Today I sit down with Vijay Yadav, head of the data science team at Merck Manufacturing Division. Vijay begins by relating his own path to adopting a data product and UX-driven approach to applied data science, andour chat quickly turns to the ever-present challenge of user adoption. Vijay discusses his process of designing data products with customers, as well as the impact that building user trust has on delivering business value. We go on to talk about what metrics can be used to quantify adoption and downstream value, and then Vijay discusses the financial impact he has seen at Merck using this user-oriented perspective. While we didn’t see eye to eye on everything, Vijay was able to show how focusing on the last mile UX has had a multi-million dollar impact on Merck. The conversation concludes with Vijay’s words of advice for other data science directors looking to get started with a design and user-centered approach to building data products that achieve adoption and have measurable impact.

In our chat, we covered Vijay’s design process, metrics, business value, and more: 

Vijay shares how he came to approach data science with a data product management approach and how UX fits in (1:52) We discuss overcoming the challenge of user adoption by understanding user thinking and behavior (6:00) We talk about the potential problems and solutions when users self-diagnose their technology needs (10:23) Vijay delves into what his process of designing with a customer looks like (17:36) We discuss the impact “solving on the human level” has on delivering real world benefits and building user trust (21:57) Vijay talks about measuring user adoption and quantifying downstream value—and Brian discusses his concerns about tool usage metrics as means of doing this (25:35) Brian and Vijay discuss the multi-million dollar financial and business impact Vijay has seen at Merck using a more UX  driven approach to data product development (31:45) Vijay shares insight on what steps a head of data science  might wish to take to get started implementing a data product and UX approach to creating ML and analytics applications that actually get used  (36:46)

Quotes from Today’s Episode “They will adopt your solution if you are giving them everything they need so they don’t have to go look for a workaround.” - Vijay (4:22)

“It’s really important that you not only capture the requirements, you capture the thinking of the user, how the user will behave if they see a certain way, how they will navigate, things of that nature.” - Vijay (7:48)

“When you’re developing a data product, you want to be making sure that you’re taking the holistic view of the problem that can be solved, and the different group of people that we need to address. And, you engage them, right?” - Vijay (8:52)

“When you’re designing in low fidelity, it allows you to design with users because you don’t spend all this time building the wrong thing upfront, at which point it’s really expensive in time and money to go and change it.” - Brian (17:11)

"People are the ones who make things happen, right? You have all the technology, everything else looks good, you have the data, but the people are the ones who are going to make things happen.” - Vijay (38:47)

“You want to make sure that you [have] a strong team and motivated team to deliver. And the human spirit is something, you cannot believe how stretchable it is. If the people are motivated, [and even if] you have less resources and less technology, they will still achieve [your goals].” - Vijay (42:41)

“You’re trying to minimize any type of imposition on [the user], and make it obvious why your data product  is better—without disruption. That’s really the key to the adoption piece: showing how it is going to be better for them in a way they can feel and perceive. Because if they don’t feel it, then it’s just another hoop to jump through, right?” - Brian (43:56)

Resources and Links:  LinkedIn: https://www.linkedin.com/in/vijyadav/

Michelle Carney began her career in the worlds of neuroscience and machine learning where she worked on the original Python Notebooks. As she fine-tuned ML models and started to notice discrepancies in the human experience of using these models, her interest turned towards UX. Michelle discusses how her work today as a UX researcher at Google impacts her work with teams leveraging ML in their applications. She explains how her interest in the crossover of ML and UX led her to start MLUX, a collection of meet-up events where professionals from both data science and design can connect and share methods and ideas. MLUX now hosts meet-ups in several locations as well as virtually. 

Our conversation begins with Michelle’s explanation of how she teaches data scientists to integrate UX into the development of their products. As a teacher, Michelle utilizes the IDEO Design Kit with her students at the Stanford School of Design (d.school). In her teaching she shares some of the unlearning that data scientists need to do when trying to approach their work with a UX perspective in her course, Designing Machine Learning.

Finally, we also discussed what UX designers need to know about designing for ML/AI. Michelle also talks about how model interpretability is a facet of UX design and why model accuracy isn’t always the most important element of a ML application. Michelle ends the conversation with an emphasis on the need for more interdisciplinary voices in the fields of ML and AI. 

Skip to a topic here:

Michelle talks about what drove her career shift from machine learning and neuroscience to user experience (1:15) Michelle explains what MLUX is (4:40) How to get ML teams on board with the importance of user experience (6:54) Michelle discusses the “unlearning” data scientists might have to do as they reconsider ML from a UX perspective (9:15) Brian and Michelle talk about the importance of considering the UX from the beginning of model development  (10:45) Michelle expounds on different ways to measure the effectiveness of user experience (15:10) Brian and Michelle talk about what is driving the increase in the need for designers on ML teams (19:59) Michelle explains the role of design around model interpretability and explainability (24:44)

Quotes from Today’s Episode “The first step to business value is the hurdle of adoption. A user has to be willing to try—and care—before you ever will get to business value.” - Brian O’Neill (13:01)

“There’s so much talk about business value and there’s very little talk about adoption. I think providing value to the end-user is the gateway to getting any business value. If you’re building anything that has a human in the loop that’s not fully automated, you can’t get to business value if you don’t get through the first gate of adoption.” - Brian O’Neill (13:17)

“I think that designers who are able to design for ambiguity are going to be the ones that tackle a lot of this AI and ML stuff.” - Michelle Carney (19:43)

“That’s something that we have to think about with our ML models. We’re coming into this user’s life where there’s a lot of other things going on and our model is not their top priority, so we should design it so that it fits into their ecosystem.” - Michelle Carney (3:27)

“If we aren’t thinking about privacy and ethics and explainability and usability from the beginning, then it’s not going to be embedded into our products. If we just treat usability of our ML models as a checkbox, then it just plays the role of a compliance function.” - Michelle Carney (11:52)

“I don’t think you need to know ML or machine learning in order to design for ML and machine learning. You don’t need to understand how to build a model, you need to understand what the model does. You need to understand what the inputs and the outputs are.” - Michelle Carney (18:45)

Links Twitter @mluxmeetup: https://twitter.com/mluxmeetup MLUX LinkedIn: https://www.linkedin.com/company/mlux/ MLUX YouTube channel: https://bit.ly/mluxyoutube Twitter @michelleRcarney: https://twitter.com/michelleRcarney IDEO Design Kit - https://tinyurl.com/2p984znh 

Dashboards are at the forefront of today’s episode, and so I will be responding to some reader questions who wrote in to one of my weekly mailing list missives about this topic. I’ve not talked much about dashboards despite their frequent appearance in data product UIs, and in this episode, I’ll explain why. Here are some of the key points and the original questions asked in this episode:

My introduction to dashboards (00:00) Some overall thoughts on dashboards (02:50) What the risk is to the user if the insights are wrong or misinterpreted (4:56) Your data outputs create an experience, whether intentional or not (07:13) John asks: How do we figure out exactly what the jobs are that the dashboard user is trying to do? Are they building next year's budget or looking for broken widgets?  What does this user value today? Is a low resource utilization percentage something to be celebrated or avoided for this dashboard user today?  (13:05) Value is not intrinsically in the dashboard (18:47) Mareike asks: How do we provide Information in a way that people are able to act upon the presented Information?  How do we translate the presented Information into action? What can we learn about user expectation management when designing dashboard/analytics solutions? (22:00) The change towards predictive and prescriptive analytics (24:30) The upfront work that needs to get done before the technology is in front of the user (30:20) James asks: How can we get people to focus less on the assumption-laden and often restrictive term "dashboard", and instead worry about designing solutions focused on outcomes for particular personas and workflows that happen to have some or all of the typical ingredients associated with the catch-all term "dashboards?” (33:30) Stop measuring the creation of outputs and focus on the user workflows and the jobs to be done (37:00) The data product manager shouldn’t just be focused on deliverables (42:28)

Quotes from Today’s Episode “The term dashboards is almost meaningless today, it seems to mean almost any home default screen in a data product. It also can just mean a report. For others, it means an entire monitoring tool, for some, it means the summary of a bunch of data that lives in some other reports. The terms are all over the place.”- Brian (@rhythmspice) (01:36)

“The big idea here that I really want leaders to be thinking about here is you need to get your teams focused on workflows—sometimes called jobs to be done—and the downstream decisions that users want to make with machine-learning or analytical insights. ” - Brian (@rhythmspice) (06:12)

“This idea of human-centered design and user experience is really about trying to fit the technology into their world, from their perspective as opposed to building something in isolation where we then try to get them to adopt our thing.  This may be out of phase with the way people like to do their work and may lead to a much higher barrier to adoption.” - Brian (@rhythmspice) (14:30)

“Leaders who want their data science and analytics efforts to show value really need to understand that value is not intrinsically in the dashboard or the model or the engineering or the analysis.” - Brian (@rhythmspice) (18:45)

“There's a whole bunch of plumbing that needs to be done, and it’s really difficult. The tool that we end up generating in those situations tends to be a tool that’s modeled around the data and not modeled around [the customers] mental model of this space, the customer purchase space, the marketing spend space, the sales conversion, or propensity-to-buy space.” - Brian (@rhythmspice) (27:48)

“Data product managers should be these problem owners, if there has to be a single entity for this. When we’re talking about different initiatives in the enterprise or for a commercial software company, it’s really sits at this product management function.”  - Brian (@rhythmspice) (34:42)

“It’s really important that [data product managers] are not just focused on deliverables; they need to really be the ones that summarize the problem space for the entire team, and help define a strategy with the entire team that clarifies the direction the team is going in. They are not a project manager; they are someone responsible for delivering value.” - Brian (@rhythmspice) (42:23)

Links Referenced:

Mailing List: https://designingforanalytics.com/list CED UX Framework for Advanced Analytics:Original Article: https://designingforanalytics.com/ced Podcast/Audio Episode: https://designingforanalytics.com/resources/episodes/086-ced-my-ux-framework-for-designing-analytics-tools-that-drive-decision-making/ 

My LinkedIn Live about Measuring the Usability of Data Products: https://www.linkedin.com/video/event/urn:li:ugcPost:6911800738209800192/ Work With Me / My Services: https://designingforanalytics.com/services

Mike Oren, Head of Design Research at Klaviyo, joins today’s episode to discuss how we do UX research for data products—and why qualitative research matters. Mike and I recently met in Lou Rosenfeld’s Quant vs. Qual group, which is for people interested in both qualitative and quantitative methods for conducting user research. Mike goes into the details on how Klaviyo and his teams are identifying what customers need through research, how they use data to get to that point, what data scientists and non-UX professionals need to know about conducting UX research, and some tips for getting started quickly. He also explains how Klaviyo’s data scientists—not just the UX team—are directly involved in talking to users to develop an understanding of their problem space.

Klaviyo is a communications platform that allows customers to personalize email and text messages powered by data. In this episode, Mike talks about how to ask research questions to get at what customers actually need. Mikes also offers some excellent “getting started” techniques for conducting interviews (qualitative research), the kinds of things to be aware of and avoid when interviewing users, and some examples of the types of findings you might learn. He also gives us some examples of how these research insights become features or solutions in the product, and how they interpret whether their design choices are actually useful and usable once a customer interacts with them. I really enjoyed Mike’s take on designing data-driven solutions, his ideas on data literacy (for both designers, and users), and hearing about the types of dinner conversations he has with his wife who is an economist ;-) . Check out our conversation for Mike’s take on the relevance of research for data products and user experience. 

In this episode, we cover:

Using “small data” such as qualitative user feedback  to improve UX and data products—and the #1 way qualitative data beats quantitative data  (01:45) Mike explains what Klaviyo is, and gives an example of how they use qualitative information to inform the design of this communications product  (03:38) Mike discusses Klaviyo data scientists doing research and their methods for conducting research with their customers (09:45) Mike’s tips on what to avoid when you’re conducting research so you get objective, useful feedback on your data product  (12:45) Why dashboards are Mike’s pet peeve (17:45) Mike’s thoughts about data illiteracy, how much design needs to accommodate it, and how design can help with it (22:36) How Mike conveys the research to other teams that help mitigate risk  (32:00) Life with an economist! (36:00) What the UX and design community needs to know about data (38:30)

Quotes from Today’s Episode “I actually tell my team never to do any qualitative research around preferences…Preferences are usually something that you’re not going to get a reliable enough sample from if you’re just getting it qualitatively, just because preferences do tend to vary a lot from individual to individual; there’s lots of other factors. ”- Mike (@mikeoren) (03:05)

“[Discussing a product design choice influenced by research findings]: Three options gave [the customers a] feeling of more control. In terms of what actual options they wanted, two options was really the most practical, but the thing was that we weren’t really answering the main question that they had, which was what was going to happen with their data if they restarted the test with a new algorithm that was being used. That was something that we wouldn’t have been able to identify if we were only looking at the quantitative data if we were only serving them; we had to get them to voice through their concerns about it.” - Mike (@mikeoren) (07:00)

“When people create dashboards, they stick everything on there. If a stakeholder within the organization asked for a piece of data, that goes on the dashboard. If one time a piece of information was needed with other pieces of information that are already on the dashboard, that now gets added to the dashboard. And so you end up with dashboards that just have all these different things on them…you no longer have a clear line of signal.” - Mike (@mikeoren) (17:50)

“Part of the experience we need to talk about when we talk about experiencing data is that the experience can happen in more additional vehicles besides a dashboard: A text message, an email notification, there’s other ways to experience the effects of good, intelligent data product work. Pushing the right information at the right time instead of all the information all the time.” - Brian (@rhythmspice) (20:00)

“[Data illiteracy is] everyone’s problem. Depending upon what type of data we’re talking about, and what that product is doing, if an organization is truly trying to make data-driven decisions, but then they haven’t trained their leaders to understand the data in the right way, then they’re not actually making data-driven decisions; they’re really making instinctual decisions, or they’re pretending that they’re using the data.” - Mike (@mikeoren)(23:50)

“Sometimes statistical significance doesn’t matter to your end-users. More often than not organizations aren’t looking for 95% significance. Usually, 80% is actually good enough for most business decisions. Depending upon the cost of getting a high level of confidence, they might not even really value that additional 15% significance.” - Mike (@mikeoren) (31:06)

“In order to effectively make software easier for people to use, to make it useful to people, [designers have] to learn a minimum amount about that medium in order to start crafting those different pieces of the experience that we’re preparing to provide value to people. We’re running into the same thing with data applications where it’s not enough to just know that numbers exist and those are a thing, or to know some graphic primitives of line charts, bar charts, et cetera. As a designer, we have to understand that medium well enough that we can have a conversation with our partners on the data science team.” - Mike (@mikeoren) (39:30)

For Danielle Crop, the Chief Data Officer of Albertsons, to draw distinctions between “digital” and “data” only limits the ability of an organization to create useful products. One of the reasons I asked Danielle on the show is due to her background as a CDO and former SVP of digital at AMEX, where she also managed  product and design groups. My theory is that data leaders who have been exposed to the worlds of software product and UX design are prone to approach their data product work differently, and so that’s what we dug into this episode.   It didn’t take long for Danielle to share how she pushes her data science team to collaborate with business product managers for a “cross-functional, collaborative” end result. This also means getting the team to understand what their models are personalizing, and how customers experience the data products they use. In short, for her, it is about getting the data team to focus on “outcomes” vs “outputs.”

Scaling some of the data science and ML modeling work at Albertsons is a big challenge, and we talked about one of the big use cases she is trying to enable for customers, as well as one “real-life” non-digital experience that her team’s data science efforts are behind.

The big takeaway for me here was hearing how a CDO like Danielle is really putting customer experience and the company’s brand at the center of their data product work, as opposed solely focusing on ML model development, dashboard/BI creation, and seeing data as a raw ingredient that lives in a vacuum isolated from people.  

In this episode, we cover:

Danielle’s take on the “D” in CDO: is the distinction between “digital” and “data” even relevant, especially for a food and drug retailer? (01:25) The role of data product management and design in her org and how UX (i.e. shopper experience) is influenced by and considered in her team’s data science work (06:05) How Danielle’s team thinks about “customers” particularly in the context of internal stakeholders vs. grocery shoppers  (10:20) Danielle’s current and future plans for bringing her data team into stores to better understand shoppers and customers (11:11) How Danielle’s data team works with the digital shopper experience team (12:02)  “Outputs” versus “Outcomes”  for product managers, data science teams, and data products (16:30) Building customer loyalty, in-store personalization, and long term brand interaction with data science at Albertsons (20:40) How Danielle and her team at Albertsons measure the success of their data products (24:04) Finding the problems, building the solutions, and connecting the data to the non-technical side of the company (29:11)

Quotes from Today’s Episode “Data always comes from somewhere, right? It always has a source. And in our modern world, most of that source is some sort of digital software. So, to distinguish your data from its source is not very smart as a data scientist. You need to understand your data very well, where it came from, how it was developed, and software is a massive source of data. [As a CDO], I think it’s not important to distinguish between [data and digital]. It is important to distinguish between roles and responsibilities, you need different skills for these different areas, but to create an artificial silo between them doesn’t make a whole lot of sense to me.”- Danielle  (03:00)

“Product managers need to understand what the customer wants, what the business needs, how to pass that along to data scientists and data scientists, and to understand how that’s affecting business outcomes. That’s how I see this all working. And it depends on what type of models they’re customizing and building, right? Are they building personalization models that are going to be a digital asset? Are they building automation models that will go directly to some sort of operational activity in the store? What are they trying to solve?” - Danielle (06:30)

“In a company that sells products—groceries—to individuals, personalization is a huge opportunity. How do we make that experience, both in-digital and in-store, more relevant to the customer, more sticky and build loyalty with those customers? That’s the core problem, but underneath that is you got to build a lot of models that help personalize that experience. When you start talking about building a lot of different models, you need scale.”  - Danielle (9:24)

“[Customer interaction in the store] is a true big data problem, right, because you need to use the WiFi devices, et cetera. that you have in store that are pinging the devices at all times, and it’s a massive amount of data. Trying to weed through that and find the important signals that help us to actually drive that type of personalized experience is challenging. No one’s gotten there yet. I hope that we’ll be the first.” -  Danielle (19:50)

“I can imagine a checkout clerk who doesn’t want to talk to the customer, despite a data-driven suggestion appearing on the clerk’s monitor as to how to personalize a given customer interaction. The recommendation suggested to the clerk may be ‘accurate from a data science point of view, but if the clerk doesn’t actually act on it, then the data product didn’t provide any value. When I train people in my seminar, I try to get them thinking about that last mile. It may not be data science work, and maybe you have a big enough org where that clerk/customer experience is someone else’s responsibility, but being aware that this is a fault point and having a cross-team perspective is key.” - Brian @rhythmspice (24:50)

“We’re going through a moment in time in which trust in data is shaky. What I’d like people to understand and know on a broader philosophical level, is that in order to be able to understand data and use it to make decisions, you have to know its source. You have to understand its source. You have to understand the incentives around that source of data….you have to look at the data from the perspective of what it means and what the incentives were for creating it, and then analyze it, and then give an output. And fortunately, most statisticians, most data scientists, most people in most fields that I know, are incredibly motivated to be ethical and accurate in the information that they’re putting out.” - Danielle (34:15)

Why design matters in data products is a question that, at first glance, may not be easily answered for some until they see users try to use ML models and analytics to make decisions. For Bill Báez, a data scientist and VP of Strategy at Ascend Innovations, realizing that design and UX matters in this context was a realization that grew over the course of a few years. Bill’s origins in the Air Force, and his transition to Ascend Innovations, instilled lessons about the importance of using design thinking with both clients and users. 

After observing solutions built in total isolation with zero empathy and knowledge of how they were being perceived in the wild, Bill realized the critical need to bring developers “upstairs” to actually observe the people using the solutions that were being built. 

Currently, Ascend Innovation’s consulting is primarily rooted in healthcare and community services, and in this episode, Bill provides some real-world examples where their machine learning and analytics solutions were informed by approaching the problems from a human-centered design perspective. Bill also dives in to where he is on his journey to integrate his UX and data science teams at Ascend so they can create better value for their clients and their client’s constituents. 

Highlights in this episode include:

What caused Bill to notice design for the first time and its importance in data products (03:12) Bridging the gap between data science, UX, and the client’s needs at Ascend (08:07) How to deal with the “presenting problem” and working with feedback (16:00) Bill’s advice for getting designers, UX, and clients on the same page based on his experience to date (23:56) How Bill provides unity for his UX and data science teams   (32:40) The effects of UX in medicine (41:00)

Quotes from Today’s Episode “My journey into Design Thinking started in earnest when I started at Ascend, but I didn’t really have the terminology to use. For example, Design Thinking and UX were actually terms I was not personally aware of until last summer. But now that I know and have been exposed to it and have learned more about it, I realize I’ve been doing a lot of that type of work in earnest since 2018. - Bill (03:37)

“Ascend Innovations has always been product-focused, although again, services is our main line of business. As we started hiring a more dedicated UX team, people who’ve been doing this for their whole career, it really helped me to understand what I had experienced prior to coming to Ascend. Part of the time I was here at Ascend that UX framework and that Design Thinking lens, it really brings a lot more firepower to what data science is trying to achieve at the end of the day.” - Bill (08:29) “Clients were surprised that we were asking such rudimentary questions.  They’ll say ‘Well, we’ve already talked about that,’ or, ‘It should be obvious.’ or ‘Well, why are you asking me such a simple question?’ And we had to explain to them that we wanted to start at the bottom to move to the top. We don’t want to start somewhere midway and get the top. We want to make sure that we are all in alignment with what we’re trying to do, so we want to establish that baseline of understanding. So, we’re going to start off asking very simple questions and work our way up from there...” - Bill (21:09)

“We’re building a thing, but the thing only has value if it creates a change in the world. The world being, in the mind of the stakeholder, in the minds of the users, maybe some third parties that are affected by that stuff, but it’s the change that matters. So what is the better state we want in the future for our client or for our customers and users? That’s the thing we’re trying to create. Not the thing; the change from the thing is what we want, and getting to that is the hard part.” - Brian (@rhythmspice) (26:33)

“This is a gift that you’re giving to [stakeholders] to save time, to save money, to avoid building something that will never get used and will not provide value to them. You do need to push back against this and if they say no, that’s fine. Paint the picture of the risk, though, by not doing design. It’s very easy for us to build a ML model. It’s hard for us to build a model that someone will actually use to make the world better. And in this case, it’s healthcare or support, intervention support for addicts. “Do you really want a model, or do you want an improvement in the lives of these addicts? That’s ultimately where we’re going with this, and if we don’t do this, the risk of us pushing out an output that doesn’t get used is high. So, design is a gift, not a tax...” - Brian (@rhythmspice) (34:34)

“I’d say to anybody out there right now who’s currently working on data science efforts: the sooner you get your people comfortable with the idea of doing Design Thinking, get them implemented into the projects that are currently going on. [...] I think that will be a real game-changer for your data scientists and your organization as a whole...” - Bill  (42:19)