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  • Vishal Vikram

Unleashing the potential of Data Governance

data governance 5 steps

We all know that organizations are making huge investments in Artificial

Intelligence and Machine learning (AI/ML). While that is being done data-driven

enterprises ought to know that data is an asset as bad data would drive bad

decisions and models. You need some form of Data Governance to drive effective

business insights and innovation. Organizations today face several challenges

related to data quality and poor data management. Fraud and security breaches

are one of their topmost concerns and this is where the data needs to be

managed and governed efficiently and Data governance comes into play.

An organization meticulously takes care of its inventory, suppliers, finances, and

employees. And that is the same way that enterprise data needs to be treated.

What is Data Governance?

Data Governance is a set of different rules, policies, processes, and standards

that manage the availability, security, and quality of data within an enterprise

system. Resolving data inconsistencies would be a task if not for data

governance. For instance, if a customer’s address is different in person,

inventory, and sales systems then it could mess up the data integration efforts.

This will not only cause issues in data integrity but will also question the

correctness of Business Intelligence (BI).

It is said that there has never been an executive who has not received two

reports with the same data but different numbers. Utilizing the data is easy if the

data is correct and of great quality. For data to benefit the organization, data

governance ensures the management of data in the correct way using quality

material. You can ethically monetize the data of your organization by utilizing the

capabilities of Data Governance.

Data Governance and Data Management

The accounts of an organization are governed by certain principles and policies

that help in auditing and helps in effectively managing the financial assets of a

company. Similar to what these principles and policies achieve for financial

assets Data governance does for Data, Information or content assets.

Now, data management is the data supply chain for a company. Data

Governance and Data Management go hand in hand and should not exist

without each other. Data management is the actual process or business function

to develop and execute the plans and policies that enhance the value of data and


To relate these two, we have the concept of governance ‘V’. The left side of the V

represents governance – providing rules and policies to ensure the correct

management of data and content life cycle, and the right represent the ‘hands

on’ data management. The V also helps understand the separation of duties and

responsibilities for both DG and DM. The DG area develops the rules, policies and

procedures and the Information managers adhere to or implement those rules.

the convergence of ‘V’ are the activities that maintain the data life cycle for  the organization

At the convergence of ‘V’ are the activities that maintain the data life cycle for the organization.

Roles and Responsibilities in DG

As mentioned earlier Data Governance requires distinct delegation of roles and

responsibilities. This is a key factor for Data Governance to survive and flourish.

This includes:

  • Data Stewards – Manage and maintain the data assets, and data quality while implementing the data policies.

  • Data Owners – Responsible for the governance and stewardship of specific data domains and sets

  • Data Governance Council – Executive body that sets the data governance policies, processes, and strategies.

  • Data Custodians- Execute and impose data security measures and access controls.

Development and Deployment of DG

Once data governance is considered in an organization, it means the problem

arising with data due to lack of governance is being acknowledged. Data

Governance is an essential element of comprehensive Enterprise Information

management (EIM). When EIM solutions like Business Intelligence (BI) or Master

Data Management (MDM) are implemented then DG is considered. MDM and DG

are always implemented together for the expansion of EIM.

The delivery framework for Data governance has five key areas of work. Each

phase has a set of activities that help enhance the DG Program. Also, it is

represented as a cycle below as it is usually iterative.


For developing and deploying a data governance framework that is robust the following activities are involved:

Engagement: Clear vision of the necessity and scope of the DG initiative.

Aligning it with the organizations strategic priorities and engaging all stake

holders to support DG

Strategy: A set of requirements built to achieve organization goals and initiatives.

Architecture & Design: Design and description of new enterprise capabilities and

operating models that are embraced by stakeholders

Implementation: Plan to deploy and invest in data governance tools and

technology. Ensure that data governance is made operational.

Operation & Changes: Operational and embedded set of BAU capabilities that

enhance any activity using data. Monitor DG activities and measure the KPIs to

assess effectiveness of the implemented framework

Use Cases of DG

There is wide usage of Data governance across industries. This includes:

Regulatory compliance assurance: A data governance framework is implemented

to comply with regulations such as GDPR, CCPA, and HIPAA.

Data Quality Improvement: Data governance processes help improve the

reliability, accuracy, and consistency of data.

Strengthen decision-making: Leveraging data governance to provide

stakeholders with access to high-quality, trusted data for informed decision-


DG Vendors and Tools

Numerous tools are available in the market to support Data Governance, listing a


Collibra: Data governance workflows and processes can be operationalized to

deliver great quality and trusted data across your enterprise

Informatica CDGC: Using Cloud Data Governance and Catalog you can discover,

understand, trust, and access your data to improve decision-making and govern


IBM InfoSphere Information Governance Catalog: A web-based tool that helps

deliver trusted and meaningful information through a governed data catalog

The first change an organization needs to bring for data monetization success is

to get its organization data literate. Data management should be as much a part

of an organization as budgets and risk. Data governance and management are

both market-driven and to achieve maximum benefit you need to have these

capabilities placed effectively.

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