We’re all awash with data. Data is all around us, being produced at a faster rate than ever. We collect it, combine it, transform it, store it, transmit it … you get the idea.
Big companies (Amazon, Target, etc.) have been mining data to reveal key insights into customers, but for smaller companies just how valuable are their data insights when they lack the teams, tools and expertise? Are we deriving full value out of, say, our financial data, even if we analyze it with the best analytics engine available? When do nuggets of information, distilled from massive databases, become the true revelations that raise our level of understanding and allow us to make better strategic decisions?
“Ah-ha” moments, almost by definition, are produced when disparate knowledge is combined in unexpected ways. Yet how many of us have the skills to identify and combine the various knowledge bases that impact our business? Weather, for example, has a huge impact on retail sales figures. Decision-makers in smaller businesses, however, rarely compare historical weather records against sales data. By taking the time to do so, organizations stand a much better chance of uncovering insights that are actionable.
Context Makes Big Data Smart
To turn Big Data into Smart Data, it’s necessary to look at all the critical factors that impact a subject. And that requires a sense of context.
Say you work in HR and are puzzled over slow response rates for your recruitment advertising. Without looking for data that reveals the broader context — perhaps employment figures for your city or region that confirm low rates of unemployment — you may not arrive at correct solutions for the problem.
Context, as it turns out, is perhaps the most overlooked component of a proper analytics effort. Data only becomes “smart” if you connect it. And it takes a bit of proactive and creative thought to realize which combination of data stores are needed to resolve a particular business problem.
One famous example of this, detailed in "The Power of Habit" by Charles Duhigg, involved an effort by Target, the mass retailer, to identify which customers in its database could be predicted to be pregnant. Pregnancy, perhaps not surprisingly, is one of the most consequential life events for both new parents and for retailers, since it generates a massive amount of purchase activity — not to mention first-time brand engagement.
Looking at purchases of traditional baby items such as nursery furniture, baby clothes and diapers were all poor indicators, since those items might be bought for expectant friends or family members. It was only when Target’s statisticians brought in data from moms-to-be who had signed up for Target’s baby registry program, that other buying habits were identified.
Second trimester moms, for example, buy unusually large quantities of unscented lotion — and later, they buy vitamins, scent-free soap, cotton balls, hand sanitizer and washcloths in huge amounts. Combining this information produced “context” for someone who is expecting a baby — and soon, Target was able to use this information to effectively cater to millions of soon-to-be mothers.
From Data to Insights
In recent years, a wealth of available data from online sources such as Facebook, Google Analytics, Twitter, Mailchimp, PayPal, Google Adwords and many others has given marketers an even stronger sense of perspective on their sales activities. The rise of tools that are in lockstep with these sources, enables companies to understand exactly what is happening in the marketplace.
It’s routine, for example, for merchants to track coupons redeemed, click-through rates and so on. But when you pit these metrics against conversion rates, cart abandonment, comparative shopping activity, etc., you take analysis to a whole new level.
In this era when Big Data is so prevalent, the potential for deep insight is closer than ever before. When we limit ourselves to the siloed storehouses of information, we do ourselves a disservice to the very task we set out to accomplish. Singular data has value — but connected data, assembled thoughtfully and with an eye to context, is truly smart. It’s from these connections that transformative insights become ripe for the picking.
Title image courtesy of Photobank gallery (Shutterstock)
Editor's Note: Read more from this month's focus on big data analytics in marketing
About the Author
Dane Atkinson is a serial entrepreneur, founder of 6 startups with 5 successful exits and an adviser to nearly a dozen companies. More recently he was CEO of Squarespace, before co-founding and becoming CEO of SumAll, a VC backed analytics startup servicing 40,000+ companies and tracking close to 300 billion social actions.
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