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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


information.


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.


 Data-governance-has-5-key-areas-of-work

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-


making.


DG Vendors and Tools


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


few:


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


analytics


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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