With the increase in data complexity and volume, organizations experience tremendous challenges with data management. To ensure consistent, reliable, and accurate data, solid data governance and quality strategies have become crucial to incorporate. This is where the concept of data governance and quality comes into being.
Data integrity and governance are crucial concepts in the data management world. They play a significant role in ensuring the accuracy, reliability, and quality of the data. While data governance is the management of the usability, availability, security, and integrity of organizational data, data quality refers to the completeness, reliability, and accuracy of the data.
A Brief on data governance and data integrity
Data integrity and data governance are related concepts. Excellent data governance helps maintain data integrity while offering a framework for ensuring complete, consistent, and accurate data. It also establishes rules and methods for managing data throughout the lifecycle, from creation to deletion. It ensures the data remains secure from unauthorized use, access, destruction, alteration, or disclosure.
On the contrary, data integrity is an outcome of effective data governance. From ensuring reliable and trustworthy data to assuring the data remains unmodified, uncorrupted, and uncompromised, data integrity does it all. It is the foundation for making well-informed decisions.
Tried and tested practices for data governance
There is no one-size-fits-all solution for any company, as every business operates differently. However, keeping the below-offered tried and tested data governance strategies will be a driving force for a company to move forward.
Key objectives of the data governance program
Before implementing initiatives, defining the goals is the most important consideration. This helps you focus on the important aspects. Plus, you can also avoid getting sidetracked with inappropriate issues.
Identify key elements and establish a policy
Establishing data governance policies is the next crucial initiative. Discover your key stakeholders and their roles, and establish the policies concerning data security and privacy. To access the data assets, you need to conduct an inventory of each data asset and evaluate its quality, relevance, and completeness to the business objectives.
Are there any redundancies in the data infrastructure? If you identify any gaps, you must develop a plan to address them. Also, define data standards for metadata, data classification, quality, and more. Your stakeholders should comprehend and agree to the standards.
Next comes implementing the data controls that ensure the data is secure, consistent, and accurate. That might include data cleansing, data validation, as well as access controls. After this, you need to recognize data elements that lead to the company’s success. Are there any critical data elements that can improve the company’s success? If yes, they must be governed first.
Data governance initiatives
Prioritizing the data governance initiatives based on data elements’ criticality and the effort needed for implementing them. The data process owner may ask you for the initiatives. So, you must keep them aware of your approach and procedures.
Initiate a pilot project
Small pilot projects are great for testing the data governance tactic and validating the efficiency. This helps identify the potential issues and make the required changes. However, it does not offer any monetary return that shows the impact of project transformation on data governance.
After this, it is time for identifying and implementing data governance tools for the following purposes:
- Data cataloging
- Metadata management
- Data lineage
These automate data management procedures & ensure consistent application of standards and policies. Then, the next step is all about automating data quality management involving automation to refrain from human error. Lastly, it is time to measure progress and execute training.
