Many organizations have pursued Big Data as a panacea. For years, there has been an implicit strategy to capture as much data as possible and then figure out what to do with all of it later.
Big Data has swelled beyond the capacity of data-driven marketers to make sense of it all. IBM estimates that 90% of the world’s data today was created in the last two years. Many companies have built large “data lakes” to capture larger and larger data sets without clear use cases in mind.
Gartner officially declared Big Data mainstream by removing it from their Hype Cycle. Yet they predict that in 2017, “60% of big data projects will fail to go beyond piloting and experimentation, and will be abandoned.”
As Big Data matures beyond the hype, organizations are re-evaluating how they approach it. Jascha Kaykas-Wolff, CMO at Mozilla, describes an interesting strategic shift from “Big Data” to “Lean Data”:
“For growth-minded companies, collecting customer data for the sake of collecting data is more risk than the rewards can usually justify. Instead, we should be looking for ways to collect less data and go lean. Why? Because our collection tools create expensive overhead and risks that are impacting the trust of our customers in a negative way…
“For the most part, compiling bigger and more complex sets of customer data will not lead to the big profit and marketshare breakthroughs that Big Data promises. Instead, we marketers must learn to live and think lean. The twin false gods of Big Data and MarTech will continue to encourage thousands of marketers to gather all the data they can wielding an ever-expanding arsenal of tools to sift through it all, with little discussion about whether the mad scramble to vacuum up customer info is worth the trouble, expense, and risk…
“I challenge the assumption that more data is always better. Rather, I say you can have too much of a good thing. And I call that sort of scrape-first think-later behavior lazy marketing.”
Here are a few other cartoons I’ve drawn about Big Data over the years.
“Big Data”, January 2014
“Big Data Analytics”, April 2014