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Data is unquestionably the primary intelligence tool of global business; it offers a method by which companies can analyze the market and their position within it to develop informed strategies and generate profit. Companies have always relied on data to make decisions, but today's managers are frequently overwhelmed by its volume.
When it comes to defining big data, the familiar alliterative “four Vs” description—volume, variety, velocity and value—comes to mind. However, this doesn't address how big data should be analyzed.
Perhaps a more useful description is: Big data analysis gives context to a complex set of information, applies sophisticated analytics that transform the information in ways that answer important questions on demand and highlights new insights yielding critical information that informs major decisions and strategy.
How can organizations leverage this ever-increasing flow to their advantage and unlock big data's transformational opportunities? Too often, technology is seen as the panacea, but experience has demonstrated that technology alone can't answer this question.
Most failed data analysis—regardless of the technology stack and software solution suites—comes from making three strategic errors: asking the wrong questions, using incorrect data and failing to treat the data as part of an ongoing process.
Executives must recognize these three mistakes to help realize their business objectives.
There are three basic best practices for dealing with big data.
First, ask the right questions.
Failure can often be traced back to bad or unclear questions; to ask the right ones, managers must look to experts with a deep understanding of the company's industry and markets. Experts who have a clear understanding of the data and analytical tools can then translate this into valuable insights for executives.
Second, look beyond your own horizon.
Big data's real power comes from its capability to integrate information from multiple sources to see the big picture and to form insights previously unnoticed. To tap this power, companies need to look beyond their immediate market and identify and use data—of any size—from outside their industry. Businesses cannot rely on past extrapolations to predict the future; instead they must pinpoint emerging trends and fine-tune models to produce forecast accuracy.
Third, understand that big data analysis is a voyage, not a destination.
Data analysis should be a process of continual improvement and fine-tuning. Failures often reveal critical insights that help make future efforts more successful. Comparing what the model predicted would happen with what actually happened provides the capability to adjust models as required. Continual refreshment of data, models and industry insights is critical to producing the most accurate projections.
John Larson is vice president of big data analytics at IHS
Posted December 5, 2014

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