摘要： Data can be anywhere. Companies store data in the cloud, in data warehouses, in data lakes, on old mainframes, in applications, on drives — even on paper spreadsheets. Every day we create 2.5 quintillion bytes of data, and there are no signs of this slowing down anytime soon.
Data can be anywhere. Companies store data in the cloud, in data warehouses, in data lakes, on old mainframes, in applications, on drives — even on paper spreadsheets. Every day we create 2.5 quintillion bytes of data, and there are no signs of this slowing down anytime soon. With so much available for data-driven decisions, you’d think every company would be relying on solid analytics to compete in the marketplace. In practice, however, one in three business leaders don’t trust the quality of the data they use to make decisions, and bad data costs the U.S. economy $3.1 trillion per year, according to Extracting Business Value from the 4 V’s of Big Data.
Rules-based Internal Data vs. Active External Data
The DAMA International Data Management Body of Knowledge, defines “high quality data” as that which is “reliable and trustworthy,” so how can companies improve and maintain the quality of their data? Not all data requires the same amount of effort to maintain.
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Full Text： Dataversity
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