The era of data-driven business has arrived. Big data analytics tools are enabling organizations to capture, manage, and mine mountains of raw chaotic data from multiple sources to gain insights that inform products, services, and marketing strategies. The challenge is that not all big data insights are relevant and meaningful enough to spark real change or drive better decisions. To be truly successful with big data adoption, businesses must find new and better ways to transform big data into actionable data.
Big Data Quality
A common big data misconception among marketers is that the more data they can collect and analyze from multiple sources, the more new relationships and insights they will discover. The problem with this mindset is that more data does not mean better data. And actionable insights don’t just suddenly reveal themselves in the midst of clutter and chaos.
Big Data Analytics
A successful big data analytics implementation necessitates pre-planning, wherein business objectives that can hopefully be solved by the big data project are clearly defined. Identifying objectives helps to better strategize the analytics process and to set parameters to qualify the data and make it more reliable, relevant, and thus more likely to lead to actionable insights.
The movie Moneyball—a movie based on the book which portrays the true story of Oakland A’s general manager Billy Bean’s adoption of statistical analysis to achieve a winning season despite limited resources—illustrates the power of actionable data to drive decisions, once objectives are clearly established. Bean’s objective was to win more games. However, instead of basing his game strategies on an athlete’s or a team’s actual performance—which is what all managers did at the time—Bean turned to number crunching and acted on what the data told him. As a result, Bean made decisions during the season—such as trading high-profile players for relative unknowns—that seemed to defy all logic and common sense. Surprisingly, many of Bean’s key decisions were based on insights obtained from relatively small bits of information, such as how many times a particular batter got on base. Focusing on his prime objective, Bean trusted what the data told him and the 2002 Oakland Athletics went on to win the American League Western Division Championship.
The takeaway from Moneyball is that clear objectives are critical for making data actionable. Armed with insights extracted from reliable data, businesses with clearly defined objectives are able to make game-changing decisions they could never make in the absence of that data.
Today’s big data analytics platforms can readily accommodate massive volumes of both structured and unstructured data. And yet while gathering quality data is an essential part of the process, the data in and of itself has no value. Value is only established when actionable insights are obtained from the data to better inform business strategies, products, and services. To find these change-producing insights data analysts need to become more adept at searching for them.
Since it’s been established that bigger data does not mean better data, companies should first make sure that they are fully utilizing the data already found within their traditional databases before attempting to take on very large unstructured datasets.
Big Data Infrastructure
When implementing a big data infrastructure such as Qubole’s cloud-based Hadoop platform, those looking for insights need to direct their searches based on a full understanding of the company’s core business strategies and objectives. This requires greater collaboration between analysts and marketing departments to make sure that insight searches utilize the right data channels and aren’t too narrow or too broad. When relevant information is delivered to the right users, insights that spark change and drive decisions can more readily come about.
The ability to make decisions based on actionable data will put businesses at a considerable advantage over their competitors going forward. By utilizing big data analytics platforms such as cloud-based Hadoop, and bringing analytics strategies in line with clearly established business objectives, companies stand to reap big benefits from making big data actionable.
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