Industryweek 14471 Bigdata

Big Data -- Are We to Repeat the Past?

Dec. 12, 2013
Harnessing this data can be game changing, but inherent in its definition is this message – it ain’t going to be easy.

RFID. While its roots are generally traced back to World War II, radio frequency identification technology did not find a supply chain toehold until the 1970s when the first patents were issued. That was the same time frame in which the U.S. government’s Department of Energy asked the Los Alamos National Laboratory to develop a system for tracking nuclear materials. Part of their solution included transponders in trucks and readers at facility gates. Here was RFID in its infancy.

So what’s happened since? In a 2003 article written by a senior consultant specializing in Internet services, he predicted that “bar code's days are numbered. There’s a new technology in town . . . that is going to be a big part of our future.” The operative word here being “future.”

Skip to 2012 and an article appears on the website of Supply & Demand Chain Executive with the title: Early Adopters Seeing Biggest Gains from Item-Level RFID. The opening sentence read, “2012 may be a watershed year for radio frequency identification technology . . . .” The next paragraph references input from an Accenture survey and states “adoption of RFID is gaining traction.”

Really? Some 40 years after the first patents and 10 years after it was forecasted to be “our future,” RFID is just now “gaining traction”! Back in the mid-1990s, I was president of a manufacturing company dealing with many U.S. Big Box retailers. One of our biggest fears was that we’d be forced by the 600-pound retail gorillas to adopt RFID technology. It simply wasn’t affordable. Doing so would have meant losing customers or jeopardizing the very survival of the company. I can’t remember precisely, but I sure hope we didn’t lose too much sleep fretting.

The year 2014 is just around the corner. I suspect that most of you have now heard about Big Data. It is being called the “next frontier” and the “killer technology of our time.” It is ubiquitous. I cannot read a supply chain-related publication (either hard copy or on the Web) without seeing some article, blog or opinion piece on the topic of Big Data. So I thought I’d add one more opinion to the mix by asking this question: Is Big Data destined to follow in RFID’s footsteps? To me there are so many similarities.

But first: What is Big Data? The amount of data in our world has been exploding. Try getting your arms around the following statistics. According to a 2009 study conducted by the University of California, San Diego, from 1980 to 2008, the number of bytes of information we consume has increased 6% each year, adding up to a 350% increase over 28 years. And at a 2013 Brussels Innovation Forum, a commissioner shared that every two days we create as much information as was created from the dawn of civilization to 2003. Yikes.

Harnessing this data can be game changing. According to Wikipedia, Big Data is “the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.” Inherent in this definition is this message – it ain’t going to be easy.

Rosy Projections, but Consider These Questions

Why Big Data? The projections are rosy. Researchers at MIT and the University of Pennsylvania found that data-driven firms performed 5% to 6% better.  And McKinsey Global Institute estimates that retailers using big data have the potential to increase operating margins by 60%. But how does one get from here to there?  

It all sounds so great. But so did RFID. Before jumping into the fray, I believe it is vitally important to take a breath and consider these Big Data-related questions . . . and answers.

  • Is it just for the fat cats?

As I conducted my research for this article, the names that kept coming up as early adopters of or those considering using Big Data were those that one would expect. Surveys were taken of the Fortune 1000 companies. Names such as eBay, Amazon, IBM, Walmart and Facebook constantly popped up.  It certainly was not the everyday company.

My concern? Those that are in a position to take advantage of Big Data are those companies that already have competitive advantages in their respective industries. This will just cause further separation between the haves and the have-nots, potentially shuttering more companies and/or putting more workers on the sidelines.

  • Is there enough talent to go around?

Collecting the data will require companies to hire staff to analyze the treasure trove of information and pounce upon opportunities revealed. Gartner predicts that there will be 1.9 million U.S. jobs created by 2015 on account of big data. However, McKinsey estimates a shortage by 2018 of 140,000 to 190,000 data scientists with deep analytical skills and a deficit of 1.5 million managers and analysts capable of utilizing the data-driven insights.

For the foreseeable future, even if the data is collected, it very likely will sit idly by. Of course, some years from now, that data will be useful to track as a base line and to create a picture of “historical” changes in the analyses. And, with a growing population of young people studying for data-related jobs, we may see this talent pool evolve (just not soon, I suspect).

  • Will technology/application break the bank?

It will for most, especially in the beginning. This will not be an inexpensive proposition. In conducting my research, it wasn’t easy finding quantifiable cost estimates for Big Data adaptation. What was available suggests that we’re talking multimillions of dollars for total cost of data (TCOD), regardless of what platform is being used – EDW (enterprise data warehouse) or something like Hadoop, an open-source library that supports distributed processing of large datasets across clusters of computer servers.

It may be that technology itself solves the problem by creating new technology applications to efficiently reduce these costs (again, not soon, I suspect).  Or that a new market is created – a market of service companies (paid by project or annual subscription) that brings these new tools to a larger array of companies.

How many companies can truly jump on this bandwagon now, having to make such an early investment? The rich will get richer, at least in the early days.

  • Why?

This might be the most important question to answer. What’s the business case for jumping in with both feet? Can’t just dabble with a toe or two. It requires a full commitment. It requires resources, both financial and talent-wise. It may require process improvements. It may require a shift in cultural attitude.

For some it will be a case of following the Joneses. “If my competitors are jumping on board, I better do the same or get left behind.” But truly, what is the value proposition? Will it lead to increased sales? Will profits climb? Costs are high, the effort is great and the advantages may be slight. CEOs, CIOs and CTOs must take a close look at the true cost of Big Data, and the timeliness of its adoption across all sizes of companies.

Is Big Data the next RFID? Yes and no. Only time will tell. I don’t believe it’ll take 40 years to write a story about Big Data “gaining traction,” but on the other hand I don’t see Big Data becoming universally employed in an efficient way anytime soon.

Lee Schwartz, former CEO and president of manufacturing and distribution companies, is principal of the Schwartz Profitability Group (SPG) that, for almost 13 years, has uncorked the operational bottlenecks of manufacturing and distribution companies, boosting their bottom line results. Lee’s clients range from smaller family-run companies to Fortune 500 firms across a multitude of industries. His consulting and operational turnaround work helps clients find solutions related to process improvement, supply chain management, inventory control, workflow design, and operational performance. Lee can be reached at [email protected] or at 310-450-2628. More info can be found at or his LinkedIn profile.

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