Making Sense of Data

A thread on a LinkedIn post got me thinking about how “data” is often confused with “insight”…

99% of the ‘insights’ I encounter are mere associations. E.g. ‘X% of millennials prefer Y’ or ‘X% of our customers switched from contract Y to Z’. These are observations, not insights.

Robert van Ossenbruggen

In every industry and market across the globe, customers are embracing the abundance of Technology that now comes practically embedded in products and services. Access to low-cost Internet is no longer a problem for most of us. And, cheap, always-on, Internet access tends to generate a whole lot of data.

To make matters worse, in today’s context, that data (structured and unstructured) no longer resides in neat database tables inside the enterprise. Mobile devices, sensors, microphones, GPS devices, software, social media, cameras – almost every electronic device or service around you is contributing to the data pool, every minute.

The Paradox of Data is that little data leads to little insight, but too much data can also lead to little insight!

Organizations need to learn how to work with the vast storehouses of data being generated each day; Actionable insights are key to building Customer Engagement.

Patterns can often emerge from large data-sets of seemingly trivial information, which raises the question: What should you begin capturing, when embarking on a new journey?

Customers may say one thing in formal C-SAT or NPS surveys, but behave differently when it comes to repeat purchases, referrals or ‘lifetime value’ realization. Which insights should key decisions be based on?

How often should data be analyzed? Which systems ought to be monitored in real-time? What happens when the instincts and hunches of key executives don’t quite match the dashboard reports and data streams being reviewed?

“Big Data” thinking allows you to do things at a large scale that simply cannot be done at a smaller one. But, it also requires a different mindset.

In a “Big Data” world, ‘more’ trumps ‘better’, messiness (in a very, very large data set) trumps high accuracy (from a small sample), and tags yield better insights than taxonomies. That said, tools do not equal analytics, and Data certainly does not equal Insight.

As always, Business goals need to drive Data & Analytics, not the other way around.

One consumer study eloquently reported: We now inhabit a world that shares more content, from more sources, with more people, more often and more quickly. Today’s cloud-native tools and advances in computing already provide us the means to collect, store and analyze enormous quantities of data, with ease.

Making sense of it all, though, is a whole different ask.

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