067 – Why Roche Diagnostics’ BI and Data Science Teams Are Adopting Human-Centered Design and UX featuring Omar Khawaja

Experiencing Data with Brian O'Neill (Designing for Analytics)
Experiencing Data with Brian T. O'Neill
067 - Why Roche Diagnostics’ BI and Data Science Teams Are Adopting Human-Centered Design and UX featuring Omar Khawaja

Episode Description

On today’s episode of Experiencing Data, I’m so excited to have Omar Khawaja on to talk about how his team is integrating user-centered design into data science, BI and analytics at Roche Diagnostics.

In this episode, Omar and I have a great discussion about techniques for creating more user-centered data products that produce value — as well as how taking such an approach can lead to needed change management on how data is used and interpreted.

In our chat, we covered:

  • What Omar is responsible for in his role as Head of BI & Analytics at Roche Diagnostics — and why a human-centered design approach to data analytics is important to him. (0:57)
  • Understanding the end-user's needs: Techniques for creating more user-centric products — and the challenges of taking on such an approach. (6:10)
  • Dissecting 'data culture': Why Omar believes greater implementation of data-driven decision-making begins with IT 'demonstrating' the approach's benefits. (9:31)
  • Understanding user personas: How Roche is delivering better outcomes for medical patients by bringing analytical insights to life. (15:19)
  • How human-centered design yields early 'actionable insights' that can lead to needed change management on how data is used and interpreted. (22:12)
  • The journey of learning: Why 'it's everybody's job' to be focused on user experience — and how field research can help determine an end-users needs. (27:26)
  • Omar's love of cricket and the statistics collected about the sport! (31:23)

Resources and Links:

Quotes from Today’s Episode

“I’ve been in the area of data and analytics since two decades ago, and out of my own learning — and I’ve learned it the hard way — at the end of the day, whether we are doing these projects or products, they have to be used by the people. The human factor naturally comes in.” - Omar (2:27)

“Especially when we’re talking about enterprise software, and some of these more complex solutions, we don’t really want people noticing the design to begin with. We just want it to feel valuable, and intuitive, and useful right out of the box, right from the start.” - Brian (4:08)

“When we are doing interviews with [end-users] as part of the whole user experience [process], you learn to understand what’s being said in between the lines, and then you learn how to ask the right questions. Those exploratory questions really help you understand: What is the real need?” - Omar (8:46)

“People are talking about data-driven [cultures], data-informed [cultures] — but at the end of the day, it has to start by demonstrating what change we want. ... Can we practice what we are trying to preach? Am I demonstrating that with my team when I’m making decisions in my day-to-day life? How do I use the data? IT is very good at asking our business colleagues and sometimes fellow IT colleagues to use various enterprise IT and business tools. Are we using, ourselves, those tools nicely?” - Omar (11:33)

“We focus a lot on what’s technically possible, but to me, there’s often a gap between the human need and what the data can actually support. And the bigger that gap is, the less chance things get used. The more we can try to close that gap when we get into the implementation stage, the more successful we probably will be with getting people to care and to actually use these solutions.” - Brian (22:20)

“When we are working in the area of data and analytics, I think it’s super important to know how this data and insights will be used — which requires an element of putting yourself in the user’s shoes. In the case of an enterprise setup, it’s important for me to understand the end-user in different roles and personas: What they are doing and how their job is. [This involves] sitting with them, visiting them, visiting the labs, visiting the factory floors, sitting with the finance team, and learning what they do in the system. These are the places where you have your learning.” - Omar (29:09)


Brian: Welcome back to Experiencing Data, everybody. I’m happy to be here with my friend, Omar. Omar, welcome to the show. We’re going to talk about design and data like we always do on the show, but I’ve got some meaty questions for you. How’s it going? How have you been?

Omar: Thanks, Brian. Great to be here. Thanks for the invitation to be on this show. Everything is doing great as it could be in these times. So, good to be here, talk about a topic which is quite close to our heart.

Brian: Excellent. Excellent. Yes. So, you’re dialing in today from Europe, and you’re at Roche, specifically in the diagnostics area. Can you tell our audience a little bit about the work you’re doing with BI and data science at Roche?

Omar: Yes, I am based out of Basel in Switzerland. I’m working at Roche, like you said, as the Head of BI & Analytics for Roche Diagnostics. We are responsible for everything and anything to do with data and analytics for our enterprise-wide diagnostics division, ranging from planning, R&D, to the entire value chain of operations, supply chain, and commercial. My team is responsible for various enterprise-wide BI tools and platforms, and work with the various business and IT teams to deliver BI projects, and now data and analytics products.

Brian: Got it, got it. So, for a lot of data people, it’s about the facts and the data, right? It’s very analytical, and obviously, we’re talking about analytics. Taking a human-centered approach to this, I think, can sound kind of fluffy, and vague, and unspecific, and soft. I don’t know, you’ve decided that, in your work and on your teams, that there’s a value in taking an approach informed by design and being user-centric. Why did you do that? And is this a hard pill for people to swallow? Like, why this change?

Omar: Love to share a short story, a background of why I decided to choose this approach over here. I’ve been in the area of this data and analytics since it was I think, twenty years, two decades ago, and out of my own learning, learn it hard way that the end of the day, whether we are doing these projects or products, they have to be used by the people. The human factor naturally comes in. How many reports we have seen that BI initiatives have failed? And it’s a very easy and simple to understand secret, I would say, that people—we forget about the people who use it.

And like any other product, whether it’s your iPhone, or your headphone, or your laptop, it has to be used by the human being at the end of the day. And that’s why it makes a lot of sense to go with a user-centered, human-centric approach of how we design products, whether it’s a dashboard, or whether it’s an algorithm, or whether it’s some other user experience that the user is expecting out of data. So, that was the thinking behind why this whole concept attracts me a lot. But when you say it’s a hard pill to swallow, I heard a saying long time ago that it’s very simple to be happy, but it’s very difficult to be simple.

Brian: [laugh].

Omar: I have no idea, I have no idea who said it; definitely it was not me. But that’s the fact of life. It’s the simple things that are eye-opener and can change what we are delivering, whether it will impact our customers—and patients in our case—or not.

Brian: You make a very good point, and I think a lot of times when we hear design people jump to, you know, aesthetics and how things look and feel and all this kind of stuff, and so much of the messaging to me is about if we do this right, no one will probably notice it because it will just, sort of—especially when we’re talking about enterprise software, and some of these more complex solutions, we don’t really want people noticing the design to begin with. We just want it to feel valuable, and intuitive, and useful right out of the box, right from the start. Was there a moment though where something shifted for you where maybe you had, I don’t know, you went through a project or something happened, and you’re like, “Man, this has to change. This isn’t working,” or was this just a—you were always thinking this way, or it was a gradual change? What happened?

Omar: It has been a gradual change, let’s say, and the journey for me personally started in 2018 when a colleague of mine in my previous job who was leading the UX team, and I don’t know whether she will like to mention her name or not, but thanks to her really it opened up my mind that there can be another fantastic approach to discover what the end-user use case is, and not start with a hardcore ETL job that needs to be written. And I’m not saying those things are not important. Please, all the listeners, don’t take me wrong, but if we don’t know how we are going to use that data product or analytical product, where is the gap? What insights that end-user needs, whether it’s a marketeer, or a sales rep, or service rep, or a finance person, how can you design something? It’s that insights understanding that really was an eye-opener, which was my experience when I worked with this UX design process and learned that it was not about learning how to make a fancy website or a dashboard—which is also part of the whole deliverable—but it was the process: it was the process of asking the right questions; it was the process of understanding, putting yourself in end-user shoes, and not about, “Hey, here you go,” you know?

There is a proper way to serve a tea, and there is a way to just take the tea and dump it on the [laugh] table at front of your customers. It’s the approach that makes the difference.

Brian: Yeah. Was there one thing that you particularly liked the most about that approach, and was there one thing that you found was really hard to apply, or doesn’t make sense for maybe analytics and data science work or just that’s going to be a difficult change for the team to make?

Omar: Yeah, I would say two, three things, here. One was, sometimes we—also including me—I used to think that this is a specialized job for a UX design expert to do and not everybody should be doing it. I think that is not the case. Or that should not be the case. At the end of the day, we are making data products, and as it goes when you are working in the area of product management, everybody has a opportunity to understand what the end-user is trying to do. And I’m not talking about a major external customer-facing products. I’m really focusing here with the enterprise internal employee using some kind of these products.

Secondly, I think it’s very important that people learn how to ask questions. And questions should be asked in terms of discovering, and finding, and not in a—and it’s an art. That’s an art. That you cannot learn just by reading a book. Or it comes with reading a book, joining courses, like we did with you, for example, or practicing.

And that’s a very big hurdle to cross. And if you recall, even when we were doing the seminar with you, there was that initial push, “Hey, how to do that? How to start that?” Question. But then it came naturally to people, that it’s a conversation. It’s a conversation, there are tools that are available that helps you. But it provides some amazing insights which makes the difference on what needs to be delivered.

Brian: Yeah. Why do you think that’s hard?

Omar: We never step back. We are so close to our jobs; we are so close to our traditional way of working. So, imagine people spending decades, two decades, three decades in their job in a very classic approach. Somebody will tell me, “I need a dashboard,” and I will deliver it. The typical approach of user requirements that IT have.

That has worked for a number of years; people have delivered good solutions, but it was a 50/50 chance at its best that whether it will work or not. Because we were all at the mercy of that person providing me requirements, whether they are really telling me what they need, or they are telling me is what they think they need. And there is a difference. And when we are doing those interviews as part of the whole user experience, you learn to understand what’s being said in between the lines, and then you learn how to ask the right questions. And when those questions, those exploratory questions really help you understand what is the real need.

And sometimes it’s an aha moment for the other person or the end-user as well that, “Hey, can you really do that? If you do that, it will make my life so much simpler.” And those are the things that you can only discover. And people needs to get out of their comfort zone, their assumptions which have been developed over the period of time. And that’s not easy to change; it requires consistent effort.

Brian: Yeah, yeah. And there are things at odds with this, right, with schedules, and if there’s, like, an engineering process, and hitting dates, and there’s always going to be a million things here [laugh] working against the service mindset of making someone’s life better by providing them some kind of value, in their eyes of what value is, not in what we think they should use. [laugh]. So, this kind of gets to this—I want to ask you about, quote, “Data culture” here. Last couple of weeks on LinkedIn with some colleagues as I know, there’s been a lot of talk about data strategy and data culture.

And some of this is rubbing me the wrong way, and I wanted to get your feel on this. I feel like data-driven culture and data culture is the thing that all data science and analytics professionals want everyone else to have so that they can come and meet us where we’re at with our stuff. It’s all about everybody else making a change to accommodate us. And it’s pushed on them and I just feel like we don’t have an—as Samir Sharma was saying, “Are we going to have a machine learning culture? Are we going to have a blockchain culture? Are we going to have a digital culture?”

It’s just like, culture is just the way crap gets done around here, right? What is your take on this? Do we need to meet in the middle with the business and the users, or is it mostly about we the data professionals really meeting people where they’re at? Is it 50/50? Is it 80/20? How do you—is that not even the right framing? What’s your take on this whole data culture thing?

Omar: So, I think culture, like any other aspect of the culture is a big topic. Let’s take a non-data example. You join a new company, you respect the culture in so many different ways. For example, for me in my current job, it’s only been one year, but everything has been virtual. I meet all my colleagues like I’m talking to you right now.

I have learned a lot about the company culture and I love it; it’s great. I’m desperate still to meet people in person, as one can imagine, and learn that aspect of the culture. Now, same thing goes for data. So, people are talking about data-driven, data-informed, and all those nice things that you said. At the end of the day, it has to start by demonstrating what the change we want.

Like Mahatma Gandhi once said, “Be the change you want to see in the world.” There will be always push and pull. Can you demonstrate and live, and as somebody says, “Eat your own dog food?” Can you do that? Can we practice what we are trying to preach?

So, am I demonstrating that in my team when I’m making decisions in my day-to-day life? How do I use the data? IT is very good at asking our business colleagues and sometimes fellow IT colleagues to use various enterprise IT and business tools. Are we using, ourselves, those tools nicely? When I create a master record of a service item, or an application, or something like that, in my nice project and portfolio management tools out there, do I really try to capture all the data attributes I need to make decision, or am I just capturing them because somebody told me to capture it, and it’s a formality, and then I have a separate spreadsheet where I really maintain the real stuff, and we use that for decision making?

So, it’s a habit, it’s a culture, and if we need to, we are able to demonstrate that. I think when we are talking to our colleagues and IT or in business, it will come directly from the heart, from our practices, what works. And sometimes these simple examples makes all the difference. It’s not the fancy words, it’s simply showing up, demonstrating how to do things. And there is another element of learning culture.

Are we open to learn new things? And if an organization or an individual is open to grow, I hope that they are willing to learn, and the learning can be in different shapes and form: it can be from a colleague, it can be from a subordinate, it can be from other companies, it can be from books, or webinars, podcasts. That is also part—it reflects in an organization context, whether somebody is willing to learn and change or not.

Brian: Is there a different kind of learning, though, which is, “Well, we built this thing. No one used it, so we learned that they didn’t like it,” or whatever, versus an active form of learning, which is, “We’re experimenting. We’re trying to iterate quickly get things in front of people, get feedback fast, learn what they liked, learn what worked, learn what didn’t. Change.” Are those different things or do you think that learning has to be kind of conscious and intentional?

Omar: It has to be both. I think, from an individual point of view, I remember that we have been told by our learning and development practitioners and experts that everyone has a different style of learning. And when we are talking about culture and learning, what it’s ‘organization?’ Organization comprises of people at the end of the day. So, how are people learning?

Everyone has a different style of learning. Some people like to focus, like you said, they might do a spike in terms of Agile terminology, and they might have a very dedicated focus on, “Hey, I want to address this problem in order to do that. This is the gap I have, and that’s what I would like to learn.” It can be a technical skill or some soft skill; could be both otherwise, the other things can also apply. And it can be a general approach of, we have enabled a platform and people will go out and learn on their own based on their own experiences, their own passion, their own drive, where they would like to grow. And I think both combination needs to work.

Brian: So, you were talking about dogfooding earlier, and I love this term. When we were working together, I know you guys were working on an initiative here, and I don’t know how much you can share about it, but I was curious if you could talk a little bit about this data-driven initiative and how your staff is going into about approaching unpacking what that means.

Omar: Yeah, I think I can talk a lot about that. Some of it is also available in public on the Roche website. So, in general, Roche is quite big on data, and you can see this a number of things. Some links are on my profile on LinkedIn or on our website, roche.com.

When we talk about these things, and we are actively involved, me and my team and some other colleagues and larger ITN business organization, they are hands-on involved in bringing that vision to life and bringing those insights to life, so that we can have a positive impact on patients’ life, and we do deliver better outcomes for them. Now, it all sounds really great, and we love that; we come to office every day for that, even virtually, but when it comes to practicing this, it goes back to that topic that we were just discussing: am I, in IT, practicing that or not? And we decided that why not come up with an approach that whatever we are preaching to the business, can we apply the same learning, same methods, same framework, use the tools that we ask business to use, ourselves, whether it’s simple as a dashboard, or a ticketing tool, or some fancy AI recommendation engine. That can actually help us in IT do our job better, generate insights from the data that we or some of our team members are generating. And that whole experience allow our teams within IT to practice what we are preaching.

And that’s really where this eat-your-own-dog-food initiative and example came in that, “Hey, we have an opportunity to show here what the best looks like.” And there is nobody actually stopping us at the end of the day, there. It’s us who are able to practice. We have support from our leadership. In many cases, we are the people on the ground who needs to really provide input and demonstrate how we will use it. So, that has been an amazing, insightful journey that we started recently.

Brian: Mm-hm. What are some of the things, like, literally what are people doing, and why? Could you just unpack, like, paint a picture of what that looks like a little bit?

Omar: In terms of applying this practice, we are currently literally focused on the first and the most important aspect of understanding our IT personas out there, that human element in the whole process of becoming data-driven. So, what are those IT roles? It’s not about a central team developing some dashboards for an IT leadership team. No. That has happened in past.

How many of us who are working in enterprise IT have seen those dashboards popping up? Most of the times, we don’t agree to the legends, we don’t agree to the colors, the reds, and the ambers, and the greens being shown on the slides. There is always a my version of an SLA and service provider version of an SLA. How can we get over these things? Why waste time in producing these slides, and PowerPoints, and dashboards, and reports which nobody agrees with?

So, how can we do this? It’s really simple. Let’s look at those personas. It’s not about the top; it’s about the people on the ground. Sometimes we are using the same information, but in a different way, as it happens in the business as well.

A person in the field will have a different view versus a person sitting in a marketing department. But the data, most likely in many cases, will be a different version of it, or different view of it. Same logic applies for IT. So, this is what we are experiencing. It’s really a joy to discover those things.

In our interview sessions, it has been a great learning that sometimes when you ask questions, you really reflect upon, “Hey, why are we really doing this?” [laugh] “Can we really—can we stop it? Can we do it differently? We don’t need to measure things anymore in this particular way.” Or, “Let’s stop measuring these things.”

So, these are some of the initial insights that are coming out. But I don’t want to set all-rosy picture; it’s just the beginning of our journey. We need to deliver the part on making and bringing it to life, what we are learning, and that, I’m confident we will make it happen.

Brian: Mm-hm. Is there a particular change in the mindset or the approach of your staff that are doing this that’s required to do this work? Is it easy to just go get going and do this if you know what the recipe is to do it, or is there a shift that has to take place? What’s been your reflection so far on that?

Omar: It’s not easy. I can safely say that. And the shift is required on both the sides. So, a typical approach in past have been, here is a dashboard based on the data that somebody was able to pull out from some system, and just threw it out there. And that’s where, in history, whenever we had this topic of data-driven, that’s where we started from.

We are not talking about data at all in our approach. And that has taken both the people who are doing the interview as well as people who are being interviewed as a pleasant surprise, “Hey, this approach is different. We are really talking about me as an individual looking at what insights I need to do my job, and by doing that, I am becoming data-driven.” So, you know, we have seen those triangles, talking about people, technology, process, data. All true, but we are not starting with technology; we are not dumping a dashboard on somebody; we are not dumping a bunch of data KPIs, I think you use the term very nicely, ‘metric toilet’ quite often and we are trying to avoid that.

So, we did a nice research as well, looking at the best practices out there. Guess what? You do a simple Google search, you will see a lot of IT KPIs out there. We don’t have to deliver all of them if we don’t even agree and understand what those are. So, it’s an element of learning which is coming through, it’s an element of fresh approach, and most importantly, it’s a realization that we are talking about people, humans, who needs to make the decision, who needs to consume things, and then go into designing and delivering that product.

Brian: Mm-hm. Mm-hm.

Omar: So.

Brian: You make some good points. A lot of the stuff that’s easy to track is not the stuff that necessarily relates to the human aspect. So, we focus a lot on what’s technically possible, but to me, there’s often a gap between [laugh] the human need and what the data can actually support. And the bigger that gap is, the less chance things get used, so the more we can try to close that gap when we get into the implementation stage, the more successful we probably will be with getting people to care and to actually use these solutions. So, thanks for sharing that. How does your leadership look at this? I mean, you report to somebody, and they probably report to somebody, and maybe this is a change for them. What’s that been like?

Omar: It is a change. And so far, it has been received very positively. And this is just a different experience that they also have. Because when we used to talk about the data-driven approach in past, the whole approach was very different. So, what are we going to do differently to get a different result?

That’s why this approach has been well received. And people when they even see the results of the insights generated from the interview itself, it’s a good reflective moment that, hey, we need to change a few things. It’s not about a dashboard or a report, it’s also about changing the way we capture the data. And in many cases, if I’m not wrong, fifty percent of the time, the realization is that we need a change management in many areas, in terms of processes, in terms of our approach towards how we will even use this data. So, for example, a typical conversation on a dashboard showing some red, amber, green lights will be always about why is this red?

And, how can I move it to green? And that’s it. Nobody talks about anything else. Those who have been involved in this industry can totally relate to yes, those traffic light signals are important and may be part of a good user design, as well, as part of the product, but how we react to that number with the person who’s responsible for. That makes heck of a difference. It’s the day and night in the approach.

And that’s the insights that we start for seeing, especially in the leadership who needs to, like I said earlier, they are there to demonstrate and live the change that we would like to see. So, it’s starting to happen. I think there is a long journey ahead of us.

Brian: Mm-hm. If I can sum up what I think you said, you know, the—and I see this a lot too—we report these descriptive analytics, we have our traffic light whatever, some qualitative ranges and all this kind of thing, and bang, you hit the wall. End of experience. [laugh]. Like, everything is over.

But what do I do about it? And the entire workflow of responding to the change—which is where the value would actually occur. Because just knowing what you’re doing right now, there can be some value in going from what I call zero to one, which is, “We have no idea what’s going on. Okay, now we know what’s going on.” But immediately right around the corner, [laugh] as I tell people all the time, right around the corner is, “Why, and what can I do about it?” [laugh].

So, in thinking through what is that process and experience, designing that entire experience and not just the initial information about current state, if you leave out the rest, and the value thing doesn’t happen. Is that kind of what you’re talking about is thinking through the entire lifecycle of whatever it is you’re reporting on, and the change, or the why, and this kind of thing?

Omar: Absolutely, Brian, and I think we use the term actionable insights quite a lot in this practice. So, what are those actions? “What I’m going to do when I see these numbers?” And, “Okay, I will take a certain action. Will it eventually have an impact on the outcomes when it comes to, let’s say, IT process or the business outcomes?”

That’s the thinking that needs to take place first. So, putting the horse before the cart and not the other way around—which we usually do—sometimes we are so much involved in our typical data stack thinking that we always think left to right, in terms of there is a data source, there is an ETL job, let’s bring it in, let’s see what will happen. At the end of the day, when a dashboard is created and the action needs, it needs to go completely other way around. We can stay on left-to-right approach, but let’s put the consumer and the action and the impact as a first step, and then the data source as the last step, and you will see the difference.

Brian: Yeah, that’s very much the last mile approach. And I think that’s hard because we often buy into the thinking that, well, with that left-to-right approach, we’re building in scalability from the beginning. Theoretically, any dashboard anyone could ever create will now be possible, or whatever the output is on the other end, dashboard, whatever the heck it is. And my feeling is, it’s just like, that’s leaving a lot to chance. Theoretically, yes, it might be possible to do it, but we’re not here to do theoretically. [laugh].

We’re here to actually produce value, to make people’s lives better, customers, and our users, and all of that. So, I’m totally with you on that, that the technology needs to serve the use case and the problem at hand. So, given the way you describe that, is there one thing when you kind of look back on where you’ve been and how you’ve gotten to where you are now that you would have changed about your approach with either building teams with this mindset or anything here, is there anything you would have gone back and done differently now?

Omar: That’s a great question. I think my own journey has been off learning as well. I think I’ve still a lot to learn, and the more I learn, the more I’m learning that I don’t know a lot. That’s a great feeling to have. It gives me that feeling of there is such a room to grow and so learn so many good things happening.

And that has been my mantra in life, at the end of the day. And when I took this role, I decided to apply this as well. It’s an opportunity for us that it’s not my job, it’s not a job of a UX designer, it’s not a job of a single role, or a department, or a person. It’s everybody’s job. And that’s what I think will make a difference.

That approach has not been applied in past. But this also means that a lot of effort will be required to bring everybody on the same page, or at least kind of on the same page. It’s never easy to take everybody on the same journey together. There are always people, based on their experiences, who wants to go fast. I think I have done that in past myself and learned hard way that, hey, let’s do it together.

I can slow down as well because there is some bigger benefit in doing that. And that’s one of the things that has been part and parcel of my own learning journey. So, it’s never about only technology, it’s never about only softer aspects. And when we are working in the—at least in the area of data and analytics, I think it’s super important to know how this data and insights will be used, which requires an element of putting yourself in user’s shoes in general, or in case of an enterprise setup, it’s important for me to understand how the end-user in different roles and personas, what they are doing, how their job is, sitting with them, visiting in—let’s say in a normal time, whenever it comes after pandemic—visiting them, visiting the labs, visiting the factory floors, sitting with the finance team, and learn what they do in the system. These are the places where you have your learning. And I think this I will do, and more and more.

Brian: Yeah. I love that. I mean, the research part, what you’re talking about is doing field research, basically, and going out and understanding what people’s jobs are like, and how do we insert ourselves into their world and support what they’re doing as opposed to asking them to come over to where we are? Because most of the time, we’re the ones in the servicing. They’re not servicing us, we are serving them, and the way to drive change is to try to fit into the natural ways things are done around here.

And I don’t think a lot of people understand that you can’t get this stuff right if you’re not going out and shadowing people—obviously with their permission—and having these conversations and really from a place of empathy. It’s really hard to get this stuff right. You would need so much domain knowledge, you’d need to understand what it’s like to be a comptroller, or a salesperson, or whatever, in order to possibly come up with something that’s going to be great, especially if you want to routinely and repeatedly do that. You can get lucky once in a while with a guess, but obviously, we want to try to optimize these processes. So, I love that you’re interested in getting out there to see that because I think a lot of people, it’s easier to hide with the tech, and focus on the creation part, and hope and pray when we throw it over the wall that someone will care and use it. And it doesn’t work. You know, it typically doesn’t work very well. So—

Omar: Yeah, what a surprise.

Brian: Yeah, yeah. No exactly. So, I want to know where people can follow you. But first, as I recall, you’re from Pakistan. So, I was curious. Is it cricket, soccer, or something else?

Omar: Yeah, of course, coming from Pakistan, you have to be a big cricket fan. So, I am, but I’m not a big sport fan in general for every sport. So, when my team is playing, I would love to watch the match, otherwise, yeah, if tournament is happening, I can look at the scores. That’s my data side, also, comes to life and gets very excited looking at all the runs, and the scores, and the statistics around it. Recently I—

Brian: That was my second question was whether you care about the data behind sports. It’s such a big—

Omar: Of course.

Brian: [laugh].

Omar: I remember one of my colleagues used to work for a software house who were very good friend, actually, who worked on the system behind the statistics of all this cricket data where it’s captured. So, it’s one of the, I think, great data sources to have, by the way.

But another area where I’m really passionate about these days is golf, which is not coming from my typical, let’s say, Pakistani roots. We don’t play that as a national sport or anything. But I was introduced quite late. But I’m very bad at it, but I love it because I think that golf teaches you—it’s very close to the real-life; it keeps you humble, keeps you grounded, keep your eye on the ball, keep your head down. There are certain things that you need to follow every time, and that reminds me every [laugh] time that that applies to life as well.

Brian: Right.

Omar: So yeah.

Brian: I’ll just say on the closing note, I just listened to a podcast. It’s not Freakonomics; it’s the guy that wrote the—it’s not Stephen Dubner, it’s the other one, the Chicago economist. And he has a show, like, people I mostly admire, and he had one of the professional golfers on, but they were talking about golf statistics, and, like, changing the way golf is scored. And there’s actually some really interesting stuff there about the math behind how golf happens and how we score better, and thinking about the value of the short game versus the long game, and it was really quite interesting. So, for any listeners out there that play golf and are interested in the stats and stuff behind that, they’ve done some really interesting work there. So, I’ll just props to that shows. But, Omar, where can people find out about you? Twitter, LinkedIn, like, are you on social media? How can they get in touch?

Omar: Yeah, I am on social media, as well as for my kids on the older [unintelligible 00:33:49] social media platforms, let’s call them, like that. So, I’m not using anything latest. I am on Twitter. My Twitter handle is @kmaomar, which is an abbreviation of my full name. And I guess we can share that in the podcast notes if required.

I’m also available on LinkedIn. I’m professionally quite active there, recently. I find that was very helpful, especially in the virtual times, to make contact. I love the community that has grown there. It’s great to be part of that; it’s great to be part of that learning culture, I think, which is demonstrated by the LinkedIn community. So, you can also find me there.

Brian: Yeah, yep. It’s been great to connect with you on there as well. So, thank you again, Omar, for coming on the show. It’s been great to get to work with you and also to just chat with you today a little bit.

Omar: My pleasure, Brian, thanks for having me here, and all the best with your next shows, and looking forward to stay connected and work with you.

Brian: All right, take care.

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Subscribe for Podcast Updates

Join my DFA Insights mailing list to get weekly insights on creating human-centered data products, special offers on my training courses and seminars, and one-page briefs about each new episode of #ExperiencingData.