Business leaders are eager to harness the power of big data. However, as the opportunity increases, it becomes exponentially more difficult to ensure that source information is trustworthy and protected. If this trustworthiness issue is not addressed directly, end users may lose confidence in the insights generated from their data—which can result in a failure to act on opportunities or against threats.
Take, for example, the case of a large multinational corporation whose CFO asked a very simple question every month: How did sales this month compare to the same month last year? The answer turned out to be quite complicated. Sales were calculated and reported in various systems: material planning, financial, rent and royalty, real estate and so forth. Each system reported a different number because each system defined a “sale” slightly differently, and therefore extracted dissimilar data from the millions of daily transactions generated around the world. So what was the truth? No one actually knew.
The sheer volume and complexity of big data means that the traditional method of discovering, governing and correcting information using manual stewardship may not apply. Information integration and governance must be implemented within big data applications, providing appropriate governance and rapid integration from the start. By automating information integration and governance and employing it at the point of data creation, organisations can boost confidence in their big data.
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