Data Governance refers to the strategy of managing and controlling data throughout the enterprise and thus ensuring to derive more value from that data. It is about enabling and encouraging good behavior regarding data, and limiting behaviors that create risks.
It is important for enterprises because it ensures that the data is reliable and consistent. Organizations rely on data to make business decision including product strategy, operations optimization, increasing revenue, makes it more critical. It is generally achieved through a combination of processes such as data stewardship, data quality and data management.
Important pillars of Data Governance:
Data Encryption – Data encryption involves translating the data into another form, or code, so that only people with access to a secret key or password can read it.
Data Security – It involves prevent the unauthorized access to data, protect data against corruption. It can be achieved by managing authentication and authorization.
Data Lineage – Data Lineage provides the complete data transformation journey from point of origin to current observation point in system. Data lineage includes the data’s origins, what happens to it and where it moves over time.
Data Auditing – Data auditing needs to track changes of key elements of datasets and capture “who / when / how” information about changes to these elements.
It helps to understand where data in the system is coming from and how it is used. An audit trail can be used to determine the particulars—the who, what, where, and when—of a data breach or attempted breach.
Data life cycle management – DLM is a policy-based approach to managing the flow of an information system’s data throughout its life cycle: from creation and initial storage to the time when it becomes obsolete and is deleted.
Data discovery – Tagging (i.e., metadata tagging) to recognize, classify and make sense of the ingested raw data in the system. Data discovery requires skills in understanding data relationships and data modeling as well as in using data analysis and guided advanced analytics functions to reveal insights.
Data governance use cases
Most important use cases of Data governance include mergers and acquisitions, business process management, legacy modernization, financial and regulatory compliance, credit risk management, analytics, business intelligence applications, data warehouses, and data lakes.
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