Experiencing Data with Brian T. O’Neill
Brian T. O’Neill from Designing for Analytics
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How do you create innovative machine learning and analytics products? Brian T. O’Neill reveals the strategies and activities that CxOs and innovative leaders in technical product management, data science and analytics are using to deliver indispensable experiences around data. From traditional analytics to machine learning and AI, Brian and his guests explore how extraordinary value can be created when the outputs of data science and analytics are turned into engaging, valuable decision support applications and user experiences centered around the humans in the loop. Experiencing Data also features special guests on design, ethics, explainable AI (XAI), and innovation who relate their expertise to the world of data-driven software. If you're in charge of creating simple, valuable, human-centered data products that produce business value in the last mile, you'll enjoy #ExperiencingData.
Transcripts available at: https://designingforanalytics.com/ed
ABOUT THE HOST
Brian T. O’Neill is a consulting product designer who helps companies create innovative ML and analytics solutions. He is also the founder and principal of Designing for Analytics.…and a professional percussionist/drummer.
Available episodes
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Nov 28, 2023
131 - 15 Ways to Increase User Adoption of Data Products (Without Handcuffs, Threats and Mandates) with Brian T. O’Neill
This week I’m covering Part 1 of the 15 Ways to Increase User Adoption of Data Products, which is based on an article I wrote for subscribers of my mailing list. Throughout this episode, I describe why focusing on empathy, outcomes, and user experience leads to not only better data products, but also better business outcomes. The focus of this episode is to show you that it’s completely possible to take a human-centered approach to data product development without mandating behavioral changes, and to show how this approach benefits not just end users, but also the businesses and employees creating these data products.
Highlights/ Skip to:
* Design behavior change into the data product. (05:34)
* Establish a weekly habit of exposing technical and non-technical members of the data team directly to end users of solutions - no gatekeepers allowed. (08:12)
* Change funding models to fund problems, not specific solutions, so that your data product teams are invested in solving real problems. (13:30)
* Hold teams accountable for writing down and agreeing to the intended benefits and outcomes for both users and business stakeholders. Reject projects that have vague outcomes defined. (16:49)
* Approach the creation of data products as “user experiences” instead of a “thing” that is being built that has different quality attributes. (20:16)
* If the team is tasked with being “innovative,” leaders need to understand the innoficiency problem, shortened iterations, and the importance of generating a volume of ideas (bad and good) before committing to a final direction. (23:08)
* Co-design solutions with [not for!] end users in low, throw-away fidelity, refining success criteria for usability and utility as the solution evolves. Embrace the idea that research/design/build/test is not a linear process. (28:13)
* Test (validate) solutions with users early, before committing to releasing them, but with a pre-commitment to react to the insights you get back from the test. (31:50)
Links:
* 15 Ways to Increase Adoption of Data Products: https://designingforanalytics.com/resources/15-ways-to-increase-adoption-of-data-products-using-techniques-from-ux-design-product-management-and-beyond/
* Company website: https://designingforanalytics.com
* Episode 54: https://designingforanalytics.com/resources/episodes/054-jared-spool-on-designing-innovative-ml-ai-and-analytics-user-experiences/
* Episode 106: https://designingforanalytics.com/resources/episodes/106-ideaflow-applying-the-practice-of-design-and-innovation-to-internal-data-products-w-jeremy-utley/
* Ideaflow: https://www.amazon.com/Ideaflow-Only-Business-Metric-Matters/dp/0593420586/
* Podcast website: https://designingforanalytics.com/podcast
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Nov 14, 2023
130 - Nick Zervoudis on Data Product Management, UX Design Training and Overcoming Imposter Syndrome
Today I’m joined by Nick Zervoudis, Data Product Manager at CKDelta. As we dive into his career and background, Nick shares insights into his approach when it comes to developing both internal and external data products. Nick explains why he feels that a software engineering approach is the best way to develop a product that could have multiple applications, as well as the unique way his team is structured to best handle the needs of both internal and external customers. He also talks about the UX design course he took, how that affected his data product work and research with users, and his thoughts on dashboard design. We discuss common themes he’s observed when data product teams get it wrong, and how he manages feelings of imposter syndrome in his career as a DPM.
Highlights/ Skip to:
* I introduce Nick, who is a Data Product Manager at CKDelta (00:35)
* Nick’s mindset around data products and how his early career in consulting shaped his approach (01:30)
* How Nick defines a data product and why he focuses more on the process rather than the end product (03:59)
* The types of data products that Nick has helped design and his work on both internal and external projects at CKDelta (07:57)
* The similarities and differences of working with internal versus external stakeholders (12:37)
* Nick dives into the details of the data products he has built and how they feed into complex use cases (14:21)
* The role that Nick plays in the Delta Power SaaS application and how the CKDelta team is structured around that product (17:14)
* Where Nick sees data products going wrong and how he’s found value in filling those gaps (23:30)
* Nick’s view on how a digital-first mindset affects the scalability of data products (26:15)
* Why Nick is often heavily involved in the design element of data product development and the course he took that helped shape his design work (28:55)
* The imposter syndrome that Nick has experienced when implementing this new strategy to data product design (36:51)
* Why Nick feels that figuring things out yourself is an inherent part of the DPM role (44:53)
* Nick shares the origins and information on the London Data Product Management meetup (46:08) Quotes from Today’s Episode
* “What I’m always trying to do is see, how can we best balance the customer’s need to get exactly the data point or insight that they’re after to the business need. ... There’s that constant tug of war between customization and standardization that I have the joy of adjudicating. I think it’s quite fun.” — Nick Zervoudis (16:40)
* “I’ve had times where I was hired, told, 'You’re going to be the product manager for this data product that we have,' as if it’s already, to some extent built and maybe the challenge is scaling it or bringing it to more customers or improving it, and then within a couple of weeks of starting to peek under the hood, realizing that this thing that is being branded a product is actually a bunch of projects hiding under a trench coat.” — Nick Zervoudis (24:04)
* “If I just speak to five users because they’re the users, they’ll give me the insight I need. […] Even when you have a massive product with a huge user base, people face the same issues.” — Nick Zervoudis (33:49)
* “For me, it’s more about making sure that you’re bringing that more software engineering way of building things, but also, before you do that, knowing that your users' needs are going to [be varied]. So, it’s a combination of both, are we building the right thing—in other words, a product that’s flexible enough to meet the different needs of different users—but also, are we building it in the right way?” – Nick Zervoudis (27:51)
* “It’s not to say I’m the only person thinking about [UX design], but very often, I’m the one driving it.” – Nick Zervoudis (30:55)
* “You’re never going to be as good at the thing your colleague does because their job almost certainly is to be a specialist: they’re an architect, they’re a designer, they’re a developer, they’re a salesperson, whereas your job [as a DPM] is to just understand it enough that you can then pass information across other people.” – Nick Zervoudis (41:12)
* “Every time I feel like an imposter, good. I need to embrace that, because I need to be working with people that understand something better than me. If I’m not, then maybe something’s gone wrong there. That’s how I’ve actually embraced impostor syndrome.” – Nick Zervoudis (41:35) Links
* CKDelta: https://www.ckdelta.ie
* LinkedIn: https://www.linkedin.com/in/nzervoudis/
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Oct 31, 2023
129 - Why We Stopped, Deleted 18 Months of ML Work, and Shifted to a Data Product Mindset at Coolblue
Today I’m joined by Marnix van de Stolpe, Product Owner at Coolblue in the area of data science. Throughout our conversation, Marnix shares the story of how he joined a data science team that was developing a solution that was too focused on the delivery of a data-science metric that was not on track to solve a clear customer problem. We discuss how Marnix came to the difficult decision to throw out 18 months of data science work, what it was like to switch to a human-centered, product approach, and the challenges that came with it. Marnix shares the impact this decision had on his team and the stakeholders involved, as well as the impact on his personal career and the advice he would give to others who find themselves in the same position. Marnix is also a Founding Member of the Data Product Leadership Community and will be going much more into the details and his experience live on Zoom on November 16 @ 2pm ET for members.
Highlights/ Skip to:
* I introduce Marnix, Product Owner at Coolblue and one of the original members of the Data Product Leadership Community (00:35)
* Marnix describes what Coolblue does and his role there (01:20)
* Why and how Marnix decided to throw away 18 months of machine learning work (02:51)
* How Marnix determined that the KPI (metric) being created wasn’t enough to deliver a valuable product (07:56)
* Marnix describes the conversation with his data science team on mapping the solution back to the desired outcome (11:57)
* What the culture is like at Coolblue now when developing data products (17:17)
* Marnix’s advice for data product managers who are coming into an environment where existing work is not tied to a desired outcome (18:43)
* Marnix and I discuss why data literacy is not the solution to making more impactful data products (21:00)
* The impact that Marnix’s human-centered approach to data product development has had on the stakeholders at Coolblue (24:54)
* Marnix shares the ultimate outcome of the product his team was developing to measure product returns (31:05)
* How you can get in touch with Marnix (33:45) Links
* Coolblue: https://www.coolblue.nl
* LinkedIn: https://www.linkedin.com/in/marnixvdstolpe/
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Oct 17, 2023
128 - Data Products for Dummies and The Importance of Data Product Management with Vishal Singh of Starburst
Today I’m joined by Vishal Singh, Head of Data Products at Starburst and co-author of the newly published e-book, Data Products for Dummies. Throughout our conversation, Vishal explains how the variations in definitions for a data product actually led to the creation of the e-book, and we discuss the differences between our two definitions. Vishal gives a detailed description of how he believes Data Product Managers should be conducting their discovery and gathering feedback from end users, and how his team evaluates whether their data products are truly successful and user-friendly.
Highlights/ Skip to:
* I introduce Vishal, the Head of Data Products at Starburst and contributor of the e-book Data Products for Dummies (00:37)
* Vishal describes how his customers at Starburst all had a common problem, but differing definitions of a data product, which led to the creation of his e-book (01:15)
* Vishal shares his one-sentence definition of a data product (02:50)
* How Vishal’s definition of a data product differs from mine, and we both expand on the possibilities between the two (05:33)
* The tactics Vishal uses to useful feedback to ensure the data products he develops are valuable for end users (07:48)
* Why Vishal finds it difficult to get one on one feedback from users during the iteration phase of data product development (11:07)
* The danger of sunk cost bias in the iteration phase of data product development (13:10)
* Vishal describes how he views the role of a DPM when it comes to doing effective initial discovery (15:27)
* How Vishal structures his teams and their interactions with each other and their end users (21:34)
* Vishal’s thoughts on how design affects both data scientists and end users (24:16)
* How DPMs at Starburst evaluate if the data product design is user-friendly (28:45)
* Vishal’s views on where Designers are valuable in the data product development process (35:00)
* Vishal and I discuss the importance of ensuring your products truly solve your user’s problems (44:44)
* Where you can learn more about Vishal’s upcoming events and the e-book, Data Products for Dummies (49:48) Links
* Starburst: https://www.starburst.io/
* Data Products for Dummies: https://www.starburst.io/info/data-products-for-dummies/
* “How to Measure the Impact of Data Products with Doug Hubbard”: https://designingforanalytics.com/resources/episodes/080-how-to-measure-the-impact-of-data-productsand-anything-else-with-forecasting-and-measurement-expert-doug-hubbard/
* Trino Summit: https://www.starburst.io/info/trinosummit2023/
* Galaxy Platform: https://www.starburst.io/platform/starburst-galaxy/
* Datanova Summit: https://www.starburst.io/datanova/
* LinkedIn: https://www.linkedin.com/in/singhsvishal/
* Twitter: https://twitter.com/vishal_singh
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Oct 3, 2023
127 - On the Road to Adopting a “Producty” Approach to Data Products at the UK’s Care Quality Commission with Jonathan Cairns-Terry
Today I’m joined by Jonathan Cairns-Terry, who is the Head of Insight Products at the Care Quality Commission. The Care Quality Commission is the the regulator for England for health and social care, and Jonathan recently joined their data team and is working to transform their approach to be more product-led and user-centric. Throughout our conversation, Jonathan shares valuable insights into what the first year of that type of shift looks like, and why it’s important to focus on outcomes, and how he measures progress. Jonathan and I explore the signals that told Jonathan it’s time for his team to invest in a designer, the benefits he’s gotten from UX research on his team, and the recent successes that Jonathan’s team is seeing as a result of implementing this approach. Jonathan is also a Founding Member of the Data Product Leadership Community and we discuss his upcoming webinar for the group on Oct 12, 2023.
Highlights/ Skip to:
* I introduce Jonathan, who is the Head of Insight Products at the Care Quality Commission in the UK (00:37)
* How Jonathan went from being a “maths person” to being a “product person” (01:02)
* Who uses the data products that Jonthan makes at the Care Quality Commission (02:44)
* Jonathan describes the recent transition towards a product focus (03:45)
* How Jonathan expresses and measures the benefit and purpose of a product-led orientation, and how the team has embraced the transformation (07:08)
* The nuance between evaluating outcomes and measuring outputs in a product-led approach, and how UX research has impacted Jonathan’s team (12:53)
* What signals Jonathan received that told him it’s time to hire a designer (17:05)
* How Jonathan’s team approaches shadowing users (21:20)
* Some of the recent successes of the product-led approach Jonathan is implementing on his team (25:28)
* What Jonathan would change if he had to start the process of moving to outcomes over outputs with his team all over again (30:04)
* Get the full scoop on the topics discussed in this episode on October 12, 2023 when Jonathan presents his deep-dive webinar to the Data Product Leadership Community. Available to members only. Apply today.
Links
* Care Quality Commission: https://www.cqc.org.uk/
* LinkedIn: https://www.linkedin.com/in/jcairnsterry
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Sep 19, 2023
126 - Designing a Product for Making Better Data Products with Anthony Deighton
Today I’m joined by Anthony Deighton, General Manager of Data Products at Tamr. Throughout our conversation, Anthony unpacks his definition of a data product and we discuss whether or not he feels that Tamr itself is actually a data product. Anthony shares his views on why it’s so critical to focus on solving for customer needs and not simply the newest and shiniest technology. We also discuss the challenges that come with building a product that’s designed to facilitate the creation of better internal data products, as well as where we are in this new wave of data product management, and the evolution of the role.
Highlights/ Skip to:
* I introduce Anthony, General Manager of Data Products at Tamr, and the topics we’ll be discussing today (00:37)
* Anthony shares his observations on how BI analytics are an inch deep and a mile wide due to the data that’s being input (02:31)
* Tamr’s focus on data products and how that reflects in Anthony’s recent job change from Chief Product Officer to General Manager of Data Products (04:35)
* Anthony’s definition of a data product (07:42)
* Anthony and I explore whether he feels that decision support is necessary for a data product (13:48)
* Whether or not Anthony feels that Tamr qualifies as a data product (17:08)
* Anthony speaks to the importance of focusing on outcomes and benefits as opposed to endlessly knitting together features and products (19:42)
* The challenges Anthony sees with metrics like Propensity to Churn (21:56)
* How Anthony thinks about design in a product like Tamr (30:43)
* Anthony shares how data science at Tamr is a tool in his toolkit and not viewed as a “fourth” leg of the product triad/stool (36:01)
* Anthony’s views on where we are in the evolution of the DPM role (41:25)
* What Anthony would do differently if he could start over at Tamr knowing what he knows now (43:43) Links
* Tamr: https://www.tamr.com/
* Innovating: https://www.amazon.com/Innovating-short-guide-making-things/dp/B0C8R79PVB
* The Mom Test: https://www.amazon.com/The-Mom-Test-Rob-Fitzpatrick-audiobook/dp/B07RJZKZ7F
* LinkedIn: https://www.linkedin.com/in/anthonydeighton/
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Sep 5, 2023
125 - Human-Centered XAI: Moving from Algorithms to Explainable ML UX with Microsoft Researcher Vera Liao
Today I’m joined by Vera Liao, Principal Researcher at Microsoft. Vera is a part of the FATE (Fairness, Accountability, Transparency, and Ethics of AI) group, and her research centers around the ethics, explainability, and interpretability of AI products. She is particularly focused on how designers design for explainability. Throughout our conversation, we focus on the importance of taking a human-centered approach to rendering model explainability within a UI, and why incorporating users during the design process informs the data science work and leads to better outcomes. Vera also shares some research on why example-based explanations tend to out-perform [model] feature-based explanations, and why traditional XAI methods LIME and SHAP aren’t the solution to every explainability problem a user may have.
Highlights/ Skip to:
* I introduce Vera, who is Principal Researcher at Microsoft and whose research mainly focuses on the ethics, explainability, and interpretability of AI (00:35)
* Vera expands on her view that explainability should be at the core of ML applications (02:36)
* An example of the non-human approach to explainability that Vera is advocating against (05:35)
* Vera shares where practitioners can start the process of responsible AI (09:32)
* Why Vera advocates for doing qualitative research in tandem with model work in order to improve outcomes (13:51)
* I summarize the slides I saw in Vera’s deck on Human-Centered XAI and Vera expands on my understanding (16:06)
* Vera’s success criteria for explainability (19:45)
* The various applications of AI explainability that Vera has seen evolve over the years (21:52)
* Why Vera is a proponent of example-based explanations over model feature ones (26:15)
* Strategies Vera recommends for getting feedback from users to determine what the right explainability experience might be (32:07)
* The research trends Vera would most like to see technical practitioners apply to their work (36:47)
* Summary of the four-step process Vera outlines for Question-Driven XAI design (39:14)
Links
* “Human-Centered XAI: From Algorithms to User Experiences” Presentation
* “Human-Centered XAI: From Algorithms to User Experiences” Slide Deck
* “Human-Centered AI Transparency in the Age of Large Language Models”
* MSR Microsoft Research
* Vera's Personal Website
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Aug 22, 2023
124 - The PiCAA Framework: My Method to Generate ML/AI Use Cases from a UX Perspective
In this episode, I give an overview of my PiCAA Framework, which is a framework I shared at my keynote talk for Netguru’s annual conference, Burning Minds. This framework helps with brainstorming machine learning use cases or reverse engineering them, starting with the tactic. Throughout the episode, I give context to the preliminary types of work and preparation you and your team would want to do before implementing PiCAA, as well as the process and potential pitfalls you may run into, and the end results that make it a beneficial tool to experiment with.
Highlights/ Skip to:
* Where/ how you might implement the PiCAA Framework (1:22)
* Focusing on the human part of your ideas (5:04)
* Keynote excerpt outlining the PiCAA Framework (7:28)
* Closing a PiCAA workshop by exploring what could go wrong (18:03) Links
* Experiencing Data Episode 106 with Jeremy Utley
* The Data Product Leadership Community
* Ask me a question (below the recent episodes)
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Aug 8, 2023
123 - Learnings From the CDOIQ Symposium and How Data Product Definitions are Evolving with Brian T. O’Neill
Today I’m wrapping up my observations from the CDOIQ Symposium and sharing what’s new in the world of data. I was only able to attend a handful of sessions, but they were primarily ones tied to the topic of data products, which, of course, brings us to “What’s a data product?” During this episode, I cover some of what I’ve been hearing about the definition of this word, and I also share my revised v2 definition. I also walk through some of the questions that CDOs and fellow attendees were asking at the sessions I went to and a few reactions to those questions. Finally, I announce an exciting development on the launch of the Data Product Leadership Community.
Highlights/ Skip to:
* Brian introduces the topic for this episode, including his wrap-up of the CDOIQ Symposium (00:29)
* The general impressions Brian heard at the Symposium, including a focus on people & culture and an emphasis on data products (01:51)
* The three main areas the definition of a data product covers according to Brian’s observations (04:43)
* Brian describes how companies are looking for successful data product development models to follow and explores where new Data Product Managers are coming from (07:17)
* A methodology that Brian feels leads to a successful data product team (10:14)
* How Brian feels digital-native folks see the world of data products differently (11:29)
* The topic of Data Mesh and Human-Centered Design and how it came up in two presentations at the CDOIQ Symposium (13:24)
* The rarity of design and UX being talked about at data conferences, and why Brian feels that is the case (15:24)
* Brian’s current definition of a data product and how it’s evolved from his V1 definition (18:43)
* Brian lists the main questions that were being asked at CDOIQ sessions he attended around data products (22:19)
* Where to find answers to many of the questions being asked about data products and an update on the Data Product Leader Community that he will launch in August 2023 (24:28) Quotes from Today’s Episode
* “I think generally what’s happening is the technology continues to evolve, I think it generally continues to get easier, and all of the people and cultural parts and the change management and all of that, that problem just persists no matter what. And so, I guess the question is, what are we going to do about it?” — Brian T. O’Neill (03:11)
* “The feeling I got from the questions [at the CDOIQ Symposium], … and particularly the ones that were talking about the role of data product management and the value of these things was, it’s like they’re looking for a recipe to follow.” — Brian T. O’Neill (07:17)
* “My guess is people are just kind of reading up about it, self-training a bit, and trying to learn how to do product on their own. I think that’s how you learn how to do stuff is largely through trial and error. You can read books, you can do all that stuff, but beginning to do it is part of it.” — Brian T. O’Neill (08:57)
* “I think the most important thing is that data is a raw ingredient here; it’s a foundation piece for the solution that we’re going to make that’s so good, someone might pay to use it or trade something of value to use it. And as long as that’s intact, I think you’re kind of checking the box as to whether it’s a data product.” — Brian T. O’Neill (12:13)
* “I also would say on the data mesh topic, the feeling I got from people who had been to this conference before was that was quite a hyped thing the last couple years. Now, it was not talked about as much, but I think now they’re actually seeing some examples of this working.” — Brian T. O’Neill (16:25)
* “My current v2 definition right now is, ‘A data product is a managed, end-to-end software solution that organizes, refines, or transforms data to solve a problem that’s so important customers would pay for it or exchange something of value to use it.’” — Brian T. O’Neill (19:47)
* “We know [the product is] of value because someone was willing to pay for it or exchange their time or switch from their old way of doing things to the new way because it has that inherent benefit baked in. That’s really the most important part here that I think any data product manager should fully be aligned with.” — Brian T. O’Neill (21:35)
Links
* Episode 67
* Episode 110
* The Definition of Data Product
* The Data Product Leadership Community
* Ask me a question (below the recent episodes)
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Jul 25, 2023
122 - Listener Questions Answered: Conducting Effective Discovery for Data Products with Brian T. O’Neill
Today I’m answering a question that was submitted to the show by listener Will Angel, who asks how he can prioritize and scale effective discovery throughout the data product development process. Throughout this episode, I explain why discovery work is a process that should be taking place throughout the lifecycle of a project, rather than a defined period at the start of the project. I also emphasize the value of understanding the benefit users will see from the product as the main goal, and how to streamline the effectiveness of the discovery process.
Highlights/ Skip to:
* Brian introduces today’s topic, Discovery with Data Products, with a listener question (00:28)
* Why Brian sees discovery work as something that is ongoing throughout the lifecycle of a project (01:53)
* Brian tackles the first question of how to avoid getting killed by the process overhead of discovery and prioritization (03:38)
* Brian discusses his take on the question, “What are the ultimate business and user benefits that the beneficiaries hope to get from the product?”(06:02)
* The value Brian sees in stating anti-goals and anti-personas (07:47)
* How creative work is valuable despite the discomfort of not being execution-oriented (09:35)
* Why customer and stakeholder research activities need to be ongoing efforts (11:20)
* The two modes of design that Brian uses and their distinct purposes (15:09)
* Brian explains why a clear strategy is critical to proper prioritization (19:36)
* Why doing a few things really well usually beats out delivering a bunch of features and products that don’t get used (23:24)
* Brian on why saying “no” can be a gift when used correctly (27:18)
* How you can join the Data Product Leadership Community for more dialog like this and how to submit your own questions to the show (32:25) Quotes from Today’s Episode
* “Discovery work, to me is something that largely happens up front at the beginning of a project, but it doesn’t end at the beginning of the project or product initiative, or whatever it is that you’re working on. Instead, I think discovery is a continual thing that’s going on all the time.” — Brian T. O’Neill (01:57)
* “As tooling gets easier and easier and we need to stand up less infrastructure and basic pipelining in order to get from nothing to something, I think more of the work simply does become the discovery part of the work. And that is always going to feel somewhat inefficient because by definition it is.” — Brian T. O’Neill (04:48)
* “Measuring [project management metrics] does not tell us whether or not the product is going to be valuable. It just tells us how fast are we writing the code and doing execution against something that may or may not actually have any value to the business at all.” — Brian T. O’Neill (07:33)
* “How would you measure an improvement in the beneficiaries' lives? Because if you can improve their life in some way—and this often means me at work— the business value is likely to follow there.” — Brian T. O’Neill (18:42)
* “Without a clear strategy, you’re not going to be able to do prioritization work efficiently because you don’t know what success looks like.” — Brian T. O’Neill (19:49)
* “Doing a few things really well probably beats delivering a lot of stuff that doesn’t get used. There’s little point in a portfolio of data products that is really wide, but it’s very shallow in terms of value.” — Brian T. O’Neill (23:27)
* “Anytime you’re going to be changing behavior or major workflows, the non-technical costs and work increase. And we have to figure out, ‘How are we going to market this and evangelize it and make people see the value of it?’ These types of behavior changes are really hard to implement and they need to be figured out during the design of the solution — not afterwards.” — Brian T. O’Neill (26:25) Links
* designingforanalytics.com/podcast: https://designingforanalytics.com/podcast
* designingforanalytics.com/community: https://designingforanalytics.com/community
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Jul 11, 2023
121 - How Sainsbury’s Head of Data Products for Analytics and ML Designs for User Adoption with Peter Everill
Today I’m chatting with Peter Everill, who is the Head of Data Products for Analytics and ML Designs at the UK grocery brand, Sainsbury’s. Peter is also a founding member of the Data Product Leadership Community. Peter shares insights on why his team spends so much time conducting discovery work with users, and how that leads to higher adoption and in turn, business value. Peter also gives us his in-depth definition of a data product, including the three components of a data product and the four types of data products he’s encountered. He also shares the 8-step product management methodology that his team uses to develop data products that truly deliver value to end users. Pete also shares the #1 resource he would invest in right now to make things better for his team and their work.
Highlights/ Skip to:
* I introduce Peter, who I met through the Data Product Leadership Community (00:37)
* What the data team structure at Sainsbury’s looks like and how Peter wound up worki…
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Jun 27, 2023
120 - The Portfolio Mindset: Data Product Management and Design with Nadiem von Heydebrand (Part 2)
Today I’m continuing my conversation with Nadiem von Heydebrand, CEO of Mindfuel. In the conclusion of this special 2-part episode, Nadiem and I discuss the role of a Data Product Manager in depth. Nadiem reveals which fields data product managers are currently coming from, and how a new data product manager with a non-technical background can set themselves up for success in this new role. He also walks through his portfolio approach to data product management, and how to prioritize use cases when taking on a data product management role. Toward the end, Nadiem also shares personal examples of how he’s employed these strategies, why he feels it’s so important for engineers to be able to see and understand the impact of their work, and best practices around developing a data product team.
Highlights / Skip to:
* Brian introduces Nadiem and gives context for why the conversation with Nadiem led to a two-part episode (00:35)
* Nadiem summarizes his thoughts on data product ma…
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Jun 13, 2023
119 - Skills vs. Roles: Data Product Management and Design with Nadiem von Heydebrand (Part 1)
The conversation with my next guest was going so deep and so well…it became a two part episode! Today I’m chatting with Nadiem von Heydebrand, CEO of Mindfuel. Nadiem’s career journey led him from data science to data product management, and in this first, we will focus on the skills of data product management (DPM), including design. In part 2, we jump more into Nadiem’s take on the role of the DPM. Nadiem gives actionable insights into the realities of data product management, from the challenges of actually being able to talk to your end users, to focusing on the problems and unarticulated needs of your users rather than solutions. Nadiem and I also discuss how data product managers oversee a portfolio of initiatives, and why it’s important to view that portfolio as a series of investments. Nadiem also emphasizes the value of having designers on a data team, and why he hopes we see more designers in the industry.
Highlights/ Skip to:
* Brian introduces Nadiem and his b…
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May 30, 2023
118 - Attracting Talent and Landing a Role in Data Product Management with Kyle Winterbottom
Today I’m chatting with Kyle Winterbottom, who is the owner of Orbition Group and an advisor/recruiter for companies who are hiring top talent in the data industry. Kyle and I discuss whether the concept of data products has meaningful value to companies, or if it’s in a hype cycle of sorts. Kyle then shares his views on what sets the idea of data products apart from other trends, the well-paid opportunities he sees opening up for product leaders in the data industry, and why he feels being able to increase user adoption and quantify the business impact of your work is also relevant in a candidate’s ability to negotiate higher pay. Kyle and I also discuss the strange tendency for companies to mistakenly prioritize technical skills for these roles, the overall job market for data product leaders, average compensation numbers, and what companies can do to attract this talent.
Highlights/ Skip to:
* Kyle introduces himself and his company, Orbition Group (01:02)
* Why Brian in…
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May 16, 2023
117 - Phil Harvey, Co-Author of “Data: A Guide to Humans,” on the Non-Technical Skills Needed to Produce Valuable AI Solutions
Today I’m chatting with Phil Harvey, co-author of Data: A Guide to Humans and a technology professional with 23 years of experience working with AI and startups. In his book, Phil describes his philosophy of how empathy leads to more successful outcomes in data product development and the journey he took to arrive at this perspective. But what does empathy mean, and how do you measure its success? Brian and Phil dig into those questions, and Phil explains why he feels cognitive empathy is a learnable skill that one can develop and apply. Phil describes some leading indicators that empathy is needed on a data team, as well as leading indicators that a more empathetic approach to product development is working. While I use the term “design” or “UX” to describe a lot of what Phil is talking about, Phil actually has some strong opinions about UX and shares those on this episode. Phil also reveals why he decided to write Data: A Guide to Humans and some of the experiences that hel…
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May 2, 2023
116 - 10 Reasons Your Customers Don’t Make Time for Your Data Product Initiatives + A Big Update on the Data Product Leadership Community (DPLC)
Do you ever find it hard to get the requirements, problems, or needs out of your customers, stakeholders, or users when creating a data product? This week I’m coming to you solo to share reasons your stakeholders, users, or customers may not be making time for your discovery efforts. I’ve outlined 10 reasons, and delve into those in the first part of this episode.
In part two, I am going to share a big update about the Data Product Leadership Community (DPLC) I’m hoping to launch in June 2023. I have created a Google Doc outlining how v1 of the community will work as well as 6 specific benefits that I hope you’ll be able to achieve in the first year of participating. However, I need your feedback to know if this is shaping up into the community you want to join. As such, at the end of this episode, I’ll ask you to head over to the Google Doc and leave a comment. To get the document link, just add your email address to the DPLC announcement list at http://designingforanalytic…
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Apr 18, 2023
115 - Applying a Product and UX-Driven Approach to Building Stuart’s Data Platform with Osian Jones
Today I’m chatting with Osian Jones, Head of Product for the Data Platform at Stuart. Osian describes how impact and ROI can be difficult metrics to measure in a data platform, and how the team at Stuart has sought to answer this challenge. He also reveals how user experience is intrinsically linked to adoption and the technical problems that data platforms seek to solve. Throughout our conversation, Osian shares a holistic overview of what it was like to design a data platform from scratch, the lessons he’s learned along the way, and the advice he’d give to other data product managers taking on similar projects.
Highlights/ Skip to:
* Osian describes his role at Stuart (01:36)
* Brian and Osian explore the importance of creating an intentional user experience strategy (04:29)
* Osian explains how having a clear mission enables him to create parameters to measure product success (11:44)
* How Stuart developed the KPIs for their data platform (17:09)
* Osian gives his…
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Apr 4, 2023
114 - Designing Anti-Biasing and Explainability Tools for Data Scientists Creating ML Models with Josh Noble
Today I’m chatting with Josh Noble, Principal User Researcher at TruEra. TruEra is working to improve AI quality by developing products that help data scientists and machine learning engineers improve their AI/ML models by combatting things like bias and improving explainability. Throughout our conversation, Josh—who also used to work as a Design Lead at IDEO.org—explains the unique challenges and importance of doing design and user research, even for technical users such as data scientists. He also shares tangible insights on what informs his product design strategy, the importance of measuring product success accurately, and the importance of understanding the current state of a solution when trying to improve it.
Highlights/ Skip to:
* Josh introduces himself and explains why it’s important to do design and user research work for technical tools used by data scientists (00:43)
* The work that TruEra does to mitigate bias in AI as well as their broader focus on AI quali…
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Mar 21, 2023
113 - Turning the Weather into an Indispensable Data Product for Businesses with Cole Swain, VP Product at tomorrow.io
Today I’m chatting with Cole Swain, VP of Product at Tomorrow.io. Tomorrow.io is an untraditional weather company that creates data products to deliver relevant business insights to their customers. Together, Cole and I explore the challenges and opportunities that come with building an untraditional data product. Cole describes some of the practical strategies he’s developed for collecting and implementing qualitative data from customers, as well as why he feels rapport-building with users is a critical skill for product managers. Cole also reveals how scientists are part of the fold when developing products at Tomorrow.io, and the impact that their product has on decision-making across multiple industries.
Highlights/ Skip to:
* Cole describes what Tomorrow.io does (00:56)
* The types of companies that purchase Tomorrow.io and how they’re using the products (03:45)
* Cole explains how Tomorrow.io developed practical strategies for helping customers get the insights they…
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Mar 7, 2023
112 - Solving for Common Pitfalls When Developing a Data Strategy featuring Samir Sharma, CEO of datazuum
Today I’m chatting with Samir Sharma, CEO of datazuum. Samir is passionate about developing data strategies that drive business outcomes, and shares valuable insights into how problem framing and research can be done effectively from both the data and business side. Samir also provides his definition of a data strategy, and why it can be complicated to uncover whose job it is to create one. Throughout the conversation, Samir and I uncover the value of including different perspectives when implementing a data strategy and discuss solutions to various communication barriers. Of course, dashboards and data products also popped up in this episode as well!
Highlights/ Skip to:
* How Samir defines a data strategy and whose job it is to create one (01:39)
* The challenges Samir sees when trying to uncover and understand a company’s existing data strategy (03:39)
* The problem with the problem statements that Samir commonly encounters (08:37)
* Samir unpacks the communication cha…
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Feb 21, 2023
111 - Designing and Monetizing Data Products Like a Startup with Yuval Gonczarowski
Today I’m chatting with Yuval Gonczarowski, Founder & CEO of the startup, Akooda. Yuval is a self-described “socially capable nerd” who has learned how to understand and meet the needs of his customers outside of a purely data-driven lens. Yuval describes how Akooda is able to solve a universal data challenge for leaders who don’t have complete visibility into how their teams are working, and also explains why it’s important that Akooda provide those data insights without bias. Yuval and I also explore why it’s so challenging to find great product leaders and his rule for getting useful feedback from customers and stakeholders.
Highlights/ Skip to:
* Yuval describes what Akooda does (00:35)
* The types of technical skills Yuval had to move away from to adopt better leadership capabilities within a startup (02:15)
* Yuval explains how Akooda solves what he sees as a universal data problem for anyone in management positions (04:15)
* How Akooda goes about designing…
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Feb 7, 2023
110 - CDO Spotlight: The Value and Journey of Implementing a Data Product Mindset with Sebastian Klapdor of Vista
Today I’m chatting with Dr. Sebastian Klapdor, Chief Data Officer for Vista. Sebastian has developed and grown a successful Data Product Management team at Vista, and it all began with selling his vision to the rest of the executive leadership. In this episode, Sebastian explains what that process was like and what he learned. Sebastian shares valuable insights on how he implemented a data product orientation at Vista, what makes a good data product manager, and why technology usage isn’t the only metric that matters when measuring success. He also shares what he would do differently if he had to do it all over again.
Highlights/ Skip to:
* How Sebastian defines a data product (01:48)
* Brian asks Sebastian about the change management process in leadership when implementing a data product approach (07:40)
* The three dimensions that Sebastian and his team measure to determine adoption success (10:22)
* Sebastian shares the financial results of Vista adopting a data produc…
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Jan 24, 2023
109 - The Role of Product Management and Design in Turning ML/AI into a Valuable Business with Bob Mason from Argon Ventures
Today I’m chatting with Bob Mason, Managing Partner at Argon Ventures. Bob is a VC who seeks out early-stage founders in the ML/AI space and helps them inform their go-to-market, product, and design strategies. In this episode, Bob reveals what he looks for in early-stage data and intelligence startups who are trying to leverage ML/AI. He goes on to explain why it’s important to identify what your strengths are and what you enjoy doing so you can surround yourself with the right team. Bob also shares valuable insight into how to earn trust with potential customers as an early-stage startup, how design impacts a product’s success, and his strategy for differentiating yourself and creating a valuable product outside of the ubiquitous “platform play.”
Highlights/ Skip to:
* Bob explains why and how Argon Ventures focuses their investments in intelligent industry companies (00:53)
* Brian and Bob discuss the importance of prioritizing go-to-market strategy over technology (…
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Jan 10, 2023
108 - Google Cloud’s Bruno Aziza on What Makes a Good Customer-Obsessed Data Product Manager
Today I’m chatting with Bruno Aziza, Head of Data & Analytics at Google Cloud. Bruno leads a team of outbound product managers in charge of BigQuery, Dataproc, Dataflow and Looker and we dive deep on what Bruno looks for in terms of skills for these leaders. Bruno describes the three patterns of operational alignment he’s observed in data product management, as well as why he feels ownership and customer obsession are two of the most important qualities a good product manager can have. Bruno and I also dive into how to effectively abstract the core problem you’re solving, as well as how to determine whether a problem might be solved in a better way.
Highlights / Skip to:
* Bruno introduces himself and explains how he created his “CarCast” podcast (00:45)
* Bruno describes his role at Google, the product managers he leads, and the specific Google Cloud products in his portfolio (02:36)
* What Bruno feels are the most important attributes to look for in a good data prod…
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Dec 27, 2022
107 - Tom Davenport on Data Product Management and the Impact of a Product Orientation on Enterprise Data Science and ML Initiatives
Today I’m chatting with returning guest Tom Davenport, who is a Distinguished Professor at Babson College, a Visiting Professor at Oxford, a Research Fellow at MIT, and a Senior Advisor to Deloitte’s AI practice. He is also the author of three new books (!) on AI and in this episode, we’re discussing the role of product orientation in enterprise data science teams, the skills required, what he’s seeing in the wild in terms of teams adopting this approach, and the value it can create. Back in episode 26, Tom was a guest on my show and he gave the data science/analytics industry an approximate “2 out of 10” rating in terms of its ability to generate value with data. So, naturally, I asked him for an update on that rating, and he kindly obliged. How are you all doing? Listen in to find out!
Highlights / Skip to:
* Tom provides an updated rating (between 1-10) as to how well he thinks data science and analytics teams are doing these days at creating economic value (00:44)…
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Dec 13, 2022
106 - Ideaflow: Applying the Practice of Design and Innovation to Internal Data Products w/ Jeremy Utley
Today I’m chatting with former-analyst-turned-design-educator Jeremy Utley of the Stanford d.school and co-author of Ideaflow. Jeremy reveals the psychology behind great innovation, and the importance of creating psychological safety for a team to generate what they may view as bad ideas. Jeremy speaks to the critical collision of unrelated frames of reference when problem-solving, as well as why creativity is actually more of a numbers game than awaiting that singular stroke of genius. Listen as Jeremy gives real-world examples of how to practice and measure (!) your innovation efforts and apply them to data products.
Highlights/ Skip to:
* Jeremy explains the methodology of thinking he’s adopted after moving from highly analytical roles to the role he’s in now (01:38)
* The approach Jeremy takes to the existential challenge of balancing innovation with efficiency (03:54)
* Brian shares a story of a creative breakthrough he had recently and Jeremy uses that to highlight…
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Nov 29, 2022
105 - Defining “Data Product” the Producty Way and the Non-technical Skills ML/AI Product Managers Need
Today I’m discussing something we’ve been talking about a lot on the podcast recently - the definition of a “data product.” While my definition is still a work in progress, I think it’s worth putting out into the world at this point to get more feedback. In addition to sharing my definition of data products (as defined the “producty way”), on today’s episode definition, I also discuss some of the non-technical skills that data product managers (DPMs) in the ML and AI space need if they want to achieve good user adoption of their solutions. I’ll also share my thoughts on whether data scientists can make good data product managers, what a DPM can do to better understand your users and stakeholders, and how product and UX design factors into this role.
Highlights/ Skip to:
* I introduce my reasons for sharing my definition of a data product (0:46)
* My definition of data product (7:26)
* Thinking the “producty” way (8:14)
* My thoughts on necessary skills f…
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Nov 15, 2022
104 - Surfacing the Unarticulated Needs of Users and Stakeholders through Effective Listening
Today I’m chatting with Indi Young, independent qualitative data scientist and author of Time to Listen. Indi explains how it is possible to gather and analyze qualitative data in a way that is meaningful to the desired future state of your users, and that learning how to listen and not just interview users is much like learning to ride a bicycle. Listen (!) to find out why pushing back is a necessary part of the design research process, how to build an internal sensor that allows you to truly uncover the nuggets of information that are critical to your projects, and the importance of understanding thought processes to prevent harmful outcomes.
Highlights/ Skip to:
* Indi introduces her perspective on analyzing qualitative data sets (00:51)
* Indi’s motivation for working in design research and the importance of being able to capture and understand patterns to prevent harmful outcomes (05:09)
* The process Indi goes through for problem framing and understanding a user’s d…
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Nov 1, 2022
103 - Helping Pediatric Cardiac Surgeons Make Better Decisions with ML featuring Eugenio Zuccarelli of MIT Media Lab
Today I’m chatting with Eugenio Zuccarelli, Research Scientist at MIT Media Lab and Manager of Data Science at CVS. Eugenio explains how he has created multiple algorithms designed to help shape decisions made in life or death situations, such as pediatric cardiac surgery and during the COVID-19 pandemic. Eugenio shared the lessons he’s learned on how to build trust in data when the stakes are life and death. Listen and learn how culture can affect adoption of decision support and ML tools, the impact delivery of information has on the user's ability to understand and use data, and why Eugenio feels that design is more important than the inner workings of ML algorithms.
Highlights/ Skip to:
* Eugenio explains why he decided to work on machine learning models for cardiologists and healthcare workers involved in the COVID-19 pandemic (01:53)
* The workflow surgeons would use when incorporating the predictive algorithm and application Eugenio helped develop (04:12)
* The quest…
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Oct 18, 2022
102 - CDO Spotlight: The Non-Technical Roles Data Science and Analytics Teams Need to Drive Adoption of Data Products w/ Iván Herrero Bartolomé
Today I’m chatting with Iván Herrero Bartolomé, Chief Data Officer at Grupo Intercorp. Iván describes how he was prompted to write his new article in CDO Magazine, “CDOs, Let’s Get Out of Our Comfort Zone” as he recognized the importance of driving cultural change within organizations in order to optimize the use of data. Listen in to find out how Iván is leveraging the role of the analytics translator to drive this cultural shift, as well as the challenges and benefits he sees data leaders encounter as they move from tactical to strategic objectives. Iván also reveals the number one piece of advice he’d give CDOs who are struggling with adoption.
Highlights / Skip to:
* Iván explains what prompted him to write his new article, “CDOs, Let’s Get Out of Our Comfort Zone” (01:08)
* What Iván feels is necessary for data leaders to close the gap between data and the rest of the business and why (03:44)
* Iván dives into who he feels really owns delivery of va…
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Oct 4, 2022
101 - Insights on Framing IOT Solutions as Data Products and Lessons Learned from Katy Pusch
Today I’m chatting with Katy Pusch, Senior Director of Product and Integration for Cox2M. Katy describes the lessons she’s learned around making sure that the “juice is always worth the squeeze” for new users to adopt data solutions into their workflow. She also explains the methodologies she’d recommend to data & analytics professionals to ensure their IOT and data products are widely adopted. Listen in to find out why this former analyst turned data product leader feels it’s crucial to focus on more than just delivering data or AI solutions, and how spending more time upfront performing qualitative research on users can wind up being more efficient in the long run than jumping straight into development.
Highlights/ Skip to:
* What Katy does at Cox2M, and why the data product manager role is so hard to define (01:07)
* Defining the value of the data in workflows and how that’s approached at Cox2M (03:13)
* Who buys from Cox2M and the customer problems that Katy…
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Sep 20, 2022
100 - Why Your Data, AI, Product & Business Strategies Must Work Together (and Digital Transformation is The Wrong Framing) with Vin Vashishta
Today I’m chatting with Vin Vashishta, Founder of V Squared. Vin believes that with methodical strategic planning, companies can prepare for continuous transformation by removing the silos that exist between leadership, data, AI, and product teams. How can these barriers be overcome, and what is the impact of doing so? Vin answers those questions and more, explaining why process disruption is necessary for long-term success and gives real-world examples of companies who are adopting these strategies.
Highlights/ Skip to:
* What the AI ‘Last Mile’ Problem is (03:09)
* Why Vin sees so many businesses are reevaluating their offerings and realigning with their core business model (09:01)
* Why every company today is struggling to figure out how to bridge the gap between data, product, and business value (14:25)
* How the skillsets needed for success are evolving for data, product, and business leaders (14:40)
* Vin’s process when he’s helping a team with a data strate…
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Sep 6, 2022
099 - Don’t Boil the Ocean: How to Generate Business Value Early With Your Data Products with Jon Cooke, CTO of Dataception
Today I’m sitting down with Jon Cooke, founder and CTO of Dataception, to learn his definition of a data product and his views on generating business value with your data products. In our conversation, Jon explains his philosophy on data products and where design and UX fit in. We also review his conceptual model for data products (which he calls the data product pyramid), and discuss how together, these concepts allow teams to ship working solutions faster that actually produce value.
Highlights/ Skip to:
* Jon’s definition of a data product (1:19)
* Brian explains how UX research and design planning can and should influence data architecture —so that last mile solutions are useful and usable (9:47)
* The four characteristics of a data product in Jon’s model (16:16)
* The idea of products having a lifecycle with direct business/customer interaction/feedback (17:15)
* Understanding Jon’s data product pyramid (19:30)
* The challenges when customers/users don’t…
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Aug 23, 2022
098 - Why Emilie Schario Wants You to Run Your Data Team Like a Product Team
Today I’m chatting with Emilie Shario, a Data Strategist in Residence at Amplify Partners. Emilie thinks data teams should operate like product teams. But what led her to that conclusion, and how has she put the idea into practice? Emilie answers those questions and more, delving into what kind of pushback and hiccups someone can expect when switching from being data-driven to product-driven and sharing advice for data scientists and analytics leaders.
Highlights / Skip to:
* Answering the question “whose job is it” (5:18)
* Understanding and solving problems instead of just building features people ask for (9:05)
* Emilie explains what Amplify Partners is and talks about her work experience and how it fuels her perspectives on data teams (11:04)
* Emilie and I talk about the definition of data product (13:00)
* Emilie talks about her approach to building and training a data team (14:40)
* We talk about UX designers and how they fit into Emilie’s data teams (18:40…
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Aug 9, 2022
097 - Why Regions Bank’s CDAO, Manav Misra, Implemented a Product-Oriented Approach to Designing Data Products
Today, I chat with Manav Misra, Chief Data and Analytics Officer at Regions Bank. I begin by asking Manav what it was like to come in and implement a user-focused mentality at Regions, driven by his experience in the software industry. Manav details his approach, which included developing a new data product partner role and using effective communication to gradually gain trust and cooperation from all the players on his team.
Manav then talks about how, over time, he solidified a formal framework for his team to be trained to use this approach and how his hiring is influenced by a product orientation. We also discuss his definition of data product at Regions, which I find to be one of the best I’ve heard to date. Today, Region Bank’s data products are delivering tens of millions of dollars in additional revenue to the bank. Given those results, I also dig into the role of design and designers to better understand who is actually doing the designing of Regions’ data products to m…
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Jul 26, 2022
096 - Why Chad Sanderson, Head of Product for Convoy’s Data Platform, is a Champion of Data UX
Today I chat with Chad Sanderson, Head of Product for Convoy’s data platform. I begin by having Chad explain why he calls himself a “data UX champion” and what inspired his interest in UX. Coming from a non-UX background, Chad explains how he came to develop a strategy for addressing the UX pain points at Convoy—a digital freight network. They “use technology to make freight more efficient, reducing costs for some of the nation’s largest brands, increasing earnings for carriers, and eliminating carbon emissions from our planet.” We also get into the metrics of success that Convoy uses to measure UX and why Chad is so heavily focused on user workflow when making the platform user-centered.
Later, Chad shares his definition of a data product, and how his experience with building software products has overlapped with data products. He also shares what he thinks is different about creating data products vs. traditional software products. Chad then explains Convoy’s approac…
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Jul 12, 2022
095 - Increasing Adoption of Data Products Through Design Training: My Interview from TDWI Munich
Today I am bringing you a recording of a live interview I did at the TDWI Munich conference for data leaders, and this episode is a bit unique as I’m in the “guest” seat being interviewed by the VP of TDWI Europe, Christoph Kreutz.
Christoph wanted me to explain the new workshop I was giving later that day, which focuses on helping leaders increase user adoption of data products through design. In our chat, I explained the three main areas I pulled out of my full 4-week seminar to create this new ½-day workshop as well as the hands-on practice that participants would be engaging in. The three focal points for the workshop were: measuring usability via usability studies, identifying the unarticulated needs of stakeholders and users, and sketching in low fidelity to avoid over committing to solutions that users won’t value.
Christoph also asks about the format of the workshop, and I explain how I believe data leaders will best learn design by doing it. As such, the new worksho…
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Jun 28, 2022
094 - The Multi-Million Dollar Impact of Data Product Management and UX with Vijay Yadav of Merck
Today I sit down with Vijay Yadav, head of the data science team at Merck Manufacturing Division. Vijay begins by relating his own path to adopting a data product and UX-driven approach to applied data science, andour chat quickly turns to the ever-present challenge of user adoption. Vijay discusses his process of designing data products with customers, as well as the impact that building user trust has on delivering business value. We go on to talk about what metrics can be used to quantify adoption and downstream value, and then Vijay discusses the financial impact he has seen at Merck using this user-oriented perspective. While we didn’t see eye to eye on everything, Vijay was able to show how focusing on the last mile UX has had a multi-million dollar impact on Merck. The conversation concludes with Vijay’s words of advice for other data science directors looking to get started with a design and user-centered approach to building data products that achieve adoption and have mea…
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Jun 14, 2022
093 - Why Agile Alone Won’t Increase Adoption of Your Enterprise Data Products
Episode Description
In one of my past memos to my list subscribers, I addressed some questions about agile and data products. Today, I expound on each of these and share some observations from my consulting work. In some enterprise orgs, mostly outside of the software industry, agile is still new and perceived as a panacea. In reality, it can just become a factory for shipping features and outputs faster–with positive outcomes and business value being mostly absent. To increase the adoption of enterprise data products that have humans in the loop, it’s great to have agility in mind, but poor technology shipped faster isn’t going to serve your customers any better than what you’re doing now.
Here are the 10 reflections I’ll dive into on this episode:
* You can't project manage your way out of a [data] product problem.
* The more you try to deploy agile at scale, take the trainings, and hire special "agilists", the more you're going to tend to measure success by how we…
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May 31, 2022
092 - How to measure data product value from a UX and business lens (and how not to do it)
Today I’m talking about how to measure data product value from a user experience and business lens, and where leaders sometimes get it wrong. Today’s first question was asked at my recent talk at the Data Summit conference where an attendee asked how UX design fits into agile data product development. Additionally, I recently had a subscriber to my Insights mailing list ask about how to measure adoption, utilization, and satisfaction of data products. So, we’ll jump into that juicy topic as well. Answering these inquiries also got me on a related tangent about the UX challenges associated with abstracting your platform to support multiple, but often theoretical, user needs—and the importance of collaboration to ensure your whole team is operating from the same set of assumptions or definitions about success. I conclude the episode with the concept of “game framing” as a way to conceptualize these ideas at a high level.
Key topics and cues in this episode include:
* An o…
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May 17, 2022
091 - How Brazil’s Biggest Fiber Company, Oi, Leverages Design To Create Useful Data Products with Sr. Exec. Design Manager, João Critis
Today I talked with João Critis from Oi. Oi is a Brazilian telecommunications company that is a pioneer in convergent broadband services, pay TV, and local and long-distance voice transmission. They operate the largest fiber optics network in Brazil which reaches remote areas to promote digital inclusion of the population. João manages a design team at Oi that is responsible for the front end of data products including dashboards, reports, and all things data visualization. We begin by discussing João’s role leading a team of data designers. João then explains what data products actually are, and who makes up his team’s users and customers. João goes on to discuss user adoption challenges at Oi and the methods they use to uncover what users need in the last mile. He then explains the specific challenges his team has faced, particularly with middle management, and how his team builds credibility with senior leadership. In conclusion, João reflects on the value of empathy in th…
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May 3, 2022
090 - Michelle Carney’s Mission With MLUX: Bringing UX and Machine Learning Together
Michelle Carney began her career in the worlds of neuroscience and machine learning where she worked on the original Python Notebooks. As she fine-tuned ML models and started to notice discrepancies in the human experience of using these models, her interest turned towards UX. Michelle discusses how her work today as a UX researcher at Google impacts her work with teams leveraging ML in their applications. She explains how her interest in the crossover of ML and UX led her to start MLUX, a collection of meet-up events where professionals from both data science and design can connect and share methods and ideas. MLUX now hosts meet-ups in several locations as well as virtually. Our conversation begins with Michelle’s explanation of how she teaches data scientists to integrate UX into the development of their products. As a teacher, Michelle utilizes the IDEO Design Kit with her students at the Stanford School of Design (d.school). In her teaching she shares some of the unlearning that…
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Apr 19, 2022
089 - Reader Questions Answered about Dashboard UX Design
Dashboards are at the forefront of today’s episode, and so I will be responding to some reader questions who wrote in to one of my weekly mailing list missives about this topic. I’ve not talked much about dashboards despite their frequent appearance in data product UIs, and in this episode, I’ll explain why. Here are some of the key points and the original questions asked in this episode:
* My introduction to dashboards (00:00)
* Some overall thoughts on dashboards (02:50)
* What the risk is to the user if the insights are wrong or misinterpreted (4:56)
* Your data outputs create an experience, whether intentional or not (07:13)
* John asks: How do we figure out exactly what the jobs are that the dashboard user is trying to do? Are they building next year's budget or looking for broken widgets? What does this user value today? Is a low resource utilization percentage something to be celebrated or avoided for this dashboard user today? (13:05)
* Value is not intrinsic…
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Apr 5, 2022
088 - Doing UX Research for Data Products and The Magic of Qualitative User Feedback with Mike Oren, Head of Design Research at Klaviyo
Mike Oren, Head of Design Research at Klaviyo, joins today’s episode to discuss how we do UX research for data products—and why qualitative research matters. Mike and I recently met in Lou Rosenfeld’s Quant vs. Qual group, which is for people interested in both qualitative and quantitative methods for conducting user research. Mike goes into the details on how Klaviyo and his teams are identifying what customers need through research, how they use data to get to that point, what data scientists and non-UX professionals need to know about conducting UX research, and some tips for getting started quickly. He also explains how Klaviyo’s data scientists—not just the UX team—are directly involved in talking to users to develop an understanding of their problem space.
Klaviyo is a communications platform that allows customers to personalize email and text messages powered by data. In this episode, Mike talks about how to ask research questions to get at what customers actually n…
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Mar 22, 2022
087 - How Data Product Management and UX Integrate with Data Scientists at Albertsons Companies to Improve the Grocery Shopping Experience
For Danielle Crop, the Chief Data Officer of Albertsons, to draw distinctions between “digital” and “data” only limits the ability of an organization to create useful products. One of the reasons I asked Danielle on the show is due to her background as a CDO and former SVP of digital at AMEX, where she also managed product and design groups. My theory is that data leaders who have been exposed to the worlds of software product and UX design are prone to approach their data product work differently, and so that’s what we dug into this episode. It didn’t take long for Danielle to share how she pushes her data science team to collaborate with business product managers for a “cross-functional, collaborative” end result. This also means getting the team to understand what their models are personalizing, and how customers experience the data products they use. In short, for her, it is about getting the data team to focus on “outcomes” vs “outputs.” Scaling some of the…
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Mar 8, 2022
086 - CED: My UX Framework for Designing Analytics Tools That Drive Decision Making
Today, I’m flying solo in order to introduce you to CED: my three-part UX framework for designing your ML / predictive / prescriptive analytics UI around trust, engagement, and indispensability. Why this, why now? I have had several people tell me that this has been incredibly helpful to them in designing useful, usable analytics tools and decision support applications.
I have written about the CED framework before at the following link:
https://designingforanalytics.com/ced
There you will find an example of the framework put into a real-world context. In this episode, I wanted to add some extra color to what is discussed in the article. If you’re an individual contributor, the best part is that you don’t have to be a professional designer to begin applying this to your own data products. And for leaders of teams, you can use the ideas in CED as a “checklist” when trying to audit your team’s solutions in the design phase—before it’s too late or expensive to make mean…
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Feb 22, 2022
085 - Dr. William D. Báez on the Journey and ROI of Integrating UX Design into Machine Learning and Analytics Solutions
Why design matters in data products is a question that, at first glance, may not be easily answered for some until they see users try to use ML models and analytics to make decisions. For Bill Báez, a data scientist and VP of Strategy at Ascend Innovations, realizing that design and UX matters in this context was a realization that grew over the course of a few years. Bill’s origins in the Air Force, and his transition to Ascend Innovations, instilled lessons about the importance of using design thinking with both clients and users.
After observing solutions built in total isolation with zero empathy and knowledge of how they were being perceived in the wild, Bill realized the critical need to bring developers “upstairs” to actually observe the people using the solutions that were being built.
Currently, Ascend Innovation’s consulting is primarily rooted in healthcare and community services, and in this episode, Bill provides some real-world examples where their machine lear…
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Feb 8, 2022
084 - The Messy Truth of Designing and Building a Successful Analytics SAAS Product featuring Jonathan Kay (CEO, Apptopia)
Building a SAAS business that focuses on building a research tool, more than building a data product, is how Jonathan Kay, CEO and Co-Founder of Apptopia frames his company’s work. Jonathan and I worked together when Apptopia pivoted from its prior business into a mobile intelligence platform for brands. Part of the reason I wanted to have Jonathan talk to you all is because I knew that he would strip away all the easy-to-see shine and varnish from their success and get really candid about what worked…and what hasn’t…during their journey to turn a data product into a successful SAAS business. So get ready: Jonathan is going to reveal the very curvy line that Apptopia has taken to get where they are today.
In this episode, Jonathan also describes one of the core product design frameworks that Apptopia is currently using to help deliver actionable insights to their customers. For Jonathan, Apptopia’s research-centric approach changes the ways in which their customers can inter…
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Jan 25, 2022
083 -Why Bob Goodman Thinks Product Management and Design Must Dance Together to Create “Experience Layers” for Data Products
Design takes many forms and shapes. It is an art, a science, and a method for problem solving. For Bob Goodman, a product management and design executive, the way to view design is as a story and a narrative that conveys the solution to the customer. As a former journalist with 20 years of experience in consumer and enterprise software, Bob has a unique perspective on enabling end-user decision making with data. Having worked in both product management and UX, Bob shapes the narrative on approaching product management and product design as parts of a whole, and we talked about how data products fit into this model. Bob also shares why he believes design and product need to be under the same umbrella to prevent organizational failures. We also discussed the challenges and complexities that come with delivering data-driven insights to end users when ML and analytics are behind the scenes.
* An overview of Bob’s recent work as an SVP of product management - and why design, UX and pro…
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Jan 11, 2022
082 - What the 2021 $1M Squirrel AI Award Winner Wants You To Know About Designing Interpretable Machine Learning Solutions w/ Cynthia Rudin
Episode Description
As the conversation around AI continues, Professor Cynthia Rudin, Computer Scientist and Director at the Prediction Analysis Lab at Duke University, is here to discuss interpretable machine learning and her incredible work in this complex and evolving field. To begin, she is the most recent (2021) recipient of the $1M Squirrel AI Award for her work on making machine learning more interpretable to users and ultimately more beneficial to humanity.
In this episode, we explore the distinction between explainable and interpretable machine learning and how black boxes aren’t necessarily “better” than more interpretable models. Cynthia offers up real-world examples to illustrate her perspective on the role of humans and AI, shares takeaways from her previous work which ranges from predicting criminial recidivism to predicting manhole cover explosions in NYC (yes!). I loved this chat with her because, for one, Cynthia has strong, heavily informed opinions from her c…
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Dec 28, 2021
081 - The Cultural and $ Benefits of Human-Centered AI in the Enterprise: Digging Into BCG/MIT Sloan’s AI Research w/ François Candelon
Episode Description
The relationship between humans and artificial intelligence has been an intricate topic of conversation across many industries. François Candelon, Global Director at Boston Consulting Group Henderson Institute, has been a significant contributor to that conversation, most notably through an annual research initiative that BCG and MIT Sloan Management Review have been conducting about AI in the enterprise. In this episode, we’re digging particularly into the findings of the 2020 and 2021 studies that were just published at the time of this recording.
Through these yearly findings, the study has shown that organizations with the most competitive advantage are the ones that are focused on effectively designing AI-driven applications around the humans in the loop. As these organizations continue to generate value with AI, the gap between them and companies that do not embrace AI has only increased. To close this gap, companies will have to learn to design trustwort…
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Dec 14, 2021
080 – How to Measure the Impact of Data Products…and Anything Else with Forecasting and Measurement Expert Doug Hubbard
Finding it hard to know the value of your data products on the business or your end users? Do you struggle to understand the impact your data science, analytics, or product team is having on the people they serve?
Many times, the challenge comes down to figuring out WHAT to measure, and HOW. Clients, users, and customers often don’t even know what the right success or progress metrics are, let alone how to quantify them. Learning how to measure what might seem impossible is a highly valuable skill for leaders who want to track their progress with data—but it’s not all black and white. It’s not always about “more data,” and measurement is also not about “the finite, right answer.” Analytical minds, ready to embrace subjectivity and uncertainty in this episode!
In this insightful chat, Doug and I explore examples from his book, How to Measure Anything, and we discuss its applicability to the world of data and data products. From defining trust to identifying cognitive b…
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Nov 30, 2021
079 - How Sisu’s CPO, Berit Hoffmann, Is Approaching the Design of Their Analytics Product…and the UX Mistakes She Won’t Make Again
Berit Hoffmann, Chief Product Officer at Sisu, tackles design from a customer-centric perspective with a focus on finding problems at their source and enabling decision making. However, she had to learn some lessons the hard way along the road, and in this episode, we dig into those experiences and what she’s now doing differently in her current role as a CPO.
In particular, Berit reflects on her “ivory tower design” experience at a past startup called Bebop. In that time, she quickly realized the importance of engaging with customer needs and building intuitive and simple solutions for complex problems. Berit also discusses the Double Diamond Process and how it shapes her own decision-making and the various ways she carries her work at Sisu.
In this episode, we also cover:
* How Berit’s “ivory tower design experience” at Bebop taught her the importance of dedicating time to focus on the customer. (01:31)
* What Berit looked for as she researched Sisu prior to joinin…
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Nov 16, 2021
078 - From Data to Product: What is Data Product Management and Why Do We Need It with Eric Weber
Eric Weber, Head of Data Product at Yelp, has spent his career developing a product-minded approach to producing data-driven solutions that actually deliver value. For Eric, developing a data product mindset is still quite new and today, we’re digging into all things “data product management” and why thinking of data with a product mindset matters.
In our conversation, Eric defines what data products are and explains the value that data product managers can bring to their companies. Eric’s own ethos on centering on empathy, while equally balanced with technical credibility, is central to his perspectives on data product management. We also discussed how Eric is bringing all of this to hand at Yelp and the various ways they’re tackling their customers' data product needs.
In this episode, we also cover:
* What is a data product and why do we need data product management? (01:34)
* Why successful data product managers carry two important traits - empathy and technical cr…
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Nov 2, 2021
077 - Productizing Analytics for Performing Arts Organizations with AMS Analytics CPO Jordan Gross Richmond
Even in the performing arts world, data and analytics is serving a purpose. Jordan Gross Richmond is the Chief Product Officer at AMS Analytics, where they provide benchmarking and performance reporting to performing arts organizations. As many of you know, I’m also a musician who tours and performs in the performing arts market and so I was curious to hear how data plays a role “off the stage” within these organizations. In particular, I wanted to know how Jordan designed the interfaces for AMS Analytics’s product, and what’s unique (or not!) about using data to manage arts organizations.
Jordan also talks about the beginnings of AMS and their relationship with leaders in the performing arts industry and the “birth of benchmarking” in this space. From an almost manual process in the beginning, AMS now has a SaaS platform that allows performing arts centers to see the data that helps drive their organizations. Given that many performing arts centers are non-profit organi…
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Oct 19, 2021
076 - How Bedrock’s “Data by Design” Mantra Helps Them Build Human-Centered Solutions with Jesús Templado
Why do we need or care about design in the work of data science? Jesús Templado, Managing Director at Bedrock, is here to tell us about how Bedrock executes their mantra, “data by design.”
Bedrock has found ways to bring to their clients a design-driven, human-centered approach by utilizing a “hybrid model” to synthesize technical possibilities with human needs. In this episode, we explore Bedrock’s vision for how to achieve this synthesis as part of the firm’s DNA, and how Bedrock adopted their vision to make data more approachable with the client being central to their design efforts. Jesús also discusses a time when he championed making “data by design” a successful strategy with a large chain of hotels, and he offers insight on how making clients feel validated and heard plays a part.
In our chat, we also covered:
* “Data by design” and how Bedrock implements this design-driven approach. (00:43)
* Bedrock’s vision for how they support their clients an…
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Oct 5, 2021
075 - How CDW is Integrating Design Into Its Data Science and Analytics Teams with Prasad Vadlamani
How do we get the most breadth out of design and designers when building data products? One way is to have designers be at the front leading the charge when it comes to creating data products that must be useful, usable, and valuable.
For this episode Prasad Vadlamani, CDW’s Director of Data Science and Advanced Analytics, joins us for a chat about how they are making design a larger focus of how they create useful, usable data products. Prasad talks about the importance of making technology—including AI-driven solutions—human centered, and how CDW tries to keep the end user in mind.
Prasad and I also discuss his perspectives on how to build designers into a data product team and how to successfully navigate the grey areas between various areas of expertise. When this is done well, then the entire team can work with each other's strengths and advantages to create a more robust product. We also discuss the role a UI-free user experience plays in some data products, some differe…
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Sep 21, 2021
074 - Why a Former Microsoft ML/AI Researcher Turned to Design to Create Intelligent Products from Messy Data with Abhay Agarwal, Founder of Polytopal
Episode Description
The challenges of design and AI are exciting ones to face. The key to being successful in that space lies in many places, but one of the most important is instituting the right design language.
For Abhay Agarwal, Founder of Polytopal, when he began to think about design during his time at Microsoft working on systems to help the visually impared, he realized the necessity of a design language for AI. Stepping away from that experience, he leaned into how to create a new methodology of design centered around human needs. His efforts have helped shift the lens of design towards how people solve problems.
In this episode, Abhay and I go into details on a snippet from his course page for the Stanford d. where he claimed that “the foreseeable future would not be well designed, given the difficulty of collaboration between disciplines.” Abhay breaks down how he thinks his design language for AI should work and how to build it out so that everyone in an organization…
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Sep 7, 2021
073 - Addressing the Functional and Emotional Needs of Users When Designing Data Products with Param Venkataraman
Episode Description
Simply put, data products help users make better decisions and solve problems with information. But how effective can data products be if designers don’t take the time to explore the complete needs of users?
To Param Venkataraman, Chief Design Officer at Fractal Analytics, having an understanding of the “human dimension” of a problem is crucial to creating data solutions that create impact.
On this episode of Experiencing Data, Param and I talk more about his concept of ‘attractive non-conscious design,’ the core skills of a professional designer, and why Fractal has a c-suite design officer and is making large investments in UX.
In our chat, we covered:
* Param's role as Chief Design Officer at Fractal Analytics, and the company's sharp focus on the 'human dimension' of enterprise data products. (2:04)
* 'Attractive non-conscious design': Creating easy-to-use, 'delightful' data products that help end-users make better decisions by focusing on the…
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Aug 24, 2021
072 - How to Get Stakeholders to Reveal What They Really Need From a Data Product with Cindy Dishmey Montgomery
Episode Description
How do you extract the real, unarticulated needs from a stakeholder or user who comes to you asking for AI, a specific app feature, or a dashboard?
On this episode of Experiencing Data, Cindy Dishmey Montgomery, Head of Data Strategy for Global Real Assets at Morgan Stanley, was gracious enough to let me put her on the spot and simulate a conversation between a data product leader and customer.
I played the customer, and she did a great job helping me think differently about what I was asking her to produce for me — so that I would be getting an outcome in the end, and not just an output. We didn’t practice or plan this exercise, it just happened — and she handled it like a pro! I wasn’t surprised; her product and user-first approach told me that she had a lot to share with you, and indeed she did!
A computer scientist by training, Cindy has worked in data, analytics and BI roles at other major companies, such as Revantage, a Blackstone real estate portf…
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Aug 10, 2021
071 - The ROI of UX Research and How It Applies to Data Products with Bill Albert
There are many benefits in talking with end users and stakeholders about their needs and pain points before designing a data product.
Just take it from Bill Albert, executive director of the Bentley University User Experience Center, author of Measuring the User Experience, and my guest for this week’s episode of Experiencing Data. With a career spanning more than 20 years in user experience research, design, and strategy, Bill has some great insights on how UX research is pivotal to designing a useful data product, the different types of customer research, and how many users you need to talk to to get useful info.
In our chat, we covered:
* How UX research techniques can help increase adoption of data products. (1:12)
* Conducting 'upfront research': Why talking to end users and stakeholders early on is crucial to designing a more valuable data product. (8:17)
* 'A participatory design process': How data scientists should conduct research with stakeholders before and during…
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Jul 27, 2021
070 - Fighting Fire with ML, the AI Incident Database, and Why Design Matters in AI-Driven Software with Sean McGregor
As much as AI has the ability to change the world in very positive ways, it also can be incredibly destructive. Sean McGregor knows this well, as he is currently developing the Partnership on AI’s AI Incident Database, a searchable collection of news articles that covers questionable use, failures, and other incidents that affect people when AI solutions are poorly designed.
On this episode of Experiencing Data, Sean takes us through his notable work around using machine learning in the domain of fire suppression, and how human-centered design is critical to ensuring these decision support solutions are actually used and trusted by the users. We also covered the social implications of new decision-making tools leveraging AI, and:
* Sean's focus on ensuring his models and interfaces were interpretable by users when designing his fire-suppression system and why this was important. (0:51)
* How Sean built his fire suppression model so that different stakeholders can optimize the s…
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Jul 13, 2021
069 - The Role of Creativity and Product Thinking in Data Monetization with ‘Infonomics’ Author Doug Laney
Doug Laney is the preeminent expert in the field of infonomics — and it’s not just because he literally wrote the book on it.
As the Data & Analytics Strategy Innovation Fellow at consulting firm West Monroe, Doug helps businesses use infonomics to measure the economic value of their data and monetize it. He also is a visiting professor at the University of Illinois at Urbana-Champaign where he teaches classes on analytics and infonomics.
On this episode of Experiencing Data, Doug and I talk about his book Infonomics, the many different ways that businesses can monetize data, the role of creativity and product management in producing innovative data products, and the ever-evolving role of the Chief Data Officer.
In our chat, we covered:
* Why Doug's book Infonomics argues that measuring data for its value potential is key to effectively managing and monetizing it. (2:21)
* A 'regenerative asset': Innovative methods for deploying and monetizing data — and the differences b…
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Jun 29, 2021
068 - Why User Adoption of Enterprise Data Products Continues to Lag with International Institute for Analytics Executive VP Drew Smith
Drew Smith knows how much value data analytics can add to a business when done right.
Having worked at the IKEA Group for 17 years, Drew helped the company become more data-driven, implementing successful strategies for data analytics and governance across multiple areas of the company.
Now, Drew serves as the Executive Vice President of the Analytics Leadership Consortium at the International Institute for Analytics, where he helps Fortune 1000 companies successfully leverage analytics and data science.
On this episode of Experiencing Data, Drew and I talk a lot about the factors contributing to low adoption rates of data products, how product and design-thinking approaches can help, and the value of proper one-on-one research with customers.
In our chat, we covered:
* 'It’s bad and getting worse': Drew's take on the factors behind low adoption of data products. (1:08)
* Decentralizing data analytics: How understanding a user's business problems by including them in the des…
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Jun 15, 2021
067 - Why Roche Diagnostics’ BI and Data Science Teams Are Adopting Human-Centered Design and UX featuring Omar Khawaja
On today’s episode of Experiencing Data, I’m so excited to have Omar Khawaja on to talk about how his team is integrating user-centered design into data science, BI and analytics at Roche Diagnostics.
In this episode, Omar and I have a great discussion about techniques for creating more user-centered data products that produce value — as well as how taking such an approach can lead to needed change management on how data is used and interpreted.
In our chat, we covered:
* What Omar is responsible for in his role as Head of BI & Analytics at Roche Diagnostics — and why a human-centered design approach to data analytics is important to him. (0:57)
* Understanding the end-user's needs: Techniques for creating more user-centric products — and the challenges of taking on such an approach. (6:10)
* Dissecting 'data culture': Why Omar believes greater implementation of data-driven decision-making begins with IT 'demonstrating' the approach's benefits. (9:31)
* Understandi…
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Jun 1, 2021
066 - How Alison Magyari Used Design Thinking to Transform Eaton’s Business Analytics Approach to Creating Data Products
Earlier this year, the always informative Women in Analytics Conference took place online. I didn’t go — but a blog post about one of the conference’s presentations on the International Institute of Analytics’ website caught my attention.
The post highlighted key points from a talk called Design Thinking in Analytics that was given at the conference by Alison Magyari, an IT Manager at Eaton. In her presentation, Alison explains the four design steps she utilizes when starting a new project — as well as what “design thinking” means to her.
Human-centered design is one of the main themes of Experiencing Data, so given Alison’s talk about tapping into the emotional state of customers to create better designed data products, I knew she would be a great guest. In this episode, Alison and I have a great discussion about building a “design thinking mindset” — as well as the importance of keeping the design process flexible.
In our chat, we covered:
* How Alison emp…
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May 18, 2021
065 - Balancing Human Intuition and Machine Intelligence with Salesforce Director of Product Management Pavan Tumu
I once saw a discussion on LinkedIn about a fraud detection model that had been built but never used. The model worked — it was expensive — but it just simply didn’t get used because the humans in the loop were not incentivized to use it.
It was on this very thread that I first met Salesforce Director of Product Management Pavan Tuvu, who chimed in on the thread about a similar experience he went through. When I heard about his experience, I asked him if he would share it with you and he agreed. So, today on the Experiencing Data podcast, I’m excited to have Pavan on to talk about some lessons he learned while designing ad-spend software that utilized advanced analytics — and the role of the humans in the loop. We discussed:
* Pavan's role as Director of Product Management at Salesforce and how he works to make data easier to use for teams. (0:40)
* Pavan's work protecting large-dollar advertising accounts from bad actors by designing a ML system that predicts and caps…
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May 4, 2021
064 - How AI Shapes the Products of Startups in MIT’s “Tough Tech” Venture Fund, The Engine feat. General Partner, Reed Sturtevant
Reed Sturtevant sees a lot of untapped potential in “tough tech.”
As a General Partner at The Engine, a venture capital firm launched by MIT, Reed and his colleagues invest in companies with breakthrough technology that, if successful, could positively transform the world.
It’s been about 15 years since I’ve last caught up to Reed—who was CTO at a startup we worked at together—so I’m so excited to welcome him on this episode of Experiencing Data! Reed and I talked about AI and how some of the portfolio companies in his fund are using data to produce better products, solutions, and inventions to tackle some of the world’s toughest challenges.
In our chat, we covered:
* How Reed's venture capital firm, The Engine, is investing in technology driven businesses focused on making positive social impacts. (0:28)
* The challenges that technical PhDs and postdocs face when transitioning from academia to entrepreneurship. (2:22)
* Focusing on a greater mission: The impo…
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Apr 20, 2021
063 - Beyond Compliance: Designing Data Products With Data Privacy As a UX Benefit with The Data Diva (Debbie Reynolds)
Debbie Reynolds is known as “The Data Diva” — and for good reason.
In addition to being founder, CEO and chief data privacy officer of her own successful consulting firm, Debbie has been named to the Global Top 20 CyberRisk Communicators by The European Risk Policy Institute in 2020. She’s also written a few books, such as The GDPR Challenge: Privacy, Technology, and Compliance In An Age of Accelerating Change; as well as articles for other publications.
If you are building data products, especially customer-facing software, you’ll want to tune into this episode. Debbie and Ihad an awesome discussion about data privacy from the lens of user experience instead of the typical angle we are all used to: legal compliance. While collecting user data can enable better user experiences, we can also break a customer’s trust if we don’t request access properly.
In our chat, we covered:
* 'Humans are using your product': What it means to be a 'data steward' when building softw…
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Apr 6, 2021
062 - Why Ben Shneiderman is Writing a Book on the Importance of Designing Human-Centered AI
Ben Shneiderman is a leading figure in the field of human-computer interaction (HCI).
Having founded one of the oldest HCI research centers in the country at the University of Maryland in 1983, Shneiderman has been intently studying the design of computer technology and its use by humans. Currently, Ben is a Distinguished University Professor in the Department of Computer Science at the University of Maryland and is working on a new book on human-centered artificial intelligence.
I’m so excited to welcome this expert from the field of UX and design to today’s episode of Experiencing Data! Ben and I talked a lot about the complex intersection of human-centered design and AI systems.
In our chat, we covered:
* Ben's career studying human-computer interaction and computer science. (0:30)
* 'Building a culture of safety': Creating and designing ‘safe, reliable and trustworthy’ AI systems. (3:55)
* 'Like zoning boards': Why Ben thinks we need independent oversight of priva…
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Mar 23, 2021
061 - Applying a Product Mindset to Internal Data Products with Silicon Valley Product Group Partner Marty Cagan
Marty Cagan has had a storied career working as a product executive. With a resume that includes Vice President of Product at Netscape and Ebay, Marty is an expert in product management and strategy.
This week, Marty joins me on Experiencing Data to talk more about what a successful data product team looks like, as well as the characteristics of an effective product manager. We also explored the idea of product management applied to internal data teams. Marty and I didn’t necessarily agree on everything in this conversation, but I loved his relentless focus on companies’ customers. Marty and I also talked a bit about his new book, Empowered: Ordinary People, Extraordinary Teams. I also spoke with Marty about:
* The responsibilities of a data product team. (0:59)
* Whether an internally-facing software solution can be considered a 'product.' (5:02)
* Customer-facing vs. customer-enabling: Why Marty tries hard not to confuse the terminology of internal employees as customers.…
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Mar 8, 2021
060 - How NPR Uses Data to Drive Editorial Decisions in the Newsroom with Sr. Dir. of Audience Insights Steve Mulder
Journalism is one of the keystones of American democracy. For centuries, reporters and editors have kept those in power accountable by seeking out the truth and reporting it.
However, the art of newsgathering has changed dramatically in the digital age. Just take it from NPR Senior Director of Audience Insights Steve Mulder — whose team is helping change the way NPR makes editorial decisions by introducing a streamlined and accessible platform for data analytics and insights.
Steve and I go way, way back (Lycos anyone!?) — and I’m so excited to welcome him on this episode of Experiencing Data! We talked a lot about the Story Analytics and Data Insights (SANDI) dashboard for NPR content creators that Steve’s team just recently launched, and dove into:
* How Steve’s design and UX background influences his approach to building analytical tools and insights (1:04)
* Why data teams at NPR embrace qualitative UX research when building analytics and insights solutions for the…
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Feb 23, 2021
059 - How Design Thinking Helps Organizations and Data Science Teams Create Economic Value with Machine Learning and Analytics feat. Bill Schmarzo
With a 30+ year career in data warehousing, BI and advanced analytics under his belt, Bill has become a leader in the field of big data and data science – and, not to mention, a popular social media influencer. Having previously worked in senior leadership at DellEMC and Yahoo!, Bill is now an executive fellow and professor at the University of San Francisco School of Management as well as an honorary professor at the National University of Ireland-Galway.
I’m so excited to welcome Bill as my guest on this week’s episode of Experiencing Data. When I first began specializing my consulting in the area of data products, Bill was one of the first leaders that I specifically noticed was leveraging design thinking on a regular basis in his work. In this long overdue episode, we dug into some examples of how he’s using it with teams today. Bill sees design as a process of empowering humans to collaborate with one another, and he also shares insights from his new book, “The? Economi…
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Feb 9, 2021
058 - IoT Spotlight: 8 UI / UX Strategies for Designing Indispensable Monitoring Applications
On this solo episode of Experiencing Data, I discussed eight design strategies that will help your data product team create immensely valuable IOT monitoring applications.
Whether your team is creating a system for predictive maintenance, forecasting, or root-cause analysis – analytics are often a big part of helping users make sense of the huge volumes of telemetry and data an IOT system can generate. Often times, product or technical teams see the game as, “how do we display all the telemetry from the system in a way the user can understand?” The problem with this approach is that it is completely decoupled from the business objectives the customers likely have-and it is a recipe for a very hard-to-use application.
The reality is that a successful application may require little to no human interaction at all-that may actually be the biggest value of all that you can create for your customer: showing up only when necessary, with just the right insight.
So, let’s dive into s…
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Jan 26, 2021
057 - How to Design Successful Enterprise Data Products When You Have Multiple User Types to Satisfy
Designing a data product from the ground up is a daunting task, and it is complicated further when you have several different user types who all have different expectations for the service. Whether an application offers a wealth of traditional historical analytics or leverages predictive capabilities using machine learning, for example, you may find that different users have different expectations. As a leader, you may be forced to make choices about how and what data you’ll present, and how you will allow these different user types to interact with it. These choices can be difficult when domain knowledge, time availability, job responsibility, and a need for control vary greatly across these personas. So what should you do?
To answer that, today I’m going solo on Experiencing Data to highlight some strategies I think about when designing multi-user enterprise data products so that in the end, something truly innovative, useful, and valuable emerges.
In total, I covered:
* Why…
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Jan 12, 2021
056 - How Design Helps Drive Adoption of Data Products Used for Social Work with Chief Data Officer Dr. Besa Bauta of MercyFirst
There’s a lot at stake in the decisions that social workers have to make when they care for people — and Dr. Besa Bauta keeps this in mind when her teams are designing the data products that care providers use in the field.
As Chief Data Officer at MercyFirst, a New York-based social service nonprofit, Besa explains how her teams use design and design thinking to create useful decision support applications that lead to improved clinician-client interactions, health and well-being outcomes, and better decision making.
In addition to her work at MercyFirst, Besa currently serves as an adjunct assistant professor at New York University’s Silver School of Social Work where she teaches public health, social science theories and mental/behavioral health. On today’s episode, Besa and I talked about how MercyFirst’s focus on user experience improves its delivery of care and the challenges Besa and her team have encountered in driving adoption of new technology.
In total, we covere…
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Dec 29, 2020
055 - What Can Carol Smith’s Ethical AI Work at the DoD Teach Us About Designing Human-Machine Experiences?
It’s not just science fiction: As AI becomes more complex and prevalent, so do the ethical implications of this new technology.But don’t just take it from me – take it from Carol Smith, a leading voice in the field of UX and AI. Carol is a senior research scientist in human-machine interaction at Carnegie Mellon University’s Emerging Tech Center, a division of the school’s Software Engineering Institute. Formerly a senior researcher for Uber’s self-driving vehicle experience, Carol-who also works as an adjunct professor at the university’s Human-Computer Interaction Institute-does research on Ethical AI in her work with the US Department of Defense.
Throughout her 20 years in the UX field, Carol has studied how focusing on ethics can improve user experience with AI. On today’s episode, Carol and I talked about exactly that: the intersection of user experience and artificial intelligence, what Carol’s work with the DoD has taught her, and why design matters when using…
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Dec 15, 2020
054 - Jared Spool on Designing Innovative ML/AI and Analytics User Experiences
Jared Spool is arguably the most well-known name in the field of design and user experience. For more than a decade, he has beena witty, powerful voice for why UX is critical to value creation within businesses. Formerly an engineer, Jared started working in UX in 1978, founded UIE (User Interface Engineering) in 1988, and has helped establish the field over the last 30 years. In addition, he advised the US Digital Service / Executive Office of President Obama and in 2016, Jared co-founded the Center Centre, the user experience design school that’s creating a new generation of industry-ready UX designers.
Today however, we turned to the topic of UX in the context of analytics, ML and AI—and what teams–especially those without trained designers on staff–need to know about creating successful data products.
In our chat, we covered:
* Jared’s definition of “design”
* The definition of UX outcomes, and who should be responsible for defining and delivering them
* Unde…
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Dec 1, 2020
053 - Creating (and Debugging) Successful Data Product Teams with Jesse Anderson
In this episode of Experiencing Data, I speak with Jesse Anderson, who is Managing Director of the Big Data Institute and author of a new book
titled, Data Teams: A Unified Management Model for Successful Data-Focused Teams. Jesse opens up about why teams often run into trouble in their efforts to build data products, and what can be done to drive better outcomes.
In our chat, we covered:
* Jesse’s concept of debugging teams
* How Jesse defines a data product, how he distinguishes them from software products
* What users care about in useful data products
* Why your tech leads need to be involved with frontline customers, users, and business leaders
* Brian’s take on Jesse’s definition of a “data team” and the roles involved-especially around two particular disciplines
* The role that product owners tend to play in highly productive teams
* What conditions lead teams to building the wrong product
* How data teams are challenged to bring together parts of th…
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Nov 17, 2020
052 - Reasons Automated Decision Making with Machine Learning Can Fail with James Taylor
In this episode of Experiencing Data, I sat down with James Taylor, the CEO of Decision Management Solutions. This discussion centers around how enterprises build ML-driven software to make decisions faster, more precise, and more consistent-and why this pursuit may fail.
We covered:
* The role that decision management plays in business, especially when making decisions quickly, reliably, consistently, transparently and at scale.
* The concept of the "last mile," and why many companies fail to get their data products across it
* James' take on operationalization of ML models, why Brian dislikes this term
* Why James thinks it is important to distinguish between technology problems and organizational change problems when leveraging ML.
* Why machine learning is not a substitute for hard work.
* What happens when human-centered design is combined with decision management.
* James's book, Digital Decisioning: How to Use Decision Management to Get Business Value from AI, wh…
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Nov 3, 2020
051 - Methods for Designing Ethical, Human-Centered AI with Undock Head of Machine Learning, Chenda Bunkasem
Chenda Bunkasem is head of machine learning at Undock, where she is focusing on using quantitative methods to influence ethical design. In this episode of Experiencing Data, Chenda and I explore her actual methods to designing ethical AI solutions as well as how she works with UX and product teams on ML solutions.
We covered:
* How data teams can actually design ethical ML models, after understanding if ML is the right approach to begin with
* How Chenda aligns her data science work with the desired UX, so that technical choices are always in support of the product and user instead of “what’s cool”
* An overview of Chenda’s role at Undock, where she works very closely with product and marketing teams, advising them on uses for machine learning
* How Chenda’s approaches to using AI may change when there are humans in the loop
* What NASA’s Technology Readiness Level (TRL) evaluation is, and how Chenda uses it in her machine learning work
* What ethical pillars…
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Oct 20, 2020
050 - Ways to Practice Creativity and Foster Innovation When You’re An Analytical Thinker
50 episodes! I can’t believe it. Since it’s somewhat of a milestone for the show, I decided to do another solo round of Experiencing Data, following the positive feedback that I’ve gotten from the last few episodes. Today, I want to help you think about ways to practice creativity when you and your organization are living in an analytical world, creating analytics for a living, and thinking logically and rationally. Why? Because creativity is what leads to innovation, and the sciences says a lot of decision making is not rational. This means we have to tap things besides logical reasoning and data to bring data products to our customers that they will love...and use. (Sorry!)
One of the biggest blockers to creativity is in the organ above your shoulders and between your ears. I frequently encounter highly talented technical professionals who find creativity to be a foreign thing reserved for people like artists. They don’t think of themselves as being creative, and believe it…
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Oct 6, 2020
049 - CxO & Digital Transformation Focus: (10) Reasons Users Can’t or Won’t Use Your Team’s ML/AI-Driven Software and Analytics Applications
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Welcome back for another solo episode of Experiencing Data. Today, I am primarily focusing on addressing the non-digital natives out there who are trying to use AI/ML in innovative ways, whether through custom software applications and data products, or as a means to add new forms of predictive intelligence to existing digital experiences.
Many non-digital native companies today tend to approach software as a technical “thing” that needs to get built, and neglect to consider the humans who will actually use it — resulting in a lack of business or organizational value emerging. While my focus will be on the design and user experience aspects that tend to impede adoption and the realization of business value, I will also talk about some org…
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Sep 22, 2020
048 - Good vs. Great: (10) Things that Distinguish the Best Leaders of Intelligent Products, Analytics Applications, and Decision Support Tools
Today I’m going solo on Experiencing Data! Over the years, I have worked with a lot of leaders of data-driven software initiatives with all sorts of titles. Today, I decided to focus the podcast episode on what I think makes the top product management and digital/software leaders stand out, particularly in the space of enterprise software, analytics applications, and decision support tools.
This episode is for anyone leading a software application or product initiative that has to produce real value, and not just a technology output of some kind. When I recorded this episode, I largely had “product managers” in mind, but titles can vary significantly. Additionally, this episode focuses on my perspective as a product/UX design consultant and advisor, focusing specifically at the traits associated with these leaders’ ability to produce valuable, innovative solutions customers need and want. A large part of being a successful software leader also involves managing teams and other…
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Sep 8, 2020
047 - How Yelp Integrates Data Science, Engineering, UX, and Product Management when Creating AI Products with Yelp’s Justin Norman
In part one of an excellent series on AI product management, LinkedIn Research Scientist Peter Skomoroch and O’Reilly VP of Content Strategy Mike Loukides explained the importance of aligning AI products with your business plans and strategies. In other words, they have to deliver value, and they have to be delivered on time. Unfortunately, this is much easier said than done. I was curious to learn more about what goes into the complex AI product development process, and so for answers I turned to Yelp VP of Data Science Justin Norman, who collaborated with Peter and Mike in the O’Reilly series of articles. Justin is a career data professional and data science leader with experience in multiple companies and industries, having served as director of research and data science at Cloudera Fast Forward Labs, head of applied machine learning at Fitbit, head of Cisco’s enterprise data science office, and as a big data systems engineer with Booz Allen Hamilton. He also served as a Marin…
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Aug 25, 2020
046 - How Steelcase’s Data Science, UX, & Product Teams Are Helping Customers Design Safer Office Workplaces Informed by Covid-19 Recommendations w/ J...
When you think of Steelcase, their office furniture probably comes to mind. However, Steelcase is much more than just a manufacturer of office equipment. They enable their customers (workplace/workspace designers) to help those designers’ clients create useful, effective, workplaces and offices that are also safe and compliant.
Jorge Lozano is a data science manager at Steelcase and recently participated as a practitioner and guest on an IIA webinar I gave about product design and management being the missing links in many data science and analytics initiatives. I was curious to dig deeper with Jorge about how Steelcase is enabling its customers to adjust workspaces to account for public health guidelines around COVID-19 and employees returning to their physical offices. The data science team was trying to make it easy for its design customers to understand health guidelines around seat density, employee proximity and other relevant metrics so that any workspace designs could be “…
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Aug 11, 2020
045 - Healthcare Analytics…or Actionable Decision Support Tools? Leadership Strategies from Novant Health’s SVP of Data Products, Karl Hightower
Healthcare professionals need access to decision support tools that deliver the right information, at the right time. In a busy healthcare facility, where countless decisions are made on a daily basis, it is crucial that any analytical tools provided actually yield useful decision support to the target customer. In this episode, I talked to Karl Hightower from Novant Health about how he and his team define “quality” when it comes to data products, and what they do to meet that definition in their daily work. Karl Hightower is the Chief Data Officer and SVP of Data Products at Novant Health, a busy hospital and medical group in the Southeast United States with over 16 hospitals and more than 600 clinics. Karl and I took a deep dive into data product management, and how Karl and his team are designing products and services that help empower all of the organization’s decision makers. In our chat, we covered:
* How a non-tech company like Novant Health approaches data product mana…
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Jul 28, 2020
044 - The Roles of Product and Design when “Competing in the Age of AI” with HBS Professor and Author Karim Lakhani
If there’s one thing that strikes fear into the heart of every business executive, it’s having your company become the next Blockbuster or Neiman Marcus — that is, ignoring change, and getting wiped out by digital competitors. In this episode, I dived into the changing business landscape with Karim Lakhani who is a Professor at Harvard Business School and co-author of the new book Competing in the Age of AI: When Algorithms and Networks Run the World, which he wrote with his friend and colleague at HBS, Marco Iansiti.
We discuss how AI, machine learning, and digital operating models are changing business architecture, and disrupting traditional business models. I also pressed Karim to go a bit deeper on how, and whether he thinks product mindset and design factor in to the success of AI in today’s businesses. We also go off on a fun tangent about the music industry, which just might have to be a future episode!. In any case, I highly recommend the book. It’s particularly pra…
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Jul 14, 2020
043 - What a Product Management Mindset Can do for Data Science and Analytics Leaders with Product School CEO, Carlos González de Villaumbrosia
I am a firm believer that one of the reasons that data science and analytics has a high failure rate is a lack of product management and design. To me, product is about a mindset just as much as a job title, and I am repeatedly hearing how more and more voices in the data community are agreeing with me on this (Gartner CDO v4, International Inst. for Analytics, several O’Reilly authors, Karim Lakhani’s new book on AI, and others). This is even more true as more companies begin to leverage AI. So many of these companies fear what startups and software companies are doing, yet they do not copy the way tech companies build software applications and enable specific user experiences that unlock the desired business value.
Integral to building software is the product management function—and when these applications and tools have humans in the loop, the product/UX design function is equally as important to ensure adoption, usability, engagement, and alignment with the business objectiv…
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Jun 30, 2020
042 - Why Machine Learning and Analytics Alone Can’t Drive Behavioral Change inside Police Departments with Allison Weil
“What happened in Minneapolis and Louisville and Chicago and countlessother cities across the United States is unconscionable (and to be clear, racist). But what makes me the maddest is how easy this problem is to solve, just by the police deciding it’s a thing they want to solve.” - Allison Weil on Medium Before Allison Weil became an investor and Senior Associate at Hyde Park Ventures, she was a co-founder at Flag Analytics, an early intervention system for police departments designed to help identify officers at risk of committing harm. Unfortunately, Flag Analytics—as a business—was set up for failure from the start, regardless of its predictive capability. As Allison explains so candidly and openly in her recent Medium article (thanks Allison!), the company had “poor product-market fit, a poor problem-market fit, and a poor founder-market fit.” The technology was not the problem, and as a result, it did not help them succeed as a business or in producing the desired…
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Jun 16, 2020
041 - Data Thinking: An Approach to Using Design Thinking to Maximize the Effectiveness of Data Science and Analytics with Martin Szugat of Datentreib...
The job of many internally-facing data scientists in business settings is to discover,explore, interpret, and share data, turning it into actionable insight that can benefit the company and improve outcomes. Yet, data science teams often struggle with the very basic question of how the company’s data assets can best serve the organization. Problem statements are often vague, leading to data outputs that don’t turn into value or actionable decision support in the last mile.
This is where Martin Szugat and his team at Datentreiber step in, helping clients to develop and implement successful data strategy through hands-on workshops and training. Martin is based in Germany and specializes in helping teams learn to identify specific challenges data can solve, and think through the problem solving process with a human focus. This in turn helps teams to select the right technology and be objective about whether they need advanced tools such as ML/AI, or something more simple to produce v…
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Jun 2, 2020
040 – Improving Potato Chips and Space Travel: NASA’s Steve Rader on Open Innovation
Innovation doesn’t just happen out of thin air. It requires a conscious effort, and team-wide collaboration. At the same time, innovation will be critical for NASA if the organization hopes to remain competitive and successful in the coming years. Enter Steve Rader. Steve has spent the last 31 years at NASA, working in a variety of roles including flight control under the legendary Gene Kranz, software development, and communications architecture. A few years ago, Steve was named Deputy Director for the Center of Excellence for Collaborative Innovation. As Deputy Director, Steve is spearheading the use of open innovation, as well as diversity thinking. In doing so, Steve is helping the organization find more effective ways of approaching and solving problems. In this fascinating discussion, Steve and Brian discuss design, divergent thinking, and open innovation plus:
* Why Steve decided to shift away from hands-on engineering and management to the emerging field of open innovation…
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May 19, 2020
039 – How PEX Fingerprinted 20 Billion Audio and Video Files and Turned It Into a Product to Help Musicians, Artists and Creators Monetize their Work
Every now and then, I like to insert a music-and-data episode into the show since hey, I’m a musician, and I’m the host 😉 Today is one of those days!
Rasty Turek is founder and CEO of Pex, a leading analytics and rights management platform used for discovering and tracking video and audio content using data science.
Pex’s AI crawls the internet for user-generated content (UGC), identifies copyrighted audio/ visual content, indexes the media, and then enables rights holders to understand where their art is being used so it can be monetized. Pex’s goal is to help its customers understand who is using their licensed content, and what they are using it for — along with key insights to support monetization initiatives and negotiations with UGC platform providers.
In this episode of Experiencing Data, we discuss:
* How the data science behind Pex works in terms of being able to fingerprint actual songs (the underlying IP of a composition) vs. masters (actual audio recordin…
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May 5, 2020
038 – (Special Co-Hosted Episode) Brian and Mark Bailey Discuss 10 New Design and UX Considerations for Creating ML and AI-Driven Products and Applica...
Mark Bailey is a leading UX researcher and designer, and host of the Design for AI podcast — a
program which, similar to Experiencing Data, explores the strategies and considerations around designing data-driven human-centered applications built with machine learning and AI.
In this episode of Experiencing Data — co-released with the podcast Design for AI — Brian and Mark share the host and guest role, and discuss 10 different UX concepts teams may need to consider when approaching ML-driven data products and AI applications. A great discussion on design and #MLUX ensued, covering:
* Recognizing the barrier of trust and adoption that exists with ML, particularly at non-digital native companies, and how to address it when designing solutions.
* Why designers need to dig beyond surface level knowledge of ML, and develop a comprehensive understanding of the space
* How companies attempt to “separate reality from the movies,” with AI and ML, deploying creative strategies…
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Apr 21, 2020
037 – A VC Perspective on AI and Building New Businesses Using Machine Intelligence featuring Rob May of PJC
Rob May is a general partner at PJC, a leading venture capital firm. He was previously CEO of Talla, a platform for AI and automation, as well as co-founder and CEO of Backupify. Rob is an angel investor who has invested in numerous companies, and author of InsideAI which is said to be one of the most widely-read AI newsletters on the planet.
In this episode, Rob and I discuss AI from a VC perspective. We look into the current state of AI, service as a software, and what Rob looks for in his startup investments and portfolio companies. We also investigate why so many companies are struggling to push their AI projects forward to completion, and how this can be improved. Finally, we outline some important things that founders can do to make products based on machine intelligence (machine learning) attractive to investors.
In our chat, we covered:
* The emergence of service as a software, which can be understood as a logical extension of “software eating the world” and the 2 hard…
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Apr 7, 2020
036 – How Higher-Ed Institutions are Using AI and Analytics to Better Serve Students with Professor of Learning Informatics and Edtech Expert Simon Bu...
Simon Buckingham Shum is Professor of Learning Informatics at Australia’s University of Technology Sydney (UTS) and Director of the Connected Intelligence Centre (CIC)—an innovation center where students and staff can explore education data science applications. Simon holds a Ph.D from the University of York, and is known for bringing a human-centered approach to analytics and development. He also co-founded the Society for Learning Analytics Research (SoLAR), which is committed to advancing learning through ethical, educationally sound data science.
In this episode, Simon and I discuss the state of education technology (edtech), privacy, human-centered design in the context of using AI in higher ed, and the numerous technological advancements that are re-shaping the higher level education landscape.
Our conversation covered:
* How the hype cycle around big data and analytics is starting to pervade education
* The differences between using BI and analytics to streamline oper…
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Mar 24, 2020
035 – Future Ethics Author and Designer Cennydd Bowles Shares Strategies for Designing Ethical Data Products That Benefit Our Business, Community and ...
Cennydd Bowles is a London-based digital product designer and futurist, with almost two decades of consulting experience working with some of the largest and most influential brands in the world. Cennydd has earned a reputation as a trusted guide, helping companies navigate complex issues related to design, technology, and ethics. He’s also the author of Future Ethics, a book which outlines key ethical principles and methods for constructing a fairer future.
In this episode, Cennydd and I explore the role that ethics plays in design and innovation, and why so many companies today—in Silicon Valley and beyond—are failing to recognize the human element of their technological pursuits. Cennydd offers his unique perspective, along with some practical tips that technologists can use to design with greater mindfulness and consideration for others.
In our chat, we covered topics from Cennydd’s book and expertise including:
* Why there is growing resentment towards the tech indust…
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Mar 10, 2020
034 – ML & UX: To Augment or Automate? Plus, Rating Overall Analytics Efficacy with Eric Siegel, Ph.D.
Eric Siegel, Ph.D. is founder of the Predictive Analytics World and Deep Learning World conference series, executive editor of “The Predictive Analytics Times,” and author of “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.” A former Columbia University professor and host of the Dr. Data Show web series, Siegel is a renowned speaker and educator who has been commissioned for more than 100 keynote addresses across multiple industries. Eric is best known for making the “how” and “why” of predictive analytics (aka machine learning) understandable and captivating to his audiences.
In our chat, we covered:
* The value of defining business outcomes and end user’s needs prior to starting the technical work of predictive modeling, algorithms, or software design.
* The idea of data prototypes being used before engaging in data science to determine where models could potentially fail—saving time while improving your odds of success.
* The…
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Feb 25, 2020
033 - How Vidant Health’s Data Team Creates Empathetic Data Products and Ethical Machine Learning Models with Greg Nelson
Greg Nelson is VP of data analytics at Vidant Health, as well as an adjunct faculty member at Duke University. He is also the author of the “Analytics Lifecycle Toolkit,” which is a manual for integrating data management technologies. A data evangelist with over 20 years of experience in analytics and advisory, Nelson is widely known for his human-centered approach to analytics. In this episode, Greg and I explore what makes a data product or decision support application indispensable, specifically in the complex world of healthcare. In our chat, we covered:
* Seeing through the noise and identifying what really matters when designing data products
* The type of empathy training Greg and his COO are rolling out to help technical data teams produce more useful data products
* The role of data analytics product management and why this is a strategic skillset at Vidant
* The AI Playbook Greg uses at Vidant Health and their risk-based approach to assessing how they will valida…
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Feb 11, 2020
032 - How and Why Talented Analytical Minds Leave People Scratching Their Head Around Data with Nancy Duarte
Nancy Duarte is a communication expert and the leader of the largest design firm in Silicon Valley, Duarte, Inc. She has more than 30 years of experience working with global companies and counts eight of the top ten Fortune 500 brands in her clientele. She is the author of six books, and her work as appeared in Fortune, Time Magazine, Forbes, Wired, Wall Street Journal, New York Times, Los Angeles Times, Cosmopolitan Magazine, and CNN.
In this episode, Nancy and I discussed some of the reasons analytics and data experts fail to effectively communicate the insights and value around data. She drew from her key findings in her work as a communication expert that she details in her new book, Data Story, and the importance of communicating data through the natural structure of storytelling.
In our chat, we covered:
* How empathy is tied to effective communication.
* Biases that cloud our own understanding of our communication skills
* How to communicate an enormous amount of data e…
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