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I’m not a robot, yet:
How data is killing Design
Dheyvi Velagapudi – SIC 2017
Dheyvi V – SIC 20172
Dheyvi V – SIC 20173
Dheyvi V – SIC 20174
Continuous Improvement
x1
@DheyviV – SIC 20175
DesignData
@DheyviV – SIC 20176
Design
objective
retroactive
measurements
subjective
proactive
ideas
Data
@DheyviV – SIC 20177
DesignData
@DheyviV – SIC 20178
Design
Data
@DheyviV – SIC 20179
Innovation
Optimization
@DheyviV – SIC 2017
How did we get here?
10
@DheyviV – SIC 201711
Cultural BusinessTactical
@DheyviV – SIC 201712
Tactical
@DheyviV – SIC 201713
Cultural
Cultural
@DheyviV – SIC 201714
Data-driven Design
@DheyviV – SIC 201715
risk
efficiency
≤
≤
Cultural
@DheyviV – SIC 201716
Business
@DheyviV – SIC 2017
What’s the problem?
17
@DheyviV – SIC 201718
Innovation
Optimization
@DheyviV – SIC 201719
@DheyviV – SIC 201720
I wake up in cold sweats every
so often thinking, what did we
bring to the world?
Did we really bring a nuclear bomb with information that can–like we see with
fake news–blow up people’s brains and reprogram them? Or did we bring light
to people who never had information, who can now be empowered?”
Tony Fadell
Nest founder, SVP Apple
(Interview with Fast Company)
@DheyviV – SIC 201721
Research by Time Well Spent
@DheyviV – SIC 201722
Complacent
@DheyviV – SIC 201723
Conservative
Modular web design - Hatched London
@DheyviV – SIC 2017
What do we do?
24
@DheyviV – SIC 201725
Data ≠ truth
Data = tool
http://www.tylervigen.com/spurious-correlations
@DheyviV – SIC 201726
More
metrics
Visceral
Don Norman - “Emotional Design”
Behavioral Reflective
DESIGN
@DheyviV – SIC 201727
Break the
bubble(s)
Adobe - “The Importance of Data in Design”
Data
Empathy Vision
DESIGN
@DheyviV – SIC 2017
So, what now?
28
@DheyviV – SIC 201729
Design
Data
@DheyviV – SIC 201730
DesignData
SIC 2016
Keep in touch!
Dheyvi Velagapudi
dheyvi@substantial.com
@dheyviv
#SICsubstantial
Will you be leaving with 1 improvement?
I’d love to hear about it.
Come visit!
www.substantial.com
Oct 25 - Queer&&Code
Oct 26 - Design Horror Stories
Nov 08 - Beer&&Code
Nov 23 - Design Thinking & Innovation Collective

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I Am Not A Robot, Yet: How Data Is Killing Design | Seattle Interactive 2017

Hinweis der Redaktion

  1. Hey I’m Dheyvi Been working in digital product / experience design for over a decade I’m terrible at pictures of myself - you can see my hair is green now
  2. I’m head of design at Substantial Full service digital product agency = from strategy to design to development In our 10th year Located in the heart of Capitol Hill We’re above a karaoke spot and Blick’s art supplies
  3. I’m not here to give you a silver bullet or tell you what to do Here in the spirit of continuous improvement - drives me and our company to do our best and create our best work If you walk away with one new piece of info to bring back to your team, one new question to challenge your company / org, one new idea to push change in the community = success and time well spent Since I’m pretty nervous, and if you’re open to the experiment, you could even give me a thumbs up if something comes up that you like (if anyone’s ever played with Periscope, it’s like that concept – but IRL)
  4. So why we’re here: the trendy topic of data and design in today’s creative industry There’s a ton that could be covered here, and I wish I had time to dive into it all, but for today let’s shift up from tactical discussions of what kinds of data there are or what design should or should not do - because it only solves for specific moments. Let’s go to the 10,000 ft view or higher - to check out the relationship between data and design, how that relationship has changed over time to stifle our creativity, and how we must reset our expectations of the relationship itself in order to get our creativity back. To make sure we’re on the same page - let’s start with some definitions.
  5. You can do a google search and you’ll get some cool, wordy, definitions on these two terms. Most of them qualify data as a noun, and design as a verb. But I’d like to think of them as nouns here - as entities and key roles in our creative process. How I’d define these entities is that… Historically as entities - these two have always been the best of friends. Design needs Data to advance and inspire ideas and solutions Data needs Design to orient focus and build understanding in measurements of information
  6. But lately, this friendship has become strained. Tension is growing and has been growing over the past few years. I’ve seen in digital and product design and am the most familiar with it in the tech industry, leading to ethical and tactical quandaries. And it stems from a change in the balance of the relationship.
  7. Where once equals - we have pulled data to sit above design. Above means: Placing higher value on the power and importance of the retrospective information in creating any solutions. Ubiquitously requiring measurement prior to making any decision or take any action
  8. Translating that into affect on creativity and what we make = we are focusing more on optimization than innovation Optimization = the small incremental improvements of any existing system, situation, or resource in hopes of finding the best of most effective use Innovation = the introduction of something completely new to shift a system, situation, or resource toward greater efficacy or unforeseen value A metaphor to help illustrate = let’s think about painting. If we were to create a painting in the sense of optimization, it would be something like paint by numbers. We’d take one step at a time, knowing we’re working toward a larger vision. We’d only be able to see the effects of each step at a time. A couple tech examples = let’s think about devices The iPhone X is far more powerful than any previous gen of the phone, but it doesn't change the paradigm of what smartphones are or could be. I mean we’re still calling them “phones!” but as my colleague has noted - they’re more like “toilet computers” (as in, a computer you can use while you’re in the bathroom) But the iPhone was born into a new space - what a phone could be, not once was
  9. So how did we end up in this state - where we are in loops of constant optimization and data supersedes intuition or creativity I believe its a combination of three factors.
  10. Tactical = the change in access and use of data within design processes and decision making Cultural = the change in value of design and specific metrics of data Business = the shift in scope of design decision making and data analysis These three factors didn’t spring up immediately, we’ve been heading down this path over several years.
  11. Tactical = the change in access and use of data within design processes and decision making. In effect - the way in which we used to gather and use data through physical manufacturing and design process was much more considered. The cost of gathering data were pretty slow and high. And the risk of misinterpretation of data equally could result in massive losses. So data was more a guiding factor in decision making - rather than a core driver. With software and the ease of changing design through code, data became more readily accessible - at smaller focus and larger scale. This access and ubiquity let data become a core factor in decision making and more rapid iteration in design. In Fast Co article (aug 2017) : Matt Webb (managing director of R/GA’s IoT Venture Studio in the U.K) put it well = “This is a unique problem of the software age. Historically, design was about making physical things, whether it be office chairs or album covers. Now, designers are coders–or at least working within the constraints of code–typing inputs into a computer that conjure up an interface that lives across millions of screens…Designers have always done user testing, of course, but it’s much harder to change a physical object than it is a piece of code.) Now, the constant tweaking of software creates a never ending design process, where every click is another piece of data to optimize.”
  12. Cultural = the change in value of design and measurement of data At the same time as the process of design and usage of data to support it was changing, we also saw a huge cultural shift in the value of design - from one that equally valued emotion, to one that favored function. Taking a look at the early versions of some pretty popular experiences today, like Facebook, Google, Craigslist and more – we prioritized utility of interactions and information. Granted we did have limitation in the ability to create aesthetic forms by the languages and browsers of the time, but we also didn’t really care. We loved these experiences for what they did. This function-first economy paved the way for a new and highly prized metric in our culture – “Engagement” ie. the number of clicks or how long an individual stays on a page. In the battle for customers, it became less about how we might feel about these experiences - because there are so many variables in that - and more about how data could prove and support ways we could capture and retain their attention against all the others’ seeking it.
  13. So - we can see how it lead to the concept of data-driven design. With our focus on capturing attention and engagement being the key factor - it was easy for us to take what data was (an objective, retroactive, set of measurements that could help guide our decisions) and turn it into the core decision making instrument for any change. For example = the infamous “41 shades of blue” test without too much risk or impediment to our customers and audiences (Google’s engineers couldn’t decide on two shades of blue for showing search results, so they tested 41 of them to see which attracted the most clicks)
  14. And again the increased speed of access to data allowed it to become far less risky to use in decision making for massively scaled or scalable software. Reduce risk in scalability - We aren’t our audiences as designers / we design for others - The more people we are designing for, the more we need to be sure our decisions aren’t going to negatively impact individuals by going for the majority Increase efficiency - Relationships between data fueling things like AI & Machine learning = help us help ourselves move quicker / process quicker / act quicker - With larger quantities of access to data, instead of speaking to people, we could use numbers to represent them - Data can settle disputes and challenges in large Stakeholder parties and huge creative team size / communication chains
  15. Business = the shift in scope of design decision making and data analysis The interesting part about putting data in the driver’s seat for design, much like that seen through the 41 shades of blue test, is that businesses also shifted the responsibility and accountability of design decisions outside of the design practices of holistic thinking and imagination, and into things like telemetry and focused problem sets. So while design was becoming increasingly important in business, the scope of ideating, exploring, and analyzing data shrank - being divided amongst feature teams or subgroups of companies. In that article by Fast Co with Matt Webb - summed it up well again: “More than ever before, designers are sitting on the C-suite of companies. Large corporations are investing in design because it makes good business sense…But as design has become integrated into the heart of companies…designers themselves, beholden to business interests that demand the most optimized, most persuasive version of something as opposed to the most useful and helpful for the user, have decreased agency. In other words, with power has come less responsibility.”
  16. Ok - so we know data’s taken the driver’s seat for design given the term “data driven design” even exists today, and if it’s reducing risk and increasing efficiency, what’s the big deal?
  17. Let’s go back to the first tension that’s resulted from it - that we’re focusing way more on optimization than innovation.
  18. You may remember this incident from previous years - this one is from last year, but its not the first time. The story goes - someone is paying attention to something very interesting on her phone and walks forward. We can presume they feel each step as they move forward and perceive that there’s a path ahead to be walked. Except by focusing on what’s right in front, and only right in front, they take a giant and unfortunate crashing step into the fountain. When we optimize, we are focused on the incremental changes that can improve our lives, our situations, our resources. But they don’t help us foresee the larger picture of what could be or what’s to come. And there can be unforeseen costs to that lack of perspective.
  19. For example - Tony Fadell (founder of Nest, instrumental in creating the iPod and iPhone) shared some of his feelings about helping mobile rise to it’s current omnipresence. When we consider his quote, we can infer that while there data was used to help design these experiences – it was only incremental enough to push for adoption and engagement, not the larger potential effects. And he’s not alone - other designers instrumental in the rise of digital adoption are starting to reflect on things in the same way. In this same interview, Faddell continued to share “A lot of the designers and coders who were in their 20s when we were creating these things didn’t have kids. Now they have kids,” he says. “And they see what’s going on, and they say, ‘Wait a second.’ And they start to rethink their design decisions.”
  20. There’s also the impact to our mental health and culture. With our creativity siphoned so heavily through data focused on attention and engagement, we’ve optimized once innovative concepts (like apps) to hog our time at the potential cost of our happiness.
  21. Aside from ethical and emotional impact - prioritizing data above design and constantly optimizing has also opened us up to dangerously complacent and conservative tendencies in the ways we work. In regards to complacency - Optimization can become dangerous because the smaller the decision we’re making, the more complacent we as humans are in determining proof. Things like confirmation bias (the tendency to search for, interpret, favor, and recall information in a way that confirms one's pre-existing beliefs or hypotheses) can run rampant in our analysis and collection of data. It’s abuse of data in favor of efficiency or avoidance of conflict.
  22. Also, with smaller decisions to be made, we become more conservative in our design decisions or might only make it to our “local optima” “Local optima” is the concept of getting stuck in small sets of data and small, incremental optimization loops that prevent us from ever finding our actual best case scenario “Copycat design” could also be a result of our conservativeness with data. How many sites and experiences have you seen that promote the same design patterns? Is it a phenomenon because we are so focused on incremental change that we’ve normalized data across scenarios and experiences? Are we flattening out our problems and therefore our designs because we don’t want to think beyond the immediate measurements?
  23. Ok & Gosh - I know that all sounds like doom and gloom and a whole slew of glasses half full around data. But I’m not trying to wag my finger at data itself. Let’s recap a bit to explain – I believe that the tension we’re facing between data and design and its affect on creativity, is the change in value and importance we’ve created between the two (primarily putting data (objective, retroactive, measurements) before design (subjective, proactive, ideas)) And that we got here because of tactical, cultural, and business developments that happened through the advent of software / digital design. We’re feeling a slow death or fading of innovation - in favor of optimization - because of this shift in the relationship, which is leading us into emotional quandaries, conservative designs, and complacent behaviors. So - what do we do? Well, I don’t have a silver bullet, but I do have some thought starters and suggestions to try. We’re all still figuring this stuff out because we understand the dependencies between data and speed and risk in business.
  24. The first thing we need to do is stop treating data as ultimate truth. Data is a tool in our design arsenal or toolkit, amongst others, and its objective only until we use it. We can preserve objectivity by avoiding complacency in it’s analysis - challenging how it relates to other data, or whether it can usurp our intuition. See example about how abuse of data can be dangerous (spurious correlations) Steve Jobs had a good point when he said – “It’s really hard to design products by focus groups. A lot of times, people don’t know what they want until you show it to them.” So instead of asking “How can you prove it?” with data. Ask “How do you know it won’t work”? Use data to find pitfalls and risks instead of blocking opportunities. Instead of being afraid we will get it wrong, we should be afraid we don’t get it at all!
  25. We also need to measure things more holistically. We are so hyper focused on behavioral metrics like awareness, engagement, retention - that we forget the other aspects of emotion embedded in the humanity of the experiences we’re building. There’s definitely safety in focusing on behavioral metrics because we can assume predicability and focus on maintaining it with an individual. That in turns leads us to more sustainable models for relating revenue to design. But we don’t behave the same way all the time - we’re not robots. Don Norman of the Nielsen Norman Group (leading international, design research group and key driver of many UX principles today) actually identified a more rounded set of dimensions that we could measure to help us understand what would lead to successful connection with people. • Visceral = is obtained through intuition rather than from reasoning or observation. This level is influenced significantly by appearance, texture and sound of objects. • Behavioral = the one we’re most familiar with and prioritize these days. ie. The actions or reactions of a person, usually in relation to the environment, to an object or person. It’s conscious and unconscious, overt and covert and voluntary and involuntary. It’s all about functionality and is influenced by pleasure and effectiveness of use (accessibility and usability). • Reflective = refers to the capability of quiet thought or contemplation. This level is influenced strongly by self-image, satisfaction, memory and the meaning of things. This is more important as products mature.
  26. And finally – we must try breaking down the walls of insulated thinking within the walls of our companies or organizations, roles, demographics, and communities in order to create balance with design. This diagram from Adobe (yay another Venn!!) about the importance of data in design seems to echo this sentiment. Data is just one facet, and not the only driver in decision making or creative thinking. Getting to empathy and vision requires us collaborating and understanding groups beyond what we normally work with. While we should get data from our customers and audiences, there’s also just understanding the larger vision of your company by talking to other teams, other groups. As companies grow and scale, we see greater delineation of responsibility and boundaries of accountability in the development of experiences. While this may help us be more efficient in management and production - it means nobody gets to see the full picture. So, get out of your team space or floor and visiting others to see what they’re working on or what they can bring to the table. And as for empathy, as sociologist Judy Wacjman mentions in an article – “Silicon Valley is notorious in particular for not being family-friendly…It’s notorious for being full of young male designers. It’s great that they’re thinking about this now that they’re having kids, but I wonder if one could envision a different design community full of people of different sexes, full of people of different ages.” While she points out the need for greater demographic empathy, there’s also industry wide empathy. Meeting with designers who aren’t just other product designers, or speaking with people who aren’t in design or your industry at all – can help build the empathy to help better understand, and reflect, upon any data you may acquire. If we do this we can live up to the Toyota way = set of principles and behaviors that underlie the Toyota’s managerial approach and production system. It consists of principles in two key areas: continuous improvement, and respect for people. Uses tactic of “nemawashi” = literally translates as "going around the roots” Informal process of quietly laying the foundation for some proposed change or project, by talking to the people concerned, gathering support and feedback, and so forth.
  27. So, thinking of data as a tool rather than truth, more metrics, and breaking our bubbles are just a few ways we can start to challenge the way we think about and use data to help rekindle the amount of innovation and creativity we had in design.
  28. And if we start here, and keep thinking and continuously improving, hopefully we can move away from this imbalance of data-driven design, where data chokes out exploration, holistic sentiments, and human potential.
  29. Let’s get back to a healthy relationship, where data shares a seat at the table, with design and with us.
  30. And that’s all I’ve got. I hope you’re leaving with at least one piece of info, question, or idea from this talk. If you have questions or want to talk more, please feel free to reach out. Or come visit me and Substantial sometime. Thanks for your time and have a great rest of SIC!