Today I am sharing some highlights for 2023 from the podcast, and also letting you all know I’ll be taking a break from the podcast for the rest of December, but I’ll be back with a new episode on January 9th, 2024. I’ve also got two links to share with you—details inside!

Transcript Greetings everyone - I’m taking a little break from Experiencing Data over December of 2023, but I’ll be back in January with more interviews and insights on leveraging UX design and product management to create indispensable data products, machine learning apps, and decision support tools. 

Experiencing Data turned this year five years old back in November, with over 130 episodes to date! I still can’t believe it’s been going that long and how far we’ve come. 

Some highlights for me in 2023 included launching the Data Product Leadership Community, finding out that the show is now in the top 2% of all podcasts worldwide according to ListenNotes, and most of all, hearing from you that the podcast, and my writing, and the guests that  I have brought on are having an impact on your work, your careers, and hopefully the lives of your customers, users, and stakeholders as well! 

So, for now, I’ve got just two links for you:

If you’re wondering how to either:

support the show yourself with a really fast review on Apple Podcasts, to record a quick audio question for me to answer on the show,  or if you want to join my free Insights mailing lists where I share my bi-weekly ideas and thoughts and 1-page episode summaries of all the show drops that I put out here on Experiencing Data.

…just head over to designingforanalytics.com/podcast and you’ll get links to all those things there.

And secondly, if you need help increasing customer adoption, delight, the business value, or the usability of your analytics and machine learning applications in 2024, I invite you to set up a free discovery call with me 1 on 1. 

You bring the questions, I’ll bring my ears, and by the end of the call, I’ll give you my best advice on how to move forward with your situation – whether it’s working with me or not. To schedule one of those free discovery calls, visit designingforanalytics.com/go

And finally, there will be some news coming out next year with the show, as well as my business, so I hope you’ll hop on the mailing list and stay tuned, that’s probably the best place to do that. And if you celebrate holidays in December and January, I hope they’re safe, enjoyable, and rejuvenating. Until 2024, stay tuned right here - and in the words of the great Arnold Schwarzenegger, I’ll be back.

In this conversation with Klara Lindner, Service Designer at diconium data, we explore how behavioral science and UX can be used to increase adoption of data products. Klara describes how she went from having a highly technical career as an electrical engineer and being the founder of a solar startup to her current role in service design for data products. Klara shares powerful insights into the value of user research and human-centered design, including one which stopped me in my tracks during this episode: how the people making data products and evangelizing data-driven decision making aren’t actually following their own advice when it comes to designing their data products. Klara and I also explore some easy user research techniques that data professionals can use, and discuss who should ultimately be responsible for user adoption of data products. Lastly, Klara gives us a peek at her upcoming December 19th, 2023 webinar with the The Data Product Leadership Community (DPLC) where she will be going deeper on two frameworks from psychology and behavioral science that teams can use to increase adoption of data products. Klara is also a founding member of the DPLC and was one of—if not the very first—design/UX professionals to join.

Highlights/ Skip to:

I introduce Klara, and she explains the role of Service Design to our audience (00:49) Klara explains how she realized she’s been doing design work longer than she thought by reflecting on the company she founded, Mobisol (02:09) How Klara balances the desire to design great dashboards with the mission of helping end users (06:15) Klara describes the psychology behind user research and her upcoming talk on December 19th at The Data Product Leadership Community (08:32) What data product teams can do as a starting point to begin implementing user research principles (10:52)  Klara gives a powerful example of the type of insight and value even basic user research can provide (12:49) Klara and I discuss a key revelation when it comes to designing data products for users, which is the irony that even developers use intuition as well as quantitative data when building (16:43) What adjustments Klara had to make in her thinking when moving from a highly technical background to doing human-centered design (21:08) Klara describes the two frameworks for driving adoption that she’ll be sharing in her talk at the DPLC on December 19th (24:23) An example of how understanding and addressing adoption blockers is important for product and design teams (30:44) How Klara has seen her teams adopt a new way of thinking about product & service design (32:55) Klara gives her take on the Jobs to be Done framework, which she will also be sharing in her talk at the DPLC on December 19th (35:26) Klara’s advice to teams that are looking to build products around generative AI (39:28) Where listeners can connect with Klara to learn more (41:37)

Links diconium data: http://www.diconium.com/ LinkedIn: https://www.linkedin.com/in/klaralindner/ Personal Website: https://magic-investigations.com/ Hear Klara speak on Dec 19, 2023 at 10am ET here: https://designingforanalytics.com/community/

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

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/

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/

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

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

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/

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

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)

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)

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

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 working there (01:54) Peter shares the 8-step product management methodology that has been developed by his team and where in that process he spends most of his time (04:54) How involved the users are in Peter’s process when it comes to developing data products (06:13) How Peter was able to ensure that enough time is taken on discovery throughout the design process (10:03) Who on Peter’s team is doing the core user research for product development (14:52) Peter shares the three things that he feels make data product teams successful (17:09) How Peter defines a data product, including the three components of a data product and the four types of data products (18:34) Peter and I discuss the importance of spending time in discovery (24:25) Peter explains why he measures reach and impact as metrics of success when looking at implementation (26:18) How Peter solves for the gap when handing off a product to the end users to implement and adopt (29:20) How Peter hires for data product management roles and what he looks for in a candidate (33:31) Peter talks about what roles or skills he’d be looking for if he was to add a new person to his team (37:26)

Quotes from Today’s Episode “I’m a big believer that the majority of analytics in its simplest form is improving business processes and decisions. A big part of our discovery work is that we align to business areas, business divisions, or business processes, and we spend time in that discovery space actually mapping the business process. What is the goal of this process? Ultimately, how does it support the P&L?” — Peter Everill (12:29)

“There’s three things that are successful for any organization that will make this work and make it stick. The first is defining what you mean by a data product. The second is the role of a data product manager in the organization and really being clear what it is that they do and what they don’t do. … And the third thing is their methodology, from discovery through to delivery. The more work you put upfront defining those and getting everyone trained and clear on that, I think the quicker you’ll get to an organization that’s really clear about what it’s delivering, how it delivers, and who does what.” – Peter Everill (17:31)

“The important way that data and analytics can help an organization firstly is, understanding how that organization is performing. And essentially, performance is how well processes and decisions within the organization are being executed, and the impact that has on the P&L.” – Peter Everill (20:24)

“The great majority of organizations don’t allocate that percentage [20-25%] of time to discovery; they are jumping straight into solution. And also, this is where organizations typically then actually just migrate what already exists from, maybe, legacy service into a shiny new cloud platform, which might be good from a defensive data strategy point of view, but doesn’t offer new net value—apart from speed, security and et cetera of the cloud. Ultimately, this is why analytics organizations aren’t generally delivering value to organizations.” – Peter Everill (25:37)

“The only time that value is delivered, is from a user taking action. So, the two metrics that we really focus on with all four data products [are] reach [and impact].” – Peter Everill (27:44)

“In terms of benefits realization, that is owned by the business unit. Because ultimately, you’re asking them to take the action. And if they do, it’s their part of the P&L that’s improving because they own the business, they own the performance. So, you really need to get them engaged on the release, and for them to have the superusers, the champions of the product, and be driving voice of the release just as much as the product team.” – Peter Everill (30:30)

On hiring DPMs: “Are [candidates] showing the aptitude, do they understand what the role is, rather than the experience? I think data and analytics and machine learning product management is a relatively new role. You can’t go on LinkedIn necessarily, and be exhausted with a number of candidates that have got years and years of data and analytics product management.” – Peter Everill (36:40)

Links LinkedIn: https://www.linkedin.com/in/petereverill/

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 management and adds context on which fields he sees data product managers currently coming from (01:46) Nadiem’s take on whether job listings for data product manager roles still have too many technical requirements (04:27) Why some non-technical people fail when they transition to a data product manager role and the ways Nadiem feels they can bolster their chances of success (07:09) Brian and Nadiem talk about their views on functional data product team models and the process for developing a data product as a team (10:11) When Nadiem feels it makes sense to hire a data product manager and adopt a portfolio view of your data products (16:22) Nadiem’s view on how to prioritize projects as a new data product manager (19:48) Nadiem shares a story of when he took on an interim role as a head of data and how he employed the portfolio strategies he recommends (24:54) How Nadiem evaluates perceived usability of a data product when picking use cases (27:28) Nadiem explains why understanding go-to-market strategy is so critical as a data product manager (30:00) Brian and Nadiem discuss the importance of today’s engineering teams understanding the value and impact of their work (32:09) How Nadiem and his team came up with the idea to develop a SaaS product for data product managers (34:40)

Quotes from Today’s Episode “So, data product management [...] is a combination of different capabilities [...]  [including] product management, design, data science, and machine learning. We covered this in viability, desirability, feasibility, and datability. So, these are four dimensions [that] you combine [...] together to become a data product manager.” — Nadiem von Heydebrand (02:34)

“There is no education for data product management today, there’s no university degree. ... So, there’s nobody out there—from my perspective—who really has all the four dimensions from day one. It’s more like an evolution: you’re coming from one of the [parallel business] domains or from one of the [parallel business] fields and then you extend your skill set over time.” — Nadiem von Heydebrand (03:04)

“If a product manager has very good communication skills and is able to break down the needs in a proper way or in a good understandable way to its tech lead, or its engineering lead or data science lead, then I think it works out super well. If this bridge is missing, then it becomes a little bit tricky because then the distance between the product manager and the development team is too far.” – Nadiem von Heydebrand (09:10)

“I think every data leader out there has an Excel spreadsheet or a list of prioritized use cases or the most relevant use cases for the business strategy… You can think about this list as a portfolio. You know, some of these use cases are super valuable; some of these use cases maybe will not work out, and you have to identify those which are bringing real return on investment when you put effort in there.” – Nadiem von Heydebrand (19:01)

“I’m not a magician for data product management. I just focused on a very strategic view on my portfolio and tried to identify those cases and those data products where I can believe I can easily develop them, I have a high degree of adoption with my lines of business, and I can truly measure the added revenue and the impact.” – Nadiem von Heydebrand (26:31)

“As a true data product manager, from my point of view, you are someone who is empathetic for the lines of businesses, to understand what their underlying needs and what the problems are. At the same time, you are a business person. You try to optimize the portfolio for your own needs, because you have business goals coming from your leadership team, from your head of data, or even from the person above, the CTO, CIO, even CEO. So, you want to make sure that your value contribution is always transparent, and visible, measurable, tangible.” – Nadiem von Heydebrand (29:20)

“If we look into classical product management, I mean, the product manager has to understand how to market and how to go to the market. And it’s this exactly the same situation with data product managers within your organization. You are as successful as your product performs in the market. This is how you measure yourself as a data product manager. This is how you define success for yourself.” – Nadiem von Heydebrand (30:58)

Links Mindfuel: https://mindfuel.ai/ LinkedIn: https://www.linkedin.com/in/nadiemvh/ Delight Software - the SAAS tool for data product managers to manage their portfolio of data products: https://delight.mindfuel.ai

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 background going from data science to data product management (00:36) Nadiem gives not only his definition of a data product, but also his related definitions of ‘data as product,’ ‘data as information,’ and ‘data as a model’ products (02:19) Nadiem outlines the skill set and activities he finds most valuable in a data product manager (05:15) How a data organization typically functions and the challenges a data team faces to prove their value (11:20) Brian and Nadiem discuss the challenges and realities of being able to do discovery with the end users of data products (17:42) Nadiem outlines how a portfolio of data initiatives has a certain investment attached to it and why it’s important to generate a good result from those investments (21:30) Why Nadiem wants to see more designers in the data product space and the problems designers solve for data teams (25:37) Nadiem shares a story about a time when he wished he had a designer to convert the expressed needs of the  business into the true need of the customer (30:10) The value of solving for the unarticulated needs of your product users, and Nadiem shares how focusing on problems rather than solutions helped him (32:32) Nadiem shares how you can connect with him and find out more about his company, Mindfuel (36:07)

Quotes from Today’s Episode “The product mindset already says it quite well. When you look into classical product management, you have something called the viability, the desirability, the feasibility—so these are three very classic dimensions of product management—and the fourth dimension, we at Mindfuel define for ourselves and for applications are, is the datability.” — Nadiem von Heydebrand (06:51)

“We can only prove our [data team’s] value if we unlock business opportunities in their [clients’] lines of businesses. So, our value contribution is indirect. And measuring indirect value contribution is very difficult in organizations.” — Nadiem von Heydebrand (11:57)

“Whenever we think about data and analytics, we put a lot of investment and efforts in the delivery piece. I saw a study once where it said 3% of investments go into discovery and 90% of investments go into delivery and the rest is operations and a little bit overhead and all around. So, we have to balance and we have to do proper discovery to understand what problem do we want to solve.” — Nadiem von Heydebrand (13:59)

“The best initiatives I delivered in my career, and also now within Mindfuel, are the ones where we try to build an end responsibility from the lines of businesses, among the product managers, to PO, the product owner, and then the delivery team.” – Nadiem von Heydebrand (17:00)

“As a consultant, I typically think in solutions. And when we founded Mindfuel, my co-founder forced me to avoid talking about the solution for an entire ten months. So, in whatever meeting we were sitting, I was not allowed to talk about the solution, but only about the problem space.”  – Nadiem von Heydebrand (34:12)

“In scaled organizations, data product managers, they typically run a portfolio of data products, and each single product can be seen a little bit like from an investment point of view, this is where we putting our money in, so that’s the reason why we also have to prioritize the right use cases or product initiatives because typically we have limited resources, either it is investment money, people, resources or our time.” – Nadiem von Heydebrand (24:02)

“Unfortunately, we don’t see enough designers in data organizations yet. So, I would love to have more design people around me in the data organizations, not only from a delivery perspective, having people building amazing dashboards, but also, like, truly helping me in this kind of discovery space.” – Nadiem von Heydebrand (26:28)

Links Mindfuel: https://mindfuel.ai/ Personal LinkedIn: https://www.linkedin.com/in/nadiemvh/ Mindfuel LinkedIn: https://www.linkedin.com/company/mindfuelai/

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 invited Kyle on the show to discuss the recruitment of technical talent for data & analytics teams (02:00) Kyle shares what’s causing companies to build out data product teams (04:49) The reason why viewing data as a product seems to be driving better adoption in Kyle’s view (07:22) Does Kyle feel that the concept of data products is mostly hype or meaningful? (11:26) The different levels of maturity Kyle sees in organizations that are approaching him for help hiring data product talent, and how soft skills are often overlooked (15:37) Kyle’s views on who is successfully landing data product manager roles and how that’s starting to change (23:20) What Kyle’s observations are on the salary bands for data product manager roles and the type of money people can make in this space (25:41) Brian and Kyle discuss how the skills of DPMs can help these leaders improve earning potential (30:30) Kyle’s observations and advice to companies seeking to improve the data product talent they attract (38:12) How listeners can learn more about Kyle and Orbition Group (47:55)

Quotes from Today’s Episode “I think data products, obviously, there’s starting to get a bit of hype around it, which I’ve got no doubt will start to lead organizations to look down that route, just because they see and hear about other organizations doing it. ... [but] what it’s helping organizations to do is to drive adoption.” — Kyle Winterbottom (05:45)

“I think we’re at a point now where it’s becoming more and more clear, day by day, week by week, the there’s more to [the data industry] than just the building of stuff.” – Kyle Winterbottom (12:56)

“The whole soft skills piece is becoming absolutely integral because it’s become—you know, it’s night and day now, between the people that are really investing in themselves in that area and how quickly they’re progressing in their career because of that. But yeah, most organizations don’t even think about that.” – Kyle Winterbottom (18:49)

“I think nine times out of ten, most businesses overestimate the importance of the technical stuff practically in every role. … Even data analysts, data scientists, all they’re bothered about is the tech stack that they’ve used, [but] there’s a lot more to it than just the tech that they use.” – Kyle Winterbottom (22:56)

“There’s probably a big opportunity for really good product people to move into the data space because it’s going to be well paid with lots of opportunity. [It’s] quite an interesting space.” – Kyle Winterbottom (24:05)

“As soon as you get to a point where if you can help to drive adoption and then you can quantify the commercial benefit of that adoption to the organization, that probably puts you up near the top in terms of percentile of being important to a data organization.” – Kyle Winterbottom (32:21)

“We’re forever talking in our industry about the importance of storytelling. Yeah, I’ve never seen a business once tell a good story about how good it is to work for them, specifically in regards to their data analytics team and telling a story about that.” – Kyle Winterbottom (39:37)

Links Kyle’s LinkedIn: https://www.linkedin.com/in/kylewinterbottom/ Orbition Group: https://www.orbitiongroup.com

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 helped shape the book’s philosophy. 

Highlights/ Skip to:

Phil introduces himself and explains how he landed on the name for his book (00:54)  How Phil met his co-author, Noelia Jimenez Martinez, and the reason they started writing Data: A Guide to Humans (02:31) Phil unpacks his understanding of how he defines empathy, why it leads to success on AI projects, and what success means to him (03:54) Phil walks through a couple scenarios where empathy for users and stakeholders was lacking and the impacts it had (07:53) The work Phil has done internally to get comfortable doing the non-technical work required to make ML/AI/data products successful  (13:45) Phil describes some indicators that data teams can look for to know their design strategy is working (17:10) How Phil sees the methodology in his book relating to the world of UX (user experience) design (21:49) Phil walks through what an abstract concept like “empathy” means to him in his work and how it can be learned and applied as a practical skill (29:00)

Quotes from Today’s Episode “If you take success in itself, this is about achieving your intended outcomes. And if you do that with empathy, your outcomes will be aligned to the needs of the people the outcomes are for. Your outcomes will be accepted by stakeholders because they’ll understand them.” — Phil Harvey (05:05)

“Where there’s people not discussing and not considering the needs and feelings of others, you start to get this breakdown, data quality issues, all that.” – Phil Harvey (11:10)

“I wanted to write code; I didn’t want to deal with people. And you feel when you can do technical things, whether it’s machine-learning or these things, you end up with the ‘I’ve got a hammer and now everything looks like a nail problem.’ But you also have the [attitude] that my programming will solve everything.” – Phil Harvey (14:48)

“This is what startup-land really taught me—you can’t do everything. It’s very easy to think that you can and then burn yourself out. You need a team of people.” – Phil Harvey (15:09)

“Let’s listen to the users. Let’s bring that perspective in as opposed to thinking about aligning the two perspectives. Because any product is a change. You don’t ride a horse then jump in a car and expect the car to work like the horse.” – Phil Harvey (22:41)

“Let’s say you’re a leader in this space. … Listen out carefully for who’s complaining about who’s not listening to them. That’s a first early signal that there’s work to be done from an empathy perspective.” – Phil Harvey (25:00)

“The perspective of the book that Noelia and I have written is that empathy—and cognitive empathy particularly—is also a learnable skill. There are concrete and real things you can practice and do to improve in those skills.” – Phil Harvey (29:09)

Links Data: A Guide to Humans: https://www.amazon.com/Data-A-Guide-to-Humans/dp/1783528648 Twitter: https://twitter.com/codebeard LinkedIn: https://www.linkedin.com/in/philipdavidharvey/ Mastodon: https://mastodonapp.uk/@codebeard

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://designingforanalytics.com/community and you’ll get a confirmation email back with the link. 

Links Join the Data Product Leadership Community at designingforanalytics.com/thecommunity My definition of “data product” is outlined on Experiencing Data Episode 105  Product vs. Feature Teams by Marty Cagan Email Brian at [email protected].

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 take on the pros and cons of how data departments are handled in regards to company oversight (21:23) Brian and Osian discuss how vital it is to listen to your end users rather than relying on analytics alone to measure adoption (26:50) Osian reveals how he and his team went about designing their platform (31:33) What Osian learned from building out the platform and what he would change if he had to tackle a data product like this all over again (36:34)

Quotes from Today’s Episode “Analytics has been treated very much as a technical problem, and very much so on the data platform side, which is more on the infrastructure and the tooling to enable analytics to take place. And so, viewing that purely as a technical problem left us at odds in a way, compared to [teams that had] a product leader, where the user was the focus [and] the user experience was very much driving a lot of what was roadmap.” — Osian Jones (03:15)

“Whenever we get this question of what’s the impact? What’s the value? How does it impact our company top line? How does it impact our company OKRs? This is when we start to panic sometimes, as data platform leaders because that’s an answer that’s really challenging for us, simply because we are mostly enablers for analytics teams who are themselves enablers. It’s almost like there’s two different degrees away from the direct impact that your team can have.” — Osian Jones (12:45)

“We have to start with a very clear mission. And our mission is to empower everyone to make the best data-driven decisions as fast as possible. And so, hidden within there, that’s a function of reducing time to insight, it’s also about maximizing trust and obviously minimizing costs.” — Osian Jones (13:48)

“We can track [metrics like reliability, incidents, time to resolution, etc.], but also there is a perception aspect to that as well. We can’t underestimate the importance of listening to our users and qualitative data.” — Osian Jones (30:16)

“These were questions that I felt that I naturally had to ask myself as a product manager. … Understanding who our users are, what they are trying to do with data and what is the current state of our data platform—so those were the three main things that I really wanted to get to the heart of, and connecting those three things together.” – Osian Jones (35:29)

“The advice that I would give to anyone who is taking on the role of a leader of a data platform or a similar role is, you can easily get overwhelmed by just so many different use cases. And so, I would really encourage [leaders] to avoid that.” – Osian Jones (37:57)

“Really look at your data platform from an end-user perspective and almost think of it as if you were to put the data platform on a supermarket shelf, what would that look like? And so, for each of the different components, how would you market that in a single one-liner in terms of what can this do for me?” – Osian Jones (39:22)

Links Stuart: https://stuart.com/ Article on IIA: https://iianalytics.com/community/blog/how-to-build-a-data-platform-as-a-product-a-retrospective Experiencing Data Episode 80 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/ LinkedIn: https://www.linkedin.com/in/osianllwydjones/ Medium: https://medium.com/@osianllwyd

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 quality management (05:10) Josh describes how user roles informed TruEra’s design their upcoming monitoring product, and the emphasis he places on iterating with users (10:24)  How Josh approaches striking a balance between displaying extraneous information in the tools he designs vs. removing explainability (14:28) Josh explains how TruEra measures product success now and how they envision that changing in the future (17:59) The difference Josh sees between explainability and interpretability (26:56) How Josh decided to go from being a designer to getting a data science degree (31:08) Josh gives his take on what skills are most valuable as a designer and how to develop them (36:12)

Quotes from Today’s Episode “We want to make machine learning better by testing it, helping people analyze it, helping people monitor models. Bias and fairness is an important part of that, as is accuracy, as is explainability, and as is more broadly AI quality.” — Josh Noble (05:13)

“These two groups, the data scientists and the machine-learning engineer, they think quite differently about the problems that they need to solve. And they have very different toolsets. … Looking at how we can think about making a product and building tools that make sense to both of those different groups is a really important part of user experience.” – Josh Noble (09:04)

“I’m a big advocate for iterating with users. To the degree possible, get things in front of people so they can tell you whether it works for them or not, whether it fits their expectations or not.” – Josh Noble (12:15)

“Our goal is to get people to think about AI quality differently, not to necessarily change. We don’t want to change their performance metrics. We don’t want to make them change how they calculate something or change a workflow that works for them. We just want to get them to a place where they can bring together our four pillars and build better models and build better AI.” – Josh Noble (17:38)

“I’ve always wanted to know what was going on underneath the design. I think it’s an important part of designing anything to understand how the thing that you are making is actually built.” – Josh Noble (31:56)

“There’s a empathy-building exercise that comes from using these tools and understanding where they come from. I do understand the argument that some designers make. If you want to find a better way to do something, spending a ton of time in the trenches of the current way that it’s done is not always the solution, right?” – Josh Noble (36:12)

“There’s a real empathy that you build and understanding that you build from seeing how your designs are actually implemented that makes you a better teammate. It makes you a better collaborator and ultimately, I think, makes you a better designer because of that.” – Josh Noble (36:46)

“I would say to the non-designers who work with designers, measuring designs is not invalidating the designer. It doesn’t invalidate the craft of design. It shouldn’t be something that designers are hesitant to do. I think it’s really important to understand in a qualitative way what your design is doing and understand in a quantitative way what your design is doing.” – Josh Noble (38:18)

Links Truera: https://truera.com/ Medium: https://medium.com/@fctry2

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 need from their products (06:10) The challenges Cole has encountered trying to design a good user experience for an untraditional data product (11:08) Cole describes a time when a Tomorrow.io product didn’t get adopted, and how he and the team pivoted successfully (13:01) The impacts and outcomes of decisions made by customers using products from Tomorrow.io (15:16) Cole describes the value of understanding your active users and what skills and attributes he feels make a great product manager (20:11) Cole explains the challenges of being horizontally positioned rather than operating within an [industry] vertical (23:53) The different functions that are involved in developing Tomorrow.io (28:08) What keeps Cole up at night as the VP of Product for Tomorrow.io (33:47) Cole explains what he would do differently if he could come into his role from the beginning all over again (36:14)

Quotes from Today’s Episode “[Customers aren't] just going to listen to that objective summary and go do the action. It really has to be supplied with a tremendous amount of information around it in a concise way. ... The assumption upfront was just, if we give you a recommendation, you’ll be able to go ahead and go do that. But it’s just not the case.” – Cole Swain (13:40)

“The first challenge is designing this product in a way that you can communicate that value really fast. Because everybody who signs up for new product, they’re very lazy at the beginning. You have to motivate them to be able to realize that, hey, this is something that you can actually harness to change the way that you operate around the weather.” – Cole Swain (11:46)

“People kind of overestimate at times the validity of even just real-time data. So, how do you create an experience that’s intuitive enough to be decision support and create confidence that this tool is different for them, while still having the empathy with the user, that this is still just a forecast in itself; you have to make your own decisions around it.” – Cole Swain (12:43)

“What we often find in weather is that the bigger decisions aren’t made in silos. People don’t feel confident to make it on their own and they require a team to be able to come in because they know the unpredictability of the scenarios and they feel that they need to be able to have partners or comrades in the situation that are in it together with them.” – Cole Swain (17:24)

“To me, there’s two super key capabilities or strengths in being a successful product manager. It’s pattern recognition and it’s the ability to create fast rapport with a customer: in your first conversation with a customer, within five minutes of talking with them, connect with them.” – Cole Swain (22:06)

“[It’s] not about ‘how can we deliver the best value singularly to a particular client,’ but ‘how can we recognize the patterns that rise the tide for all of our customers?’ And it might sound obvious that that’s something that you need to do, but it’s so easy to teeter into the direction of building something unique for a particular vertical.” – Cole Swain (25:41)

“Our sales team is just always finding new use cases. And we have to continue to say no and we have to continue to be disciplined in this arena. But I’d be lying to tell you if that didn’t keep me up at night when I hear about this opportunity of this solution we could build, and I know it can be done in a matter of X amount of time. But the risk of doing that is just too high, sometimes.” – Cole Swain (35:42)

Links Company website: https://Tomorrow.io Twitter: https://twitter.com/colemswain

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 challenges that lead to negative business outcomes when developing data products (14:05) An example of how improving research and problem framing solved a problem for Samir’s first big client (24:33) How speaking in a language your users understand can open the door to more exciting and valuable projects (31:08)

Quotes from Today’s Episode “I don’t think business teams really care how you do it. If you can get an outcome—even if it’s quick and dirty. We’re not supposed to be doing these things for months on end. We’re supposed to be iterating quickly to start to show that result and add value and then building on top of that to show more value, more results.” — Samir Sharma (07:29)

“Language is so important for business teams and technical teams and data teams to actually be able to speak a common language which has common business constructs. Why are organizations trying to train 20,000 people on data literacy, when they’ve got a ten-person data team? Why not just teach the ten people in the data team business language?” — Samir Sharma (10:52)

“I will continuously talk about processes because there’s not enough done actually understanding processes and how data is an event that occurs when a process is kicked off. … If you don’t understand the process and how data is enabling that process, or how data is being generated and the trigger points, then you’re just building something without really understanding where I need to fit that product in or where I need to fit that workflow in.” – Samir Sharma (11:46)

“But I start with asking clear questions about if I built you this dashboard, what is the decision you’re going to make off the back of it? Nine times out of ten, that question isn’t asked, if I build you this widget on this dashboard, what decision or action are you going to make or take? And how is that going to be linked back to the map that strategic objective? And if you can ask that question, you can build with purpose.” – Samir Sharma (19:27)

“You show [users] a bit of value, you show them what they’ve been dying to have, you give them a little bit extra in that so they can really optimize their decisions, and suddenly, you’ve got both sides now speaking a language that is really based on business outcomes and results.” – Samir Sharma (32:38)

“If the people in that conversation are the developers on one side, the business team, and they’re starting to see a new narrative, even the developers will start to say, “Oh! Now, I know exactly why I’m doing this. Now, I know why I’m building it.” So, they’re also starting to learn about the business, about what impacts sales, and maybe how marketing then intertwines into that. It’s important that that is done, but not enough time has been taken on that approach.” – Samir Sharma (24:05)

The thing for me is, business teams don’t know what they don’t know, right? Most of the time, they’re asking a question. If I was on the data team and I’d already built a dashboard that would [answer that question], then I haven’t built it properly in the first instance. What I’ve done is I’ve built it for the beauty and the visualization instead of the what I would class is the ugliness and impact that I need.” – Samir Sharma (17:05)

Links datazuum: https://datazuum.com/ LinkedIn: https://www.linkedin.com/in/samirsharma1/

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 for multiple user types (personas) (06:29) Yuval describes how using Akooda internally (dogfooding!) helps inform their design strategy for various use cases (09:09) The different strategies Akooda employs to ensure they receive honest and valuable feedback from their customers (11:08) Yuval explains the three sales cycles that Akooda goes through to ensure their product is properly adapted to both their buyers and the end users of their tool (15:37) How Yuval learned the importance of providing data-driven insights without a bias of whether the results are good or bad (18:22) Yuval describes his core leadership values and why he feels a product can never be simple enough (24:22) The biggest learnings Yuval had when building Akooda and what he’d do different if he had to start from scratch (28:18) Why Yuval feels being the first Head of Product that reports to a CEO is both a very difficult position to be in and a very hard hire to get right (29:16)

Quotes from Today’s Episode “Re: moving from a technical to product role: My first inclination would be straight up talk about the how, but that’s not necessarily my job anymore. We want to talk about the why and how does the customer perceive things, how do they look at things, how would they experience this new feature? And in a sense, [that’s] my biggest change in the way I see the world.” — Yuval Gonczarowski (03:01)

“We are a very data-driven organization. Part of it is our DNA, my own background. When you first start a company and you’re into your first handful of customers, a lot of decisions have to be made based on gut feelings, sort of hypotheses, scenarios… I’ve lived through this pain.” — Yuval Gonczarowski (09:43)

“I don’t believe I will get honest feedback from a customer if I don’t hurt their pocket. If you want honest feedback [from customers], you got to charge.” — Yuval Gonczarowski (11:38)

“Engineering is the most expensive resource we have. Whenever we allocate engineering resources, they have to be something the customer is going to use.” – Yuval Gonczarowski (13:04)

When selling a data product: “If you don’t build the right collateral and the right approach and mindset to the fact that it’s not enough when the contract is signed, it’s actually these three sales cycles of making sure that customer adoption is done properly, then you haven’t finished selling. Contract is step one, installation is step two, usage is step three. Until step three is done, haven’t really sold the product.” — Yuval Gonczarowski (16:59)

“By definition, all products are too complex. And it’s always tempting to add another button, another feature, another toggle. Let’s see what we can remove to make it easier.” – Yuval Gonczarowski (26:35)

Links Akooda: https://akooda.co/ Yuval’s Email: [email protected] Yuval’s LinkedIn: https://www.linkedin.com/in/goncho/

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 product approach (12:56) The size and scale of the data team at Vista, and how their different roles ensure success (14:30) Sebastian explains how Vista created and grew a team of 35 data product managers (16:47) The skills Sebastian feels data product managers need to be successful at Vista (22:02) Sebastian describes what he would do differently if he had to implement a data product approach at a company again (29:46)

Quotes from Today’s Episode “You need to establish a culture, and that’s often the hardest part that takes the longest -  to treat data as an asset, and not to treat it as a byproduct, but to treat it as a product and treat it as a valuable thing.” – Sebastian Klapdor (07:56)

“One source of data product managers is taking data professionals. So, you take data engineers, data scientists, or former analysts, and develop them into the role by coaching them [through] the product management skills from the software industry.” – Sebastian Klapdor (17:39)

“We went out there and we were hiring people in the market who were experienced [Product Managers]. But we also see internal people, actually grooming and growing into all of these roles, both from these 80 folks who have been around before, but also from other areas of Vista.” – Sebastian Klapdor (20:28)

“[Being a good Product Manager] comes back to the good old classics of collaborating, of being empathetic to where other people are at, their priorities, and understanding where [our] priorities fit into their bigger piece, and jointly aligning on what is valuable for Vista.” – Sebastian Klapdor (22:27)

“I think there’s nothing more detrimental than saying, ‘Yeah, sure, we can deliver things, and with data, it can do everything.’ And then you disappoint people and you don’t stick to your promises. … If you don’t stick to your promise, it will hurt you.” – Sebastian Klapdor (23:04)

“You don’t do the typical waterfall approach of solving business problems with data. You don’t do the approach that a data scientist tries to get some data, builds a model, and hands it over to data engineer who should productionize that. And then the data engineer gets back and says certain features can’t be productionized because it’s very complex to get the data on a daily basis, or in real time. By doing [this work] in a data product team, you can work actually in Agile and you’re super fast building what we call a minimum lovable product.” – Sebastian Klapdor (26:15)

“That was the biggest learning … whom do we staff as data product managers? And what do we expect of a good data product manager? How does a career path look like? That took us a really long time to figure out.” – Sebastian Klapdor (30:18)

“We have a big, big, big commitment that we want to start stuffing UX designers onto our [data] product teams.” - Sebastian Klapdor (21:12)

Links Vista: https://vista.io LinkedIn: https://www.linkedin.com/in/sebastianklapdor/ Vista Blog: https://vista.io/blog

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 (03:42) How Bob views the career progression from data science to product management, and the ways in which his own career has paralleled that journey (07:21) The role customer adoption and user experience play for Bob and the companies he invests in, both pre-investment and post-investment (11:10) Brian and Bob discuss the design capabilities of different teams and why Bob feels it’s something leaders need to keep top of mind (15:25) Bob explains his recommendation to seek out quick wins for AI companies who can’t expect customers to wait for an ROI (19:09) The importance Bob sees in identifying early adopters during a sales cycle for early-stage startups (21:34) Bob describes how being customer-centric allows start-ups to build trust, garner quick wins, and inform their product strategy (23:42) Bob and Brian dive into Bob’s belief that solving intrinsic business problems by vertical increases a start-up’s chance of success substantially over “the platform play” (27:29) Bob gives insight into product trends he believes are going to be extremely impactful in the near future (29:05)

Quotes from Today’s Episode “In a former life, I was a software engineer, founder, and CTO myself, so I have to watch myself to not just geek out on the technology itself because the most important element when you’re determining if you want to move forward with investment or not, is this: is there a real problem here to be solved or is this technology in search of a problem?” — Bob Mason (01:51)

“User-centric research is really valuable, particularly at the earliest stages. If you’re just off by a degree or two, several years down the road, that can be a really material roadblock that you hit. And so, starting off on the right foot, I think is super, super valuable.” – Bob Mason (06:12)

“I don’t think the technical folks in an early-stage startup absolve themselves of not being really intimately involved with their go-to-market and who they’re ultimately creating value for.” – Bob Mason (07:07)

“When we’re making an investment decision, startups don’t generally have any customers, and so we don’t necessarily use the signal of long-term customer adoption as a driver for our initial investment decision. But it’s very much top of mind after investment and as we’re trying to build and bring the first version of the product to market. Being very thoughtful and mindful of sort of customer experience and long-term adoption is absolutely critical.” – Bob Mason (11:23)

“If you’re a scientist, the way you’re presenting both raw data and sort of summaries of data could be quite different than if you’re working with a business analyst that’s a few years out of college with a liberal arts degree. How you interpret results and then present those results, I think, is actually a very interesting design problem.” – Bob Mason (18:40)

“I think initially, a lot of early AI startups just kind of assumed that customers would be patient and let the system run, [waiting] 3, 6, 9, 12 months [to get this] magical ROI, and that’s just not how people (buyers) operate.” – Bob Mason (21:00)

“Re: platform plays: Obviously, you could still create a tremendous platform that’s very broad, but we think if you focus on the business problem of that particular vertical or domain, that actually creates a really powerful wedge so you can increase your value proposition. You could always increase the breadth of a platform over time. But if you’re not solving that intrinsic problem at the very beginning, you may never get the chance to survive.” – Bob Mason (28:24)

Links Argon Ventures: https://argon.vc/ LinkedIn: https://www.linkedin.com/in/robertmason/details/experience/ Email: [email protected]