The changing volume and variety of data is obvious to nearly everyone, but far fewer of us understand the concept of veracity. Treating all data as though it were equally accurate and reliable can adversely affect the quality of business decisions and business outcomes.
"There are two core risks: making decisions based on 'information fantasy,' and compliance. If you're not representing the real world, you can be fined and your CFO can be imprisoned. It all comes down to that that one point: If your systems don't represent the real world, then how can you make accurate decisions?" said Steve Jones, global VP of the big data practice at global consultancy Capgemini.
The topic of data quality is not generally well understood, because it has been treated as an IT problem. Collecting, storing, and processing data require a lot of technical expertise to do right -- and achieving data quality targets can take considerably more time to do right than others in the organization expect.
[Having trouble making sense of disparate data? Read Data Visualizations: 11 Ways To Bring Analytics To Life.]
"[Data quality] is the most underappreciated part of a project. It's the part that takes the most time," said Moshe Kranc, CTO of Ness Software Engineering Services. "Once you get the data normalized and all the bad records removed, and the incorrect records are cleaned, the rest of the project is doing the analytics and seeing the results. It's the easier half compared to the 60% [spent] getting data where you want it in a clean, normalized format, so you can use it."
As more people use data and analytics in their everyday jobs, the importance of data quality is leading to new organizational roles, including the chief data officer, data stewards, and data governance teams. Because businesses run on data, it's important that people in the organization understand some of the basics so they can be confident that the data quality is reliable. Here's a guide to what's required to achieve business-driven data quality.