3 Big Data Pitfalls and How to Avoid Them

pitfalls11 150x148 photo (data analytics big data )Google has indexed 1.5 million results for the term “big data,” a testament to its quick march to buzzword status.

pitfalls11 150x148 photo (data analytics big data )Google has indexed 1.5 million results for the term “big data,” a testament to its quick march to buzzword status.

But unfortunately the hype is creating potential pitfalls that organizations must maneuver around to be able to achieve the large business returns that C-level executives believe big data can provide.

One of the most daunting challenges to successfully exploiting big data is ensuring that the right questions are being used to analyze big data to get true business insight.

Here are three potential big data pitfalls and how to avoid them:

  • A multitude of big data definitions: While one of the first definitions of big data focuses on large volumes of data that flows into an organization very quickly in a wide variety of formats, the term has evolved to encompass much more.
  • Technology buyer chaos: The options for technology buyers are growing quickly as a wide variety of vendors not formerly in the analytical space attach big data labels to their hardware and storage solutions.
  • Aversion to problem solving: In too many companies, the discussion around big data has shifted away from the business problem to looking at technology in isolation.

The bottom line is that organizations should focus on creating business value from big data, according to Forbes contributor and CEO of Constellation Research R. “Ray” Wang.

Start each project by asking the following:

  • What are the questions that need to be asked?
  • What are the answers that help us move from data to decisions?
  • Can we shift insight into action?
  • How do we tie information to business process?
  • Who needs what information at what right time?
  • How often should this information be updated, delivered, and shared?

In a recent Wall Street Journal blog post, PricewaterhouseCoopers Principals Bill Abbott and Chris Curran agree, noting that the first question executives need to ask before diving in to big data is: “Why?”

Exploratory questions can be as simple as understanding how products, profits or behaviors are distributed across customers, the pair go on to say. After answering these questions, it might make sense to investigate and understand whether to enhance relationships with customers, reconfigure supply chains or repair the company’s brand reputation.

Moreover, while most businesses historically have focused on using technology to support routine decisions rather than complex or highly coordinated ones, that has meant pouring money into keeping the business running without much innovation, adds Marc Demarest, CEO and principal of management consultancy Noumenal Inc., at a recent big data analytics conference. Big data, though, could mean big innovation, he says.

Organizations should begin by scrutinizing the data they’re already collecting and find decisions that are routine or algorithmic, take a long time to complete, or are performed by only one person.

“And you want to find the ones that are closely connected to top-level financial metrics or key performance indicators  because this is all about determining outcomes,” he notes.

Thus, businesses can monitor how the process is working and make the necessary adjustments to fully exploit adding big data analysis to the mix.

Next Steps:

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