This will be the introduction of additional posts to appear. In this article we will look at the problems that have led to the rise of Data Governance in various areas within Data Governance that has led to its growth and rise in the current future.
Did you know what led to the creation and popularity of Data Governance?
First and foremost There is no single source of truth. What happened to organizations at the beginning of the last decade was that the values of the same data elements in different systems were different. This has been due to a number of reasons such as data updates not flowing properly, out of sync systems, etc. This is called multiple versions of truth. There was therefore a need to mark specific systems that are authorized to provide specific business data where there are multiple systems with similar information.
This brings us to the second challenge.
Although there were well-defined applications or system owners, there were no data owners. So if there was a problem, no one knew who should fix it or no one would take responsibility for fixing it, which often leads to chaos and even financial loss sometimes. Third, there was no defined purpose or value of data sets or data features in the organization. This often prevented critical decisions from being made.
There was therefore a need to explain the meaning or context of the data. And in the end there were no established documents for easy access to information and understanding of the data. This was there. This will ensure that there is less dependence on people to understand the data.
In simple terms, Data Governance is the process of establishing accountability and authority over all aspects of data and environments. This is a sector that has seen rapid acceptance by large corporations over the past decade at an unprecedented rate because organizations have come to realize the importance of not storing bad data. We will now be discussing different areas within Data Governance. Much of this may seem interdependent and intertwined, but it is well planned. Also, some organizations prefer to categorize Master Data Management or MDM under Data Governance, but we will categorize it under data storage concepts for effective learning information.
Data Stewardship is the accountability we just talked about. Data managers are basically the owners of data. Traditionally, ownership was at the application level, business level or product level, but data is an entity that combines all of these factors. It is therefore important to establish data ownership and lead to a data management system.
Next, the data policy is a set of guidelines for managing objects in and around the data effectively. A data policy is usually developed by a Data Management/Governance Council consisting of data-related managers and key employees in an organization with adequate representation from all business lines. Next, data standards ensure a consistent approach to capturing, recording and storing data across a company.
Metadata management so that we can access data using metadata.it related to recording, recording and storing metadata. Next, Data Lineage tracks the movement of the data from its source and the conversion of the recording.
Data Cataloging involves the creation of business glossary and most importantly connects technical metadata and business metadata.
Data quality is basically a measure of data usage and is very important to ensure that data can achieve its purpose. And finally, data security interacts with security-related features such as access control. One key feature that Data Security faces these days is the identification and flagging of personally identifiable information. This is important for compliance purpouse