Dirty data is costing you: Four solutions to common data preparation issues

If you’ve ever analyzed data, you know the pain of digging in only to find that it is poorly structured, full of inaccuracies, or just plain incomplete. “Dirty data” is a common pain point that can have a major financial and cultural impact on an organization.

Today, more people expect data to be in the right shape and structure before they even bring it into a business intelligence tool. But preparing data for analysis is typically complex, time-consuming, and restricted to a few users—leading to backlogs and frustration.

This paper outlines actionable ways to overcome common data preparation issues, including tips on how to:

• Keep up with the demand for clean, ready-to-use data
• Establish a company standard for “clean data”
• Break down silos of data preparation that cause inaccuracies and confusion
• Democratize data preparation across an organization