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The 6 Cs of Data Quality: A Framework for Improving the Quality of Your Data

There’s no doubt about it: bad data is bad for business. Data quality impacts business performance, with low quality data leading to problems such as lackluster customer service, needless marketing spend, and poor decision-making. The explosive growth of data in 2021 is only making the problems surrounding poor data quality more apparent.

While you likely understand that your business needs high data quality for the advanced analytics and reporting necessary for making critical, strategic decisions you may be wondering how to get to the place where your team can really trust the data. Thankfully, Temberton Analytics has a framework for helping businesses improve their data quality.

What are the Six Characteristics of Data Quality?

There are six characteristics of data quality that you can use to assess the quality of your data – all of which conveniently start with the letter C. As such, Temberton Analytics calls this the 6 Cs of Data Quality. Ensuring your data is current, complete, clean, consistent, credible and compliant will lead to more trust in the data.

Let’s take a closer look at how each of these six characteristics of data quality – the six “C’s” – contribute to ensuring high-quality data.

Current data: Data is often time-sensitive, and as such, the data must be up-to-date across all systems, taking into account any changes that may render it obsolete or worthless.

Complete data: Data completeness refers to the comprehensiveness or wholeness of the data. If essential data elements are missing, the data is not reliable.

Clean data: Date cleaning or data cleansing is the process of detecting and correcting any corrupt or inaccurate records. Any data errors must be addressed and resolved for data to be clean, and this must be done on a consistent basis.

Consistent data: Consistency means data reflects the same information across all systems within the organization. The format of the data also must be standardized, and redundancies need to be removed as a part of the preparatory step known as data normalization.

Credible data: Data credibility is the extent to which a data source can be relied upon to ensure the data is correctly represented. In other words, the data must be verified as coming from a reliable source to be credible.

Compliant data: Data compliance is the practice of ensuring that sensitive data is organized and managed in order to meet enterprise business rules as well as legal and governmental regulations. In order to ensure your data is compliant, you’ll need to look at the types of sensitive data you’re collecting and how it’s being handled.

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Temberton Analytics, Inc.