The phrase “big data” puts the emphasis on quantity — and it’s true that the data available in the world continues to grow exponentially every year. But for businesses wanting to make the most of big data, it’s not quantity that counts, but what you do with the data. Companies are losing millions of dollars because they are not fully exploiting the data they are already gathering. Given the complexity of big data, visualisation software tools are required to help companies avoid this mistake.
The amount of data we generate today boggles the mind. As much as 90% of all data in the world today was created in the past 2 years, writes Keith Carter, adjunct associate professor in the Department of Decision Sciences at National University Singapore Business School and author of the book "Actionable Intelligence: A Guide to Delivering Business Results with Big Data Fast!".
With a billion gigabytes of data generated every 24 hours, it is no wonder that discussions about big data focus on its bigness. Businesses seeking to learn more about their customers are able to accumulate more data than ever before. Every purchase, every visit to a website, every mention on social media, every interaction in almost any form produces another piece of data — piles of data that can be numeric or qualitative.
Recognizing the myriad and growing opportunities for generating data, companies are investing heavily into harvesting this data. Simply accumulating piles of isolated pieces of data is of limited value, however. The question that companies must answer is, what does the data mean? Big data is only valuable when it can be transformed into what Professor Carter calls “actionable intelligence” — information that gives companies key knowledge about their customers and their competitors that they can use to develop responses and strategies to improve business performance.
Turning data into actionable intelligence requires more than analysing the data. Companies must understand the interconnections and linkages among all the data. These interconnections and linkages reveal the insights and patterns that businesses can use to take action.
Given the complexity and sheer bulk of the data in question, drawing out these interconnections and linkages requires sophisticated data visualisation software, such as QlikView, SAP Visual Intelligence and others. Data visualisation platforms translate this myriad data collected — which captures all the layers of the complex, constantly evolving, real time activity of a business — into easy-to-understand graphics.
Big data will only continue to get bigger. What we consider absolutely normal today in terms of data — for example, enterprise resource planning (ERP) systems that enable manufacturing companies to monitor in real time the performance of every line in every plant in every location around the world — was almost unimaginable 30 years ago. Where will we be 30 years from now?
Technology will continue to find new ways of generating data; successful companies will be the ones that have the people, processes and tools in place to leverage that data.
The disconnect between data generation and organizing that data into actionable intelligence is, surprisingly, not a new phenomenon. The invention of paper allowed a much greater dissemination of ideas… except that there were not enough writers to use the paper. Printing allowed a much greater distribution of material, but storage became an issue.
In today’s context, companies need to focus less on increasing the accumulation of data, and more on the maximum exploitation of the data that is gathered. Often, this means starting with the data you already have. Worry about extracting the most actionable intelligence from this data before finding ways to accumulate even more. Explore the latest data visualisation tools that can deliver the actionable intelligence that is most effective for your company and your industry.
By some estimates, the utility industry could extract $150 billion of “unrealized efficiencies” by using the data it already has in smarter ways. What unrealized efficiencies are you leaving on the table?
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