Data Governance and Frameworks

- Overview

Data is one of a company's most valuable assets. Data is critical to the growth and continued success of companies, especially data-driven ones. In essence, data is an evolving legacy that companies can use to understand where they started and how they should move forward and improve.

A data governance framework is an indispensable compass for the digital age. It is a set of guidelines, protocols, processes, and rules that enable an organization to effectively manage its data. Establishing a well-defined framework is critical to a data governance program.

However, how well a company manages asset quality, governance and ownership will largely determine the company's overall success. Effective data governance requires engagement and accountability across the enterprise, from data stewards to executives. To ensure a successful implementation, one should understand how data governance works.

- Data Governance Program and Its Structure

Data governance is the process of managing the quality, availability, usability, integrity, and security of data in enterprise systems, based on internal data standards and policies that also control data usage. Effective data governance ensures that data is consistent, trusted and free from misuse. This becomes increasingly important as organizations face new data privacy regulations and increasingly rely on data analytics to help optimize operations and drive business decisions. 

A data governance framework is a defined structure used to guide the implementation of data governance in an organization. It is the foundation of a data governance program. It should clearly show how to ensure the quality, integrity, security, discoverability, accessibility and availability of data assets.

A well-designed data governance program typically includes a governance team, a steering committee that acts as the governing body, and a group of data stewards. Together, they develop standards and policies for managing data, as well as implementation and enforcement procedures primarily performed by data stewards. Ideally, executives and other representatives from the organization's business operations are involved in addition to the IT and data management teams.

Generally, Data Governance Coordinators and Data Stewards work as individuals at the "heart" of the Data Governance process. Several important peripheral roles may initiate, support, inform, or draw inspiration from the process. 

Several groups should also work to move the program forward, identifying data issues and working together to develop responses. The core group includes the Data Governance Council and the Data Stewards Working Group. Two peripheral groups, the Data Policy Committee and the Data Request Review Committee, also play an important role.

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- Cloud Data Governance

Cloud Data Governance manages the accessibility, integrity, usage, and security of cloud computing systems to meet important business objectives. In a multi-cloud or hybrid cloud computing setup, where data is stored in different locations and data governance rules differ between databases, cloud data governance presents new complexities.

In Cloud Data Governance, you should meet the following criteria:

• Improve data privacy and security
• Update data analytics for improved operations and better decision-making
• Sensitive data access is monitored and regulated
• Obtain and maintain data privacy and security agreements on a regular basis
• Avoid cybersecurity risks and data breaches
• Modern tools that allow data engineers and compliance teams to automate data governance, data access rules, and privacy protection through web application interfaces can help data teams manage the complexities of cloud data governance.

 - Data Governance Frameworks

Creating a data governance framework is essential for organizations that want to be truly data-driven. The data governance framework provides the basic structure required for the core elements of data governance (data privacy and data security).

A data governance framework has four pillars that enable organizations to get the most out of their data.

• Identify different use cases
• Quantitative value
• Improve data capabilities
• Develop a scalable delivery model

[More to come ...]