WHAT IS DATA GOVERNANCE?

摘要： Data governance is becoming increasingly essential as businesses confront new data privacy regulations and rely more on data analytics to optimize operations and make business decisions.

Data governance is becoming increasingly essential as businesses confront new data privacy regulations and rely more on data analytics to optimize operations and make business decisions.

Data governance is the process of collecting, managing, and utilizing data to provide improved business decision-making. It encompasses everything a company and its teams do to ensure data is safe, private, accurate, accessible, and usable. It includes activities as well as processes and technologies that companies must acquire to apply data governance procedures throughout the data lifecycle. Data governance handles integrity, availability, and security of data according to policies, standards, and regulations. Enterprises can avoid inconsistencies and mistrust in data through effective data governance initiatives.

DATA GOVERNANCE SIMPLIFIED

Companies must understand who has access to which data, how they use them, and their security privileges in the organization. Data governance processes address these needs by establishing the infrastructure and technology, developing and maintaining data procedures and policies, identifying individuals or positions in an organization with both authority and responsibility for managing and safeguarding the data.

WHAT DOES IT MEAN TO GOVERN DATA?

Data Governance Institute (DGI) describes data governance as a framework for understanding, managing, and protecting data to identify and meet the information needs of the various parties in an enterprise.

“Data governance is a framework for managing data throughout its lifecycle, from acquisition to usage and disposal”

WHAT ARE THE BENEFITS OF DATA GOVERNANCE?

The notion that information and knowledge are commodities has evolved into data being the core corporate asset determining a company’s success. Data runs the range of computer and technology processes, including accounting and finance, planning and control, order management, customer service, scheduling, process control, engineering, design, etc.

Given the importance of trustworthy and dependable information for businesses, organizations must pay close attention to data generation, quality, management, and security. It makes company systems and databases trusted to represent reality, allowing decision-making and company growth.

Internal corporate regulations, regulatory requirements frameworks, and standards are frequently the driving forces behind initiatives in this area. However, the advantages of establishing clear data-related standards and procedures extend beyond compliance:

BETTER DECISION-MAKING

It ensures that corporate users access the data they need to reach customers, deliver services, design or improve products, and take new revenue opportunities. Better and more reliable data leads to fewer misunderstandings and better decisions.

IMPROVED RISK AND COST MANAGEMENT

It streamlines audits and makes day-to-day operations more efficient and effective. It protects companies from the risks caused by decisions made based on erroneous or outdated information. Companies that carry out effective data governance processes have the chance to improve their customer service by knowing the correct status of their operations, inventories, and workforce availability.

DEMOCRATIZATION OF DATA

It allows more people to access accurate data and boosts confidence that democratizing data will not negatively impact the organization. The advantage of having all of the organization and decision-makers using the same data is immeasurable. Data governance ensures there are no data duplications and confusion about the validity of the data.

COMPLIANCE

Since auditors and regulatory oversight officials focus on how data is generated, handled, and safeguarded rather than examining the information itself, solid data management procedures are crucial for data governance. Today’s complex regulatory environment has made it even more necessary for businesses to have robust procedures for governing data. Only in this way can the dangers of non-compliance be proactively eliminated.

WHAT DIFFERENTIATES DATA GOVERNANCE?

Data governance is often confused with other data disciplines, such as data stewardship and master data management (MDM). Although they need to work together, data governance differs in its focus. While data management includes all the necessary functions to collect, control, change, protect, and deliver data, governing data ensures quality and reliability.

Data stewardship includes the implementation of data accuracy and reliability procedures determined by data governance. Data stewardship manages and oversees the techniques and tools used to handle, store, and protect data according to rules. Master data management (MDM), on the other hand, is an approach that emerged in line with the idea of a single source for enterprise data. MDM has to work with data governance to get the best results. To understand this approach, one must first understand ‘master data’. Master data is the core data required for all business functions, and it is indispensable for companies for their business transactions, such as purchases, investments and invoices.

DATA GOVERNANCE FRAMEWORK AND PROCESSES

The framework is a set of rules, processes, and policies established as part of a governance program. The framework also explains the program’s mission statement, goals, and KPIs. It also determines who will decide the program’s various components and be responsible for them. The governance framework of an organization should be written down and circulated internally to show how the program will run so that everyone understands right away.

The task falls heavily on software for the technology side of the term. Data governance software streamlines the governance program by automating the various aspects of managing a governance program. Tools are not essential framework components, but they simplify the program, workflow management, collaboration, governance policies development, process documentation, data catalogs, etc. Companies can utilize these tools with data quality and management tools.

轉貼自Source： dataconomy.com

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