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

080 – How to Measure the Impact of Data Products…and Anything Else with Forecasting and Measurement Expert Doug Hubbard

Finding it hard to know the value of your data products on the business or your end users? Do you struggle to understand the impact your data science, analytics, or product team is having on the people they serve?  

Many times, the challenge comes down to figuring out WHAT to measure, and HOW. Clients, users, and customers often don’t even know what the right success or progress metrics are, let alone how to quantify them. Learning how to measure what might seem impossible is a highly valuable skill for leaders who want to track their progress with data—but it’s not all black and white. It’s not always about “more data,” and measurement is also not about “the finite, right answer.” Analytical minds, ready to embrace subjectivity and uncertainty in this episode! 

In this insightful chat, Doug and I explore examples from his book, How to Measure Anything, and we discuss its applicability to the world of data and data products. From defining trust to identifying cognitive biases in qualitative research, Doug shares how he views the world in ways that we can actually measure. We also discuss the relationship between data and uncertainty, forecasting, and why people who are trying to measure something usually believe they have a lot less data than they really do. 

Episode Description

• A discussion about measurement, defining “trust”, and why it is important to collect data in a systematic way. (01:35)
• Doug explores “concept, object and methods of measurement” - and why most people have more data than they realize when investigating questions. (09:29)
• Why asking the right questions is more important than “needing to be the expert” - and a look at cognitive biases. (16:46)
• The Dunning-Kruger effect and how it applies to the way people measure outcomes - and Bob discusses progress metrics vs success metrics and the illusion of cognition. (25:13)
• How one of the challenges with machine learning also creates valuable skepticism - and the three criteria for experience to convert into learning. (35:35)

Quotes from Today’s Episode

• “Often things like trustworthiness or collaboration, or innovation, or any—all the squishy stuff, they sound hard to measure because they’re actually an umbrella term that bundles a bunch of different things together, and you have to unpack it to figure out what it is you’re talking about. It’s the beginning of all scientific inquiry is to figure out what your terms mean; what question are you even asking?”- Doug Hubbard (@hdr_frm) (02:33)

• “Another interesting phenomenon about measurement in general and uncertainty, is that it’s in the cases where you have a lot of uncertainty when you don’t need many data points to greatly reduce it. [People] might assume that if [they] have a lot of uncertainty about something, that [they are] going to need a lot of data to offset that uncertainty. Mathematically speaking, just the opposite is true. The more uncertainty you have, the bigger uncertainty reduction you get from the first observation. In other words, if, you know almost nothing, almost anything will tell you something. That’s the way to think of it.”- Doug Hubbard (@hdr_frm) (07:05) 

• “I think one of the big takeaways there that I want my audience to hear is that if we start thinking about when we’re building these solutions, particularly analytics and decision support applications, instead of thinking about it as we’re trying to give the perfect answer here, or the model needs to be as accurate as possible, changing the framing to be, ‘if we went from something like a wild-ass guess, to maybe my experience and my intuition, to some level of data, what we’re doing here is we’re chipping away at the uncertainty, right?’ We’re not trying to go from zero to 100. Zero to 20 may be a substantial improvement if we can just get rid of some of that uncertainty, because no solution will ever predict the future perfectly, so let’s just try to reduce some of that uncertainty.”- Brian T. O’Neill (@rhythmspice) (08:40)

• “So, this is really important: [...] you have more data than you think, and you need less than you think. People just throw up their hands far too quickly when it comes to measurement problems. They just say, ‘Well, we don’t have enough data for that.’ Well, did you look? Tell me how much time you spent actually thinking about the problem or did you just give up too soon? [...] Assume there is a way to measure it, and the constraint is that you just haven’t thought of it yet. ”- Doug Hubbard (@hdr_frm) (15:37)

• “I think people routinely believe they have a lot less data than they really do. They tend to believe that each situation is more unique than it really is [to the point] that you can’t extrapolate anything from prior observations. If that were really true, your experience means nothing.”- Doug Hubbard (@hdr_frm) (29:42)

• “When you have a lot of uncertainty, that’s exactly when you don’t need a lot of data to reduce it significantly. That’s the general rule of thumb here. [...] If what we’re trying to improve upon is just the subjective judgment of the stakeholders, all the research today—and by the way, here’s another area where there’s tons of data—there’s literally hundreds of studies where naive statistical models are compared to human experts […] and the consistent finding is that even naive statistical models outperform human experts in a surprising variety of fields.”- Doug Hubbard (@hdr_frm) (32:50)

Links Referenced

• How to Measure Anything: https://www.amazon.com/gp/product/1118539273/
• Hubbard Decision Research: https://hubbardresearch.com

 

079 - How Sisu’s CPO, Berit Hoffmann, Is Approaching the Design of Their Analytics Product…and the UX Mistakes She Won’t Make Again

Berit Hoffmann, Chief Product Officer at Sisu, tackles design from a customer-centric perspective with a focus on finding problems at their source and enabling decision making. However, she had to learn some lessons the hard way along the road, and in this episode, we dig into those experiences and what she’s now doing differently in her current role as a CPO.

In particular, Berit reflects on her “ivory tower design” experience at a past startup called Bebop. In that time, she quickly realized the importance of engaging with customer needs and building intuitive and simple solutions for complex problems. Berit also discusses the Double Diamond Process and how it shapes her own decision-making and the various ways she carries her work at Sisu.

 

In this episode, we also cover:

• How Berit’s “ivory tower design experience” at Bebop taught her the importance of dedicating time to focus on the customer. (01:31)
• What Berit looked for as she researched Sisu prior to joining - and how she and Peter Bailis, Founder and CEO, share the same philosophy on what a product’s user experience should look like. (03:57)
• Berit discusses the Double Diamond Process and the life cycle of designing a project - and shares her take on designing for decision making. (10:17)
• Sisu’s shift from answering the why to the what - and how they approach user testing using product as a metric layer. (19:10)
• Berit explores the tension that can arise when designing a decision support tool. (31:03)

Quotes from Today’s Episode

• “I kind of learned the hard way, the importance of spending that time with customers upfront and really digging into understanding what problems are most challenging for them. Those are the problems to solve, not the ones that you as a product manager or as a designer think are most important. It is a lesson I carry forward with me in terms of how I approach anything I'm going to work on now. The sooner I can get it in front of users, the sooner I can get feedback and really validate or invalidate my assumptions, the better because they're probably going to tell me why I'm wrong.”- Berit Hoffmann (03:15)

 

• “As a designer and product thinker, the problem finding is almost more important than the solutioning because the solution is easy when you really understand the need. It's not hard to come up with good solutions when the need is so clear, which you can only get through conversation, inquiry, shadowing, and similar research and design methods.” - Brian T. O’Neill (@rhythmspice) (10:54)

 

• “Decision-making is a human process. There's no world in which you're going to spit out an answer and say, ‘just go do it.’ Software is always going to be missing the rich context and expertise that humans have about their business and the context in which they're making the decision. So, what that says to me is inherently, decision-making is also going to be an iterative process. [...] What I think technology can do is it can automate and accelerate a lot of the manual repetitive steps in the analysis that are taking up a bunch of time today. Especially as data is getting exponentially more complex and multi-dimensional.”- Berit Hoffmann  (17:44)

 

• “When we talk to people about solving problems, 9 out of 10 people say they would add something to whatever it is that you're making to make it better. So often, when designers think about modernism, it is very much about ‘what can I take away that will help it make it better?’ And, I think this gets lost. The tendency with data, when you think about how much we're collecting and the scale of it, is that adding it is always going to make it better and it doesn't make it better all the time. It can slow things down and cause noise. It can make people ask even more questions. When in reality, the goal is to make a decision.”- Brian T. O’Neill (@rhythmspice) (30:11)

 

• “I’m trying to resist the urge to get industry-specific or metric specific in any of the kind of baseline functionality in the product. And instead, say that we can experiment in a lightweight way in terms of outside of the product, health content, guidance on best practices, etc. That is going to be a constant tension because the types of decisions that you enact and the types of questions you're digging into are really different depending on whether you're a massive hotel chain compared to a quick-service restaurant compared to a B2B SAAS company. The personas and the questions are so different. So that's a tension that I think is really interesting when you think about the decision-making workflow and who those stakeholders are.”- Berit Hoffmann (32:05)

Links Referenced

• Sisu: https://sisudata.com
• Berit Hoffmann on LinkedIn: https://www.linkedin.com/in/Hoffmannn-berit/
• Sisu on LinkedIn: https://www.linkedin.com/company/sisu-data/

078 - From Data to Product: What is Data Product Management and Why Do We Need It with Eric Weber

Eric Weber, Head of Data Product at Yelp, has spent his career developing a product-minded approach to producing data-driven solutions that actually deliver value. For Eric, developing a data product mindset is still quite new and today, we’re digging into all things “data product management” and why thinking of data with a product mindset matters.

In our conversation, Eric defines what data products are and explains the value that data product managers can bring to their companies. Eric’s own ethos on centering on empathy, while equally balanced with technical credibility, is central to his perspectives on data product management. We also discussed how Eric is bringing all of this to hand at Yelp and the various ways they’re tackling their customers' data product needs.

In this episode, we also cover:

• What is a data product and why do we need data product management? (01:34)
• Why successful data product managers carry two important traits - empathy and technical credibility. (10:47)
• A discussion about the levels of problem-solving maturity, the challenge behind delivering solutions, and where product managers can be the most effective during the process. (16:54)
• A look at Yelp’s customer research strategy and what they are focusing on to optimize the user experience. (21:28)
• How Yelp’s product strategy is influenced by classes of problems – and Yelp’s layers of experimentation. (27:38)
• Eric reflects on unlearning and talks about his newsletter, From Data to Product. (34:36)

Quotes from Today’s Episode

• “Data products bring companies a way to think about the long-term viability and sustainability of their data investments. [...] And part of that is creating things that are sustainable, that have a strategy, that have a customer in mind. And a lot of these things people do - maybe they don't call it out explicitly, but this is a packaging that I think focuses us in the right places rather than hoping for the best.”-  Eric Weber (@edweber1) (02:43)

• “My hypothesis right now is that by introducing [product management] as a role, you create a vision for our product that is not just tied to a person, it's not just tied to a moment in time of the company. It's something where you can actually have another product manager come in and understand where things are headed. I think that is really the key to seeing the 10 to 20-year sustainability, other than crossing your fingers and hoping that one person stays for a long time, which is kind of a tough bet in this environment.”- Eric Weber (@edweber1) (07:27)

• “My background is in design and one of the things that I have to work on a lot with my clients and with data scientists in particular, is getting out of the head of wanting to work on “the thing” and learning how to fall in love with the customer's problem and their need. And this whole idea of empathy, not being a squishy thing, but do you want your work to matter? Or, do you just write code or work on models all day long and you don't care if it ships and makes a difference? I think good product-minded people care a lot about that outcome. So, this output versus outcome thing is a mindset change that has to happen.”- Brian T. O’Neill (@rhythmspice) (10:56)

• “The question about whether you focus on internal development or external buying often goes back to, what is your business trying to do? And how much is this going to cost us over time? And it's fascinating because I want [anyone listening] to come across [the data product] field as an area in motion. It's probably going to look pretty different a year from now, which I find pretty awesome and fascinating myself.”- Eric Weber (@edweber1) (27:02)

• “If you don't have a deep understanding of what your customer is trying to do and are able to abstract it to some general class of problem, you're probably going to end up building a solution that's too narrow and not sustainable because it will solve something in the short term. But, what if you have to re-architect the whole thing? That's where it becomes really expensive and where having a product strategy pays off.”- Eric Weber (@edweber1) (31:28)

• “I've had to unlearn that idea that I need to create a definitive framework of what someone does. I just need to be able to put on different lenses. [For example] if I'm talking to design today, these are probably the things that they're going to be focused on and concerned about. If I'm talking to our executive team, this is probably how they're going to break this problem down and look at it. So, I think it's not necessarily dropping certain frameworks, it's being able to understand that some of them are useful in certain scenarios and they're not in others. And that ability is something that I think has created this chance for me to look at the data product from different spaces and think about why it might be valuable.”- Eric Weber (@edweber1) (35:54)

Links

• Subscribe to “From Data to Product” on Substack
• LinkedIn
• Yelp

 

077 - Productizing Analytics for Performing Arts Organizations with AMS Analytics CPO Jordan Gross Richmond

Even in the performing arts world, data and analytics is serving a purpose. Jordan Gross Richmond is the Chief Product Officer at AMS Analytics, where they provide benchmarking and performance reporting to performing arts organizations. As many of you know, I’m also a musician who tours and performs in the performing arts market and so I was curious to hear how data plays a role “off the stage” within these organizations. In particular, I wanted to know how Jordan designed the interfaces for AMS Analytics’s product, and what’s unique (or not!) about using data to manage arts organizations.

Jordan also talks about the beginnings of AMS and their relationship with leaders in the performing arts industry and the “birth of benchmarking” in this space. From an almost manual process in the beginning, AMS now has a SaaS platform that allows performing arts centers to see the data that helps drive their organizations. Given that many performing arts centers are non-profit organizations, I also asked Jordan about how these organizations balance their artistic mission against the colder, harder facts of data such as ticket sales, revenue, and “the competition.”  

In this episode, we also cover:

• How the AMS platform helps leaders manage their performing arts centers and the evolution of the AMS business model. (01:10)
• Benchmarking as a measure of success in the performing arts industry and the “two buckets of context” AMS focuses on. (06:00)
• Strategies for measuring intangible success and how performing arts data is about more than just the number of seats filled at concerts and shows. (15:48)
• The relationships between AMS and its customers, their organizational structure, and how AMS has shaped it into a useful SaaS product. (26:27)
• The role of users in designing the solution and soliciting feedback and what Jordan means when he says he “focuses on the problems, and not the solutions” in his role as Chief Product Officer. (35:38)

 Quotes from Today’s Episode

• “I think [AMS] is a one-of-a-kind thing, and what it does now is it provides what I consider to be a steering wheel for these leaders. It’s not the kind of thing that’s going to help anybody figure out what to do tomorrow; it’s more about what’s going on in a year from now and in five years from now. And I think the need for this particular vision comes from the evolution in the business model in general of the performing arts and the cultural arts in America.”- Jordan Gross Richmond (@the1jordangross) (03:07)

• “No one metric can solve everything. It’s a one-to-one relationship in terms of data model to analytical point. So, we have to be really careful that we don't think that just because there's a lot of charts on the screen, we must be able to answer all of our [customers'] questions.”- Jordan Gross Richmond (@the1jordangross) (18:18)

• “We are absolutely a product-led organization, which essentially means that the solutions are built into the product, and the relationship with the clients and the relationship with future clients is actually all engineered into the product itself. And so I never want to create anything in a black box. Nobody benefits from a feature that nobody cares about.”- Jordan Gross Richmond (@the1jordangross) (29:16)

• “This is an evolution that's driven not by the technology itself, but [...] by the key stakeholders amongst this community. And we found that to be really successful. In terms of product line growth, when you listen to your users and they feel heard, the sky's the limit. Because at that point, they have buy-in, so you have a real relationship. ”- Jordan Gross Richmond (@the1jordangross) (31:11)

• “Successful product leaders don't focus on the solutions. We focus on the problems. And that's where I like to stay, because sometimes we kind of get into lots of proposals. My role in these meetings is often to help identify the problem and make sure we're all solving the same problem because we can get off pretty easily on a solution that sounds sexy [or] interesting, but if we're not careful, we might be solving a problem that doesn't even exist.”- Jordan Gross Richmond (@the1jordangross) (35:09)

• “It’s about starting with the customer’s problems and working backwards from that. I think that you have to start with the problem space that they're in, and then you do the best job you can with the data that's available. [...] So, I love the fact that you're having these working groups. Sometimes we call these design partners in the design world, and I think that kind of regular interaction and exposure, especially early and as frequently as possible, is a great habit.”- Brian T. O’Neill (@rhythmspice) (40:26)

Links Referenced

https://www.ams-analytics.com/

076 - How Bedrock’s “Data by Design” Mantra Helps Them Build Human-Centered Solutions with Jesús Templado

Why do we need or care about design in the work of data science? Jesús Templado, Managing Director at Bedrock, is here to tell us about how Bedrock executes their mantra, “data by design.” 

 

Bedrock has found ways to bring to their clients a design-driven, human-centered approach by utilizing a “hybrid model” to synthesize technical possibilities with human needs. In this episode, we explore Bedrock’s vision for how to achieve this synthesis as part of the firm’s DNA, and how Bedrock adopted their vision to make data more approachable with the client being central to their design efforts. Jesús also discusses a time when he championed making “data by design” a successful strategy with a large chain of hotels, and he offers insight on how making clients feel validated and heard plays a part.

 

In our chat, we also covered: 

• “Data by design” and how Bedrock implements this design-driven approach. (00:43)
• Bedrock’s vision for how they support their clients and why design has always been part of their DNA. (08:53)
• Jesús shares a time when he successfully implemented a design process for a large chain of hotels, and some of the challenges that came with that approach. (14:47)
• The importance of making clients feel heard by dedicating time to research and UX and how the team navigates conversations about risk with customers. (24:12)
• More on the client experience and how Bedrock covers a large spectrum of areas to ensure that they deliver a product that makes sense for the customer. (33:01)
• Jesús’ opinion on why companies should consider change management when building products and systems - and a look at the Data Stand-Up podcast (35:42)

Quotes from Today’s Episode

“Many people in corporations don’t have the technical background to understand the possibilities when it comes to analyzing or using data. So, bringing a design-based framework, such as design thinking, is really important for all of the work that we do for our clients.” - Jesús Templado (2:33)

 

“We’ve mentioned “data by design” before as our mantra; we very much prefer building long-lasting relationships based on [understanding] our clients' business and their strategic goals. We then design and ideate an implementation roadmap with them and then based on that, we tackle different periods for building different models. But we build the models because we understand what’s going to bring us an outcome for the business—not because the business brings us in to deliver only a model for the sake of predicting what the weather is going to be in two weeks.”- Jesús Templado (14:07)

 

“I think as consultants and people in service, it’s always nice to make friends. And, I like when I can call a client a friend, but I feel like I’m really here to help them deliver a better future state [...] And the road may be bumpy, especially if design is a new thing. And it is often new; in the context of data science and analytics projects.”- Brian T. O’Neill (@rhythmspice) (26:49)

 

“When we do data science [...] that’s a means to an end. We do believe it’s important that the client understands the reasoning behind everything that we do and build, but at the end of the day, it’s about understanding that business problem, understanding the challenge that the company is facing, knowing what the expected outcome is, and knowing how you will deliver or predict that outcome to be used for something meaningful and relevant for the business.”- Jesús Templado (33:06)

 

“The appetite for innovation is high, but a lot of the companies that want to do it are more concerned about risk. Risk and innovation are at opposite ends of the spectrum. And so, if you want to be innovative, by definition—you’re signing up for failure on the way to success. [...] It’s about embracing an iterative process, it’s about getting feedback along the way, it’s about knowing that we don’t know everything, and we’re signing up for that ambiguity along the way to something better.”- Brian T. O’Neill (@rhythmspice) (38:20)

 

Links Referenced

• Bedrock: https://bedrockdbd.com
• Data Stand-Up podcast: https://bedrockdbd.com/podcast/
• LinkedIn: https://www.linkedin.com/in/Jesústg/

075 - How CDW is Integrating Design Into Its Data Science and Analytics Teams with Prasad Vadlamani

How do we get the most breadth out of design and designers when building data products? One way is to have designers be at the front leading the charge when it comes to creating data products that must be useful, usable, and valuable.

 

For this episode Prasad Vadlamani, CDW’s Director of Data Science and Advanced Analytics, joins us for a chat about how they are making design a larger focus of how they create useful, usable data products. Prasad talks about the importance of making technology—including AI-driven solutions—human centered, and how CDW tries to keep the end user in mind. 

Prasad and I also discuss his perspectives on how to build designers into a data product team and how to successfully navigate the grey areas between various areas of expertise. When this is done well, then the entire team can work with each other's strengths and advantages to create a more robust product. We also discuss the role a UI-free user experience plays in some data products, some differences between external and internally-facing solutions, and some of Prasad’s valuable takeaways that have helped to shape the way he thinks design, data science, and analytics can collaborate.

 

In our chat, we covered: 

 

• Prasad’s first introduction to designers and how he leverages the disciplines of design and product in his data science and analytics work (1:09)
• The terminology behind product manager and designer and how these functions play a role in an enterprise AI team (5:18)
• How teams can use their wide range of competencies to their advantage (8:52)
• A look at one UI-less experience and the value of the “invisible interface” (14:58)
• Understanding the model development process and why the model takes up only a small percentage of the effort required to successfully bring a data product to end users (20:52)
• The differences between building an internal vs external product, what to consider, and Prasad’s “customer zero” approach. (29.17)
• Expectations Prasad sets with customers (stakeholders) about the life expectancy of data products when they are in their early stage of development (35:02)

074 - Why a Former Microsoft ML/AI Researcher Turned to Design to Create Intelligent Products from Messy Data with Abhay Agarwal, Founder of Polytopal

Episode Description

The challenges of design and AI are exciting ones to face. The key to being successful in that space lies in many places, but one of the most important is instituting the right design language.

For Abhay Agarwal, Founder of Polytopal, when he began to think about design during his time at Microsoft working on systems to help the visually impared, he realized the necessity of a design language for AI. Stepping away from that experience, he leaned into how to create a new methodology of design centered around human needs. His efforts have helped shift the lens of design towards how people solve problems.

In this episode, Abhay and I go into details on a snippet from his course page for the Stanford d. where he claimed that “the foreseeable future would not be well designed, given the difficulty of collaboration between disciplines.” Abhay breaks down how he thinks his design language for AI should work and how to build it out so that everyone in an organization can come to a more robust understanding of AI. We also discuss the future of designers and AI and the ebb and flow of changing, learning, and moving forward with the AI narrative. 

In our chat, we covered:

• Abhay’s background in AI research and what happened to make him move towards design as a method to produce intelligence from messy data. (1:01)
• Why Abhay has come up with a new design language called Lingua Franca for machine learning products [and his course on this at Stanford’s d.school]. (3:21)
• How to become more human-centered when building AI products, what ethnographers can uncover, and some of Abhay’s real-world examples. (8:06)
• Biases in design and the challenges in developing a shared language for both designers and AI engineers. (15:59)
• Discussing interpretability within black box models using music recommendation systems, like Spotify, as an example. (19:53)
• How “unlearning” solves one of the biggest challenges teams face when collaborating and engaging with each other. (27:19) 
• How Abhay is shaping the field of design and ML/AI -- and what’s in store for Lingua Franca. (35:45)

Quotes from Today's Episode

“I certainly don’t think that one needs to hit the books on design thinking or listen to a design thinker describe their process in order to get the fundamentals of a human-centered design process. I personally think it’s something that one can describe to you within the span of a single conversation, and someone who is listening to that can then interpret that and say, ‘Okay well, what am I doing that could be more human-centered?’ In the AI space, I think this is the perennial question.” - Abhay Agarwal (@Denizen_Kane) (6:30)

 

“Show me a company where designers feel at an equivalent level to AI engineers when brainstorming technology? It just doesn’t happen. There’s a future state that I want us to get to that I think is along those lines. And so, I personally see this as, kind of, a community-wide discussion, engagement, and multi-strategy approach.” - Abhay Agarwal (@Denizen_Kane) (18:25)

 

“[Discussing ML data labeling for music recommenders] I was just watching a video about drum and bass production, and they were talking about, “Or you can write your bass lines like this”—and they call it reggaeton. And it’s not really reggaeton at all, which was really born in Puerto Rico. And Brazil does the same thing with their versions of reggae. It’s not the one-drop reggae we think of Bob Marley and Jamaica. So already, we’ve got labeling issues—and they’re not even wrong; it’s just that that’s the way one person might interpret what these musical terms mean” - Brian O’Neill (@rhythmspice) (25:45)

 

“There is a new kind of hybrid role that is emerging that we play into...which is an AI designer, someone who is very proficient with understanding the dynamics of AI systems. The same way that we have digital UX designers, app designers—there had to be apps before they could be app designers—there is now AI, and then there can thus be AI designers.” - Abhay  Agarwal (@Denizen_Kane) (33:47)

 

Links Referenced

• Lingua Franca: https://linguafranca.polytopal.ai
• Polytopal.ai: https://polytopal.ai
• Polytopal email: hello@polytopal.ai
• LinkedIn: https://www.linkedin.com/in/abhaykagarwal/
• Personal Twitter: https://twitter.com/Denizen_Kane
• Polytopal Twitter: https://twitter.com/polytopal_ai

073 - Addressing the Functional and Emotional Needs of Users When Designing Data Products with Param Venkataraman

Episode Description

Simply put, data products help users make better decisions and solve problems with information. But how effective can data products be if designers don’t take the time to explore the complete needs of users?

To Param Venkataraman, Chief Design Officer at Fractal Analytics, having an understanding of the “human dimension” of a problem is crucial to creating data solutions that create impact.

On this episode of Experiencing Data, Param and I talk more about his concept of ‘attractive non-conscious design,’ the core skills of a professional designer, and why Fractal has a c-suite design officer and is making large investments in UX. 

 

In our chat, we covered:

• Param's role as Chief Design Officer at Fractal Analytics, and the company's sharp focus on the 'human dimension' of enterprise data products. (2:04)
• 'Attractive non-conscious design': Creating easy-to-use, 'delightful' data products that help end-users make better decisions by focusing on their needs. (5:32)
• The importance of understanding the 'emotional need' of users when designing enterprise data products. (9:07)
• Why designers as well as data science and analytics teams should focus more on the emotional and human element when building data products. (16:15)
• 'The next version of design': Why and how Param believes the classic design thinking model must adapt to the 'post-data science world.' (21:39)
• The core competencies of a professional designer and how it relates to data products. (25:59)
• Why non-designers should learn the principles of good design — and how Fractal’s internal Phi Design System helps frame problems from the perspective of a data product's end-user, leading to better solutions. (27:51)
• Why Param believes the coming together of design and data still needs time to mature. (33:40)

Quotes from Today’s Episode

“When you look at analytics and the AI space … there is so much that is about how do you use ... machine learning … [or] any other analytics technology or solutions — and how do you make better effective decisions? That’s at the heart of it, which is how do we make better decisions?” - Param Venkataraman (@onwardparam) (6:23) 

 

“[When it comes to business software,] most of it should be invisible; you shouldn’t really notice it. And if you’re starting to notice it, you’re probably drawing attention to the wrong thing because you’re taking people out of flow.” - Brian O’Neill (@rhythmspice) (8:57)

 

“Design is kind of messy … there’s sort of a process ... but it’s not always linear, and we don’t always start at step zero. … You might come into something that’s halfway done and the first thing we do is run a usability study on a competitor’s thing, or on what we have now, and then we go back to step two, and then we go to five. It’s not serial, and it’s kind of messy, and that’s normal.” - Brian O’Neill (@rhythmspice) (16:18)

 

“Just like design is iterative, data science also is very iterative. There’s the idea of hypothesis, and there’s an idea of building and experimenting, and then you sort of learn and your algorithm learns, and then you get better and better at it.” - Param Venkataraman (@onwardparam) (18:05)

 

“The world of data science is not used to thinking in terms of emotion, experience, and the so-called softer aspects of things, which in my opinion, is not actually the softer; it’s actually the hardest part. It’s harder to dimensionalize emotion, experience, and behavior, which is … extremely complex, extremely layered, [and] extremely unpredictable. … I think the more we can bring those two worlds together, the world of evidence, the world of data, the world of quantitative information with the qualitative, emotional, and experiential, I think that’s where the magic is.” - Param Venkataraman (@onwardparam) (21:02)

 

“I think the coming together of design and data is... a new thing. It’s unprecedented. It’s a bit like how the internet was a new thing back in the mid ’90s. We were all astounded by it, we didn’t know what to do with it, and everybody was just fascinated with it. And we just knew that it’s going to change the world in some way. … Design and data will take some time to mature, and what’s more important is to go into it with an open mind and experiment. And I’m saying this for both designers as well as data scientists, to try and see how the right model might evolve as we experiment and learn.” - Param Venkataraman (@onwardparam) (33:58)

Links Referenced

• Fractal Analytics: https://fractal.ai
• LinkedIn: https://www.linkedin.com/in/parameswaranv/
• Twitter: https://twitter.com/onwardparam

072 - How to Get Stakeholders to Reveal What They Really Need From a Data Product with Cindy Dishmey Montgomery

Episode Description

How do you extract the real, unarticulated needs from a stakeholder or user who comes to you asking for AI, a specific app feature, or a dashboard? 

On this episode of Experiencing Data, Cindy Dishmey Montgomery, Head of Data Strategy for Global Real Assets at Morgan Stanley, was gracious enough to let me put her on the spot and simulate a conversation between a data product leader and customer.

I played the customer, and she did a great job helping me think differently about what I was asking her to produce for me — so that I would be getting an outcome in the end, and not just an output. We didn’t practice or plan this exercise, it just happened — and she handled it like a pro! I wasn’t surprised; her product and user-first approach told me that she had a lot to share with you, and indeed she did!  

A computer scientist by training, Cindy has worked in data, analytics and BI roles at other major companies, such as Revantage, a Blackstone real estate portfolio company, and Goldman Sachs. Cindy was also named one of the 2021 Notable Women on Wall Street by Crain’s New York Business.

Cindy and I also talked about the “T” framework she uses to achieve high-level business goals, as well as the importance for data teams to build trust with end-users.

 

In our chat, we covered:

• Bringing product management strategies to the creation of data products to build adoption and drive value. (0:56)
• Why the first data hire when building an internal data product should be a senior leader who is comfortable with pushing back. (3:54)
• The "T" Framework: How Cindy, as Head of Data Strategy, Global Real Assets at Morgan Stanley, works to achieve high-level business goals. (8:48)
• How building trust with internal stakeholders by creating valuable and smaller data products is key to eventually working on bigger data projects. (12:38)
• How data's role in business is still not fully understood. (18:17)
• The importance for data teams to understand a stakeholder's business problem and also design a data product solution in collaboration with them. (24:13)
• 'Where's the why': Cindy and Brian roleplay as a data product manager and a customer, respectively, and simulate how to successfully identify a customer’s problem and also open them up to new solutions. (28:01)
• The benefits of a data product management role — and why 'everyone should understand product.' (33:49)

Quotes from Today’s Episode

“There’s just so many good constructs in the product management world that we have not yet really brought very close to the data world. We tend to start with the skill sets, and the tools, and the ML/AI … all the buzzwords. [...]But brass tacks: when you have a happy set of consumers of your data products, you’re creating real value.” - Cindy Dishmey Montgomery (1:55)

 

“The path to value lies through adoption and adoption lies through giving people something that actually helps them do their work, which means you need to understand what the problem space is, and that may not be written down anywhere because they’re voicing the need as a solution.” - Brian O’Neill (@rhythmspice) (4:07)

 

“I think our data community tends to over-promise and under-deliver as a way to get the interest, which it’s actually quite successful when you have this notion of, ‘If you build AI, profit will come.’ But that is a really, really hard promise to make and keep.” - Cindy Dishmey Montgomery (12:14)

 

“[Creating a data product for a stakeholder is] definitely something where you have to be close to the business problem and design it together. … The struggle is making sure organizations know when the right time and what the right first hire is to start that process.” - Cindy Dishmey Montgomery (23:58)

 

“The temporal aspect of design is something that’s often missing. We talk a lot about the artifacts: the Excel sheet, the dashboard, the thing, and not always about when the thing is used.” - Brian O’Neill (@rhythmspice) (27:27)

“Everyone should understand product. And even just creating the language of product is very helpful in creating a center of gravity for everyone. It’s where we invest time, it’s how it’s meant to connect to a certain piece of value in the business strategy. It’s a really great forcing mechanism to create an environment where everyone thinks in terms of value. And the thing that helps us get to value, that’s the data product.” - Cindy Dishmey Montgomery (34:22)

Links Referenced

• LinkedIn: https://www.linkedin.com/in/cindy-dishmey/

071 - The ROI of UX Research and How It Applies to Data Products with Bill Albert

There are many benefits in talking with end users and stakeholders about their needs and pain points before designing a data product. 

 

Just take it from Bill Albert, executive director of the Bentley University User Experience Center, author of Measuring the User Experience, and my guest for this week’s episode of Experiencing Data. With a career spanning more than 20 years in user experience research, design, and strategy, Bill has some great insights on how UX research is pivotal to designing a useful data product, the different types of customer research, and how many users you need to talk to to get useful info.

 

In our chat, we covered:

• How UX research techniques can help increase adoption of data products. (1:12)
• Conducting 'upfront research': Why talking to end users and stakeholders early on is crucial to designing a more valuable data product. (8:17)
• 'A participatory design process': How data scientists should conduct research with stakeholders before and during the designing of a data product. (14:57)
• How to determine sample sizes in user experience research -- and when to use qualitative vs. quantitative techniques. (17:52)
• How end user research and design improvements helped Boston Children's Hospital drastically increase the number of recurring donations. (24:38)
• How a person's worldview and experiences can shape how they interpret data. (32:38)
• The value of collecting metrics that reflect the success and usage of a data product. (38:11)

Quotes from Today’s Episode

“Teams are constantly putting out dashboards and analytics applications — and now it’s machine learning and AI— and a whole lot of it never gets used because it hits all kinds of human walls in the deployment part.” - Brian (3:39)

 

“Dare to be simple. It’s important to understand giving [people exactly what they] want, and nothing more. That’s largely a reflection of organizational maturity; making those tough decisions and not throwing out every single possible feature [and] function that somebody might want at some point.” - Bill (7:50)

 

“As researchers, we need to more deeply understand the user needs and see what we’re not observing in the lab [and what] we can’t see through our analytics. There’s so much more out there that we can be doing to help move the experience forward and improve that in a substantial way.” - Bill (10:15)

 

“You need to do the upfront research; you need to talk to stakeholders and the end users as early as possible. And we’ve known about this for decades, that you will get way more value and come up with a better design, better product, the earlier you talk to people.” - Bill (13:25)

 

“Our research methods don’t change because what we’re trying to understand is technology-agnostic. It doesn’t matter whether it’s a toaster or a mobile phone — the questions that we’re trying to understand of how people are using this, how can we make this a better experience, those are constant.” - Bill (30:11)

 

“I think, what’s called model interpretability sometimes or explainable AI, I am seeing a change in the market in terms of more focus on explainability, less on model accuracy at all costs, which often likes to use advanced techniques like deep learning, which are essentially black box techniques right now. And the cost associated with black box is, ‘I don’t know how you came up with this and I’m really leery to trust it.’” - Brian (31:56)

Resources and Links:

• Bentley University User Experience Center: https://www.bentley.edu/centers/user-experience-center
• Measuring the User Experience: https://www.amazon.com/Measuring-User-Experience-Interactive-Technologies/dp/0124157815
• www.bentley.edu/uxc: https://www.bentley.edu/uxc
• LinkedIn: https://www.linkedin.com/in/walbert/