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How to Build an Effective Data Quality Management Program

You want to make the most of your big data. However, data quality is paramount to achieving that goal. Implementing an effective data quality management program ensures that your data is of the highest caliber, making it not only useful but also profitable.

Read on to learn what you need to know about building an effective data quality management program, including assessing your current state of data quality, putting data quality management strategies in place, and maintaining data quality management best practices.

Assess the State of Your Current State of Data Quality

The first step to managing the quality of your data is to determine what state your data quality is in. Why is that so crucial?

If you can’t trust your data, you can’t make the right decisions. The old saying “garbage in, garbage out” is true—the information going into the system won’t provide the right results in the end if it’s not accurate or consistent.

How do you assess the state of your data quality? The process can be carried out with specialized software. It determines what the data quality rules are, defines them against the source data, and then reports on the results.

4 Ways to Measure Data Quality

Putting Data Quality Management Strategies in Place

Once you figure out what data quality issues exist within your information, it’s time to create data quality management strategies and then put them in place.

The first step is to define data quality. You need standards to determine what good data is and what it isn’t. Business users can provide input on data quality management standards, which will shape corporate policy.

Data Quality Management

Maintain Data Quality Management Best Practices

What you must remember is that data quality management isn’t a one-and-done process. It’s ongoing—you can’t stop doing it, or you’ll find yourself in a situation in which you can’t trust your information.

What steps can you take on a regular basis to uphold data quality management best practices? Performing regular data validity checks is one step you can take—you can’t sit back and hope no data problems arise.

What happens when data problems do crop up? You have to log, track, and manage them. By ignoring them, you’re only letting data quality management issues fester and multiply. Conversely, when you know what those issues are, you can solve them to improve your data quality.

It’s also critical that everyone in the organization is aware of data quality management best practices and adheres to them. If no one understands why data quality matters, they won’t take steps to maintain it. An education campaign helps spread this message throughout the firm, ensuring adherence to data quality management standards.

Data is a source of value to organizations—as long as that data is consistent, accurate, and high quality. How do you ensure that? The answer is data quality management; by implementing data quality management best practices, you know your data is reliable and that it can be used to make the right decisions. To learn more, read our eBook: 4 Ways to Measure Data Quality.

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