Big Data – Blog by YY
Data Quality, the term being mentioned last week, has educed the following question —
whether there is any criterion, as standards, that force factors in Data Quality to follow.
That is, Data Governance, the overall management of the availability, usability, and security of data used in enterprises.
Data governance makes enterprises acquire benefits from afterwards processing due to the assurance of both consistency and trustworthy on data.
However, developing a successful data governance strategy is very complicated and requires careful planning, right people, appropriate tools, and, of course, technologies that corresponding to the internal needs within an enterprise.
The following covers fundamentals key factors regarding the development of data governance strategy.
A. Data Stewardship –
fulfill important tactical functions by supporting enterprise data governance initiatives in different ways. The major objective is to assure data quality in terms of completeness, timeliness, validity, integrity, consistency, and accuracy.
B. Data Quality –
the processes behind most data governance activities. Besides, accuracy, completeness, and consistency among data sources are the crucial factors of successful initiatives.
C. Master Data Management –
a discipline that establishes a main reference to ensure the use in consistency of data across organizations. Master Data Management is a comprehensive method of enabling an enterprise to link
Data governance is a crucial component of business process management, financial compliance, business intelligence applications, data warehouses, data lakes and fields related to business units(BUs) in enterprises. Various high-profile data breaches have made data security as more central part in data governance. How to perform solid and secure data governance within projects require more comprehensive thinkings before projects getting started.