Businesses across sectors are clamoring after big data, chasing unforeseen insights that can help one company exceed the capabilities of its competitors. However, analysts may not necessarily find those diamonds of information in the vast piles they collect, even with the desire to harness insights and budgets that allow for larger databases and analytics platforms. They need data quality to achieve those goals.
In a recent post for the Obsessive Compulsive Data Quality blog, Jim Harris compares the relationship between data and data quality to the one that connects philosophy and science. The former without the latter does not generate tangible findings, while a reverse scenario doesn't lead to discoveries. This same is true for big data and the information that fuels it, he explains in the entry.
"I would argue that clearly what organizations need is a crash course in data science - a way to humanize data science so that it can be both appreciated and judged by an informed business community. Big data is useless if you don't have a business context to interpret it," Harris writes.
This is because analytics requires accountability, according to Phil Simons who recently penned a post for the Data Roundtable Blog. If there aren't checks and balances, inaccuracies can enter data and corrupt results.