The phrase “big data” has been creeping into the mainstream in recent months, but it has been thrown around in business analytics for a number of years. If it seems a bit fuzzy as to what falls into the realm of big data that’s probably because it has been kept initially fuzzy. Big data is regularly defined as a dataset that is too large for normal database tools to handle or analyze; therefore, the definition continues to shift as the capabilities of database tools and the availability of data storage continue to improve.

When you look at the world you interact with on a daily basis it’s easy to see how these big datasets are being built. What did you do over the weekend? Shop on Amazon.com for a new Christmas sweater? Use GPS or Google Maps to find the hotel where your office Christmas party was being held? Update your facebook status to “Oops – didn’t know the office party was formal wear only”? All three of these actions create additional data that is captured and analyzed by the database owners, and all three of these examples of big data have significant implications for business decisions if analyzed properly. For example, Amazon has reported that as much as 30% of their sales come from the recommendation engine they utilize to offer suggestions to users of other products they might be interested in based on Amazon’s incredibly large database of customer purchases and your specific preferences.

With today’s globally interconnected economy, the growth of such data is astronomical. Some estimates put it as high as a 40% growth in global data each year for the foreseeable future (McKinsey Global Institute, 2011). We’re talking about filling more than 60,000 Libraries of Congress every year with new data.

Of course, with such large amounts of data being collected on consumers there is bound to be some repercussions due to concerns about privacy, security and even the intellectual property rights of the findings that come from analysis of the big data. However, consumers (particularly younger consumers) have consistently shown less concern about their privacy when there is a direct benefit to them. For example, consumers with smartphones will regularly share their current location for access to information on shops or restaurants near them or discounts at such establishments. There are also fewer and fewer consumers who object to offers tailored to their interests that come their way on facebook, or job openings they are notified of by LinkedIn, even though the basis of these suggestions is the data they have provided on themselves in their profiles and through their actions on the sites.

The question for us as marketers is how such big data can be turned into insights that will help us meet the needs of our customers better and bring our products and services to them in the most efficient manner possible. In the McKinsey Global Institute’s 2011 publication, Big data: The next frontier for innovation, competition, and productivity, they suggest a broad range of insights that can be the product of big data analysis (see table).

Breakdown of potential uses of big data by business functional area by the McKinsey Global Institute (2011).

An example of one area where marketers of products and services could greatly benefit from the availability of big data (whether your own or data acquired from secondary sources) is customer micro-segmentation. Being able to tailor product and service offerings and promotions to like consumers with similar needs, possibly even with real-time information based on their online or mobile actions, could revolutionize a sector’s go-to-market strategy.

Of course, obtaining big data and having the analysis tools and expertise to extract actionable marketing insights are two very different things. It is almost like data is the new raw material of our economy – technology and human capital are still essential for any value to be created; the data is just the first step.

Once the analysis is complete, the algorithms run, and the results spit out, the insights still have to be served up in such a way that they can be grasped and strategies and tactics can be based on them. This often takes the form of data visualization, such as an infographic that makes large amounts of information easier for viewers to understand.

Visualization of big data from the New York Talk Exchange program of MIT’s Senseable City Laboratory (2008). Shows call traffic to other regions of the world by neighborhood within New York City.

What kind of big data do you have access to in your business? What could you do with access to more? We’re happy to discuss what implication big data insights could have for your business, just drop us a note.