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, also known as a data management framework, is a set of rules, guidelines, and processes that help organizations manage their data. It's important to have a well-defined framework for data governance initiatives.

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).

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. 

Please refer to the following for more information:

• Wikipedia: Regulation of Artificial Intelligence

• Wikipedia: Data Governance

• Wikipedia: Data Governance Framework

- AI Governance

AI governance is the ability to direct, manage, and monitor an organization's AI activities. It includes processes that: 

• Trace and document the origin of data, models, and associated metadata (data about data)
• Have pipelines for audits
• Bring accountability and traceability to machine learning models

The main focus areas of AI governance are: 

• AI as it relates to justice
• Data quality
• Autonomy

Some ethical frameworks for AI include: 

• Non-maleficence
• Responsibility or accountability
• Transparency and explainability
• Justice and fairness
• Respect for various human rights, such as privacy and security

- Data Privacy and Regulations

Data privacy is a crucial aspect of data science. Data privacy is the ability to control how sensitive data is collected, analyzed, and stored. It's also a branch of data management that involves handling personal data in compliance with data protection laws and regulations. 

Data protection laws are regulations that ensure data is used fairly and properly. The United States has many data protection laws enacted at both the state and federal levels. Data privacy laws are present in almost all major countries around the world.

Virtually every country has enacted some sort of data privacy law to regulate how information is collected, how data subjects are informed, and what control a data subject has over their information once it is transferred. 

Failure to follow applicable data privacy laws may lead to fines, lawsuits, and even prohibiting a site's use in certain jurisdictions. Navigating these laws and regulations can be daunting, but all website operators should be familiar with data privacy laws that affect their users.

- Data Governance Frameworks

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.

A data governance framework creates a single set of rules and processes for collecting, storing, and using data. It can help with: 

• Streamlining and scaling core data governance
• Maintaining policy and regulatory compliance
• Ensuring data quality, accessibility, and security
• Enhancing decision-making

The four pillars of data governance are:

• Data quality
• Data stewardship
• Data protection and compliance
• Data management

Some key data governance principles include:

• Accountability
• Standardized rules and regulations
• Data stewardship
• Data quality standards
• Transparency

Data quality is the process of ensuring that data is accurate, complete, and consistent. It includes: 

• Processes for data validation
• Data cleansing
• Data matching
• Data quality metrics
• Data quality reporting

Data security and privacy is another aspect of data governance. It ensures that data is protected from unauthorized access, disclosure, and misuse.

[Arizona, U.S.A. - Civil Engineering Discoveries]

- 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.

- Cloud Data Governance

Enterprise-level businesses are increasingly adopting cloud-based environments, which means cloud data governance is more important than ever. If you want to ensure that your cloud data is stored, processed, and accessed in a secure and compliant manner, it's time to consider your cloud data governance strategy. 

Cloud data governance is a concept that organizations need to be familiar with if they have data in the cloud or plan to migrate their data to the cloud.

Cloud governance is a set of processes that ensure data stored in cloud environments is secure, accurate, and compliant with all relevant data regulations and policies. It also helps organizations identify and classify sensitive data, define usage, and manage access to data.

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.

[More to come ...]