Big Data has become a big buzzword, but many companies are just beginning to determine how to put Big Data to work for them. During Foley & Lardner’s annual FOLEYTech Summit in Boston late in 2013, a panel of experts from companies on the front lines of delivering Big Data technologies and services highlighted the key issues companies should consider before taking the Big Data plunge.
Focus on the Big Questions, Not the Big Data
Although the term “Big Data” appears to be focused on the size of the data set, the real power of Big Data is the analytics. As one Big Data executive put it: it’s easy to collect and store vast amounts of data… the hard part is turning that data into a source of valuable business insights. The key is to start small and simple.
According to industry experts, the first step for any company considering a Big Data initiative is to identify the most important questions they want to answer. At the top of the list for many executives: how can the company save more money and increase revenues? But Big Data can be used to drive a much broader range of decision-making across an organization, from increasing customer engagement, to better targeting marketing initiatives, to deploying personnel more effectively, to predicting the best time to purchase equipment. For companies beginning a Big Data initiative, it can be tempting to try to answer all of these questions at once. But the experts recommend keeping it simple: pick one question to focus on, and use it as a pilot to build a model that can be refined and tailored over time. Once the company begins to get useful analysis from the pilot project, it can use the results to identify new questions that will drive follow up projects.
Take a Hard Look at Your Data
Big Data-driven analysis is only as good as the data it’s based on, and many companies conducting their first Big Data projects are surprised at how poor their data is. As one executive of a Big Data services provider noted: It’s like dropping 10,000 feet in the middle of an airplane flight. Companies come in thinking that they have a good, clean data set, but in most cases, that is not the case. It can be gut wrenching.
Data sets suffer from a number of common problems: the data is incomplete, has errors or is missing key information; data is stored in proprietary data formats that make it difficult to access and analyze data; and data captured from different sources can be difficult to integrate. Although these problems are not insurmountable, they can require a significant upfront investment of time and effort to prepare the data set before the analysis can begin.
The Big Data experts at the Foley Tech Summit also recommend that companies take a hard look at their IT practices and investments to determine if they support a Big Data driven approach. For example, companies interested in leveraging Big Data should make sure there are storing their data in flat files that are platform agnostic to make the data as accessible to Big Data analysis as possible. Likewise, for businesses that want real-time analysis, it is critical to invest in infrastructure that permits the real-time transfer and analysis of data sets.
Consider Whether the Company Is Ready to Rely on Data-Driven Decisions
One of the most powerful aspects of Big Data — data-driven decision making — can also be one of the most challenging. Many companies, experts say, are not prepared for their Big Data analysis to produce a recommendation that conflicts with the leadership’s intuition. Those situations can produce “eye opening” moments for a company, and require the company leadership to take a leap of faith and embrace a data-driven approach to business decision making. Similarly, the results of a Big Data analysis may require the company to make significant changes to its operations in order to implement the findings or address the source of a problem the company is trying to solve. There, too, the company’s leadership needs to be prepared to not only invest in the Big Data analysis needed to identify the source of the problem, but also in making the changes needed to implement the solution recommended by the Big Data analysis.