Responsive image
September 20, 2016

Importance Of Data In Design Thinking


“China’s banking system is riskier than it has ever been, the latest global data shows”

“Data Shows Urgent Need for Civil Services Reform in India”

“Neanderthals made their own jewellery, new data confirms”

“One in three Saudi air raids on Yemen hit civilian sites, data shows”

Data. How important is data in design thinking?  Every day, on every social media and news platform, we are bombarded with “data”-based news. From economic trends to evolving sex habits, from global pollution studies to the link of chocolate and longevity.

Now I am neither questioning the research, nor challenging the findings. I am sure that ALL the above (and others) are based on solid field work, analysis and compilation. And that the insights drawn are watertight.

And in our work in the twin spaces of Innovation and Design Thinking, data is key. We often say that “without data, a belief is a bias”.

And we also say, and say often, that “there are lies, damned lies, and statistics.” That data, badly collected, poorly collated and deliberately misinterpreted, can be dangerous. For products and governments (just ask the BREXIT supporters).

We often say that “without data, a belief is a bias”.

How do we reconcile, then, between the two extremes? Between the dependence on data and the possible misinterpretations and resultant fallacious conclusions?

In our work as a Design Thinking consulting firm, we always prioritize on data collection and assessments, even more than the sexier bits like brainstorming and prototyping. The assumption, often validated by results, is that if the data collection and analysis is done well, we can get to some fascinating insights. And, as we all know, these leads to excellent reframing of problem statements, helping us focus on real issues and needs.

Creativity and data analytics are both fundamental to design thinking and the framework of design thinking includes three, non-sequential, phases: c ideation and implementation. During this process, vast amounts of structured and unstructured data are generated that provide the insights required for human-centred innovations. Without a thorough understanding what drives our customers, it becomes impossible to create customer-focused products and services.

Susan Wojcicki, Google’s Senior Vice President of Advertising, had given an example in her article on the Eight Pillars of Innovation in her company that has remained with me. Under the pillar called “Spark with imagination, fuel with data”, she had written:

“What begins with intuition is fueled by insights. If you’re lucky, these reinforce one another. For a while the number of Google search results displayed on a page was 10 simply because our founders thought that was the best number. We eventually did a test, asking users, ‘Would you like 10, 20 or 30 search results on one page?’ They unanimously said they wanted 30. But 10 results did far better in actual user tests, because the page loaded faster. It turns out that providing 30 results was 20 percent slower than providing 10, and what users really wanted was speed. That’s the beautiful thing about data – it can either back up your instincts or prove them totally wrong.”

The last line is key. And in my experience, how we look at data is the critical piece of the puzzle. And let me share two examples from our recent work, on how data can impact research, and results. For reasons of confidentiality, I am not sharing the names of the clients.

“That’s the beautiful thing about data – it can either back up your instincts or prove them totally wrong.”

A Bookstore in Bangalore

When this client, which runs a chain of bookstores across the city came to us, they came with their own research and defined the problem simply as “How do we get more people to visit our stores?” The focus was on increasing footfalls, with the hope that the more visitors the stores have, the more books they will sell.

With the problem, we received two Insights from the management:

  1. Books are not being sold as much as they were a decade ago because the public’s tastes have changed – they don’t read as much any more.
  2. People are not visiting bookstores any more as they have too many other sources for entertainment.

These claims, however, made us suspicious that these were more assumptions than insights. So, we started with some Secondary Research, to find out the trends in book publication and sales in India.


What we found was interesting: the Indian publishing industry is growing at an impressive pace and India is one of the few major markets in the world which is still seeing growth in both print and digital publishing.

India’s book market, currently worth Rs 261 billion making it the sixth largest in the world and the second largest of the English language ones, is expected to touch Rs 739 billion by 2020, says a Nielsen survey. The study estimates a CAGR (compound annual growth rate) of 19.3 per cent for the industry in the next five years.

Although organised retail is growing in India at 15% per annum it still only accounts for 7% of book sales in India. Flipkart claimed earlier this year that it had sold 30 million books in its 8 years of existence. And then came a clincher: while analysts once predicted e-books would overtake print by 2015, e-book sales fell by 10% in the first five months of 2015, according to Association of American Publishers.

To us, this negated the assumption that “Books are not being sold as much as they were a decade ago because the public’s tastes have changed – they don’t read as much any more”? Clearly, more books are being consumed in India than ever before, and coupled with the slowing down of e-book sales, the market for offline bookstores could be promising.

Next, we embarked on Primary Research to ascertain the tastes and preferences of the public, who are seen to shunning bookstores. Our team interviewed close to a hundred respondents in Bangalore and found out some even more interesting.

When we asked the respondents how they spent their weekends, almost 50% mentioned that they visit bookstores at least once a month (even more than the National Book Trust findings)! That too, mostly over weekends! And yet, none of them had mentioned the bookstore visits when asked how they liked to spend their weekends.

So, clearly, the second assumption that “People are not visiting bookstores any more as they have too many other sources for entertainment” also did not seem to hold water.

The apparent incongruity was soon sorted out when we dived deeper. Most people who said “yes” to bookstores mentioned that they visited a bookstore on the way to other places. They often just “dropped in” to pick up stationary or gifts, and as bookstores make space for toys and mobile phones and groceries, all other kind of stuff. When we probed more, a number of reasons for not wanting to spend too much time in the bookstore came up: the location of the store, traffic on the roads, easier to buy online featured high on the reasons. Other reasons included poor staff quality in terms of knowledge and communication skills, poor ambience and limited titles on offer, lack of discounts vis-a-vis online options, no options for family entertainment while browsing topped the list.

Our team went back to the store owners with our findings. They were surprised, but agreed that our research had thrown some strikingly new angles into the equation. We met a couple of times, and finally managed to Reframe the problem statement.

The new statement was different: “How can we make the visitors bookstore experience more memorable?”

So, what did the data lead us to? And why did it work? Before I provide an answer, let me talk about a second, more recent case. (Those interested in the story can read it here, on the website)

Employability in India

On a recent project for a adult education institute based in Mumbai, we embarked on a journey to figure out how we can help India’s growing rural and semi-urban youth find better employment. Here’s the background: today, India is the world’s second largest emerging economy, but the rapid economic growth has not transformed the labor market in India, even when 62% of the population are of the working age (Census, 2011). And in 2014-2015, the annual dropout rates for students between class I and VIII was 4% across India and even higher among social and economically backward communities. Secondary level dropout are attributed to low levels of learning and poor engagement, inadequate school infrastructure and poor functioning schools, and many parents felt existing education does not equip child for life or skills for earning livelihood.

In 2014, report published by the Institute for Human Development indicated that the labor-force to the working age population ratio (15-60) at 56%, compared to the global average of 62%. And 92% of the existing workforce are employed in the informal sectors, that offers no social protection and very little pay. 30% or fewer workforce have secondary education or higher, less than one-tenth received formal/informal vocational training.

With this data, the client, a well-known chain of vocational training institutes, approached up to conduct an extensive research with our corporate clients, to find out:

  1. What do they look for when they recruit those candidates who are from such backgrounds?
  2. What do they consider when they promote those candidates?

During the initial discussions, we submitted that the scope of the work should be largely exploratory, to discover the motivations of companies and their recruitment leaders, and then use these findings to generate insights about recruiter preferences and behaviors. The assumption was to use this data to identify current gaps in the schooling system, the curriculum, the opportunities in vocational training etc., and see how we could brainstorm and create alternative approaches.

With this in mind, we created a questionnaire which had the objective of engaging and observing the respondents, to unearth motivations and interests. We deliberately kept the questions open-ended and broad, to allow respondents to express themselves freely, without prejudice or provocation. We vetted this with the client who gave us a big “thumb up” to proceed.

As per the contract, we used this questionnaire to interview 10 HR managers, and the findings looked really good. The respondents not only shared information and opinions, but also seemed to enjoy responding to the open questions that allowed them space to talk and share how they felt. The data was also very interesting, and in some cases, surprising (yes, the “juicy” bits that can make for exciting insights and problem statements).

When we submitted the findings, we got a an even bigger surprise. The client reverted by rejecting the responses as being too open and generic, and questioned the questionnaire itself as not designed effectively to capture real data. We questioned them on the reasons, and they promptly send us their own questionnaire!

This questionnaire was a very different animal. It opened well, but very quickly changed tracks into giving respondents a curated list of options to choose, whether it on recruitment criteria or leadership skills that need to be demonstrated. Yes, multiple choice questions that demanded that the respondents pick from a set of alternatives. Only.

When we got on to a call to question this, we were told that this will help narrow down the requirements and help them plan better for the interventions. Being me, I objected, stating that “this was validation, not discovery”. We also questioned the basis for selection of those choices from which the respondents were required to pick, and got that dreaded answer.

“Our experience.”

So there it was – we were just getting data to bolster what these good folks thought were important issues. And leaving out what could be the nuances, the key interests and underlying motivations. In other words, we were being asked to collect data to support decisions already made, possibly based on biases and exposures. While we all value experiences and exposure, can we consider that enough to base decisions on? And if yes, do we need to collect fresh data at all?


That, in my opinion, brings out the essential contrast in the way we look at and deal with data. It is our attitude, our openness and our focus on the true objectives. In the case of the bookstore owners, their readiness to reject their own assumptions and go with the data helped them (and us) co-create some very interesting ideas. For the vocational training institute, however, their rejection of data and the collection methodology to explore ground realities severely restricted what could have emerged and helped reframe beliefs. At the end, it was the mindset that mattered.

Beginner’s Mind

If we have open minds, what is called the “Beginner’s Mind”, we find that to be very useful to look at data and be open to be surprised.

Kenneth Cohen put this beautifully here: “A “Beginner’s Mind” feels open and aware. When we cultivate it, we free ourselves from expectation, but we experience greater anticipation. Because we are alert and constantly taking in new information and experiences, we are renewed moment by moment. An open mind can relieve you from stress, preconception, and prejudice and enrich every aspect of your life.

“The wise person,” said Mencius, in the fourth century b.c., “is one who doesn’t lose the child’s heart and mind.” ”

There is no right or wrong way, just different ways. And data is therefore key to support or question preconceived ideas, those ways we live by, and travel on…

To quote Susan Wojcicki again, “data – it can either back up your instincts or prove them totally wrong.”

Anirban Bhattacharya

Anirban Bhattacharya

Now Hiring! Founder at ubqt Design Thinking School. Founder at The Painted Sky

Leave a Reply

Your email address will not be published. Required fields are marked *