Data-driven marketing insights start with collaboration


Part of the problem is that most of the big analytics solution providers promote a misperception that generating these all-important insights with their tools is easy.

Tableau’s homepage, for instance, suggests that with them you can “get actionable insights fast.” The Google Analytics homepage claims its users can “get insights only Google can give you.” Meanwhile, Microsoft Power BI’s homepage contends you can “go from data to insights in minutes.”

If getting insights from data were such a piece of cake, then why do only 15% of decision-makers surveyed by Forrester feel their business intelligence initiatives provide a competitive advantage and only 11% say these initiatives led to new or better products and services?

The reality is that true, impactful insights are much more difficult to produce than these companies would otherwise have you believe.

What is an insight, after all? To paraphrase Arthur Schopenhauer, the 19th-century German philosopher, one might say that an insight is to see what everyone else sees, but notice what few else do.

Post-It Notes, Darwin’s theory of natural selection, YouTube, Dove’s Campaign for Real Beauty — all these ideas started with fairly rote observations about science, technology and human nature, but ultimately were born from thinking differently about what the information means and how it could be used.

The point is that none of these ingenious ideas was the result of one person’s sudden epiphany. Rather they, as with most great ideas, actually germinated over time — and not in a bubble, but after much research, experimentation and perhaps most importantly, active discourse with others in a larger community of friends, family, coworkers and colleagues.

In other words, good insights and ideas do not happen in a vacuum. They are most likely to occur with the help of a network or, as the best-selling author, media theorist and TED presenter, Steven Johnson, puts it, “Chance favors the connected mind.”

Think about it. Why would Netflix offer a $1m prize to anyone who could improve the accuracy of their industry-leading recommendation algorithm by 10%? Because back in 2006, Netflix realized that crowdsourcing a solution by giving 30,000 people access to their data would be more efficient than by doing it themselves.

In a more recent example, in November 2017, the fitness-tracking app Strava publicly released heat maps of every single exercise route ever uploaded to its system — 3 trillion points of data. This would have been no big deal had a 20-year old Australian university student studying international security not noticed a few months later that some of the routes clearly showed the location and exercise activity of US military bases in such countries as Syria, Yemen, Niger, Afghanistan and Djibouti. 

Neither Strava nor the entire US intelligence community picked up on this potentially huge national security risk. It was not until a student, who happened to be in the right place at the right time, was able to put two and two together.

As Emily Balcetis, a social psychologist at NYU, says in one of her TED Talks, “We all see the world in our mind’s eye.” No one at Strava looked at its data through the lens of a security risk. Likewise, no one in the US military ever saw the practice of soldiers publicly logging their run routes as a problem. Would this insight have ever occurred had the data not been exposed for the whole world to see? It is hard to say. But the chance that a breakthrough insight like this would occur increased significantly when it was.

This is exactly why companies ought to find ways to democratize their data to as many people across their organization as possible instead of limiting the role of insight generation to a few who happen to have business intelligence, customer insights, data science or analytics in their job titles.

Finding true, impactful insights is not easy. No one really knows where the best ideas will come from, so it is always worth letting as many people as possible have a crack at your data.


Originally published as “Editorial” in Applied Marketing Analytics, Volume 4, Issue 1, Pages 4-5, by Henry Stewart Publications (2018). Edited for style.

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