7 Effective Ways to Assess Data Health

  • In today’s data-driven world, assessing the health of your company’s data is crucial. 
  • Poor data quality can have a significant impact on your business, from inefficient processes to lost revenue. 
  • This article reveals the seven effective ways to assess the health of your company’s data.

In today’s data-driven world, assessing the health of your company’s data is crucial. Poor data quality can have a significant impact on your business, resulting in inefficient processes and lost revenue. A 2022 report by Monte Carlo, which surveyed 300 data professionals across industries, revealed that poor data quality impacted about 26% of business revenue on average.

Considering such after-effects of bad data, it becomes all the more vital to ensure that your company’s data is always healthy. Let’s explore seven ways to assess the health of your company’s data.

1. Define Your Data Quality Standards

The first step in assessing the health of your company’s data is to define your data quality standards. These standards should reflect the needs of your business and should be specific to the type of data you are collecting. For example, if your company relies heavily on customer data, your data quality standards should focus on accuracy, completeness, and consistency.

According to a June 2022 report by Talend, about 80% of organizations have standard metrics to evaluate and describe the quality of their data.

2. Evaluate Data Accuracy

Data accuracy is critical to ensuring that your business makes informed decisions. If your data is inaccurate, you could be making decisions based on inaccurate information. One way to evaluate data accuracy is to compare it to external data sources. For example, if you are collecting customer demographic data, you can compare it to census data to ensure accuracy.

3. Assess Data Completeness

Data completeness refers to the degree to which your data captures all relevant information. If your data is incomplete, you may miss critical information that could impact your business decisions. To assess data completeness, you can review your data against a checklist of required fields or compare it to other data sources to ensure that all relevant information is captured.

4. Ensure Data Consistency

Data consistency refers to the degree to which your data is uniform and follows a standard format. Inconsistent data can lead to confusion and errors in your business processes. To assess data consistency, you can review your data for inconsistencies in naming conventions, formatting, and data types.

5. Evaluate Data Timeliness

Timeliness is essential in data analysis. If your data is not up-to-date, you may be making decisions based on outdated information. To assess data timeliness, you can review the frequency of data updates and the time lag between data collection and analysis.

On failing to ensure data timeliness, a workforce can end up spending a significant amount of time in data cleaning processes. A 2022 Monte Carlo survey identified that data professionals spend about 40% of their workday on data quality management.

6. Assess Data Security

Data security is crucial for today’s data-centric organizations. If your data is not secure, you could be putting your business and your customers at risk. To assess data security, you can review your policies and procedures, conduct regular security audits, and implement data encryption and access controls.

7. Monitor Data Governance

Data governance refers to the policies and procedures that govern the management of your company’s data. To ensure that your data is healthy, you must have a strong data governance framework in place. To assess data governance, you can review your data management policies and procedures, conduct regular audits of data governance processes, and ensure that all employees are trained on data governance best practices.

According to a 2022 survey of 350 data professionals who attended the ‘MIT Chief Data Officer and Information Quality Symposium,’ 51% of the chief data officers (CDOs) spend their time formulating effective data governance strategies. The survey also found that 48% of CDOs were involved in improving the overall data quality. These figures highlight the importance of data governance in ensuring good data health.

Moreover, with artificial intelligence and machine learning initiatives driving the business operations of most organizations in recent times, implementing data governance strategies is inevitable to simplify day-to-day operations and make strategic business decisions.

See More: Quality Over Quantity: Understanding the Importance of Data Quality


Assessing the health of a company’s data is vital for several reasons. 

  • Firstly, it ensures that the data is accurate, complete, and consistent, which is essential for making informed business decisions. 
  • Secondly, it helps identify any data quality issues or errors, allowing them to be corrected before they cause severe problems. 
  • Thirdly, it helps ensure the data is secure and protected from unauthorized access or breaches. 

Finally, assessing a company’s data health can help identify opportunities for improvement and optimization, allowing the organization to make better use of its data resources and gain a competitive advantage in the marketplace.

Do you think there are better ways to measure the overall health of your organization’s data? Comment below or let us know on FacebookOpens a new window , TwitterOpens a new window , or LinkedInOpens a new window . We’d love to hear from you!


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