The big data steamroller era

Big data, therefore, requires people, but it may be a management task to make certain that big data is more than a distraction. The flashy graphics and intricacies of analytics might carry another cost burden. Users may not know the weaknesses of the underlying data or understand the configuration of the churning numerical recipes. A decision could appear to be well informed, but that decision could be incorrect, little more than a guess guided by flawed Hollywood-style graphics.

Big data examples

Although the laws might encapsulate some marketing hoo-hah, there are examples of firms using big data to address problems in knowledge management. I’ll highlight three examples:

• An organization engaged in the development of pharmaceuticals can use software to intercept e-mail, convert spoken conversations via telephone devices and generate relationship maps of interactions among employees and other watched personnel. The data processed and generated by those intercept systems are voluminous. Cybertap LLC offers a robust system that can make life easier for a person monitoring an organization’s information flows for indicators of security issues.
• IBM has published “IBM Big Data: A Collection of Big Data Client Success Stories” (see http://ibm.co/1n5uPkK). Among the firms generating payoffs from big data are several hospitals and IBM itself. Another “success” is Barnes & Noble, the troubled owner of bookstores and the Nook electronic reading device, among other properties. IBM’s recent round of layoffs and its “bet big” investments in Watson, do not suggest that big data has a universal upside. IBM’s big data winner is an application that allows IBM professionals to connect with other IBM professionals. In the client success document mentioned previously, IBM’s Sara Weber asserts: “We could not have developed Faces without the distributed processing capabilities Hadoop provides. The Faces application has really highlighted the power of Hadoop and has helped us address a major pain point for all IBMers.”
• The Wall Street Journal reports that Dollar General has “gained insights such as that people who bought Gatorade most frequently bought laxatives as well.” The payoff, according to Bits, a blog supplementing traditional newspaper coverage, added: “Dollar shortly moved its entire data warehouse onto 1010data, including all point-of-sale and inventory data, making up 17 billion and 35 billion rows of data respectively. The retailer provides consumer goods companies with access to the data about its own products and, for a higher price, products of its competitors, with some limitations. For example, companies can’t see inventory data on competing products. (See Scott Denne, “Big Data Success Stories: 1010data,” at http://on.wsj.com/1cKjD3p.)

The company 1010data may have been the big winner from this big data case. The analytics firm provided the software and system that flushed out the high-value details from the discount retailer’s purchase and inventory information.

Those are representative examples that help illustrate the value of big data. For an organization, information is indisputably important. The concept of big data is interesting, but when stripped of its marketing veneer, big data becomes more mundane. An individual reviews data, thinks about it, does additional investigation and formulates ideas.

Knowledge is not automatically generated by outputs from a big data system. Management processes are not improved because of big data. Big data can obfuscate as well as reveal. Many organizations are desperate for ideas, innovation, new opportunities and new ways to reduce costs and generate revenue.

As Winston Churchill allegedly said, “An optimist sees an opportunity in every calamity; a pessimist sees a calamity in every opportunity.” Sir Winston did not have to steamroll big data, just a palpable enemy.