Data governance is an essential part of any overall corporate governance program, ensuring that data management and data use are aligned with the business and its policies. In today’s digitally-driven environment, where data is arguably the most valuable asset of the business, effective management is critical in ensuring that the data is both safe and accessible.

Effective data governance involves setting the parameters for data management and usage, establishing processes for resolving data-related issues, and enabling business users to make decisions based on high-quality data and well-managed information assets.

Implementing a data governance framework . Complicating factors such as questions over data ownership, inconsistencies across different departments, and the expanding collection and use of big data in companies often come into play.

Without proper data management, organisations open themselves up to risk. Data governance prioritizes data according to the financial benefit it delivers, while mitigating the risk of poor data practices and poor data quality.

Besides being essential to achieving compliance, information governance can also prove to be beneficial in other ways. For example, it can actually reduce the overall cost of compliance by providing a framework that can be quickly applied when new regulations are put into effect. In addition, having clear data governance policies in place reassures customers of the ethical use of their data, and ensures the organisation can trust the quality of their own data internally as well.

Developing a successful data governance strategy requires careful planning, the right people and appropriate tools and technologies. An essential guide should include best practices and advice for managing data governance projects, an exploration of data stewardship and details about common problems that organizations have experienced while instituting data governance programs and how they solved them.

While data governance, IT governance and data management are not the same thing, they are all intrinsically related, as both data governance and IT governance are essential for data management. Data governance lays the foundation for other forms of governance, include risk and financial management, by ensuring that clean, quality, accurate and valid data is available.

Data Governance and Data Stewardship are most times used interchangeably, but there is a difference. Data Governance pieces together cross-functional teams to make interdependent rules to resolve issues or to provide services to data stakeholders. These cross functional teams Data Stewards/Data Governors generally come from the Business side of operations. They set policy that IT and Data groups will follow as they establish their architectures, implement their own best practices, and address requirements. Data Governance can be considered the overall process of making this work.

Data Stewardship is concerned with taking care of data assets that do not belong to the stewards themselves. Data Stewards represent the concerns of others. Some may represent the needs of the entire organization. Others may be tasked with representing a smaller constituency: a business unit, department, or even a set of data themselves.

Here are some guiding principles put forward by the Data Governance Institute to help stakeholders come together to resolve the types of data related conflicts that are inherent in every organization.

  • Integrity

Data Governance participants will practice integrity with their dealings with each other; they will be truthful and forthcoming when discussing drivers, constraints, options, and impacts for data-related decisions.

  • Transparency

Data Governance and Stewardship processes will exhibit transparency; it should be clear to all participants and auditors how and when data-related decisions and controls were introduced into the processes.

  • Auditability

Data-related decisions, processes, and controls subject to Data Governance will be auditable; they will be accompanied by documentation to support compliance-based and operational auditing requirements.

  • Accountability

Data Governance will define accountabilities for cross-functional data-related decisions, processes, and controls.

  • Stewardship

Data Governance will define who is in charge of particular stewardship activities that are assigned to individual contributors, as well as the tasks under groups of Data Stewards.

  • Checks and Balances

Data Governance will define accountabilities in a manner that introduces checks-and-balances between business and technology teams as well as between those who create/collect information, those who manage it, those who use it, and those who introduce standards and compliance requirements.

  • Standardization

Data Governance will introduce and support standardization of enterprise data.

  • Change Management

Data Governance will support proactive and reactive Change Management activities for reference data values and the structure/use of master data and metadata.

Data governance ensures that metrics are defined consistently across the organization, so when managers or analysts talk about conversion rates or unique visitors, everyone else knows precisely what they’re talking about. Without clearly documented standards around metrics, decisions may be made around false assumptions.

Analysis and reporting issues are most often data governance problems, not technology problems.
Many organizations are quick to blame their tools or technology when there is confusion about the meaning of Web analytics data or lack of clarity in reports.

Data governance guides all other analysis activities. Analytics teams need to guide and structure their most important activities using data. In this sense, data governance informs everything from analytics software implementation to page tagging to report design.

Ultimately, data governance saves money. Having a firm grip on how you define your core metrics can help you better manage your organization and keep track of what exactly is going on.

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