According to an Experian global data management report, 78% of organizations believe they are losing money due to poor governance of data assets, although 85% consider data to be one of their most valuable business assets.
The recent Gartner Data and Analytics Summit 2023, emphasized the need to strengthen the data governance program with new data analytics techniques to draw better, adaptable, resilient, and data-literate organizations.
Data governance is essential for delivering business value. With the help of a data governance program, organizations ensure that their data is accurate, secure, complete, and consistent. This is essential for making better decisions, reducing costs, and mitigating risks. Moreover, it leads an organization towards better decision-making.
In this blog, you will learn 5 components that are vital for achieving success in your organization’s data governance program.
Components of A Successful Data Governance Program
- Clear data governance strategy
- Data governance framework and policies
- Data stewards and data governance team
- Data quality management
- Continuous monitoring and improvement
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Clear Data Governance Strategy:
Establishing a well-defined data governance strategy is the foundation of a successful program. This strategy outlines the organization’s objectives, responsibilities, and policies related to data management. A clear vision and roadmap enable stakeholders to understand the importance of data governance and align their efforts accordingly.
- According to the reports of Gartner, by 2026, 20% of large enterprises will use a single data and analytics governance platform to unify and automate discrete governance programs.
2. Data Governance Framework and Policies:
In the Dataversity report, 48% of companies ranked regulatory compliance as their primary factor for data governance.
Regulatory compliance is very important for an organization to ensure and validate that the organization’s data-related practices are according to the requirements set forth by relevant laws and regulations.
A robust data governance framework and policies play a crucial role in defining the structure, processes, and guidelines for data management. It helps in data stewardship, data quality management, data access controls, data lifecycle management, and data privacy policies.
Thus, data governance is a comprehensive code that defines how data is being used and managed in the organization. These frameworks and policies also outline the responsibilities of team members included in the team of data governance and systems management. It defines their responsibilities and how they have to process the systems used for data management.
3. Data Stewards and Data Governance Team:
Data stewards are individuals responsible for overseeing and managing data assets within specific areas of the organization according to the defined policies of the data governance program.
Their expertise and experience effectively help in data accuracy and quality management of data governance systems. Moreover, the data governance team is also responsible for implementing data governance policies, resolving data-related issues, and ensuring data quality and compliance. The team is typically composed of representatives from different departments, which helps in fostering collaboration and accountability in data governance. Thus, a dedicated data governance team is very important to ensure the success of the data governance program.
4. Data Quality Management:
According to the reports of Gartner research, “the average financial impact of poor data quality on organizations is $12.9 million per year.” IBM also discovered that in the US alone, businesses lose $3.1 trillion annually due to poor data quality. Gartner recently discovered many businesses lose thousands of dollars each year as a result of “lost productivity” stemming from poor-quality data. Gartner also predicts that by 2024, 50% of organizations will adopt modern data quality solutions to better support their digital business initiatives.
- Another Gartner report “Magic Quadrant for Data Quality Solutions” states: By 2024, 90% of data quality technology buying decisions will focus on ease of use, automation, operational efficiency, and interoperability as the critical decision factors.
- By 2025, 80% of mainstream data quality vendors will expand their product capabilities to provide greater data insights by discovering patterns, trends, and relationships of data, in addition to error resolution.
Maintaining data quality is very important for an organization to prevent financial loss and improve the profit stream of the organization. It’s one of the most critical factors to ensure the success of the Data governance program. In a data governance program, all the processes of data from data collection, storage, archiving, and deletion are covered with deep analysis.
A successful data governance program implements data quality management practices, such as data profiling, data cleansing, and data validation, to ensure that data is reliable and trustworthy.
5. Continuous Monitoring and Improvement:
Organizations need to monitor and improve the quality of data with the recent standards and data governance policies. Continuous monitoring and reporting of data can play a significant role in the success of data governance programs. It’s very helpful for organizations to identify and address data quality issues in real-time, and ensure the accuracy, completeness, and consistency of data.
For continuous monitoring, there are multiple ways that organizations can incorporate to ensure the continuous monitoring of data. Here we will discuss only two ways. One simple way is the use of a data quality management (DQM) tool. DQM tools are widely used to monitor data quality for various metrics, such as accuracy, completeness, consistency, and timeliness.
The second approach is data warehousing. Data warehouses analyze data from a variety of different sources. This approach allows organizations to get a holistic view of their data and to identify trends and patterns that may not be visible from individual data sources.
Data governance is an ongoing process, and continuous monitoring is vital to maintaining its effectiveness. Regular audits, assessments, and reviews of data governance practices help identify areas for improvement and ensure adherence to data policies and regulations. Feedback from stakeholders and data consumers can also provide valuable insights for refining the data governance program.
Benefits of Data Governance Program:
- Improved data quality
- Lower data management costs
- Increased access to needed data
- Enhanced data security
- Better data compliance
- Improved data sharing
Final Words
In a nutshell, a strong data governance program is vital for keeping your data safe. By incorporating these five key components, your data governance program will build a solid base for managing data effectively, meeting regulations, and using data for smarter decisions that drive business success.
Ready to take control of your data? BITLogix is here to help your organization. Let’s break down data barriers and use secure, well-managed data for better decisions. Start your journey to data excellence with us today.