Thanks to modern technology, businesses of all sizes have access to rich, granular data about their customers and operations. However, understanding what to do with these massive amounts of data can be difficult and costly, and even with the right tools, it can be overwhelming.

With so much complexity around big data, businesses can easily make mistakes when working with their data sets. We asked a panel of Forbes Technology Council members the most common of these mistakes and the best way to correct them. Follow their tips to help your company avoid these big data pitfalls.

1. Analysis Paralysis

A lot of companies jump into big-data initiatives with an overwhelming amount of data collection. This almost inevitably leads to stalled projects and analysis paralysis. For your first foray into the world of big data, start with a small, well-defined initiative. Your data should clearly support your hypothesis or refute it. If your data produces ambiguous results, keep paring it down. - Brent Yax, Awecomm Technologies

2. Not Appointing A ‘Data Czar’

Every company I talk to complains about data quality and accuracy. The mistake is that we often don’t have central oversight on how we collect data, so we end up with duplication, columns being used incorrectly or just bad input. Make sure that there is a role (or committee) responsible for data hygiene in your organization and give them the mandate to keep it clean and train your users. - Josh Caid, Cherwell Software

3. Trying To Put The Whole Puzzle Together At Once

Big data is like a big puzzle. In order to solve a big puzzle, you have to work it area by area, piece by piece. There’s no other option. If you take this approach, a big-data challenge becomes a small-data challenge, which organizations are better equipped to tackle. - Hoony Youn, MackeyRMS

Read more