7 Ways To Ensure Data Health On World Health Day 2023

Data health is the accuracy, completeness, and reliability of data used by organizations to make informed decisions.

• It is vital to assess the health of your company’s data in today’s data-driven world.
• Bad data quality can have a major impact on your business. This could be in the form of inefficient processes or lost revenue.
• This article will show you seven ways to evaluate the health of your company’s data.

It is vital to assess the data health of your company in today’s data-driven environment. Bad data quality can cause significant business problems, including inefficiency and loss of revenue. Monte Carlo surveyed 300 data professionals from different industries and found that poor data quality had an average impact of 26% on business revenue.

It is vital that you ensure your company’s data remains healthy, considering the potential consequences of bad data. Let’s look at seven ways you can assess the health and safety of your company’s data.

1. Set Quality Standards for Your Data

Your data quality standards are the first step to assessing the health of your company’s data. These standards should reflect your business’s needs and be specific to the data you collect. If your business relies on customer data heavily, then your data quality standards must be focused on accuracy, completeness, and consistency.

A Talend report from June 2022 found that about 80% of organizations use standard metrics to assess and describe the quality of their data.

2. Assess Data Accuracy

To ensure that your business can make informed decisions, data accuracy is essential. You could be making incorrect decisions if your data is not accurate. Comparing data to other sources is one way to assess data accuracy. To verify the accuracy, you could compare customer demographic data with census data.

3. Verify Data Completion

Data completeness is the extent to which all information in your data has been captured. Data that is not complete can lead to missing critical information that can impact your business decisions. You can check the completeness of your data by comparing it with other sources or a checklist to determine if there are any missing fields.

4. Data Consistency

Data consistency is the standardization of your data. Inconsistent data can cause confusion and lead to errors in business processes. You can check your data for inconsistent data types, naming conventions, and formatting to determine data consistency.

5. Timelines for Data Evaluation

Data analysis requires that you act quickly. You may be making poor decisions if your data is out of date. You can assess the data timeliness by looking at the frequency of data updates as well as the time lag between collection and analysis.

If data timeliness is not maintained, employees can spend a lot of time cleaning up data. According to a Monte Carlo survey, data professionals spend approximately 40% of their workday in data quality management.

6. Security Assessment

Data security is essential for data-centric companies today. Your business and customers could be at risk if your data security is poor. You can review your security policies and procedures to assess data security. Regular security audits are also conducted. Finally, data encryption and access control measures can be implemented.

7. Monitoring Data Governance

Data governance is the set of policies and procedures that regulate the management of data within your company. A strong data governance framework is essential to ensure your data’s security. You can assess your data governance by reviewing your data management policies and procedures. Also, make sure that you regularly audit data governance processes and train all employees on best data governance practices.

A survey of 350 data professionals attending the MIT Chief Data Officer Symposium in 2022 revealed that 51% of chief data officers (CDOs), spend their time developing effective data governance strategies. A survey found that 48% of CDOs are involved in improving data quality. These numbers highlight the importance and necessity of data governance to ensure good data health.

Furthermore, with AI and machine-learning initiatives driving most organizations’ business operations in recent years, it is imperative to implement data governance strategies to simplify day-to-day operations and make strategic business decisions.

Conclusion

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

• It ensures that data is consistent, accurate, and complete. This is crucial for informed business decisions.
• It also helps to identify data quality issues and errors before they become serious problems.
• It also helps to ensure that data is protected against unauthorized access and breaches.

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