7 Ways to Stop Data Quality Issues in Their Tracks

Data quality is one of the most important aspects of any successful data strategy, and it’s essential to ensure that the data you collect and store is accurate and reliable. Poor data quality can lead to costly mistakes in decision-making, inaccurate predictions, and ineffective strategies. Data quality is essential in any organization, and there are a few key strategies you can use to instantly improve your data quality. Here are seven strategies to improve data quality:

1. Automation of Data Entry

Automating data entry is one of the most effective strategies for improving data quality. Automation helps ensure that data is entered accurately and quickly, reducing the risk of human error. Automation also allows you to quickly identify any errors or inconsistencies in the data, which allows you to trust the data you use to make decisions. Automation can help reduce the time spent manually entering data, freeing up more time for other tasks.

2. Data Standardization

Data standardization is another key strategy for improving data quality. Data standardization helps to ensure that data is consistent and reliable, and that data is entered in the same format across the organization. This helps to ensure that data is comparable and easy to analyze. Standardizing data also helps to reduce the risk of errors due to different formats and versions.

3. Data Verification

Data verification is another essential strategy for improving data quality. Data verification helps to ensure that the data is accurate, and it helps to detect any discrepancies or errors in the data. Data verification can also help you identify any patterns or anomalies that could indicate a problem with the data or your data pipelines. This allows staff to diagnose and resolve issues faster.

4. Use Data Integration Tools

Data integration tools are a great way to improve data quality. Data integration solutions, like Actian Data Platform, allow you to quickly and easily combine data from multiple sources, which helps to ensure that the data is accurate and up-to-date. Data integration tools can also help you automate the process of combining data and transformation, which can help to reduce the amount of time spent manually entering data.

5. Encourage Self-Service Data Quality

Encouraging self-service data quality is another excellent strategy. Self-service data quality empowers users to take ownership of the data they enter. By providing users with easy-to-use tools, training, and support, you can help ensure that data is entered correctly and quickly.

6. Implement Data Profiling

Data profiling helps to identify any patterns or anomalies in the data, which can help you identify any potential issues with the data. Implement tools or processes that can easily identify and segregate data that doesn’t adhere to your organization’s data standards.

7. Integrate Data Quality into your Pipelines

Create profiling and quality rules that can be integrated into your pipelines. Data management tools vary wildly in capabilities, so look for products that can provide a quick “at-a-glance” view of data quality based on the rules you’ve established. This can make it easier for staff to determine if there are expected results in data quality anomalies or something that could single a more significant problem at an earlier stage in the pipeline.

Benefits of Improving Data Quality

Improving data quality can have a number of benefits for any organization. Here are a few of the key benefits of improving data quality:

1. Improved Decision-Making: When data is accurate and reliable, it can help improve decision-making by ensuring that decisions are based on accurate and up-to-date data.
2. Enhanced Efficiency: Improved data quality can also help to improve efficiency, as it reduces the amount of time spent manually entering and verifying data, freeing up more time for other tasks.
3. Better Customer Service: Improved data quality can also help to improve customer service, as it helps to ensure that customer data is accurate and up-to-date.
4. Cost Savings: Improved data quality can also help save costs, as it reduces the time and resources spent manually entering and verifying data.

Get Started!

Automation of data entry, data standardization, data verification, data integration tools, and data quality processes are great strategies for improving data quality. Data governance is also essential for ensuring data accuracy and reliability. By following these strategies, you can ensure that your data is accurate and reliable, which can help to improve decision-making, enhance efficiency, and improve customer service. It can also help save costs, as it reduces the time and resources spent manually entering and verifying data. Actian’s Data Platform can support you in implementing these strategies to get the most out of your data.

About Traci Curran

Traci Curran serves as Director of Product Marketing at Actian focused on the Actian Data Platform. With more than 20 years of experience in technology marketing, Traci has previously held senior marketing roles at CloudBolt Software, Racemi (acquired by DXC Corporation), as well as some of the world’s most innovative startups. Traci is passionate about helping customers understand how they can accelerate innovation and gain competitive advantage by leveraging digital transformation and cloud technologies.