Understanding The Role of Data Governance in The Age of Cloud Computing

Aditya Kumar Pandey
2 min readJul 18, 2021
Photo by Sigmund on Unsplash

As companies continue to rely heavily on cloud, there is no detest from the fact that the marriage between data governance and cloud computing is imminent. Sensing way ahead about the importance of these critical IT infrastructures, these companies have invested heavily in creating important cloud infrastructures that simplify their operations and, in return, help them save substantially.

However, the use of cloud computing is not limited to the storage and retrieval of data. The horizons of cloud computing (coupled with data governance) expands across real-time product or service recommendations, customer 360s, and service recommendations, hence, helping the industry grow by leaps and bounds.

On the other hand, from a data governance point of view, the integration of data connectivity, cataloging, and data quality, comes under a single roof, making it a one-point stop for all data needs.

Today, with this article, we understand the importance of data governance concerning cloud computing.

#1: Metadata Intelligence

The concept of metadata intelligence highlights assembling data from numerous sources and using it under a single domain such as on customer, product, and supply chain transformation. This differs totally from what metadata has been previously used for and could not be ignored at all costs.

#2: Data Cataloging

Next in the series comes data cataloging. In this process, the team is assigned to creating and identifying spots in the cloud where data could be categorized and stored. Again, this part of data governance is infiltrated by advanced software solutions where the first task is cataloging.

The data is then classified and assigned a spot in the cloud, thus, helping in easier retrievals and understanding of data in future. The following step also leads to better use cases of classified data.

#3: Data Modeling

The most important aspect of data engineering is data modeling. Unfortunately, data modeling is particularly time-consuming and depends upon traditional methods and software solutions that do not complement today’s requirements.

Data modeling enables users to create scientific models driven by numbers. The choice of attributes could not be ignored as it allows engineers work on the desired set of data.

#4: Data Stewardship

Data stewardship is the umbrella term for all the above factors, i.e., metadata intelligence, data modeling, and data cataloging. Data stewardship leads to better governance for a layperson and hence is essential.

Furthermore, heterogeneous data could be stored across enterprises with systems ranging from cloud, on-premise applications, and edge.

Concluding Remarks

While medium-sized businesses or individuals use clouds with limitations, larger enterprises have extensively used these solutions. The real-time access, no cost for maintenance, and distributed networks worked miraculously for the multinational companies and hence could not be ignored at all costs.