Experiencing Data w/ Brian T. O’Neill (UX for AI Data Products, SAAS Analytics, Data Product Management)

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If you’re a leader tasked with generating business and org. value through ML/AI and analytics, you’ve probably struggled with low user adoption. Making the tech gets easier, but getting users to use, and buyers to buy, remains difficult—but you’ve heard a ”data product” approach can help. Can it? My name is Brian T. O’Neill, and on Experiencing Data—one of the top 2% of podcasts in the world—I offer you a consulting designer’s perspective on why creating ML and analytics outputs isn’t enough to create business and UX outcomes. How can UX design and product management help you create innovative ML/AI and analytical data products? What exactly are data products—and how can data product management help you increase user adoption of ML/analytics—so that stakeholders can finally see the business value of your data? Every 2 weeks, I answer these questions via solo episodes and interviews with innovative chief data officers, data product management leaders, and top UX professionals. Hashtag: #ExperiencingData. PODCAST HOMEPAGE: Get 1-page summaries, text transcripts, and join my Insights mailing list: https://designingforanalytics.com/ed ABOUT THE HOST, BRIAN T. O’NEILL: https://designingforanalytics.com/bio/

Episodes

Tuesday Nov 14, 2023

130 - Nick Zervoudis on Data Product Management, UX Design Training and Overcoming Imposter Syndrome

Tuesday Nov 14, 2023

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)

129 - Why We Stopped, Deleted 18 Months of ML Work, and Shifted to a Data Product Mindset at Coolblue

Tuesday Oct 31, 2023

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)

128 - Data Products for Dummies and The Importance of Data Product Management with Vishal Singh of Starburst

Tuesday Oct 17, 2023

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)

127 - On the Road to Adopting a “Producty” Approach to Data Products at the UK’s Care Quality Commission with Jonathan Cairns-Terry

Tuesday Oct 03, 2023

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

Tuesday Sep 19, 2023

126 - Designing a Product for Making Better Data Products with Anthony Deighton

Tuesday Sep 19, 2023

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)

125 - Human-Centered XAI: Moving from Algorithms to Explainable ML UX with Microsoft Researcher Vera Liao

Tuesday Sep 05, 2023

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

Tuesday Aug 22, 2023

124 - The PiCAA Framework: My Method to Generate ML/AI Use Cases from a UX Perspective

Tuesday Aug 22, 2023

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

Tuesday Aug 08, 2023

123 - Learnings From the CDOIQ Symposium and How Data Product Definitions are Evolving with Brian T. O’Neill

Tuesday Aug 08, 2023

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

Tuesday Jul 25, 2023

122 - Listener Questions Answered: Conducting Effective Discovery for Data Products with Brian T. O’Neill

Tuesday Jul 25, 2023

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)

121 - How Sainsbury’s Head of Data Products for Analytics and ML Designs for User Adoption with Peter Everill

Tuesday Jul 11, 2023

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)