What is Data Governance?
Data Governance is a collection of practices and processes that helps to ensure the formal management of data assets within an organization. It encompasses both the strategies and the technologies required to achieve the goal of data usage that complies with the regulations, restrictions, and policies applicable to that use. It includes data cataloging, data lineage, data usage labeling, data access policies, and much more.
Why Data Governance?
Every organization is dealing with data more than ever before – individual bits of information about its operations, its products and services, its finances, its employees, its suppliers, distributors, and competitors, and most significantly, its customers. Data Governance allows to create profiles of individuals that contain many dimensions of personal information. These profiles can be used to categorize individuals by various characteristics into groups, document their preferences and interests, and predict their behavior. It helps to restrict usage of data on how it can be used by those with whom it’s shared, both to protect its business value and limit risk. Data governance is essential to data-driven success. Data governance starts with the following:
- Understanding the regulations, contractual obligations, and company policies that apply to your data (“Data Usage Policies”)
- Classification of data by labeling it with appropriate meta-data
- Defining Data Usage Policies based on various compliance, legal and corporate policies
The Need for Data Governance
Data governance enables organizations to access the full value of data, while protecting that data from risk. As data is growing across all areas of the enterprise – on the average, at the rate of 1.5 to 2.5 times a year. Data quality, Master Data Management (MDM), and data migration initiatives are booming as a result of this growth in data, demand, and regulation. As these data initiatives proliferate, they need governance to ensure they fit the needs of the enterprise and work with one another. Effective data governance creates a framework for the use of data that fits each individual enterprise. Here are few benefits of having data governance.
- It improves operational efficiency.
- Improves application effectiveness.
- Helps to minimize risk.
- Ensures that the right people get the right information at the right time and in a right way.
Why Enterprises Struggle with Data Governance?
Here are few barriers to success for data governance initiatives:
- Organizational – Different groups within an organization must communicate and coordinate well with one another
- Data quality, MDM, and data migration integration – Applications and data must speak to one another, and this must be addressed up front and planned for in any integration initiative
- Accountability and ownership of data – People must be held accountable for information assets and supported with technology to ensure the integrity of the assets
- Cost – Data governance initiatives must be implemented in such a way that costs are recouped, and business value is proven
Core Components of Data Governance
A data governance program has four core components:
- Data Stewardship
- Data Quality
- Data Security/Privacy
- Metadata Management
Data stewardship is one of the most important components of a data governance program. It supports the base for the continuance of the program beyond the initial implementation and ensures proper representation across the organization. It is the aspect of data governance that focuses on managing data throughout its lifecycle. It also provides the appropriate access to business and IT users, helping them to understand the data and take ownership of the quality and security of the data.
Data quality is a primary concern for every industry in today’s world. Data quality has one basic purpose: to collect and cleanse data and make sure that it is complete, timely, and accurate. Few data quality errors can be:
- Missing Data
- Duplicate Data
- Incomplete Data
- Inconsistent Data
Data security is not merely a business requirement for the organizations, rather an obligation and commitment. It is important that close attention is given from all stakeholders to ensure that standard security guidelines are adopted, monitored, and always improved. Organizations are encouraged to frame their own policies and measures beyond standard guidelines. After all, a well-rounded data security plan is part of a good data governance practice.
It describes, explains, locates, or makes it easier to retrieve, use or manage any data. It is also known as “data about data”. Metadata management can give visibility as to where the data resides to illuminate the context, and later to re-purpose – to utilize effectively to further wring out the desired information for insights. This strategy needs buy in from both the technical and business constituents. It is suggested to lead with the business users to highlight the business lineage as well as specific technical lineage.
Common Pitfalls of Data Governance
While most enterprises agree that data governance is an important cog in the wheel, there can be different reasons for data governance not delivering the desired results. Here are few of the failure points for data governance programs:
- Lack of accountability and strategic participation
- Lack of data standardization across organizations IT infrastructure
- Lack of awareness of business value of data
- Failure to manage data quality early in the data governance process
- Cross-divisional/cross-departmental issues
- Failure to recognize outcome specific measures (KPIs)
- Lack of compliance monitoring
- Lack of proper training and awareness of data governance policies