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.
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-
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.