5 Key Steps to Develop a Data Governance Strategy

Resources 8 min read
In this article
Oct 10, 2022
Author Varsha

According to Grand View Research, the global multi-cloud management market is expected to grow at an annual growth rate of 27.5% from 2022 to 2030. 

With a 92% cloud adoption rate across industries, it’s clear that the future of enterprise analytics is in the cloud. Organizations seek to create, consume, and control their data, and information assets to gain valuable insights from it. Data governance requires a system and a strategy. It’s crucial to have a solid framework of the people, processes, and technologies to maintain compliance with regulations, and mitigate risks.  

Building an effective data governance strategy applies:

  • Defining a clear set of principles.
  • Establishing accountability for decisions.
  • Selecting a correct operating model of governance.
  • Future-proofing their governance strategies and safeguarding global information to attain exceptional flexibility and control. 

We have listed a few frameworks for future-proofing data governance strategies for businesses.

5 Key Steps For Future-proofing Data Governance

A good governance strategy must mitigate the risk related to poor data quality, minimize the compliance risk, encourage data utilization, and demonstrate success through short-term goals. 

  5 Frameworks For Future-proofing Data Governance

Here are 5 frameworks that companies must undertake.

1. Eliminate Redundant Data: Redundancy is an apparent reason for inefficient data quality and poor governance. The best way to govern every bit of data is to ensure it is trustworthy and consistent! The identical copies of transactional data, records, billing, etc., could exist in multiple sources like data lakes, data warehouses, on-premise data marts, and others. These copies increase the chances of data getting misused and make it  prone to cyber crimes.

Data virtualization is an approach that prevents the creation of duplicate data, analytical data marts, or additional data repositories allowing virtual data creation on top of existing data repositories. This ensures maintenance of a single authoritative source. 

2. Centralize Data Access Control: Centralized access control allows access to all applications, websites and other computing systems from a single profile anytime, anywhere. It allows the user to login using the same credentials from any location. Formulating a logical data fabric with data virtualization would centralize and simplify the data access control. Data fabric architecture facilitates end-to-end integration of various data pipelines and cloud environments. It helps unify data, embeds data governance, strengthens security, and provides more data access to users. 

3. Decoupling Data Security from Data Repository: Modern data security policies are complex and need real-time controls. For this, organizations need security measures that are technology agnostic. Decoupling the security measures from repositories and defining them across all BI tools and repositories would map the policies across all data sources.

4. Data Separation: When an organization uses a multi-tenant environment, like  multi-cloud, the best way to secure data governance is through data separation. Multi-cloud data can be separated in the application layer, where it resides easily and can be accessed anytime when required. This data rests in an independent layer and can be accessed through any cloud resource. 

A user can access information without breaching the privacy of other tenants. Hence, organizations need to map their data streams and establish buffers to minimize damage and avoid compromise. 

  • Governing Unstructured Data With AI: Storing unstructured data in the cloud has made it more vulnerable. Most of this data is redundant, obsolete, and trivial. Organizations should consider these factors before choosing an AI solution:
  • A solution has a toolbox full of AI algorithms and can map the solution to its  data corpus.
  • A solution that uses AI deep learning visuals to track visuals like personally identifiable information (PII) in any location within documents.
  • A solution that can scale and efficiently run all of its functionality on a large scale.

With AI innovation, unstructured data can be easily organized and included in data governance programs. Innovations in artificial intelligence (AI) have led to the powerful data management of unstructured data, which SMEs can accomplish. 

The practices mentioned above are a great way to start data governance in a company. Implementing easy and simple steps of setting accountability will do great work with agreeable objectives.


Data Governance is the Key Element of a Data Product

Data must be treated like a product to ensure accessibility, compliance, and reliability. Only the best technologies and tactics will not be enough.

Businesses find it difficult to manage data governance. They must first list all parameters and determine who is in charge of them. Data assets must therefore be decentralized in order to disperse data ownership. Organizations must design ownership & accountability, clear, quantifiable measurements, and data quality statistics in order to reduce the risks associated with data governance.

Techment provides multiple solutions around data touching each facet of a secure cloud perimeter. To know more about our cloud data solutions, connect with us.

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