Data Governance

While Data management is part of the overall management of data. Data governance in short is just documentation, practices, policies, and procedures in place. Data Governance is a core component of an overall data management strategy. With Data Governance, we are assuring data consistency and trustworthy by having the process of managing the availability, usability, integrity, quality, and security of the data based on internal standards and policies throughout the data lifecycle. Data governance in comprehensive way comprises the principles, practices, and tools to manage data assets throughout their lifecycle by aligning with organization’s business strategy.

Data Governance Framework

The main components of a data governance framework are people, process, and technology.

PEOPLE: Follow roles can be considered: Steering Committee (Chief Data Officer can be also the head of IT) and executives from each business unit. They can set policies, standards, and goals; Governance Team: Led by a data governance manager, this team implements and maintains the systems and tools; Data stewards: This team manages the datasets and is responsible for the enforcement of rules and day-to-day needs of the business.

PROCESS: Formal processes to ensure consistent execution and enforcement of the usage policies and data standards set by the steering committee.

TECHNOLOGY: Tools and techniques used to efficiently maintain and manage the security, integrity, lineage, usability, and availability of data.

Benefits of data governance

Implementation of data governance approach can leverage data assets. This is essential to gain competitive edge ensuing data privacy practices while maintaining its availability.

Decision-making

As Data Governance are integrated in all stages of Data Management, it can promote data quality by ensuring data accuracy and consistency leading to faster data based decision making.

Enhanced collaboration, security, and privacy

Data Governance help build better data culture maximizing the use of data. It can streamline the use of data in organization across teams, business unit and partners. If well implemented can mitigate security and privacy risks while keeping transparency.

Regulations and standards

Effective data governance results in better compliance with regulatory requirements, this protects the organization’s reputation, avoids potential financial and legal consequences, and increases stakeholder trust.

Standard procedures in place help respond the follow questions:

·?????? Who has ownership of the data?

·?????? Who can access what data?

·?????? Which Data Analytics can be available for internal and/or external use?

·?????? What security measures are in place to protect data and privacy?

·?????? How much of our data is compliant with new regulations?

·?????? Which data sources are approved to use?

·?????? Risk mitigation on data assets

Data governance implementation

Either we need to improve existing documentation and procedures or we need to create data governance strategy from scratch, we need starting points:

·?????? Identify data assets and existing informal governance processes.

·?????? Increase the data literacy and skills of end users.

·?????? Decide how to measure the success of the governance program.

Elements of Data Governance

Data discovery is the act of gather amounts of data from various sources.

Data mapping and classification, document the dataset and flows of data through organization. Dataset must be classified based on factors like, whether they contain personal information or other sensitive data.

Business glossary contains definitions of business terms and concepts used in an organization.

Data catalog is collection of metadata that can help users to find the data that they need. In this system, we create an indexed inventory of available data assets.

Data lineage is a powerful tool that helps organizations ensure data quality and trustworthiness by capture relevant metadata and events throughout data’s lifecycle understanding, recording, and visualizing data as it flows from data sources to consumption providing an end-to-end view of how data flows across an organization’s data estate.

Data sharing and collaboration is the process of making the same data resources available to multiple applications, users, or organizations.

Data stewardship is responsible for a portion of an organization's data, help implement and enforce data governance policies. Data stewards collaborate with data stakeholders, also they help identify data requirements and issues.

Data quality is based on factors like accuracy, completeness, consistency, reliability, relevance, and timeliness.

Data security is process of protect information throughout its entire life cycle, preventing it from corruption, theft, or unauthorized access. The data must be defined and labeled based on level of risk, creating secure access points and keep balance between data access and security.

Data transparency, every piece of the process and all of procedures should work within a model of transparency.

Data auditing is a critical aspect of data governance and security.? By understanding who has access to what data and tracking it, organization can proactively identify security issues.

Best practices when managing data governance initiatives

Gartner analyst Saul Judah has recommended an adaptive data governance approach that applies different governance policies and has listed these seven foundations:

·?????? A focus on business value and organizational outcomes.

·?????? Internal agreement on data accountability and decision rights.

·?????? A trust-based governance model that relies on data lineage and curation.

·?????? Transparent decision-making that hews to a set of ethical principles.

·?????? Risk management and data security included as core governance components.

·?????? Ongoing education and training, with mechanisms to monitor their effectiveness.

·?????? A collaborative culture and governance process that encourages broad participation.

Data governance challenges

Different views of parts involved can make the implementation of Data Governance difficult. Not understand the importance of data governance, lack the necessary skills to implement it effectively, poor data quality, employee resistance to changes of procedures can make organizations fail to overcome data governance challenges.

Strategic Practices

High-level plan that defines and outlines the goals and direction for data governance within an organization. In this level, clear objectives are set tailored to the organization's vision and mission.

Tactical Practices

In this level, team identifies and resolves data definitions, production, and usage issues across business. They are directly linked to data creations, transformations, storage, updates, and deletions, rules and standards and train their teammates and managers in data-related matters.

Operational Practices

This is part of day-by-day activities that include create, use, store, define, archive, manage, or delete data to according to Data Governance processes, standards, and procedures.

Support Practices

Those at this level help run Data Governance program along with assistance from partners.

Different Types of Data Governance Programs

Command-and-Control: A top-down approach that sets the Data Governance rules and assigns employees to follow them.

Formalized: Training programs constructed as part of an organization’s data literacy initiative to encourage Data Governance practices.

Non-Invasive: A formalization of existing roles.

Adaptive: A set of Data Governance principles and definitions that can be applied flexibly and made part of business operations using a combination of styles

Data Governance Advantages

·?????? Make ease to understand Data Architecture and data models

·?????? Ensuring regulatory compliance

·?????? Improving Data Quality for decision-making and operations

·?????? Improving data security and privacy while making data available

·?????? Mapping of data and flows

·?????? Metadata in place that help users find what they need

·?????? Building data trust and integrity with employees, customers, and vendors

·?????? Integrated data for sharing across business operations

·?????? Inventor of all data assets

·?????? Critical data terminology definition through a business glossary or data dictionary

·?????? Promotion of awareness and usability of data across the organization

·?????? Improved data accuracy, completeness, and consistency

·?????? Prevention of data misuse

·?????? Agreement on common data definitions

·?????? Removal of data silos between departments and systems

·?????? Increased trust in data for analytics and decision making

·?????? Easier to locate data making all data more available

·?????? Lower data management costs.

·?????? More-informed business decisions based on better data.