Like cells are the basic unit of life, data simply refers to the primary format of information. Often in texts and numbers, these characters are factual and used as a basis for reasoning, discussion, or calculation. It’s safe to ask how important data is to require governance?
In answering this question and considering today’s world of technological boom, it might be helpful to conceptualize data as tiny representations of human beings and other valuables. It has become essential to ensure best practices in data management, which gives rise to the need for heightened discussions about data governance and data governance systems.
Data Governance Explained
Public institutions worldwide are investing in databases, while large companies are building the capacity of senior management to prioritize data assets in all lines of business. Data governance has become a priority for almost all organized bodies. It’s essential for universal definitions and a more streamlined approach in conceptualizing issues around data in this age of information technology.
Generally, a data governance system is a complex combination of efforts (roles, responsibilities, and responsibilities) for efficient data management. However, this system may have different implications for specific groups of people and, in effect, different indicators in measuring data governance processes.
Generally, for a specific group of people, data governance systems seek to answer who a data owner can be, what include in their data systems, the means of data access, and other issues related to data integrity, data usability, etc. To preserve the accepted data standards of any data governance system, stakeholders (which range from an executive sponsor to a beneficiary) might often have to subscribe to specific data governance policies and data models.
For corporate bodies, data governance systems are of high business value and may take shape in different systems based on the planned business benefits from the system. From harnessing insights for competitive advantage to creating leverages with large amounts of metadata for the goodies associated with economies of scale, corporate bodies may develop custom data governance tools with a well-resourced data governance program.
The Data Governance Framework
While data governance relates to “what’s” and the “why’s,” data governance frameworks focus on the “how’s”. A data governance framework sets out the methods and principles for data governance. A correctly done governance framework empowers data owners to define guidelines for managing the data being collected and encompasses every part of an organization’s data management process, down to individual technologies, databases and data models.
What then is the difference between a data governance system and a data governance framework? For starters, while it’s worth noting that there could be a chicken and egg situation, they both enforce data governance for a particular people. Technically, a data governance framework enables the system, while the system establishes the framework, like a country and its constitution—the country being the latter and the constitution, the former.
Another way to communicate the difference is that, whereas there may only one system for a particular framework, there could be several frameworks for a system depending on how complex the said system might be.
Benefits of Data Governance
Again, the differences in data governance objectives may give rise to benefits for different stakeholders. While corporate bodies may be pouring millions of dollars into data governance for capital benefits, public institutions, on the other hand, would be managing a data governance strategy aimed at policing these large companies and where they can and can’t poke their data governance noses.
Notwithstanding, there are some general benefits of data governance, and they include: data privacy, preventing fraud, identity theft and exploitation, risk management solutions, etc.
