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Should You Start a Big Data Project?

When does it make sense to start up a Big Data program? If your email marketing system isn't talking to your sales force automation system, and neither is synched up with your online purchase system, are you really ready to tackle a Big Data project? The answer may surprise you as we examine Big Data and its impact on the next generation digital experience in this second installment of our ongoing series "Are You Ready for Big Data?"

"If your organization stores multiple petabytes of data, if the information most critical to your business resides in forms other than rows and columns of numbers, or if answering your biggest question would involve a 'mashup' of several analytical reports, you’ve got a Big Data opportunity."

That’s according to Thomas H. Davenport, a visiting professor at Harvard Business School who writes frequently about Big Data. Davenport advises companies to push beyond the buzzword to define their projects more precisely.

photo of Tom Davenport
Tom Davenport,
Harvard Business School

"Because the term is so imprecise," he says, "organizations need to deconstruct it a bit in order to refine their strategies and signal to stakeholders what they are really interested in doing with these new types of data."

For example, instead of saying, “We’re embarking on a Big Data initiative,” Davenport recommends that a company says, "We’re going to analyze video data at our ATM’s and branches to better understand customer relationships."

Similarly a health care organization can get more specific with its Big Data project by saying that it intends to “Combine electronic medical records and genomic data to create personalized treatment regimens for patients.”

Not only is this approach more precise, Davenport says, but it also avoids endless discussions about whether the data involved are big or small. (Few organizations, he points out, confess to working with “small data,” even though it’s a perfectly respectable activity).

Bill Franks, chief analytics officer for Teradata, and a faculty member of the International Institute for Analytics, also advises companies not to be awed by the Big Data label.

"In many cases, Big Data is used for the exact same kind of analytics you've been doing for some time but with more data points from new data sources added to the mix."

photo of Bill Franks
Bill Franks,

Franks, who is the author of the book Taming the Big Data Tidal Wave (John Wiley & Sons, April 2012), points out that forward-looking companies are always struggling with new data types. In the late 1990’s and early 2000’s, for example, many organizations were struggling to use transactional data for broad analytics purposes. Now transaction data is "not much of a challenge," he says.

More recently, companies are getting used to working with online browsing history, a data type that was once considered daunting.

Big Data, according to Franks, is "simply a continuation of the struggle we've always had to incorporate ever-growing and ever more diverse data sources into analytics to enable better business decisions."

That's why the definition of Big Data, according to Mike Gualtieri, a principal analyst with Forrester Research in Cambridge, Mass., includes the word "frontier.” The push to incorporate ever larger and more various data is an essential part of a Big Data project.

To get the most from a Big Data project, experts say, you should start with a goal in mind.

Do you want to measure the effectiveness of your marketing and advertising? How about incorporating the "voice of the consumer" in your product lifecycle decisions? Big Data can also be used to create brand new information products and services.

Be explicit about your business goals. That will shape your project, and increase the odds of a successful outcome.

If you don't have a goal, take a look at the Examples of Big Data. Maybe one of those project goals can be adapted to work for your company or organization?

Remember, too: most often Big Data "a-ha moments" result from the intersections among a variety of data sources. Large collections of data tend to be stored in silos. Powerful new strategies and insights emerge when you cut across those vertical containers.

Shawndra Hill, who works with and teaches about Big Data in the Operations and Information Management Department at The Wharton School of the University of Pennsylvania, advises that a company, "should first understand the state of the art in data mining for their domain in order to identify the best benchmarks for their project and to see whether some existing solution is available to solve their problems."

photo of Shawndra Hill
Shawndra Hill,
The Wharton School, University of Pennsylvannia

The next step, according to Hill: "Calculate the expected gain from implementing the project in the best and worst cases of success and compare the estimates to the expected cost of taking on the project… No Big Data project should start just because it's fashionable."

The most successful Big Data projects are also "action-oriented," with a strong internal push towards acting on the insights that emerge from the analysis.

This is why consultants like Mike Gualtieri caution companies to avoid accepting Big Data projects that generate "lazy data."

"If you have a data warehouse and you're just producing reports, that's not Big Data," Gualtieri says. "You have to be able to use the information to create a competitive advantage in your markets."

Editor’s Note: This is the second post in the ongoing series “Are You Ready for Big Data?” by DC Denison. Download the complete "Are You Ready for Big Data" ebook to learn more about Big Data, its applications in creating the next generatlon digital experience, and what it takes to get into the game.

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