Rick Ferguson, CLMP
Through a special arrangement, presented here for discussion is a summary of a current article from The Wise Marketer, a website and newsletter serving the global loyalty industry.
About a decade ago, the phrase “data is the new oil” swept the globe as the twin corporate power centers of IT and marketing realized their companies had more data than they knew what to do with. "Drinking from the fire hose” became a metaphor for the struggles of extracting actionable insights from data.
About five years ago, the usual suspects in IT consulting and cloud-based analytics began to trumpet the phrase “Big Data” to sell into companies hoping to extract that resource.
Now, as Slate’s Will Oremus points out, the phrase Big Data has become passé — in part because we just call it data now, and in part because the rush to rely solely on data for business decision-making has often revealed the limitations of data-based decisions.
Our over-confidence in these tools often hinders our ability to see the forest for the trees. We often fall victim to what data scientist Shane Brennan calls the “Ten Fallacies of Data Science.” For a variety of reasons, the data may be inaccessible, indecipherable, outdated, lacking enough granularity for analysis or prohibitively expensive to get or have processed, Mr. Brennan contends. Often, a marketer’s lack of understanding or pre-conceived notions leads to poor decisions.
“Garbage in/garbage out” results in marketing campaigns or loyalty offers that are ineffective or even harmful to your business.
Should all data analysis be eschewed in favor of, say, gut feel, astrological charts or throwing darts at a board? Not at all. Data analytics is being successfully used to fuel personalization, construct relevant offers and build differentiated experiences.
But by understanding the limits of data science, your efforts on data can best correlate with current customer value or be most predictive of future customer value. Call it “small data” or “customer-centric-data,” or just continue to call it data. If customer behavior shifts in a profitable direction because of your analysis, then you’ll know you’re on the right track. Be diligent, question your assumptions and be aware of your biases. Big Data may be over, but data science, like any scientific pursuit, is forever.