Data is the base on which the artificial intelligence use cases are being built and deployed. It is very important to make sure that the integrity and fidelity of the data is established and maintained. Data governance is essential for the quality and security of data.
This brings us to the next question. How do we define Data Governance? We have the definition from the leading providers in this area namely Google, Amazon and Microsoft and the leading user ChatGPT.
According to google- Data governance is everything you do to ensure data is secure, private, accurate, available, and usable. It includes the actions people must take, the processes they must follow, and the technology that supports them throughout the data life cycle.
Data governance means setting internal standards—data policies—that apply to how data is gathered, stored, processed, and disposed of. It governs who can access what kinds of data and what kinds of data are under governance. Data governance also involves complying with external standards set by industry associations, government agencies, and other stakeholders.
According to Microsoft - The definition of data governance includes the collection of processes, policies, roles, metrics, and standards that ensures an effective and efficient use of information. This also helps establish data management processes that keep your data secured, private, accurate, and usable throughout the data life cycle.
A robust data governance strategy is crucial for any organization that uses data to drive business growth, make improved decision-making, and ensure successful outcomes in a competitive market. When collecting vast amounts of internal and external data, you'll need to have a strategy that manages risks, reduces costs, and executes business objectives effectively.
According to Amazon Web Services- Data governance includes the processes and policies that ensure data is in the proper condition to support business initiatives and operations. Modern organizations collect data from various sources at scale to enhance operations and service delivery. However, data-driven decision-making is effective only when data meets required quality and integrity standards.
Data governance determines roles, responsibilities, and standards for data usage. It outlines who can take what action, upon what data, using what methods, and in what situations. With more data being used to support artificial intelligence (AI) and machine learning (ML) use cases, it has become critical that all data usage meets regulatory and ethical requirements. Data governance balances data security with tactical and strategic objectives to ensure maximum effectiveness.
According to ChatGPT- Data governance refers to the collection of policies, procedures, and standards that ensure the effective management, quality, security, and usability of an organization's data. It encompasses the processes, roles, and responsibilities designed to ensure that data is accurate, consistent, and used in a compliant and ethical manner across the organization. Key elements of data governance include:
Data Quality: Establishing standards and processes to maintain data accuracy, completeness, and consistency.
Data Security and Privacy: Implementing measures to protect data from unauthorized access and to ensure compliance with privacy regulations.
Data Stewardship: Assigning roles and responsibilities for data management, including data stewards or custodians who oversee data lifecycle management.
Policy and Compliance: Creating and enforcing policies that guide data usage and ensure compliance with legal and regulatory requirements.
Data Lifecycle Management: Managing data from creation to retirement, ensuring it remains reliable and relevant throughout its lifecycle.
In essence, data governance provides a framework that aligns data management with business objectives, thereby enabling organizations to maximize the value of their data while mitigating risks.
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