• by Keith Laska , Roman Stanek , May 16, 2013

How do you get started with Big Data, and how does rapid digital globalization impact the Big Data equation? More importantly, how do you turn Big Data intelligence into international ROI in today’s undeniably global economy? Here are some insights we have developed on how to turn “Sehr große Data” into “Big Data” and deliver a real (and real-time) return on a global Big Data investment.

The first step is understanding the relationship between Big Data and an increasingly global economy -- and how businesses navigate these two converging dynamics. One hurdle is recognizing cultural differences -- something technology by itself cannot address. It’s a human issue. It starts with understanding that people interpret their world differently.

The second hurdle is language. It’s equally important for companies to recognize that the Internet is now part of daily life -- and English is no longer the de facto or dominant digital language. Companies will increasingly need access to all their data in every language and in real-time or near-real-time in order to compete effectively in this new global marketplace.

In addition, global enterprises should realize that the ROI of Big Data lies in the depth and granularity of customer insights that it can potentially deliver. Big Data cannot deliver on that potential promise without factoring what we call “Big Language” into the equation.

The return and value of Big Data lies in its ability to empower customer experience management and more deeply connect you with your customers. It does this through intelligence and insight that allows you to make highly informed and accurate business decisions, as well as maximize your presence and profitability in a given market. Those customers and insights could potentially exist in 6,000 different languages (the number of languages on this planet today). In reality, about a dozen of those languages are now critical competitive components in today’s global economy.

With Big Data initiatives, it is important to consider language early on, because the massive volume of information being created today is further compounded by the number of languages in which it exists. If this Big Language complexity is not considered up front, it limits the ultimate value of a Big Data investment.

So how do organizations get started with Big Data and Big Language, which appears to be such a massive and overwhelming undertaking? Companies should begin by identifying the answers they want to uncover: The goal of Big Data is to mine insights that are both useful and actionable. But it doesn’t have to be overwhelming. You just need to clearly define your objectives so you can identify the right questions.

For example, if one of your company’s key strategic objectives is to build a globally recognized brand, then focus on a Big Data use case centered around sentiment analysis. For this use case, just try to be as specific as possible: Is it whether people have a positive or negative view of, say, certain foods? If positive, what -- exactly -- do they like or not like about it, and how do they express those sentiments? Such analysis could help California rice growers overcome food biases in Asia, for example.

From a Big Language perspective, once a company decides what information it wants to find, begin with the languages that are the most critical to the business. This might be the languages spoken in the regions that you’re currently operating in. Or perhaps it is the languages within your expansion markets. Instead of worrying about every language you need to leverage for Big Data insights and new revenue streams, prioritize your Big Language goals in alignment with your C-level objectives.

These strategies will help you avoid the risk of Big Data and Big Language analysis paralysis. Start where you have the opportunity to drive the most growth and revenue, test and prove the model, and then expand.