How to Assess Data Health: 7 Best Ways

How to Assess Data Health

It is important to assess the data health in today’s data-driven world. Poor data quality has a major impact on business. It can cause inefficient processes and even lead to revenue loss. This article will reveal seven ways that you can assess the health of your company’s data.

Assessing the health of data in today’s data-driven world is essential. Poor data quality will have a negative impact on your company, leading to inefficient processes and revenue loss. Monte Carlo’s 2022 report, which surveyed over 300 data professionals from different industries, found that poor data quality affected about 26% of average business revenue.

In light of these negative effects, it is even more important to make sure that the data in your company is always clean. Explore seven ways to evaluate the health of your company’s data.

1. Define your Data Quality Standards

Data quality standards are the first step to assessing the health of your company’s data. These standards should reflect your company’s needs and be specific to the data that you collect. If your business relies heavily upon customer data, for example, you should set data quality standards that focus on accuracy and completeness.

A report from Talend in June 2022 shows that 80% of companies use standard metrics to describe and evaluate the quality of data.

Also read: What is Data Interpretation and How to Collect Accurate Data?

2. Evaluate Data Accuracy

Accurate data is essential to ensure that your business can make informed decisions. You could make decisions based on inaccurate data if your data is incorrect. Comparing data to other data sources is one way to assess data accuracy. Assessing data completeness is important. You can compare your data to a list of fields that are required or to data from other sources to make sure all the relevant information has been captured.

3. Assess the Data Completion

The completeness of your data is the extent to which it contains all relevant information. You may miss important information if your data is incomplete. This could have a negative impact on your business. You can check your data for completeness by comparing it with other data sources or comparing it against a list of required fields.

4. Ensure Data Consistency

Data consistency is the degree of uniformity and standardization in your data. Data that is inconsistent can cause confusion and errors to occur in your business process. You can assess data consistency by examining your data to look for inconsistent naming conventions and formatting.

5. Evaluate Data Timeliness

Data analysis requires a high level of accuracy. You may be making decisions using outdated data if your data is out of date. You can assess the timeliness of data by examining the frequency with which data is updated and the lag time between data collection, and data analysis.

A workforce that fails to maintain data accuracy can spend a lot of time cleaning data. In a Monte Carlo study conducted in 2022, data professionals spent about 40% of their day on data quality management.

6. Assess Data Security

Data security is crucial for today’s data-centric organizations. Your business and customers could be at risk if your data isn’t secure. You can assess data security by reviewing your policies and procedures. Conducting regular security audits and implementing data encryption and access control are also options.

Also read: What is Continuous Data Protection (CDP)?

7. Monitor Data Governance

Data Governance is the set of policies and procedures that governs the management of data in your business. A strong data governance framework is essential to ensure your data’s health. You can assess data governance by reviewing your data management procedures and policies, conducting regular audits of the data governance processes, and ensuring that all employees have been trained in data governance best practices.

A survey conducted in 2022 of 350 data professionals attending the MIT Chief Data Office and Information Quality Symposium revealed that 51% of chief data officers spend their time developing effective data governance strategies. In addition, the survey found that CDOs are involved in improving data quality. These statistics highlight the importance data governance has in ensuring the good health of data.

Moreover, Artificial intelligence and machine-learning initiatives have been driving business operations in most organizations over the past few years. implementing data governance strategies will simplify your day-to-day operations, and help you make strategic decisions.


It is important to assess the health of data in a business for many reasons.

First, It ensures that the data are accurate, complete, and consistent. This is crucial for making informed decisions. It helps to identify data quality problems or errors and allows them to be corrected prior to causing serious problems. Thirdly, this helps to ensure that the data is protected and secure from unauthorized access or breaches.

Finally, assessing the data health of a company can identify areas for improvement and optimization. This will allow the organization to better utilize its data resources, and gain an advantage in the market.

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