UX STRAT Interview: Dr. Eva Deckers, Philips

Paul: Hi Eva, thanks for taking time out to talk with me today. Can you start by telling us a little bit about yourself?

Eva: My name is Eva Deckers and I’ve worked at Philips for four and a half years. I’ve recently taken the role of Director of Data Enabled Design. This is a competence we started building officially since last year. Before joining Philips, I worked at Eindhoven University of Technology, where I obtained my PhD on a topic that I could now frame as shaping a designer point of view on artificial intelligence--so, how do you design for artificial intelligence, not merely from a cognitive point of view, but how do you do it from a perceptual, emotional, and social point of view? After finishing my PhD, I joined Philips Design in Eindhoven. I took a little bit of a different road, where I think I was the first official strategic designer on the team. We built that competence in the first three years, leading also to, from a design point of view, one of the Philips focus areas around maternal and infant health. I also set up a design thinking program for business strategy; so, strategy on a business level and not on a proposition level. That’s also what will be the topic of my speech at the conference.

Paul: When you say strategic design, what kinds of things does that include?

Eva: For me, that especially means ensuring that design thinking is embedded in our company-wide, business-wide strategic work. We also team up with our corporate strategy team and ensure that we bring our customer/people/user perspective into building business strategy. There are different activities that we have in our design strategy team, but that is at the core of my work in that direction. So, we’ve developed this program that we call the Business Value Proposition Program, in which we help businesses, from this customer point of view, build a strategy--and this compliments the established strategy processes, so it’s not replacing any of that. It’s bringing in this customer/user perspective.

Paul: So, you had a corporate strategy team that was basically looking at markets and products and services, and doing projections several years out? And then you were responsible for discovering the customer benefits and prioritizing those alongside the business priorities?

Eva: Yes, absolutely. And the difference between what we--I mean the design thinking process, was quite well-established in our company, but it was quite well-established on a portfolio level, on an offer level, when it’s really about the strategy around the product, around the service. What’s different about this program is it’s really on the business level. So, it’s translating our company mission/vision/strategy/brand values into the business level, where we have different businesses operating, of course within the Philips context, but in their own domains. So, that’s one, translating that to the business level, but also ensuring overarching value proposition that is above the portfolio.

Paul: I see. So, more of an ecosystem approach.

Eva: The idea, of course, behind that is if you’re going to want to deliver in the knowledge paradigm, or you want to deliver a network ecosystem--deliver value in a network situation rather than a linear one, then you also need a different way of describing your strategy, because you have to grasp complexity and you also need a different way of framing value. So, value is not only the direct action between two players, and not only in terms of, “You give me something, I give you money for it.” So, to be able to frame that and understand that, we need a framework that helps the business understand the benefits they are delivering on.

Paul: Was there a natural progression from that role to data enabled design, or was that a completely different path?

Eva: I think it comes from the same challenge in the sense that we are looking for ways on how do you actually deliver in a network paradigm, right? So, how do you deliver ecosystems, internet of things, data, artificial intelligence, right? All these topics that “personalized” is one of the words that comes to mind. So, that’s also why we had to look at what is the new business reality, so that has a more strategic part. But you also have to wonder, from the design point of view, how do you actually go about and deliver value in such an ecosystem--which is a bit different than delivering value on an offering level. And yes, I appreciate that our offering is not always just a single product or single service or software, it can be a combination of those. But still, normally they are quite touch point solutions. And, of course, Philips has gone into a direction of solution selling, where we really want to deliver more than the sum of the different elements--which, of course, from a (inaudible, 07:04, company-level strategy has to be translated Offering-level strategy?), to deliver on this systems thinking, this internet of things, also in healthcare… But that doesn’t necessarily tell us how to design for that. And what you could say is that data enabled design is a competence that we develop to design with data, with intelligence, to deliver meaning in this network reality. In that sense, the reason of why we deliver that strategy process and that we are developing this competence, I think is very much the same. The fact that I am leading this data enabled design competence also calls back a bit more closely to my background, where I have a PhD on artificial intelligence--designer point-of-view on artificial intelligence. Actually, I defended my thesis in 2013, and actually I’m now carrying my thesis around again to actually hand it out to people. So, apparently the time is there to pick that content up again.

Paul: Okay, so it seems like before you were more top-down oriented and now you’re more bottom-up oriented. Now you’re more helping practitioners to be more effective as they design products, whereas before you were more setting the landscape and getting value propositions together. They seem to be kind of opposite ends of the stream.

Eva: I think that’s also interesting, and I’m doing both - it works together, right? If we choose to develop a competence to deliver on something, that’s not going to work. If you just top-down push for something that should be, it will also not work. And I think also, as a design organization, we have to sometimes “eat our own dog food.” We’ve been talking about how the world is evolving, how value is changing, how people are perceiving what value is… And there’s a paradigm story that was published years ago, and in getting that data, and work more from a strategic angle. But we also need the competence to deliver on these solutions.

Paul: Well, it seems to me that you’re the Poster Child that we would like to model this conference on. Your work is the core of why we’re doing this conference in the first place. There are a lot of conferences obviously around design and developing competencies of design, but we’re trying to help design leaders find the most intelligent way forward. I think that has to be both in a business strategy context and in a data-enabled context. So, how about if we talk a little bit about what you’re going to be talking about at UX STRAT Europe?

Eva: Yes. I will introduce the team and explain why we have the team. In our organization, we’ve identified that we need to up our game in terms of competence when it comes to data and intelligence. I actually refer to a paper that was published last year, where our academic colleagues did a good job of portraying some challenges around machine learning, but I think it’s a bit broader, where they interview multiple UX designers and they came to three challenges in this area. And I think these areas apply to our design community in Philips, as well. So, designers can still be quite naïve --like even when it comes to designing with and for data and intelligence. If we have data, everything is possible, make it a little bit black and white. But we also miss some opportunities because we don’t get the nuance of what you can and cannot do with data. So, that’s the first challenge.

The second challenge that they portrayed is that we don’t have the tools, as designers, to actually work with data and intelligence, right? So, we have our pen and paper, we have our modeling tools, we have our Illustrator, we now have, of course, also digital tools we can use; but when it comes to really working with data and intelligence, there are no design tools readily available in which you can freely explore from a design perspective with this material, I should call it. And we’re also not trained as designers; so, the most established design schools, you won’t find this in their curriculum.

And then the third challenge is that designers are not well enough embedded in the discussion around intelligence, or what to do and what not to do with data and intelligence. So, we much more should portray an opinion and a vision on how you design with and for data and intelligence.

So, I use these three challenges to frame my work because I think they very well capture also what is going on in our organization, and I’ll show how I build a team to actually work on these challenges. And there are five ways we are actually tackling trying to address these, and these five ways I’m illustrating during my talk.

The first one is that we are really working and gathering data in our projects. If we don’t have the data, we go and ensure that we’ve gathered it, maybe we hack the devices together and maybe we use available public data sources, within the company we look for the data. My designers on the project work with data. That’s the goal.

The second one is that we actually have a look at existing tools and also build new tools to go from data to insight. So, of course we have customer decision journeys, we have service blueprints, we have experience flows and all these different names, of course, of ways in which you actually translate data to insight. But they’re more geared towards the qualitative, and we’re not really used to handling vast amounts of data in such a way. We also use digital tools, of course, more in the field of growth hacking, where things like this have gone. We’re really investing in, “Okay, how can you build design research dashboards where you can quickly go into the data, explore, combine quantitative and qualitative data, and really build a design environment to work with that?” Data visualization is quite a part of that, as a way to dive into the data and get an understanding.

The third point is growing the impact of data visualization competence. So, actually we’ve already had data visualization on our agenda for the past few years, but it was quite oriented for the innovation program and research program, not really geared towards business. So, really driving actively on getting the existing capability much more embedded within our business project, where you can both use data visualization as a means to gear the creative process, as well as the proposition itself.

Then I’m really working on integrating--I also have service design competence in my team, with the idea that the service designers are the ones that should adapt to this data part and ensure that we also build differentiating propositions that encompass this data and intelligence. So, I’m really driving this integration of data design and service design with the reason to really accelerate (inaudible, 16:30, build also?) the propositions and not just use the data in the creative process, but also ensure that we deliver something meaningful to our users.

And the last point that I will touch upon is that we actually also explore how we can work with intelligence in the design process. So, we’ve made some first steps there, and I’ll show some examples where we, for example, visually can identify experiences in the data. So, if we go from a classic data analytical point of view, you will go for patterns and events and so forth, and as a designer you’re much more looking to an experience or behavior or context input. So, also looking, from a design point of view, how could I find such events from an experience point of view in my data, and can I build tools for designers to use that. I’m looking for experiences rather than routines or patterns. So, that’s what we are looking for, and part of that is also looking into how can we combine qualitative and quantitative information to really go from behavior to experience to context, so we can understand how we’re actually delivering value to.

Paul: It seems like you’re going to have to be a bit of a philosopher as well, if you’re going to be deciding, “This is what a high quality experience looks like.” Versus it’s not data only, it’s also exploring qualitatively, “What is a good experience?”

Eva: Yeah, I think that also represents that it’s designers doing this, and why it’s also interesting to have a designer look at it, right? From a design point of view, you have thought about and have an opinion about what is a good experience, based on user research. And I think that’s what makes this interesting.

So, it’s not just what I see from people or what I hear from people, but it’s also what I can see in the data that they do, which also means that I can use it in the proposition, which is difficult when you’re just doing it by interviewing and observing people, how then you could also see things that you could act upon, but how? Because you have to also know this in their day-to-day life. I see that many propositions within Philips, but also outside, they kind of thrive on knowing someone, right? Knowing what you need when and where. I think in general, not only within our company, I haven’t seen that many great examples of people actually getting to know their customers at that level. So, that’s something we have to work on.

Of course there are examples where we claim to be personalized, but you see them especially in a very digital environment, a UI environment--where, of course, the big digital companies do A/B testing. But that’s on a quite a bit different level, right? That’s really on the interaction level and not necessarily on the experience level.

Paul: Are you still focused on health sciences and medical sciences, or are you more broad than that now?

Eva: We’re actually in health technology, so that means both personal health as well as health systems, as we call them, within Philips, so that’s quite a range. As I said, we started this endeavor of building competence more from a personal health perspective, working with data, working not in a medical context--as you can imagine, that makes working with data, being present in people’s lives and so forth a little bit harder than when they are patients, right? But we did make a step, so I also have a study now in mind in which we gather and work with data of actual patients.

If you look at more of the data visualization part, that’s actually much more oriented at the health care side of things. So, there we look much more into population health management, really helping our oncology business, building, for example, solutions for multidisciplinary team meeting to determine treatment. So, how do you visualize data for a group of clinicians working together on a multidisciplinary team. So, as I said, population health management, financial dashboards to social economic dashboards, or different parts… Trying to ensure that we mix and match and learn on both sides.

Paul: Historically, design has been a very “right brain” activity, but data analysis is a very “left brain” activity. Have you also had to train your designers to think a little differently than what they’ve been trained to do? Have you had some interventions where you’re having to make a connection between the two sides of the brain for them?

Eva: Yeah, of course when we brought this team together last year, we actually had people that made that connection already, right? They are on that team. Maybe not all of them; so, I think half of my team are able to hands on work with data, program, set up the whole data structure supports themselves. It might not be at the level of a data scientist, but they can do it themselves.

So I’m also growing the team, I’m really looking for these hands-on people that actually work with data themselves. Because I think that if you want to design for it, you also have to design with it, so you have to be able to do that. That means, especially, as we integrate also the service design capability, with the idea that these are the first designers who really use data in their service proposition work.

And there, of course, I’m having to make sure that the data designers and service designers work together very closely. So, just to make it very practical in their goals, I make sure that the service designers show that this year they actually worked with the data themselves. So, I’m fine if they want to use available tools for that, right? If they want to use Microsoft Power BI, if they want to use Adobe Analytics… we have all of these different environments where it’s a little bit easier to dive into detail. So, I don’t have restrictions on that; I just want them to do it.

On the other hand, I also make sure that data designers have, as a goal, to really help another designer to use the data in the creative process. So, in that way I’m actually forcing them to really work together and ensure that the service designer helps the data designer to build differentiating propositions, and that the data designer helps the service designer to use the data in the creative process. That’s an ongoing activity. I’ll also more actively have some openings on my team, and I’m very actively now searching for someone who can integrate service and data design points of view.

Paul: I think our UX STRAT audience is going to be really interested to hear about this. I think the challenge will be how to present some very specific examples of perhaps customers gaining these benefits and customers realizing, while sanitizing those and keeping everything secret that needs to be secret, but to actually dive down deep into an application to show here’s how it actually works in practice. I think that’s going to be the key. Can you put on your “future glasses” for a moment, looking into the next three to five years, obviously data is only increasing, so we only have increasing amounts of data, sensors, better ways to manage data… Since you bring design and data together in your work, how do you see that playing out three to five years from now in the general user experience field?

Eva: The competence I’m driving now, if we haven’t doubled it at least in the next two or three years, it’s going to be difficult. It doesn’t mean there will have to be new people, but it means especially also making the people on the digital teams and the UI and UX teams--give them access to the tools to actually do that. Really embedding all that kind of data into our development process is of a different nature that we still have to establish. So that is very concrete; we have to get that done. There are a few straightforward things that I think in the next few years have to be an established way of working, to really up our game.

And then moving forward, I think what I have to position us for at this moment is that we will really have a say in the artificial intelligence conversation. So, artificial intelligence also in our company is quite functional, so we really look at making our image quality better, or maybe even the functioning of hiring, in the personnel area. And we don’t really develop an experience point of view, that is not really established yet, and I don’t see it really happening that much yet in the world. I think as design as a whole, we have to really up our game.

But that also means that we need designers who actually know what they’re talking about when it comes to AI. So, first we have to know what we’re talking about when it comes to data, and then we have to also know what we’re talking about in terms of intelligence. That goes hand-in-hand. Of course the designers more and more run into these kinds of solutions, and I wanted to go a little bit further than interaction with intelligence, right? So, the first thing we often think about is UI for AI, so how to interact with the intelligence. For me, it really goes a step further. What is the design point of view on AI? A people point of view on artificial intelligence. That means it has much more to do with how we interact as people and how do you interact with a system, rather than just how do we think as people.

Paul: Thank you, Eva. I’m very much looking forward to your talk, and also to the Interactive Thinktank portion of the program, where we can dive into these kinds of issues with the whole UX STRAT audience.