May 28 2014
The rise of big data analytics is well known but less well understood.
New data is being generated at an exponential rate — it’s been estimated that in the current decade data creation will see a 50-fold increase, up to 40 ZB (zettabytes) — and many retailers and product manufacturers are using big data analytics to tap into this expanding resource for measurable gains, from streamlining their supply chain to improving ROI on marketing initiatives.
But as data streams multiply and the ocean of big data rises, many businesses are pushing out from shore without a star to sail by … and finding themselves adrift.
Big Data, Small Reward?
Want to find out the meaning of life? It’s easy.
You already know exactly where to look, because you’ve seen it a hundred times on movies and TV (and countless New Yorker cartoons): a ragged seeker crawls to the top of a mountain in search of enlightenment from a wise old sage, who rouses from a meditative slumber to explain the point of existence in a tidy phrase or two.
At least, that’s how it’s supposed to go. Unfortunately, instead of giving answers, the bearded guru sitting cross-legged at the peak tends to miss the mark. “Google it,” says one. “User name and password?” asks the next.
Business leaders have been promised a lot from big data analytics:
- The truth about shopper behavior
- The path to supply chain efficiency
- The key to customer engagement.
But many suspect that those promised insights will turn out to be like the pearls of wisdom at the top of the mountain — shallow, impractical, unreliable. What if it’s all more trouble than it’s worth?
Bridging the Insight Gap
There’s a good reason many business worry about the value of big data analytics: they aren’t getting any!
A report from Forrester research indicates that, “while 68% of retail CIOs report they are actively collecting big data, a mere 25% are using it to actually improve customer service initiatives and business performance.”
That’s a lot of energy going into data management systems, and often little to show for it. Why the disparity? One major issue is that retailers and vendors are failing to identify the relevant data. It’s an easy mistake to make, since a lot of data simply isn’t pertinent to their business objectives.
Phani Nagarjuna, founder and CEO of Nuevora, suggests that only 2% of all big data streams are relevant to solving a given business problem. “The question is how to enable organizations to quickly identify this 2% of data, apply that to solving a given problem, and move on to the next 2% for the next problem,” he says. “Do not try to boil the big data ocean.”
Finding a Line of Sight on Big Data Analytics
Without enterprise-level data management, all the extra information really is useless. The mass of big data allows business leaders to distinguish relevant content from what’s actually there, not what they hope is there. Whether or not it converts to obvious value, all data provides visibility.
Think of a DSLR camera shooting RAW: it takes in literally all the visible information, absolutely everything that the sensor picks up. This is vastly more than will appear in the final image. (There’s really no comparison to dark room developing, because no physical process could ever contain all that information.)
By contrast, the JPEG format chooses which bits of image data to preserve, based on whatever parameters the photographer programs into the camera from the outset. So, for example, JPEG records 256 “levels of brightness,” whereas RAW records up to 16,384. Those extra levels, which are usually translated into “bits,” don’t all come out in the final image. Technically, they’re irrelevant.
But with all those bits of information, the photographer can enter that data into an advanced developing tool like Photoshop and enjoy a dramatically expanded range of possibilities for image modification. Within this new field of adjustments, the skilled artist can obtain better detail, more refined prints, greater possibility for correction, and better exposure.
Complaining about the 95% of useless data in big data analytics would be like looking at the photographer’s final image and saying, “See! It looks good like that. Why did you need all that other stuff?”
Getting to the Summit
The climber standing on the summit doesn’t look down at the mountain and think, “What a waste of rock!” Likewise, businesses that take advantage of efficient big data analytics aren’t going to scoff at the mass of big data when they’re able to take advantage of dynamic pricing, or predict shopper behavior at the retail level, or engage in low-risk localized marketing experiments with real-time visibility.
When the most useful data rises to the top, it becomes a vista where business leaders can look out onto a broader customer landscape. From this vantage, businesses can create more accurate maps of how consumers travel to their products, and thereby compose better signage along the way.
For more on how advanced big data analytics engines can eat up inefficiencies and launch retailers and vendors to peak visibility, download our white paper, “The Retail Collaboration Quadrant.”