How to Improve Data Quality

Data quality is a critical component of any organization’s success. With the right data, you can make better decisions and take more informed actions. With bad data, your company could be in serious trouble.

Data quality is the degree to which data has minimal errors, omissions, and discrepancies. As such, organizations are taking measures to improve data quality as part of their growth plans.

This article explores tips on how to improve data quality.

Develop Your Data Quality Management Plan

A data quality plan ensures that your company’s processes are in place to improve data quality.

The plan should include an overview of the current state of your organization’s data and a description of how to improve it. It should also discuss where the sources of error are, and define what you will do about them.

Minimize Process Errors

Data errors can occur when inaccurate or incomplete information is entered into systems or databases. Common input mistakes involve incorrect:

  • Names
  • Addresses
  • Account numbers
  • Dates and times

Additionally, erroneous updates could cause users to search for the wrong business.

Reduce Duplication

Duplicate data is information that already exists and has not been identified as such. For example, you might have the same person in different places in your records system with different identifiers.

This can cause problems when you process information because there will be duplicate records. When this happens, it can lead to inconsistent reporting and decision-making based on incorrect information.

The problem is complicated because different units may duplicate records across multiple locations. It will happen when each tries to improve their own databases without consulting others.

In addition, while most organizations focus on reducing redundancy in common fields (e.g., customer address), they overlook redundant or overlapping content for other files such as product descriptions and competitors’ names.

Increase Data Consistency

Data consistency refers to the degree to which data from multiple sources is consistent within and across each source. It involves verifying that information in various databases relates to the same subject or is otherwise compatible, without errors such as misspellings.

It is also among data quality dimensions that ensure there are no differences between records in different databases.

Develop a Data Quality Review Protocol

A data quality review protocol is a framework for improving the accuracy of information.

The protocol (such as the one set by MedPro Systems®) should include a checklist detailing every field. The ultimate goal is to improve the accuracy of the inputted data and make it available when users need it for decision making.

Improve Data Quality in Your Organization

Poor data quality can affect your bottom line. When you improve its quality, you can improve your company’s profit and also know that what is being presented will be accurate.

When you improve data quality in your organization, you improve decision-making and communicate optimal decisions. This leads to better business outcomes.

For more informative content, check out our other posts.

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