Data governance defines the process of using and maintaining data. This includes stating who is in authority over decisions related to information, who is accountable for the data assets, and how the data assets will be used. The process of data governance includes human resources, technologies and a clear structure and framework.
Data governance ensures that high quality data is maintained throughout the data lifecycle and that adequate data controls are maintained. Data governance has 5 main areas:
· Data availability
· Data Quality
· Data consistency
· Data security
· Data integrity
Through these five areas data governance allows for transformation of the data into meaningful information such that the employees have higher confidence in decision making and the income generation potential of data is maximised.
Data governance is critical to DataOps for ensuring data analytics produce best possible quality results and at the fastest speed. Data governance allows for a secure data catalogue and maintenance of data, allowing every user to edit, contribute and share data and at the same time protecting the data against any misuse. It also allows for tracing data lineage, managing data lakes and putting data into a well-planned categorisation for efficient data usage.
Data governance is said to be the backbone of applied artificial intelligence, which is the intersection of artificial intelligence and machine learning. Applied artificial intelligence is here to stay and will completely transform the way of doing business. Given that the quality of results produced by artificial intelligence is dependent on the quality of data given to it, data governance is surely critical for reaping the benefits of artificial intelligence.
To take full advantage of what data governance can offer to a business, it is essential to have a data governance framework in place. There can be many confusions regarding how to initiate the process of establishing a data governance framework.
Factors like investment amount or the change in culture or systems can act as a deterring factor. Here it is important to understand the wisdom and value of data governance. Transforming the business into being truly data driven is a continuous journey. It is important to review the data governance framework time and again to identify required modifications in the strategy, tools and technologies used.
Through a data governance framework the data is profiled according to its relevance to the business goals. In the initial stages of setting a data governance framework it is important to define the rules and procedures which will govern the data across the organisation. After the rules are decided the process of data catalogue begins.
Data governance framework impacts every aspect of data usage from the data models to the technologies employed in data management. It also impacts the data analytic process to help the business make efficient and speedy decisions. The goal of any data governance framework is to develop methods and processes to manage, integrate, secure, and store data to allow businesses to use data to its best potential. Through implementation of a data governance framework, the treatment of data is more consistent and purpose driven.