“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Rachael Hadaway, vice president of solutions strategy at 84.51°.
I read news articles decrying the failures of big data all the time. They usually begin with how the massive amount of data we create every day is growing exponentially, followed by the obscene amount of money organizations are spending on their big data initiatives and the profound opportunities to solve humanity’s biggest challenges.
These narratives usually pivot to the alarmingly low success rates many organizations are experiencing. Capgemini famously reported [PDF] that only 27% of executives surveyed would describe their big data initiatives as “successful” and only 8% would say “very successful.”
It’s clear that big data has developed a reputation as yet another technology trend that failed to live up to its potential. Unfortunately, the narrative that has emerged is too often overly focused on helping reduce organizational and financial risks. But this reduction of risk runs the potential of reducing big data’s potential to disrupt incumbent processes, alter industries and produce truly game-changing outcomes for all.
But harnessing big data isn’t easy, and many unexpected challenges arise during the process.
Integrating Data Alone Will Not Deliver Big-Data ROI
Most organizations have data all over the place, generally due to legacy IT investments, mergers and acquisitions. Many companies on this journey start by developing an exhaustive and expensive data integration road map to put all of their data in one place.
This action is the culinary equivalent of buying a new spice rack, with shiny new jars, easy-to-use lids and fresh labels. You can pour your old spices into these jars, buy new spices to fill out your collection and get rid of the dusty flakes bought a decade ago. While the outcome of this effort will feel satisfying, you will have done nothing to improve your ability to cook gourmet meals.
Good, Accurate And Clean Data May Conflict
Once companies have likely spent millions of dollars executing their integration plan, many will hit a dip of confidence.
Even when data is disjointed and siloed, organizations operate everyday with the insights they believe are most trusted and reliable. But once new data is joined, there is a period of time where new approaches and outcomes must be vetted and validated. New insights will challenge and conflict with long-held beliefs. If the business is neither patient nor able to trust new outcomes, the entire costly endeavor could seem in vain.
Focusing On The Signal And Discounting The Noise Will Limit Results
Companies that experience the “dip in confidence” often fall back into former habits. One such habit is to revert to seeking the mythical “one source of truth.” In many cases this plays out as a desire for one data type or metric that will reveal all the company needs to know, allowing the rest of the “noise” to fade into the background.
This thinking has generally been best applied to talking about the past – what happened – but it’s incredibly limited when assessing the impact moving forward. If a doctor were to ask a patient how their weight-loss effort was going, the most straightforward metric would be to analyze their daily weight records; however, focusing on weight records alone tells the doctor very little about how to improve the results and make them long-lasting. For that, he needs to know what the patient ate, how he or she exercised, the medicines and vitamins taken, hydration levels and so on.
In life, we seldom ignore the additional context that adds meaning to a hairy problem, but to simplify and reduce risk in many big data initiatives, we are doing just that.
Stopping Before You Get Started
In many cases, executives get frustrated with big data efforts because they are evaluating success and failure at the wrong times. Trying to gauge success before teams and techniques have matured nearly guarantees that the organization will attempt to seek immediate, short-term value. This means falling back on the same data and insights they had before the expensive technology and data integration began.
Bad press shouldn’t stop top organizations from investing in the right big data programs. Each and every day, the industry is maturing. Technology and tools are becoming less expensive and fit for purpose. Where there was once a scarcity of talent, there are more skilled and experienced professionals than ever before. This rising expertise is accomplishing the only thing that will end the negativity – reducing the time gap between investment and transformative result.
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