Do you know how to more effectively manage Big Data in 2014? In part one of this series, I only delivered the first step toward more effective Big Data management, but it was a very important step: create a clear plan. Creating a powerful, clear plan is the best way to put your organization on the path to better Big Data management. Remember, that plan should include the three key components: data, analytic models, and tools. In today’s article, we will discuss the next practical steps toward better managing your organization’s Big Data.

Why does it matter? Effective Big Data management gives organizations the edge. In McKinsey Global Institute’s (MGI) report, “Big Data: What’s Your Plan?” they caution: “Exploiting data is an increasingly important source of advantage. Companies whose early efforts struggle risk getting lapped by competitors.”

According to Harvard Business Review’s “Competing on Analytics,” what separates the leading organizations from the rest of the competition in the future is an understanding and an investment in the management techniques available today:

“Analytics competitors understand that most business functions—even those, like marketing, that have historically depended on art rather than science—can be improved with sophisticated quantitative techniques.”

In today’s blog, we will continue with the next three steps toward Big Data management, besides securing a budget.

2 | Hire an Analytics Pro

Today, new titles are emerging at the C-suite level.

For example, “Mobilizing Your C-Suite for Big-Data Analytics” reports that there are now chief analytics officers (CAOs), chief data officers (CDOs), and the ubiquitous chief information officers (CIOs). It was not long ago that the CIO and chief marketing officer (CMO) were the new chiefs in town. Now many organizations are restructuring and thinking through hybrids of current positions or creating entirely new positions within their structure to effectively manage, analyze, and put their Big Data assets to good use. Where is your organization in terms of personnel? Do you have anyone on staff with analytics knowledge or expertise?

Someone within your organization needs to have working knowledge of analytics, its advantages, benefits, and best use cases. If your CIO or CMO doesn’t have expertise in analytics, you need to consider hiring and investing in someone who does. MGI calls analytics “the world’s hottest market for advanced skills,” and the leading organizations are snatching up personnel with these skills sets, making them expensive to hire and in high demand. Another viable option to consider is training your key C-suite level staff in analytics beyond the basics.

3 | Educate Everyone

Just like mandatory kindergarten, no one in an organization should be exempt from Big Data management and analytics basic training today. What are the basics everyone should know? Thomas Davenport, President’s Distinguished Professor of Information Technology and Management at Babson College, explains:

“They need to know what data are available and all the ways the information can be analyzed; and they must learn to recognize such peculiarities and shortcomings as missing data, duplication, and quality problems.”

While some of your key personnel will go on to continue their education in analytics, it won’t be necessary for others. But as a minimum, everyone should be “on-boarded” and given the opportunity to become vested in this key organizational change. In this case, familiarity does not breed contempt, it breeds operational efficiency.

In “Big Data, What’s Your Plan?” MGI warns that unless organizations “develop the skills and training of frontline managers, many of whom don’t have strong analytics backgrounds, those investments won’t deliver.” Spending the time, money, and energy to educate your organization will pay off in the long run. MGI goes on to note that as you put your Big Data plan into place, a good rule of thumb is “a 50–50 ratio of data and modeling to training.” Equal parts training to action and systems change. The people and educational component of this investment is just as vital as the technology and systems themselves.

So call it what you want—Big Data basic training, professional development days, or something else entirely—but your organization must take the time to invest in a heavy educational component during the period of Big Data management systems adoption. This will naturally create a break from operations as usual. It will interrupt the daily flow and possibly, temporarily, your organizational output, but if done properly and at regularly scheduled intervals, it will pay off. The investment you make now to invest in your employees, and the Big Data systems you are building will ultimately reap benefits in the long-term future.

4 | Consider Forming an “Uberanalytics” Group

Should you create a specialized group of people who are tasked with championing all-things analytics within your organization? Is there a more effective way to spread your plan throughout the organization besides top down? Would creating a centralized group with interdepartmental influence be more effective for affecting change?

Competing on Analytics” cites Procter & Gamble as an example. They created an “überanalytics” group of 100 analysts from various departments within the organization. P&G uses the expertise of this group to weigh in on key issues the affect multiple departments. “For instance, sales and marketing analysts supply data on growth opportunities in existing markets to supply-chain analysts, who can then design more responsive supply networks.” This obviously creates opportunity for greater collaboration and harmonization within the organization, as well as accompanying challenges.

Turn Your Big Data into Actionable Data

Today, many organizations have created Mobile Centers of Excellence (MoCe) in order to stay ahead of the mobile technology adoption curve. Now, why not take McKinsey Global Institute’s advice and consider the creation of a formal Data Analytics Center of Excellence (DACE)? Or, at minimum, take these first four practical steps toward turning your Big Data into actionable data?

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