7 Skills for Improved Data Governance

What is data governance? Data governance term used on both a micro and a macro level. Micro-level sociology focuses on large-scale processes, like social stability as well as change. While macro-level sociology focuses on small-scale interactions between individuals, like group dynamics and conversation. The latter is a management concept which forms part of data governance; the former is a political concept which forms part of the internet governance and international relations.

Micro level: Here the focal point is on individual companies. The data management concept concerning the capacity that empowers an organization to safeguard high data quality exists through the complete life cycle of that data. The key focus is usability, availability, data integrity and security, and availability and includes instituting processes to ensure data management on every level of the business such as responsibility for the adverse result of poor data quality and ensuring that the data a company has can be used by the entire corporation – a role tasked to a data steward. A steward ensures that proper channels are followed, and guidelines are enforced, and recommends improvements.

Data governance framework involves processes, people and information technology needed to design a consistent and effective pattern across the enterprise such as: increased consistency in decision making, decreasing risk of regulatory fines, maximizing income generation, improving data security, including defining and verifying requirements for data distribution policies, designating accountability for information quality, minimizing or eliminating re-work, enable better planning by supervisors, optimize staff effectiveness, acknowledge and hold all gain, and establish performance procedure to enable improvement efforts.

Macro level: The governing of cross-border data flows by countries is what’s referred to here. It’s a new field that consists of principles, norms and regulation governing varied types of data.

These goals must be realized through the implementation of data and its programs, or initiatives using Change Management proficiency. When organizations are required, or desire to gain control of their data, they authorize their employees to set up processes or acquire the help of technology professionals to do it.

Data governance framework is a blueprint for success. It’s a quality control discipline for managing, assessing, improving, using, maintaining, monitoring, and protecting the company’s sensitive information. While the governance of data initiatives can be propelled by a desire to enhance quality, they’re more often propelled by C-Level leaders, wittingly or under pressure, responding to external regulations. In a report conducted recently by CIO WaterCooler community, 54% of companies affirmed the primary driver was efficiencies in procedures, while for 39% of them, it was regulatory requirements, and about 7% customer service.

Sarbanes-Oxley Act, HIPPA, Base I, Base II, cGMP, GDPR among others are examples of these regulations. To comply with these regulations, organization procedures and controls require conventional management processes to help govern the data subject to these regulations. Successful and effective programs identify drivers relevant to both executive leadership and supervisory.

It’s a structure of decision rights and responsibilities for information-related procedures, implemented regarding agreed-upon models describing who should take what actions and with what information, under what circumstances, when, and using what methods. Themes common among external regulations focuses on the need to minimize and manage risk. Among those risks are inadvertent release of sensitive data, financial misstatement, or needful data quality for important decisions.

Processes to manage these risks vary greatly from organization to organization. Examples of frequently referenced best guidelines and practices include ISO/IEC 38500, COBIT, among others. The rapid production of standards and regulations creates challenges for data management specialists, especially when multiple regulations converge the data being managed. In most cases, corporations usually launch preventive initiatives to address these challenges before they arise.

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