VI. A good product doesn’t need dark patterns.
3 Lessons I Learned as the “Chief Data Analyst” of a Silicon Valley–funded Start-up
From 2015 till 2017 I helped grow HoloBuilder Inc., a start-up providing virtual reality solutions for the construction industry, as their VP of Customer & Data Analytics & Optimization, which roughly translates to “Chief Data Analyst”. The company is headquartered in San Francisco while I was a part of their R&D lab in Aachen, Germany. I was responsible for the whole data analytics* pipeline — from collecting data on the web platform using Google Analytics and own trackers to processing the data in Google BigQuery and visualizing it using tools like Power BI and Klipfolio. During my time in Aachen I learned lots of valuable lessons. Here, I want to share with you the three most important ones that are directly concerned with data analytics (please scroll down for a TL;DR).
1. Data Analytics ≈ UX Design
Data analytics is a lot like UX design. You have specific target audiences that expect to experience what you provide them with in the most optimal way — concerning content, presentation, and possibly interactivity. For instance, providing data for C-level management and for potential investors are two completely different stories. While management requires low-level insights concerning the software itself, among other things, for VCs we usually prepared more high-level business metrics including projections and forecasts, due to the different requirements. Moreover, internal data would usually be provided through dynamic dashboards that could be adjusted and customized while data for investors would rather be delivered in the form of PowerPoint slides that matched the layout of the pitch deck. Therefore, it is crucial to have the definition of a target audience (potentially even personas) and a requirements elicitation from that audience at the beginning of every data science process. At HoloBuilder Inc., this lesson became especially clear because of the split between San Francisco and Germany and the fact that most of the (potential) VCs were residing in the Silicon Valley.
I am convinced that a data analyst without some proper UX skills — and, of course, adequate requirements and input — cannot be successful.
2. Ask the Right Questions — and Do So early
This one goes hand in hand with requirements elicitation. Don’t provide analyses just for the sake of it!
This whole “let’s just analyze everything we can get” thing doesn’t work! It’s extremely important to define the questions you intend to answer beforehand. Tracking is cheap, so you can (and should!) track more than you need (at the moment). But the processing and visualization of data that nobody ever looks at eats up a whole lot of resources that would be required for the meaningful analysis and presentation of the few nuggets that are buried in your giant pile of big data. Also, having concrete questions in mind greatly helps with tailoring data structures more precisely to your specific needs. Of course, this doesn’t mean that your infrastructure doesn’t have to be flexible enough to quickly react to changing and new questions that need to be answered. In an optimal world, the data for answering new questions is already there and you “just” have to do the processing and visualization. In general: Expect surprise on-demand questions anytime! Therefore, anticipate and be prepared!
(While the questions that need to be answered can be seen as part of the requirements elicitation, I treat them separately here, because I give requirements a more technical connotation — e.g., “possibility to toggle between line/bar charts” or “include difference to previous period in %” — compared to key questions such as “Why do we lose users?”.)
3. Data is Meaningless …
… unless you give it meaning by interpreting it. For this, it’s inherently important to not think in silos. A data analytics team has to closely cooperate with the UX team and (almost) all other teams in the company in order to find meaningful interpretations or reasons for the collected data. Yet, this is still not the norm in industry. For instance, there is still the widely believed misconception that A/B testing = usability testing.
To ensure meaningful data analytics, at HoloBuilder Inc., marketing manager Harry Handorf and I developed a boilerplate for a weekly KPI report that posed three crucial questions:
- Which data did we collect?
- What are the reasons the data looks like that?
- What actions must/should be taken based on the above?
That is, the first part delivered the hard facts; the second part explained these numbers (e.g., less sign-ups due to change in UI); and the third part presented concrete calls to action (e.g., undo UI change). The report looked at those questions from the platform as well as the marketing perspective. Therefore, we had to extensively collaborate with software engineers, designers, UX people, marketing and sales to find meaningful answers. According to the second learning above, the basis of the report always were higher-level questions defined beforehand, such as: “Does the new tutorial work?”, “How can we gain more customers?”, and “Have we reached our target growth?”. In general, the interpretation of data is based on the processed data and the questions to be answered, rather than on technical requirements (see infographic above).
Again, because this is really important: Your data is worth nothing without proper interpretation and input from outside the data analytics department.
Ultimately, to conclude this article, I don’t want to withhold from you Harry’s take on the topic:
You might have heard of the metaphor for life feeling like a tornado. It perfectly applies to working with data of a young business — it spins you around with all of its metrics, data points and things you COULD measure. It’s noisy and wild. A good data scientist figures out how to step out of it. But that does not mean getting out of the tornado completely, letting it do its thing and becoming a passive spectator. It means getting inward, to the eye. Where silence and clarity allow for a better picture of what’s going on around you, defining appropriate KPIs and asking the right, well thought-out questions.”
—Harry Handorf (tornado tamer)
- Data analytics is a lot like UX design! As a data analyst, you have to define target audiences and elicit requirements. Tailor content & presentation of your analyses to those.
- Define the questions to be answered beforehand, then process and interpret the data necessary to answer those questions. Don’t analyze everything you can just for the sake of it.
- Data is meaningless without interpretation. Extensively collaborate with other departments — especially UX — to ensure meaningful data analytics.
* What we did at HoloBuilder Inc. was clearly a mix of data analytics and data science. But since it was closer to the analytics part, I refer to it as data analytics in this article. In case you are interested in the specific differences between the two (and how difficult it is to tell them apart), I recommend reading the Wikipedia articles about data science and data analytics, as well as “Data Analytics vs Data Science: Two Separate, but Interconnected Disciplines” by Jerry A. Smith.
Special thanks go to Harry for proofreading the article & his valuable input.
Traurig sein hat keinen Sinn,
die Sonne scheint auch weiterhin.
— Farin Urlaub: “Sonne” (2005)
I know, I know. Not yet another one of those “How to find happiness” articles 🙄. So why am I writing this anyway? First of all, I think that writing is probably the best way of self⸗reflection—sadly, an art that is performed way too little by those who would need it the most. Second, because I moved to the United States recently—which can be considered a pretty significant life event I guess—I thought it might be a neat idea to read the various diaries and notebooks I’ve kept over the past ten years. So, while I was going through my diary entries—dating back as far as January 2007 (three months before I started my Bachelor studies at the University of Koblenz)—I noticed there might be some interesting patterns. And since I’m a guy who likes to play around with data from time to time, I prepared an Excel sheet and did a little number crunching. I’m not going to give you the raw data (because privacy), but it looks something like this:
|met friends often?||had a goal?||worked out regularly?||read a lot?||…||happy?|
Here are the higher⸗level results I found (spoiler alert: the following is kind of obvious). It seems like I was happier at times when I (in no particular order)
- didn’t worry about money
- often met with good friends
- worked out a lot
- did competitive sports on a regular basis
- pursued my hobbies
compared to times when I did none or only few of the above.
You’re still reading the article? That’s nice! Of course, from a statistical perspective it must be noted that the above are mere correlations (threshold = 0.7). This means, it could be that I just work out more when I’m happy for different reasons and working out doesn’t actually contribute to my happiness, but that’s beyond the scope of this article. Since all of this is about myself, I can ensure that a certain degree of causality is given.
However, the point of all this is not primarily to just tell you what makes me personally happy. Rather, I’ve observed that quite some people are not really aware of what actually contributes to their happiness. As I noticed, in this case, it can be very helpful to keep a diary and do the same little analysis that I’ve done. It’s really insightful to simply note down what you did and didn’t do at times when you were happy and at times when you were not, then search for underlying patterns. Because every now and then, we all easily miss the obvious.
“Sorry, we are out of stock!” is an unpleasantly common sentence customers of Indian retailers hear every day. This is because due to a lack of appropriate systems, retailers are having problems with maintaining their stock and accurately anticipating the necessary quantities of each product. With their business network Ganges, SAP intend to tackle this and other problems in the supply chain that prevent Indian retailers from making the most of their businesses. To be able to optimally achieve this, during the creation process Design Thinking was applied to understand retailers’ and the other involved parties’ needs and pain points. I have had the opportunity to talk to Abhijit De (pictured)—who was one of the driving forces behind Ganges—about their solution, the unique value Design Thinking has contributed, and its pros and cons.
Max: First of all, could you please give a quick intro of yourself?
Abhijit De: My name is Abhijit De. I grew up in Uganda, Africa, worked and lived in the USA for 15 years and spent the last 5 years in India. I have always oscillated, alternating between corporate jobs—Intel, Microsoft, Infosys, and SAP—and entrepreneurial start-ups. For the past five years, I was VP of new business incubation at SAP with a particular focus on business networks and IoT. I recently transitioned out of SAP to plan my next start-up. Additionally, I also hold a Master’s degree in Management of Technology as well as an MBA.
Personally, I like to help initiate and execute projects that have high impact on improving life on this planet. I like to travel and interact with different cultures. My motto is: “Work hard and play hard, but leave a better planet behind.”
Max: Together with your team, you have created SAP Ganges, which is a business network in the retail sector. What exactly is a business network?
Abhijit De: I had the opportunity to set up an innovation business at SAP and chose a business network in the retail sector. A business network is an IT infrastructure that allows different personas from different types of companies in one trade ecosystem to transact business together. One example for this is SITA, which is a travel network that allows airlines, hotels, travel agents, financial institutions, customers, airports, etc. to do business in a coercive framework and information infrastructure. At SAP, the executive strategy was in the direction of business networks with the acquisition of Ariba, which is a business network in procurement, Fieldglass, etc.
In the case of Ganges we were building a business network for the retail ecosystem. This means that retailers, wholesalers, FMCG manufacturers [fast-moving consumer goods], banks, fellow travelers, etc. can conduct business seamlessly.
Max: To create SAP Ganges, your team has applied Design Thinking; you have talked to numerous retailers and distributors to learn about their pain points. What is the unique value Design Thinking has added to the system compared to traditional engineering approaches?
Abhijit De: I believe there are two types of innovation: First, push innovation and second, pull innovation. Furthermore, all innovation manifests as one or more of the following frameworks: incremental innovation, disruptive innovation, fundamental innovation, and alternative application innovation.
A push innovation use case example would be Intel processors. Intel designs processors based on fundamental research innovation and it sometimes takes ten years to get from ideation to offering as there are long cycles of designing and building the technology fabrication plant. Hence, at ideation Intel cannot always accurately predict the types of applications that will use the said processor. This is a “build it and they will come” strategy. As such, Intel “pushes out” products and offerings without intensive end-user input.
A pull innovation use case example would be Apple’s iPhone 7 or Facebook. These are based on a deeper understanding of users’ pain points and their root cause analysis. Value created is accessed at the ideation phase of the product or service.
The Design Thinking and value engineering methodologies together work as a perfect process to mitigate most calculated risks. Hence help create a “viral” product or service as it results in “capabilities” of highest value to the “user”. It is grounded in time-to-market boundaries based on an additional feasibility and viability framework.
Max: What is the main disadvantage of Design Thinking?
Abhijit De: First, Design Thinking is a pull innovation methodology and not very effective on push innovation scenarios. Second, Design Thinking curtails radical creative or artistic opportunities. And third, Design Thinking does not dig deep into addressing ecosystem change nor does it address fellow traveler requirements. Examples for this are offerings based on new material innovation or fundamental research that cannot be improved using Design Thinking methodology effectively.
Max: In the official video about SAP Ganges, you say that your team did “constraint innovation”; that most of the project was done with zero budget and most of the work with crowdsourcing. Can you please dive a little deeper into that?
Abhijit De: Today there is a lot of waste of resources in corporate environments due to power plays, fiefdom politics, unaligned divisional strategies, siloed organization structures, etc. Constrained innovation starts with building effective networks within the organization to enable the empowerment and creation of vested stake holders, starting with internal fellow travelers. Nurturing win-win objectives between resource owners and your team/project creates a strong ecosystem of support that opens various types of resources as needed by your project [cf. Juggaar innovation]. Moreover, enabling systematic structured internal crowdsourcing also facilitates HR objectives in increasing employee satisfaction and growth.
Max: The main motivation behind the system is to maximize retailers’ businesses. What are the non-economic implications?
Abhijit De: There are several:
- Increasing financial inclusion;
- elevating the poverty level from $1/day to $2/day in India;
- banking the unbanked;
- optimizing the FMCG supply chains;
- moving to a cashless society;
- creating new channels and markets for cross-pollination, i.e., alternative uses;
- eliminating parallel economy; and finally
- increasing education and awareness reach.
Max: How do you guarantee your Design Thinking–based technology is available to those who need it?
Abhijit De: For this, we build on an awareness generation program due to success stories.
Max: Can you please explain in one or two sentences how this works?
Abhijit De: Based on use-case success stories we let the merit of the methodology speak for itself. For example, through articles in the Harvard Business Review etc.
Max: Where is SAP Ganges today?
Abhijit De: As all products and services need to have exit strategies, the corporate strategic focus changed and SAP Ganges was morphed into the SAP IoT world; meanwhile the Indian government has finally moved towards a cashless society enabling several similar business networks to germinate ecosystems with the same objectives.
Max: If a company came to you and said “We want to do Design Thinking because it’s hip and everyone else does it”, what would you tell them?
Abhijit De: Follow the “MAN” rule!
- Do you have the money?—It’s an expensive investment.
- Do you have the top down buy-in commitment?—authority
- Do you have the need?
Any two of these are sufficient to create the third, but all three are necessary to successfully enter into a Design Thinking–based project.
Max: To conclude the interview: What is Design Thinking?
Abhijit De: It’s a tool like many others with pros and cons and not always the right tool for every scenario. It is however a great tool/methodology that can design “viral” products and services when executed well.
Max: Thank you for taking the time to provide your insights on this topic! It’s very much appreciated!
(This article has also been published in theuxblog.com on Medium.)
Abstract: In the first year, a clear majority of the investigated companies secured more than $0.395m of seed or angel funding; in the second year, a clear majority secured more than $4.366m of Series A or venture funding; and in the third year, a clear majority secured more than $11.131m of Series B funding.
At bitstars GmbH / HoloBuilder Inc. I’ve been a part of “start-up grad school” for almost two years now. Most of the time the early years of a start-up are a constant fight for a good valuation and big investments. The first steps towards a real, innovative product, the hunt for customers who (are going to) pay actual money to use it and pitching nice figures to potential investors are at the core of this process. Recently, I’ve repeatedly asked myself what the early rounds of funding of the most successful start-ups looked like and whether they might all have something in common. So I’ve done some number crunching on the topic.
First off, I needed a list of companies that are commonly recognized as highly successful start-ups. The most prominent features of these are high valuations and huge amounts of raised money. I went with Inc. magazine’s “15 Most Valuable Startups in the World”, the Telegraph’s “most valuable start-ups in the world” and Verge HQ’s “Top 20 Startups of All Time”. The 44 companies found in these three articles (Uber, Airbnb and Dropbox, among others) are referred to as “Group A” in the following. To complement the list with some very successful start-ups that have not (yet) reached the status of the above big players, I’ve created an additional “Group B”. It comprises 16 companies found in “10 Wildly Successful Startups and Lessons to Learn From Them” by Inc. magazine and the top 7 exits in the portfolio of “leading global venture capital seed fund and startup accelerator” 500 Startups.
In the next step I consulted CrunchBase and for every company in my list—as far as the data was available—looked up the money raised in the first three major rounds of funding and in which month/year it happened.1 Using the Consumer Price Index (CPI), all numbers have been inflation-adjusted, i.e., they are the equivalent amount of money that would (have to) be raised in October 2016. There was no data available for 5 companies from Group A, which left me with a total of 55 data sets—39 in Group A (71%) and 16 in Group B (29%).
In the following I report on my findings for groups A and B as well as both groups combined. They particularly focus on how much money was raised in the different rounds of funding and how many months after the founding of a company it happened.2 Before the analysis, outliers were removed from both data series (the amounts of money raised and months after founding) separately using Tukey’s test for outliers based on groups A and B combined.
The raw data I used for my analysis can be publicly accessed at https://docs.google.com/spreadsheets/d/1ge7RaUe6Pc6bpBM6PtbkVlgg35mmTJTMVSx3vbmb2s8/pubhtml.
1 Multiple investments of the same type (e.g., “Series A”) that happened in a relatively short time span were aggregated considering the month/year of the latest investment as the effective date.
2 The date of the first investment was assumed as the founding date of a company if it was earlier than the founding date given by Crunchbase. In case only the founding year of a company was given, I assumed June of that year as the founding date; or January if the first investment already happened in June or earlier.
1st Round of Funding
The seed or first angel investment (in case there was no dedicated seed round) of a start-up was considered as the first major round of funding. For this, Crunchbase provided data on 16 companies from Group A and 8 from Group B.
When looking at Group A—the most valuable start-ups—on average they raised roughly $0.932m (σ ≈ $0.692m) and reached this milestone an average 7 months (σ ≈ 5) after the company was founded. Interestingly, the Group B start-ups on average raised more money in this first round, i.e., $1.374m (σ ≈ $0.976m), which averagely happened 6.5 months after founding (σ ≈ 6.5).
Combining the two groups gives us an average of $1.080m (σ ≈ $0.804m). This money was raised roughly 7 months after founding the start-up (avg. ≈ 6.8, σ ≈ 5.5). Neglecting outlier investments1, 75% of all considered companies raised more than $0.395m and managed to do so within the first 10.5 months after founding. For Group A only, these numbers are $0.395m and 11.25 months.
1 That is, an investment that is an outlier in either the money or the time dimension.
2nd Round of Funding
The Series A or first VC investment (in case there was no dedicated Series A) of a company was considered as the second major round of funding. Crunchbase provided data on 37 companies from Group A and 16 from Group B for this.
Group A secured an average $11.372m (σ ≈ $7.573m) in this round, roughly 16 months after founding (avg. = 16.25, σ ≈ 10.12). Group B falls a little short, with “only” $7.032m raised on average (σ ≈ $5.361m), but in a similar timeframe (avg. = 16.8, σ ≈ 10.73). The average amount raised by Group B is about 62% of that raised by Group A.
Combining the two groups yields an average of roughly $9.925m (σ ≈ $7.160m) raised after approximately 16.5 months (avg. ≈ 16.43, σ ≈ 10.20). Not considering outlier investments, 75% of the start-ups in both groups achieved a Series A/Venture funding of over $4.366m within the first 23.25 months of their existence. When looking at Group A only, these numbers change to $5.471m and 23 months.
3rd Round of Funding
The Series B investment was considered as the third major round of funding. For this, Crunchbase provided data on 31 companies from Group A and 13 from Group B.
Group A companies raised an average investment of roughly $25.619m (σ ≈ $14.093m) in this round, at an average 27.54 months (σ ≈ 12.35) after having been founded. The relative difference to Group B stays almost constant compared to the second round of funding, with an average investment of roughly $15.614m (σ ≈ $14.093m). These are 61% of the funding secured by Group A. Group B companies secured their investments an average 32.92 months (σ ≈ 17.22) after having been founded.
When looking at both groups combined, the average investment is roughly $22.196m (σ ≈ $12.998m), averagely 29.24 months (σ ≈ 14.09) after the founding of the company. 75% of all considered start-ups secured an investment of at least $11.131m within the first 35 months (without outlier investments). For Group A only, these numbers are $13.167m and 33 months.
The question posed in the title of this article is “What Do Highly Most Successful Start-ups Have in Common?”. So let’s see what we’ve learned. First off, Seed/Angel funding seems to be usually secured within the first, Series A/Venture funding within the second and Series B funding within the third year of existence.
When looking at the amounts of money raised, it becomes evident that the difference between the most successful (Group A) and the slightly less famous (Group B) companies is neglectable in the first round of funding, but becomes more considerable in the following two rounds. This is most probably due to a mutual effect of “If you raise more money you become more famous” and “If you are more famous you can raise more money”. Still, the first huge investment usually comes before the fame. Therefore, the numbers given in this article can be (cautiously) considered a common trait of highly successful start-ups.
Hence, to answer the initial question: Based on the first quartiles determined earlier we can state that in the first year, a clear majority of the investigated companies secured more than $0.395m of seed or angel funding; in the second year, a clear majority secured more than $4.366m of Series A or venture funding; and in the third year, a clear majority secured more than $11.131m of Series B funding.
Additionally, the following scatter plot maps all investigated investments (without outliers) from all three rounds. As can be seen, the rounds overlap and the variance in both money and time becomes bigger with each round of funding.
Finally, it is important to note that my analysis—as originally intended—only investigates the commonalities in the funding of the considered companies. What I haven’t done was to look at what explicitly distinguishes these (highly) successful start-ups from start-ups that failed. That being said, although proper funding most of the time is a key factor to success, you can raise just as much money as the companies described above in the same amount of time and still fail if you don’t make proper use of your investments. The other way round, it’s of course also possible to fall short of the figures above and still build a highly successful start-up. Always bear in mind that it takes more than money to transform a start-up idea—no matter how awesome it is—into a successful company!
- Use a notebook
- Sketch every idea you have
- Communicate visually
- Observe your target audience
- Build prototypes
- Talk to people without domain knowledge
- Discuss with people you don’t know
- Never neglect crazy ideas
- Think about technology later
- Don’t stick to a particular order
- Test every hypothesis
- Never just assume things
- Practice empathy
- Be satisfied with ‘good enough’
- Strive to improve people’s lives
- Always collect feedback
- Consider cultural differences
- Never let sunk costs bias you
- Start over if necessary
(This article has also been published in theuxblog.com on Medium.)
From 2013 till 2016 I’ve done an industrial Ph.D. at Unister GmbH in Leipzig in cooperation with Chemnitz University of Technology. This means that I worked in Unister’s R&D department, which at that time developed a novel semantic search engine, and wrote my thesis about the part I contributed to the project. A few days ago—in an old notebook—I found a list of blog posts I wanted to write with the entry “Pros & Cons of an Industrial Ph.D. Program” not yet crossed out. I remember that I added this idea to the list after talking to Jürgen Cito, a Ph.D. student at the University of Zurich, at the 2014 International Conference on Web Engineering (ICWE). After chatting about the topic for a bit, Jürgen said something like “The pros and cons of an industrial Ph.D. program would really make a good blog post”. So here it is, in terms of an infographic made with Adioma, which I wanted to play around with for a pretty long time now.
I have to note that the con “less contact to your professor” wasn’t too bad for me because my second advisor Dr. Andreas Both, who was the Head of R&D at Unister, attached great importance to good and quality scientific work and publishing scientific results. This, however, isn’t something you can expect at any company. Overall, I list one more pro than cons because I had a really good experience with my industrial Ph.D. program and would definitely do it again!
When looking at current research, there is plenty of existing work inquiring into how users use search engines1 and how future search interfaces could look like2. Yet, an investigation of users’ perceptions of and expectations towards current and future search interfaces is still missing.
Therefore, at this year’s International Conference on WWW/Internet (ICWI ’16) my co-author Martin Gaedke presented our paper “REFOCUS: Current & Future Search Interface Requirements for German-speaking Users”, which we wrote together with Andreas Both. To give you an idea of what our work aims at, I’m going to provide a step-by-step explanation of the research paper’s title.
REFOCUS. An acronym for Requirements for Current & Future Search Interfaces.
Search Interface Requirements. From an exploratory study with both qualitative and quantitative questions we have derived a set comprising 11 requirements for search interfaces. The initial set of requirements was validated by 12 dedicated experts.
Current. The requirements shall be valid for current search interfaces. According to the experts’ reviews, this applies to eight of the requirements.
Future. Also, the set of requirements shall inform the design and development of future search interfaces. According to the experts’ reviews, this applies to ten of the requirements. Supporting the design of future search interfaces is particularly important with the wide variety of Internet-capable novel devices, like cutting-edge video game consoles, in mind.
German-speaking Users. Due to the demographics of our participants, the set of requirements can be considered to be valid for German-speaking Internet users. 87.3% of the participants were German while 96.6% lived in a German-speaking country at the time of the survey.
1 For instance, http://www.pewinternet.org/2012/03/09/search-engine-use-2012/ (accessed November 8, 2016).
2 For instance, Hearst, M. A. ‘Natural’ Search User Interfaces. In Commun. ACM 54(11), 2011.
Before you receive feedback from users, the user experience and usability of your website are both ‘good’ and ‘bad’ at the same time.* Through a good design process you can only raise the chances of user experience and usability manifesting as ‘good’** once the feedback arrives.
* That is, the factors and of a linear combination with are unknown.
** Subject to definition. For instance, or .
According to Patnaik (2009), Design Thinking is “any process that applies the methods of industrial designers to problems beyond how a product should look.” The term was already used as early as 1987 by Rowe in his eponymous book in an architectural context and has lately become popular through research done at Stanford University and the Hasso Plattner Institute in Potsdam, Germany (Schmalzried, 2013). In his introductory article about Design Thinking, Brown (2008), the CEO and president of IDEO, uses the example of Thomas Edison to illustrate the underlying methodology. While Edison invented the lightbulb—which undoubtedly was a significant innovation in itself from a pure engineering perspective—he did not stop at that point. Rather, he understood that the lightbulb alone would be of no use to people, so he also created “a system of electric power generation and transmission to make it truly useful” (Brown, 2008). This means that “Edison’s genius lay in his ability to conceive a fully developed marketplace, not simply a discrete device” (Brown, 2008), which underpins that one of the prime principles of Design Thinking is to consider a broader context with the user at its center.
Three Requirements of Design Thinking
Brown (2008) characterizes Design Thinking as “a discipline that uses the designer’s sensibility and methods to match people’s need with what is technologically feasible and what a viable business strategy can convert into customer value and market opportunity”. Therefore, Design Thinking does not solely concentrate on users, but also takes into account the company perspective. This is necessary since without profitable companies, no human-centered products could be realized, which clearly do not come at no cost. Hence, Design Thinking focuses on people and industry to ultimately yield a methodology that serves both sides—if applied correctly; i.e., companies should not see designers as pure means to make existing products more beautiful, but to “create [new] ideas that better meet consumers’ needs and interests” (Brown, 2008). From all this, we can derive three requirements that have to be met if one wants to successfully apply Design Thinking for the creation of a new product. A product that is based on Design Thinking
- matches people’s needs (Brown, 2008),
- is based on feasible technological requirements (Brown, 2008), and
- creates customer value and market opportunity based on a viable business strategy (Brown, 2008).
According to Schmalzried (2013), these are similar to the “R-W-W” method by Day (2007), which can be summarized as: “Is it real? Can we win? Is it worth?”
To give an example, consider the Search Interaction Optimization methodology and toolkit that are at the heart of my PhD thesis. As for requirement (1) above, in the context of my thesis I had to consider two target groups. First, I developed means for human-centered design and development that are both, effective and efficient from a company’s point of view. Therefore, my primary target group were the stakeholders, designers and developers applying the new Search Interaction Optimization methodology and toolkit. Then, the secondary target group were the users who are ultimately provided with more usable products by the companies applying the approach. Hence, matching people’s needs corresponded to matching companies’ and users’ needs in that specific case.
What is a Design Thinker?
When it comes to the characterization of a person who applies Design Thinking, Brown (2008) specifies that Design Thinkers are empathic, which supports the successful application of a “people first” approach. Furthermore, they exert integrative thinking (Martin, 2009), i.e., thinking beyond the scope of purely analytical approaches in order to create “solutions that […] dramatically improve on existing alternatives” (Brown, 2008). Third, a Design Thinker is optimistic, which means they believe that there exists at least one solution that is better than the status quo Brown (2008). In addition, a certain amount of experimentalism is required, i.e., a Design Thinker must be happy to try out new (and potentially radical) things instead of just doing “incremental tweaks” (Brown, 2008). Finally, and probably most importantly, Design Thinkers collaborate, particularly in an interdisciplinary manner and also have experience in multiple fields (Brown, 2008).
Three Spaces of Design Thinking
Contrary to existing processes and methodologies that are established and predominantly used in today’s IT industry—i.e., “linear, milestone-based processes” (Brown, 2008)—, Design Thinking does not happen sequentially. Rather, Brown (2008) states that it “is best described metaphorically as a system of spaces rather than a pre-defined series of orderly steps.” These spaces are given as follows:
Inspiration relates to actions such as investigating the status quo, defining potential target audiences, exploring the context the new product will be embedded in, going beyond that context to obtain a broader view, observing people, observing the current market situation etc. All of these are actions that “motivate the search for solutions” (Brown, 2008).
Ideation In the ideation space, scenarios and user stories are created, prototypes are built and tested (both informally and formally), outcomes are communicated etc., all in multiple iterations. That is, the “generati[on], develop[ment], and testing [of] ideas that may lead to solutions” (Brown, 2008).
Implementation The implementation space does not correspond to the technical implementation alone, i.e., programming tasks carried out by developers. Rather, it again involves a huge amount of interdisciplinary communication to pave the path to a usable product that is put into a broader context. This particularly includes business solutions and marketing, i.e., the implementation space “chart[s] […] a path to market” (Brown, 2008).
While a Design Thinking process usually starts in the inspiration space, transition between any two of the spaces is possible at any time (Brown, 2008), which clearly distinguishes it from established business processes. One example could be that while working in the implementation space, a Design Thinker notices that the new product can not be well communicated to customers, which might make it necessary to enter the inspiration space (again) and perform a new analysis of the current market situation and customers needs. If it turns out that a different marketing strategy would be sufficient, they can then return to the implementation space. A second example would be a series of several paper prototypes that all indicate the previously developed user stories to be irrelevant. This could result in a return to the inspiration space for defining new target audiences or paying closer attention to specific groups of users.
Design Thinking as a Process
A more concrete implementation of the Design Thinking methodology has been realized in the Human-Centered Design Toolkit by IDEO.org (2011). That is, although the underlying principles remain the same, they build on a more defined process. The toolkit guides designers (which, according to the Design Thinking methodology, can be project managers, developers etc.) through a three-step process, i.e., “Hear”, “Create” and “Deliver”. This happens in order to provide solutions that are “desirable, feasible and viable” (IDEO.org, 2011) from a human-centered point of view.
Complementary to this, David Kelley, the founder of IDEO and d.school, defines five elements for the Design Thinking process: Empathize, Define, Ideate, Prototype and Test (Kliever, 2015). By empathizing, you understand “the beliefs, values, and needs that make your audience tick” (Kliever, 2015). In the next step, the collected information are analyzed and translated into insights about the audience and the challenge to be faced (Kliever, 2015). Once the challenge has been defined, in the Ideation phase (which is also one of Brown’s Design Thinking spaces), everything is about finding possible solutions, i.e., it “is a brain dump of ideas, and nothing is off limits” (Kliever, 2015). Finally, in the last two stages, multiple ideas are translated into prototypes and tested with the audience (Kliever, 2015). Depending on whether or not a prototyped and tested solution proves suitable, it might be necessary to iterate one or more of the previous steps (Kliever, 2015).
It might seem counterintuitive to talk about Design Thinking processes after having introduced Design Thinking spaces earlier (“Design Thinking does not happen sequentially”). However, Kelley’s process is perfectly in line with the concept of Design Thinking spaces, where you do not follow a predefined path. The spaces and processes of Design Thinking go hand in hand. While you follow the above process, you necessarily move through the three spaces of Inspiration, Ideation and Implementation in no particular order, iterating previous steps if required.
To conclude, I would like to quote Steve Jobs, who said:
Design is a funny word. Some people think design is how it looks. But of course, if you dig deeper, it’s really how it works.
Moreover, I would like to refer to Google’s principle
Focus on the user and else will follow.
To give just one example for this, if you write a blog post, it will not become popular because you are using some fancy SEO tools. It will become popular if and only if you have created a piece of great content that fulfills the needs of your audience. As a Design Thinker, be bold, be unpredictable, be creative! You do not (necessarily) have to be a Photoshop artist for this. Hypothesize, but make your hypotheses testable—and test them. But still, do not rely on data alone as a starting point, which might prevent radical and potentially better solutions. Finally—and most importantly—do not let legacy processes restrict yourself. Yet, at the same time make sure that Design Thinking remains too unpredictable to become a legacy process itself.
(This article has also been published in theuxblog.com on Medium.)
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IDEO.org (2011). Human-Centered Design Toolkit. isbn: 978-0-9914063-0-2.
Kliever, Janie (2015). “Design Thinking: Learn How to Solve Problems Like a Designer”. https://designschool.canva.com/blog/design-thinking/ (July 9, 2015).
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