Powering Growth with Data Governance Prescience Decision Solutions November 24, 2022

Powering Growth with Data Governance

Data is the oxygen in today’s information age. Data that is well-managed and well-governed helps organizations to derive deep and actionable insights to outpace their competitors. Not just that, business initiatives that are built around insights and analytics always try to leverage data to improve their business outcomes.

However, riding on the data bandwagon requires a good measure of caution and foresight. It is a fact that many of these initiatives start off with extremely promising proof of concepts or pilots. However, as time progresses, only a few achieve sustainable business outcomes at the enterprise level in the long run.

This is often due to the lack in consistency and quality of the data getting streamed into the systems/models for insights. With no clear ownership of data, businesses start suspecting the insights delivered by these systems, resulting in a slow death of the data initiative. With data management forming 60–70% of the effort around data analytics, these efforts will not yield the desired results if the data is not governed holistically and its lineage defined, quality measured and tracked. Data governance is extremely critical to the effective management of data across the enterprise. Data governance plays the key role within the enterprise, setting the parameters for data management and usage, creating processes for resolving data issues and enabling business users to make decisions based on data that can be trusted and other information assets that are managed well.


Data Governance is the core component of data management, tying together many other disciplines such as data quality, reference and master data management, data security, etc. Some of the key components of data governance that require upfront focus are:

1. Business glossary
Establishing a common vocabulary for an organization. This vocabulary furnishes clear meaning and business context and can be linked to the underlying technical metadata to provide a direct association between business terms and objects.

2. Discovery
Extraction of relevant data, connecting to authoritative sources and the process of curating critical data elements.

3. Data ownership/stewardship
Identifying a legal ownership/stewardship of enterprise-wide data. A specific organization or the data owner/steward has the permission to create, edit, modify, share and restrict access to the data.

4. Data quality assessment and cleansing
Establishing a process of scientifically and statistically evaluating data in order to determine whether they meet the quality required for projects or business processes. In addition, publishing DQ assessment dashboard with DQ score while also suggesting the cleansing scripts to correct the errors.

5. Master and reference data management
A method to enable an enterprise to link all critical data to a common point of reference, called master, leading to fewer errors and less redundancy in business processes. Reference data pertains to management of data external to enterprise but is still key to internal processes. Examples of reference data include address, currency conversion rates, and more.

Data management must make sense in the organizational context and an enterprise needs to adapt best practices to goals of the organization.


The maturity of an organization in implementing a data governance framework is classified across five different levels of increasing maturity, as define by the CMMI Institute.

Measuring an organization’s maturity across this framework will help in benchmarking against industry best practices and provide a roadmap to leveraging value from data. An enterprise needs to adopt this model to practices within the organization and goals of the organization.


An enterprise can move up the data maturity framework and get better control of data assets through a standardized and structured approach to implementing data governance practices.


Implementing data governance steps is not enough if it is not supported by the right sponsorship and involvement across business and IT functions across the organization. Given below is an example of an organization structure that is designed to provide the right ownership structure for the data assets and governance processes.


With a robust data quality process and data governance framework in place, data management and quality will improve over time. Some of the multiple benefits organizations can reap by putting in place robust data governance capabilities are:

  • Better insights from data analytics
  • Accountability for data
  • Reduction in rework and costs
  • Ability to track lineage and hence better business and IT agility
  • Better compliance and reduced costs for compliance reporting