Getting Real About Big Data: Six Tips for Starting Your Own Plan

Share it on Twitter  
Share it on Facebook  
Share it on Linked in  
Slide Show

Big Data Disruptions Can Be Tamed with Enterprise Architecture

This week, I've been looking at what it takes to get real about Big Data, starting with a look at the people and roles you'll need. Next, I shared why experts say a business plan is uniquely important with Big Data.


Today, I'm sharing six tips experts say will help you formulate a business plan for Big Data.


First, ask yourself: "What can't we do today that Big Data could help us do?" advises Jill Dyche, vice president of Thought Leadership for DataFlux Corporation and an expert on data management issues.


Second, limit your scope to the top three or four business problems and figure out which can be helped by Big Data, suggests Ram Chandrasekar, director of the Analytics Center of Excellence and Strategy at pharmaceutical giant Bristol-Myers Squibb. By identifying a few key problems, you'll avoid the trap of trying to do too much, which Rick Sherman, a data integration and management consultant, says is a common problem.


If you can't identify a problem, then don't bother with Big Data. "Business leaders must be able to provide guidance on the problem they want Big Data to solve, whether you're trying to speed up existing processes (like fraud detection) or introduce new ones that have heretofore been expensive or impractical (like streaming data from 'smart meters' or tracking weather spikes that affect sales)," says Dyche. "If you can't define the goal of a Big Data effort, don't pursue it."


Third, figure out how those problems map to data both inside and outside the organizations, says Chandrasekar. You'll also need to think through integrating those data sets, but more on that later this week.


Fourth, keep the Big Picture in mind while you focus on a few problems. Even though you're going to start small, think bigger. For instance, the trend is toward self-service business intelligence, in which end users can conduct their own research. This will be a huge focus in Big Data analytics, according to Lyndsay Wise, a TechTarget contributor.


"Having a robust IT infrastructure that can handle large data sets and both structured and unstructured information is important, of course," she writes. "But so is developing a system that is usable and easy to interact with, and doing so means taking the varying needs of users into account. That requires different levels of interactivity that match user expectations and the amount of experience they have with analytics tools "


Fifth, definitely involve the business - since it is, you know, a business plan. "Analytics only produces business value if it is incorporated into business processes, enabling business managers and users to act upon the findings to improve organizational performance and results," says Sherman.


Sixth, don't mistake buying Big Data tools for a Big Data plan, warns Sherman. While tools are a critical part of Big Data, they aren't enough.


"Building an analytics system, especially one involving big data, can be complex and resource-intensive. As a result, many organizations hope the software they deploy will be a silver bullet that magically does it all for them," he writes. "People should know better, of course - but still they have hope. But big data analytics is only as good as the data being analyzed and the analytical skills of those using the tools."


Tomorrow, I'll look more at the "infrastructure" - the processes, programs and tools - you'll need to get real about Big Data.