Data Enabled Design
Data Enabled Design
Understanding the Data Enabled Design team, part of Philips Design. This new competence team aims at making data and artificial intelligence part of the creative process and deliver on meaningful propositions that encompass data and AI. In this session Eva will elaborate on the need for this competence; the needed skills, expertise of designers and collaborations with data scientist, analysts and developers; the methodologies and tools we develop; illustrated with examples.
All right. Thank you. Thank you for having me. And yeah, well, good to be here and speak to you all about how do we actually ensure and discuss how do we bring UX into what we are doing as a business and how we keep it there, and how we also evolve that moving forward. So I'll talk a lot a little bit about the team that I'm leading: the data-enabled design team.
Let's say something about data to start with. So design with, by, for, through, data, right? So we all know the examples, and I don't have to probably inform this audience about our peers and the digitally born companies where A/B testing is a daily reality, and where we do data design, data-driven design practices, and where we very well understand how you validate and understand the impact or the effectiveness of an incremental change in your digital solution. On the other end of it, we also see first examples of where actually artificial intelligence is used to make design decisions. Right? So can AI actually pick the colour of your product? Or can the AI and that's the example here, actually decide what your homepage looks like every day?
Again, we also see examples more leaning into the marketing side of things, where actually we use growth hacking, right? More digitally understanding, like where we growing our business or more in building personal profiles to actually targeted advertisements. At the same time, as we also know, for example, Procter and Gamble reduced the investments in profiling like that tremendously, because so far, they hadn't seen the impact of it. And advertisement didn't necessarily end up at the right place at the right time.
So, so much for personalization. Our anecdote is that actually with the huge forest fires in the US during the summer, is that actually the navigation so far was actually leading people into the fire, because oh, it was so nice and quiet drive over there, no traffic at all. Right?
So let's see, there is something to say still about that. And what we think about that as designers, because probably some of you actually mastered this A/B testing part. Actually, most designers still go in sort of a defence mode when you start talking about AI and AI taking over design decisions, that can never be as good as the intuition of the designer, or the experience, or the skill of the designer. And if you look at the tagline of this conference track, continuous delivery, validating assumptions, and non-stop iteration, getting these right means excellent execution.
Well, I couldn't actually agree more. However, in my daily reality, we need, I think, a bit of a different understanding of how you use data in your design process than most of our digitally born peers. That so we bring a digital solution, not saying that that is better or worse, but it's just not cutting it for us. So I'm at Phillips and Phillips is a health tech company. And it reduced actually, to that essence recently, and we actually house a portfolio of businesses, ranging from Precision diagnostics, into oral health care, from mother & childcare into image-guided therapy. And actually, all these businesses are asked to more and more deliver solutions that go across these different silos.
So we are asked to move from touchpoints to solutions, from single products to systems. And with that, of course, also connect these different offerings that we already have in our portfolio. At Philips, actually, I have nothing to complain about the user not being in the center of what we do. So the mission and the vision and the strategy of Philips as the whole, the user is in the middle of that. And we are delivering solutions that are in part of the so called Health Continuum. And we're also delivering solutions that go across. But of course a few challenges with that, how do you connect them up in a meaningful manner? How do you actually move from your heritage and mainly hardware products into this connected reality or system?
From a design point of view, we thought we had to give an answer to this by bringing a team together like one and a half years ago. That's called Data Enabled Design. And that's our answer to how do you bring data into the design process. We are actually driven by two main things. So we set out to understand the value of data to users and with data, maybe also going into artificial intelligence or related developments. So how do we ensure that propositions that encompass data are actually meaningful to our users? And in the healthcare setting, actually, most of moving forward, most of the propositions we bring, they have part of it are driven by data, or maybe it's even at the core of it. Another element is that if you want to do that, I think you should also use data as creative material. Why you should be able to both quantitatively and qualitatively synthesize from data to understand truly what's going on.
The design community in my perspective doesn't do that enough yet. And we try to treat data as creative material. Obviously, it's not about the data itself in the end, right? Data in itself is not really worth something. It's about what you learned from it, what you the insights that you gained from it.
So it's more about the questions. How do you design personalized systems for ecosystems? Or what does the design perspective on the artificial intelligence of Internet of Things look like? I'm not the only one who wonders that so actually, I got this from research done in some design, research, and academic environments. And it did some interviews with quite a large group of UX designers on their encounter with machine learning. And I freely translated that to data and intelligence.
And there are three learnings. So it's actually quite difficult to envision what data and intelligence can and cannot do. And I think that applies to our design community. Right? So either we are naive when we have the data, we can do anything, right? Various, who knows something about data, it can be really ugly. And it's the question if that's what you think is in there is in there. The opposite thing is also true. We also because we lacked the knowledge, also miss opportunities that are in there. And we're often also not so well placed to challenge our business or research and development stakeholders or colleagues on if what they are doing with the data or with the intelligence is truly meaningful to our end-users or to our customers.
I can also turn it around if you look more at my computer science, data science, developer colleagues, is that they might actually know these kinds of elements, but then not make the translation to why, what, when it's relevant to our customer. So designers are in general, and that doesn't go for everyone. But I think most designers are not trained and like the tools to work with data and intelligence. Right? It's not part of the regular design curricula. And it's actually also quite a difficult material to grasp, right? It's not tangible, you have to somehow be able to code the program, or have some kind of tool that gives you access to it.
And then the third point is that designers are not enough involved in the development of AI, and internet of things. So what is now an experience-driven viewpoint on these technology developments? Of course, the ethical viewpoint is there. But that's, you know, gaining quite a bit of traction, of course.
But what if we argue from the experience point of view, what have we are you found the behavioural, from the experiential and contextual point of view. As a team, we actually set five goals or ways of working that we actually want to address these challenges. So these are the five I will go through them one by one.
So the first one is that everyone in my team is actually working and gathering and working with data in the projects that they do. So this means both quantitative and qualitative data. And this might sound easier than it is. Because although we might have a lot of devices in our portfolio, it is not said that we just have ready access to the data, or the data actually contains these behavioural, experiential, or contextual data points that we as designers normally use in our qualitative research. So what is actually really in there in these data logs about our user, right? If it's just about the workflow, when did someone hit the button? Does that really tell us about someone how stressed someone is maybe in an emergency situation, does it tell us something about that patient, and what he or she is going through in that care path.
So what we do is we build and hack our own assets. And we use third party devices to actually get an insight into this behaviour, into this experience and into that context. We also build manual trackers, in which we actually get people to think about it a little bit different than trying to think about what they are tracking and why they are tracking it. And the third example that you see is actually the first exploration more on the big data side, where we try to have a different perspective on population health data, and I'll show a little bit more about that later.
The second thing we do is, we're using existing tools, and we're building new tools to move from data to insight. As I said before, data is one thing, but what you want, of course, is in the design process, to move to insights and to knowledge and be able to synthesize on that. So we have more digital tools like Growth Hacking, we, of course, also in our studio, have our experience flows and our service blueprints, they will be there as well. But what are my team is especially working on is how do we actually get insight into the data from a user experience research point of view?
So how do we build the dashboards or tools that actually are able to bring this quantitative and qualitative view together? And really, makers understand how do we get from this to a design intervention that makes sense. An important element, I think, is the integration of data design and service design. So service designers are inherently the people, the designers that oversee the bigger service flow, that understand how touchpoints belong to one another and what happens if you change one of these touchpoints. So you need this thinking, the service thinking, the system thinking, to combine with the people that actually understand the data flows and these kinds of services, or systems. And that together, I think, is the way to build meaningful propositions differentiating propositions around data and intelligence.
We have already, for a few years, invested in data visualization. But we have some problems, like really kicking it off in our organization. So we’re not really landing it outside of the innovation program into the business programs. There are multiple reasons for that one of it is that data visualization, the impact, and the difference it makes often are only understood once you really experience it, and when you're working with it.
Another thing is, is that the business choices for underlying platforms sometimes just make it impossible to design for data visualizations that make sense, right? So if your business decides to work with a BI tool like Tableau, we are done, right? So we can work within the Tableau boxes, which might work fine for the direct needs of the business. But from a data visualization point of view, it's very limited. So we have to think about how also do we advise and work together with our business to make choices from a financial point of view, from a functional point of view, from a technology point of view, also from a user experience point of view. And this is done both about how do you use visualization to get insight into data, but also how you use data visualization as a proposition in itself.
The last part is that we actually building the tools to make exploring the data and intelligence in the design process more accessible to more designers in our organization. So here, you actually see that we build this two ways you can build chatbots, conversations on the fly, and combine that with sensor and data input that we are getting in or from a real-life setting. The middle one is where you see that we actually try to annotate data from an experience point of view, instead of listing events, we're actually looking at this part of the data actually showed a good experience around brushing your teeth. So how could we actually use that kind of insight and find it back throughout the data points that we have? And there you see also the flow of this to motion AI or flow AI, where we can actually really start to build these flows, use some automation to see if we can also let a system act without actually where's it going.
So let me show you two examples of what my team our team works on. So the first one is on Population Health. So Population Health is all about understanding for a care provider healthcare provider. How is my population, my patients? How are they doing? And how do I understand the bigger picture of health in the area that I'm responsible for as a care provider? One of the insights was is that the things that make us healthy are not necessarily the thing that we spend the most money on. So there's a lot of behavioural and social-cultural elements that make up our healthy, how healthier we are? But it's not so much used in our healthcare delivery.
So how can we actually give insight into this and make that useful for our customers? So I'm now showing you static images, where this is done a prototype, and it goes near to industrialization. But it's a prototype, where we actually, together with our research colleagues and the business, think about how could we use this to show social-economical determinants of health? And how do we make them insightful to our customer? And what you can see here is up to a zip code level, you can see what kind of social and cultural elements influence the social health index. So you see areas that have a low scoring index, and the region does have the highest score in the index. We are then involved because what is this index? How do you calculate that? That's a machine-learning algorithm that tells you something about which attributes actually say something about how healthy people in a zip code area are.
So what you see is that we have different elements. So you can see your areas, you can see the attributes, you also have an idea of how accurate your algorithm is based on the filters you choose. And as a healthcare provider, you can actually use this to get more insights. So you can have a look at actually what for the people you're actually caring for what kind of care are they actually consuming? And what would be maybe the care that they need? Or where are they actually getting their care, right? If I'm in one zip code, where do I go to actually get the care that I need? And in this way, we actually build a differentiating proposition by three elements. So we help our customers to think about their market expansion. So which care and that sounds very commercial, but in the end, it's really about which care do I deliver to which area? So who needs what, not all zip codes actually need a non smoking campaign, others might.
The second thing is that we actually do care optimization. So this relates the same thing. So how do you dedicate the care to the area that you're providing it to?
And the third one is that you actually normalize the quality. So sometimes it appears that some care organizations in one place are performing less than the other. If you look at the social determinants of health, it might actually be that one care facility is in a little bit more difficult circumstance than the other. If you can normalize you can actually see what is the quality, what is the difference they are making, which is valuable information to our customers.
Another showcase I want to show you is around Patient Track data in healthcare. So what we notice is that there's a lot to do about this, so the patient getting more and more in charge of their own health. And we've just seen an interesting talk about that, of course. But we also see on the healthcare professional side that there are doubts or worries, the worries are a bit more prevalent than the opportunities in what to do with all of this data. And if the data that is struck by the patient is actually accurate and valid, and if you could base decisions on that.
So this first started off with a project that we did around infant feeding. And we just wanted to learn more about the experience of infant feeding by actually hooking up some sensors into the bottle and understanding what was happening. This was to accelerate on a single connected device, bottle feeding. And the interesting thing is that it gave insights to our experts in a company that literally know everything about this that they had never thought of before, never seen before. And why as that, we focus on bottle feeding. So we focus on the bottle, we focus on the feed, and we about the drinking. Where is bottle feeding of infant feeding has everything to do with? Where did I come from? Where was I in a rush? Is there a lot of noise? Am I at home? Right?
So there are a lot of different elements that actually determine, let's say, the success of a feed. This led us also to think about how can this parent tracked baby data be relevant to healthcare professionals. And, of course, this is in the context of a business that we have and the proposition that we are actually offering.
And what we started to do is, we really put a, it's not it's more probe, not really a prototype, into the family's homes, so they can track the data. They have contact with the healthcare professional via that kit as well. So we do in the actual context, we use the data as creative material. And I'll show you through a few steps in the process, how we've done that, we're really thinking qualitatively and quantitatively, what are we learning about the situation about the behaviour again, about the experience in a context, and how does that translate into a design intervention? And that comes through the explorative and continuous part. This is not a pre-built prototype that we put where the customer let them test it and then take the learnings back.
No, we actually put it out there and let it evolve while it's out there. So it's not prototype, test, prototype, test. Now get the insights, and make sure that we kind of evolve on the go. I'll show you how we've done that. So we have the parent tool kit that gives the parents the ability to actually track some data freely, right? So they can, there are some buttons and they can themselves indicate what I'm using them for? Am I tracking the sleep? Am I tracking, crying intensity? Am I tracking the feedings, right? So the different things one can track.
There's a healthcare professional dashboard, where the professional actually get access to the data, and where they can both communicate. And then on our side, we have a researcher dashboard where we can follow the things that happen. And then we can also from that part, intervene with new design concepts. As I said, it's in context. So we actually have a set of professionals that recruit their own patients to participate in a small scale study, like this one. So that also babies that actually have a care question, right? So it is something around these parents and babies that there is a question around the care.
Just give you an idea of how small this test actually is. It gives a lot of insights. And then we go in. So this is actually what it looks like in the home, right? So I try to convince my business partners very often about the fact that we make beautiful pictures of what something looks like. But that's not what the home looks like. Right? So here you see and that's what we also value.
So people start tracking data. Very quickly, actually notice that they want complete data overviews. Because they are tracking for their health care professionals, they find it very important that it's complete and accurate. That might seem like a small thing. But it's actually big thing for the parents, because they want to ensure that they have the right dialogue with the healthcare professional. So we quickly make sure that there is actually a way to adjust data points in the systems. And then you can also annotate the data points.
At the same time, actually, the healthcare professional in this case, a preventative nurse gets a data overload, right? So there's too much going on, and she doesn't know where to look. So we built a way to actually filter the data so she can more browse through it. That still is a bit difficult, because he says yeah, there's a lot of data points, but I still don't really get the context. Right? So I miss the conversation with the parents explaining what's going on.
So what we actually what she says is, normally I actually work on a question basis. So parent actually has a question or problem and I try to anticipate that. So when we do is that we make sure that they can have a more richer dialogue about what the data means. So by changing some of the elements of the design, we actually put more emphasis to what are the trackers actually used for? So the preventive nurse and the parents actually have a discussion about are you tracking? Is it actually valuable to track the duration of the sleep of the baby? Or actually, do you want to track how quickly the baby falls asleep? Right? So to get more nuance into what is it actually that they are after, which is a complete difference from all the baby apps that are out there. Now, when you have a standard set of trackers that you want to track varies for every parent, there might be a slightly nuance to the problem or the challenge the encounter.
So every what we also do is that we give them an opportunity to actually start adding videos to a data point, right? So if they tract something about crying, they can then to that exact same data point, they can add the video of the crying. So the nurse actually has an understanding of what the parents think, is crying your lungs out, right? So to have a better idea of the experience and the context. And this then results into a concept in which we saw that the question was about expressing milk, which is quite a serious topic for a young mom, right? She was not expressing enough breast milk for the baby and she wanted to increase that.
She got advice, or she asked the question Is he said, these are the data points, I think are relevant for this question and then the nurse could actually see that particular data. After a week, it was not going better. So actually, the advice didn't help. And they had to revise that. They both agreed that if that didn't happen, that moment, she would have stopped expressing milk already, right? So you make actually an impact in someone's life. For the more higher care, the second or third level care professionals, we see a more controlled approach where the care professional actually says, this is the things that I want you to measure up from the first consult. So we have this case of reflux where the baby actually spits up. And we see that the healthcare professional gets this data about when was the baby getting a bottle? When was it crying? And when was it spitting up. And the interesting thing is, this is all an average, if this child had reflux, they would be spitting in the first half an hour after each bottle, right?
So in one go, the healthcare professional would say this is not reflux, it cannot be, it would have taken this healthcare professional 1, 2, 3 consult to actually find out that it was not that problem.
So this is where we are. Give you an idea, showed you the challenge. This is what we are after. And there are a few things that are actually next on the agenda. So I need to strengthen the bridge to data science and development teams, right? So my designers are actually very hands on, they mind structure, build, prototype, program with data themselves. But of course, if we want to scale up and bring more business impact, we have to build a bridge.
Another important point that I made is how do we influence from an experience point of view, the choices that are made around platforms and software? I can talk quite a bit more about the points that are there. One that I'm very passionate about is how do we portray designedly point of view towards artificial intelligence? So how do you use experience attributes in your AI? And also how, as a design function organization, do you take accountability for your design?
So how do you go from designing it to actually understanding the impact and what impact I mean, in this case, on health outcomes, right? On sustainability outcomes, not per se on the conversion rates, but how can we go one step further? How can we actually show what our design interventions mean to the people we designed for?