5 Best Practices to Improve Data Quality

Improving data quality is extremely important for organizations of all sizes. A lack of clean, validated, high-quality data can result in easily avoidable errors that may, at times, prove to be costly for the organization. Recent data from Gartner shows that unorganized data is responsible for average annual losses of up to $15 million.

As business environments continue to become more complex and organizations leverage data available in various file formats and cloud locations, improving data quality is absolutely crucial to ensure that your decisions are not being driven by data that is unreliable or inaccurate. Interested in improving your data quality to make better business decisions? Here’s everything you need to know about improving data quality and how it can help your organization.

What is Data Quality and Why is it Important?

Data quality can mean different things to different organizations. Some might prioritize metrics such as accuracy and consistency to measure quality while others may focus more on reliability and completeness. Regardless of how you define the term, high-quality data enables businesses to build far more accurate projections and forecasts, anticipate and resolve operational issues, and create proactive strategies to win over customers and prospects.

Needless to say, when you’re working with data that hasn’t been cleaned and validated beforehand, you need to be extra cautious to ensure that the reports and analyses that come from this data are accurate and not laden with errors. By improving their data, these organizations can automate their data integration and analytics processes without worrying about data that is out of date, inaccurate, or unreliable.

5 Best Practices to Improve Data Quality

Ensuring data quality is crucial for organizational success. Key best practices include:

• Establishing a process to investigate data quality problems.
• Setting clear guidelines for data governance.
• Training teams.
• Exploring Customer360 initiatives.
• Making high-quality data a priority.

Additionally, standardizing data formats, regular data cleansing, effective integration, monitoring with Key Performance Indicators (KPIs), utilizing data profiling tools, encouraging feedback, collaboration, and fostering a culture of continuous improvement collectively contribute to maintaining accurate, reliable, and valuable datasets for informed decision-making.

1. Establish a process to investigate data quality problems

Understanding data quality issues and how they can affect your business is the most important step in improving data quality. After all, you will only be able to make improvements to your data quality once you identify what the problem is and why it is important to resolve these issues for your organization.

Looking into data incoherencies is also important because certain problems may cause greater issues in some scenarios than others. For instance, a slight misspelling in the “occupation” field in a customer database may not be too big of an issue in case you just need to send a promotional email to a customer, but an incorrect name can make a huge difference in case you’re in the ticketing or insurance space.

Here are some metrics that you can use to determine the quality of your data:

• Completeness: Establishing a process to measure data completeness can help you ensure that there are no gaps in your data analysis. Data completeness needs to be measured to determine whether crucial information is missing to ensure that insights derived from this data can be used to design reliable strategies and make projections.
• Accuracy: Checking data accuracy is extremely important. A slight difference in the format of your data can render it invalid and useless. For instance, if the Date of Birth field in your employee database accepts dates in the MM/DD/YYYY format and an employee enters 13/01/1983 in the field, the data will be inaccurate and should not be processed further.
• Uniqueness: Duplicates and repetitive values can cause inconsistencies in your data pipelines. Ensure your data is unique by eradicating redundant values that can affect accuracy and reliability, especially when creating intricate integration pipelines with multiple data streams.
• Up-to-date entries: Up-to-date data is essential in several scenarios, including forecasting and allocating budgets. Since most companies today need to work with real-time data and create reports quickly, it is important to ensure that all data that is being collected is up-to-date to mitigate the chances of errors.

2. Set clear guidelines for data governance

Abiding by data governance laws and regulations is absolutely essential. Failure to do so can result in fines, penalties, and harsher repercussions.

Since organizational and customer data is used by different teams in different ways, it’s best to conduct company-wide discussions to create data governance guidelines and decide how they can be implemented. These guidelines should cover every aspect of data collection and management including where and how data is stored and which personnel will be allowed to process it.

From a data quality standpoint, implementing these guidelines could mean creating automated pipelines to ensure that certain data is deleted as soon as it is processed or that data in some fields are only formatted in a particular way.

3. Train your teams

Improving data quality is pretty much a life-long process and should be treated as such. As your organization continues to source its data from different locations, it’s important to ensure that your teams do not start slacking and are always up-to-date on the latest procedures when it comes to improving data.

Here are a few pointers you can use to conduct your next data quality training:

• Basic concepts of how poor-quality data can affect the organization
• Challenges of improving the quality of data, especially when data is integrated from multiple channels
• The cost of poor-quality data (both in terms of resource utilization and failed projects)
• Creating department or project-specific use cases to understand how data quality works in real-life situations

4. Explore customer360

Customer360 is an interesting concept to minimize duplicates and promote the use of accurate, reliable, and consistent data to drive business decisions. Data streams can be automated and integrated with each other and data quality can be improved by removing irrelevant, duplicate, or corrupt entries from what will act as your single source of truth.

Since this data will be cleansed and updated, it will be easy to use the customer 360 data across the organization to ensure that there are not any issues caused by the lack of standardization or by using inconsistent data streams.

5. Make high-quality data a priority

This may sound like a no-brainer but it’s actually one of the most important steps you can take to improve data quality. The quality of data often takes a back seat because more time and effort goes into the organization’s larger and more important goals.

After all, who would want to focus on improving quality when you can work on improving your sales pitch or create new strategies to minimize overheads?

Understanding how data quality affects all of these other areas of your organization and its success makes a world of difference. Once you realize that improving data can help you improve targeting, keep leads in the sales funnel, and reduce costs associated with managing poor quality data, you and your teams will willingly prioritize cleaning, validating, and scrubbing data to extract more value from it.

Improve the Quality of Your Data with Astera Centerprise

As an enterprise-grade end-to-end data integration tool, Astera Centerprise comes complete with multiple features and capabilities to enhance quality to ensure that you never have to work with inconsistent or unreliable data again.

The Data Cleanse object allows users to validate their data through regular expressions and remove whitespaces, the Data Quality Rules object features dozens of functions to check the quality of data, and the Expression transformation gives users the chance to build custom expressions to clean and validate data just how they want to. Supporting data extraction and integration from 40+ sources, Astera Centerprise also comes with a Distinct transformation giving users the chance to deduplicate data, ensuring that only relevant unique data is passed to the next step in the integration pipeline.

Ready to see Astera Centerprise in action? Get in touch with us to view a demo or discuss your specific data quality use case.