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Four Ways Not to Get Lost in Big Data

Introduction

The term ‘Big Data’ has been around for a few years now. Anyone in the business world knows that Big Data refers to the business phenomenon that has sprung up around the analysis of massive and ever-growing sets of data, and that it’s valuable to businesses because of the insights that they glean from the analysis of virtually infinite data points. But, as the Wall Street Journal’s Vipal Monga recently pointed out, putting a number on the value of data remains a very difficult task.

Executives know that it takes manpower and money to harness the value of Big Data. But for many, that’s as specific as it gets. Beyond the definition and a vague knowledge of its cost, Big Data remains shrouded with questions: How does an executive set expectations for a Big Data initiative? How does a business leader track progress?

Most importantly, is it worth the effort?

There are a number of steps that any executive interested in Big Data can take to make sure that he has covered his bases. Like any truly important undertaking, a successful Big Data initiative begins in the astute completion of many little tasks.

Consult With Experts

Not every organization has internal access to Big Data experts, but the Big Data consulting business is thriving. Many consultants can help teach teams how to plan, manage and evaluate Big Data projects. More importantly, they offer strategies for monetizing projects so that gains or losses involved with project can be measured. Delivering quantifiable results is necessary, but with analytics projects, value often becomes a difficult thing to define. Fortunately, there are people who understand how to translate datasets into monetary terms.

Getting the right people on board with Big Data projects is unequivocally necessary. A slight majority (51 percent) of failed advanced analytics projects don’t succeed because of a lack of expertise.

Given the option of consulting, hiring someone full-time clearly isn’t necessary to inject knowledge into any team’s Big Data strategy. But if top American companies are any indication, it might not be a bad idea: the Harvard Business Review recently named Data Scientist the ‘sexiest occupation of the 21st century.’ PhDs who wrote dissertations on genome mapping are moving to high-growth startups and commanding salaries well into six figures. Salary range is a rudimentary but significant means of determining that Big Data offers high value for progressive companies.

Go Beyond the Spreadsheet

Many teams still haven’t found a replacement for Microsoft Excel. In reality, this says just as much about the program’s genius as it does about the conservative nature of business professionals. It would be crazy, and probably very stupid, to completely ditch Excel.

But successful projects often reach breakthroughs when data migrates off of spreadsheets and into new forms of manipulation and visualization. Visual data analytics programs in particular offer a fresh perspective on data: a typical data visualization program can make it 28 percent more likely to find valuable information. 

Visualizing data becomes especially useful when the time comes to discuss results with those who exist outside of the IT sector of a company. People who look at a busy spreadsheet are liable to throw their hands up in the air and call it quits. They know the information is there, but they don’t trust themselves to find it. In fact, people who use Business Intelligence tools are more than twice as likely to require the help of IT if they do not have access to data visualization.

Access is King

In the study Go Big or Go Home: Maximizing the Value of Analytics and Big Data, the Aberdeen Group separated companies into two categories: Leaders (those that maximized the value of Big Data) and Followers (those that did not). One of the starkest divides between Leaders and Followers existed in the category of ‘access,’ or the ability to deliver necessary data in the amount of time required for relevant analysis. Leaders provided adequate access to data 82 percent of the time. Followers only managed to hit 62 percent. Leaders also improved twice as fast as Followers in terms of the amount of data they were able to make available to their employees.

Too many business leaders cautiously test Big Data projects, don’t immediately see incredible results, and then write off the entire category. That’s like test-driving a Ferrari and never exceeding 25 mph.

Involve Different Types of People

Many successful political leaders make sure to include people with vastly different opinions and perspectives among their aides. When managed productively, this leads to improved governing, more comprehensive policies and better leadership. The same approach can benefit IT teams. Mixing in different ideas and approaches increases the value of projects immensely.

Of course, this type of strategy only succeeds when human creativity matters. That’s why it’s especially important to make Big Data teams well-rounded: The rise of self-serve data analytics has made creativity more valuable than ever. Because it’s now possible to invent new metrics, reshape data into innumerable forms and run completely original reports, the data analysts sitting at the desk must have razor-sharp skills and open minds 

Therefore, when managers form teams they should focus on pulling employees from a variety of backgrounds. Managed the right way, differences of opinion and approach can yield results that would be impossible for a homogeneous team to find.

Conclusion

Knowing what Big Data means makes it no easier to adopt technologies and strategies that adequately use its power to improve the bottom line. But that’s why people become Big Data experts in the first place. It’s an incredibly complex field that few business executives ever master. That doesn’t mean, however, that businesses should abandon Big Data, and they haven’t: 73 percent of companies will have invested in Big Data by 2016.

By reaching out to experts, investing in data visualization tools, providing adequate access and taking a multifaceted human resources approach to Big Data, even executives who can’t become Big Data authorities can make sure their businesses aren’t left in the dust.