7 Ways to Safely Consume Big Data Analytics

Big Data isn’t a technology or solution set that gets dropped into organization, ready to deliver compelling insights that will put the business on an upward trajectory of intelligence and prosperity. Rather, it is a gradually building wave that organization’s leaders will need to learn to ride, or else get swamped on the sidelines. Understanding and working effectively with big data will take a lot of practice.

That’s the theme of a new book co-authored by Michael Minelli, vice president of information services for MasterCard Advisors, along with Michele Chambers, formerly general manager and VP of Big Data analytics at IBM, and Ambiga Dhiraj, head of client delivery for Mu Sigma.

In the book, Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses, Minelli, Chambers and Dhiraj lay out the ways organizations can prepare to consume big data analytics.

1) Consider who is handling the “last mile” in data analysis: You need people who can look at the big picture with big data, and be able to explain its implications to the business. The authors quote Dr. Usama Fayyad, who talks about the crucial last mile in data analytics – the people “who are basically there to deliver the results of the analysis and put them in terms the business can understand. This last-mile group is made up of data analysts who know enough about the business to present to the CMO or the CEO.” At issue is the ability to find and hire these people, which is not an easy task. Also, a mistake many organizations make is putting these people to work on tactical assignments. “That’s a mistake, because these are people who can help develop and guide strategy, move the needle, and grapple with big issues,” Fayyad is quoted as saying.

2) Introduce the power of “geospatial intelligence”:  Geospatial intelligence involves the gathering and analysis of data to form more of a 3D view of what’s happening around the organization. It’s about “using data about space and time to improve the quality of predictive analysis.” Minelli and his co-authors quote IBM’s Jeff Jonas: “It’s going to come from weaving together data that has traditionally not been woven together.” This means location data generated from sensors and smartphones, as well as social media data.

3) Separate the signal from the noise: With so much data and extremely large datasets, there’s going to be a lot of noise, with a lot of conflicting signals. “As data gets larger, it becomes increasingly difficult to fully grasp the meaning and magnitude of the data through exploratory analysis.” the authors state. The best way to help analysts decipher the nuggets of information needed is through visualization tools. For example, a “word cloud” of relevant terms plucked from a site or journal – and the most mentions, the larger the font – will provide, at a glance, the topics mentioned most often.

4) Collaborate: “successful analytics is a collaborative endeavor,” Minelli  and his co-authors state. The first step in the process is to take your analytics intent beyond your core team and sell it to a wider group of decision makers – the prospective daily consumers of analytics in your organization.”

5) Learn to lead: “organizations that successfully consume analytics are driven by leadership, which builds consensus in the organization and allows for moving ahead without the need to have everyone on board every step of the way,” the authors state. “Strong leadership has been found to be the most important trigger in the wider analytics adoption in organizations.”

6) Measure, measure, measure:  “Use analytics to measure itself,” Minelli and his co-authors urge. They add that hard numbers actually aren’t necessary to gauge any progress – the availability of analytics may elevate discussions and awareness of what the business needs. “One often but profound change in organizations is the maturing of a culture of objective debates, arguments and viewpoints driven by data and not just ‘gut feel,’” the authors state.

7) Change your incentives:  Big data analytics implementations will shake up the organization will shake up the flow of information across the organizations, and thus re-arrange the hierarchy. Such projects will “bring in new stakeholders in employees’ decisions as well as higher levels of oversight,” the authors point out. “Sometimes, a general tendency of status quo bias exists, and employees do want to venture out of their comfort zone. You need to create robust incentives to overcome these barriers.”

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