Data quality measures steer users down the right path

The phrase "big data" yields excitement or concern, depending on who you ask. Businesses are thrilled with the potential of the analytics strategy, which allows them to compile all the content floating around on the internet and pair it with the rows of content stored on their internal servers to learn, for example, which product color will be the most popular among their loyal customers.

On the other hand, some consumers are worried that big data means that organizations can spy on them, according to Jeff Bladt, director of data products and analytics at DoSomething.org, and Bob Filbin, chief data scientist at Crisis Text Line - the duo who recently authored an article for Harvard Business Review. However, the two are quick to point out that big data is not a computer or technology tool that is harvesting information about people, nor is it capturing what it means to be human.

All the data in the world doesn't have any meaning without human context, Filbin and Bladt explain in the article. 

Because it's not an automated process, there is still room for human error. Companies looking for one of the best ways to lessen the likelihood of big data failure can also invest in data quality tools and implement governance plans that verify the accuracy of every piece of information that goes in, according to InfoWorld.