CEOs and corporate boards want to see big data deliver results in company revenues, operations, and financial performance. The C-suite's desire to get these results faster than ever before is causing many big data leaders to feel more pressure in four specific areas.
In a 2014 Wall Street Journal article, Gartner analyst Douglas Laney observed that, "It's flummoxing that companies have better accounting for their office furniture than their information assets.... You can't manage what you don't measure." And in that same WSJ article, Leonard Nakamura, an economist at the Federal Reserve Bank of Philadelphia, estimated that corporate holdings of data and other intangible assets could be worth more than $8 trillion.
Data isn't on corporate balance sheets yet, but as companies begin to evolve P&L functions where data is packaged and resold to others, it could be. Even for companies with no current plans to commercialize data, the data under management could be considered an untapped source of future business spinoffs and revenue generation, so companies have to derive ways to value it. This squarely places pressures on IT leaders and other data stewards to understand what this accumulation of data consists of and how best to mine it.
There are so many types of data manipulation, extraction, cleaning, normalization, etc., tools that it is hard for organizations to decide which ones are the best in class for their environments. Companies are also trying to figure out how to actually use these tools.
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