Data Governance and Data Management (DGDM)
Data Governance and Data Management (DGDM) is the overall management of the availability, usability, integrity, and security of the data used in an organization.It is the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.(DAMA International). It includes the processes, roles, standards, and metrics that ensure that data is correctly used and maintained. Data governance helps organizations make sure that their data is accurate, consistent, and properly protected, and that it is used in a way that is consistent with their policies and goals. This involves establishing policies and procedures for the creation, maintenance, use, and disposal of data, as well as defining roles and responsibilities for those who work with data within the organization. DGDM is related to idea of data stewardship (managing and protecting the data) and data quality.
Data Stewardship involves ensuring that the data is accurate, complete, and up-to-date, and that it is used in a way that is consistent with the policies and goals of the organization. Data stewards are responsible for ensuring that the data is properly managed and protected throughout its lifecycle, from creation to disposal.
Key components of Data Governance are :
- Policies and procedures: Data governance involves establishing policies and procedures for the creation, maintenance, use, and disposal of data within an organization. These policies and procedures help to ensure that data is used consistently and in a way that is consistent with the policies and goals of the organization.
- Roles and responsibilities: Data governance involves defining roles and responsibilities for those who work with data within the organization. This includes data owners, who are responsible for the quality and integrity of the data, and data stewards, who are responsible for managing and protecting the data.
- Standards: Data governance involves establishing standards for data quality, accuracy, and completeness, as well as for the storage, access, and use of data. These standards help to ensure that data is consistently high quality and is used in a way that is consistent with the policies and goals of the organization.
- Metrics: Data governance involves establishing metrics for measuring the effectiveness of the data governance program and for tracking progress over time. These metrics may include measures of data quality, data usage, data security, and other key indicators.
- Governance structure: Data governance involves establishing a governance structure to oversee the implementation of the data governance program and to ensure that it is aligned with the overall governance and risk management strategy of the organization. This structure may include a steering committee, working groups, and other organizational units.
- Data Quality involves ensuring that the data is accurate, complete, and up-to-date, and that it is used in a way that is consistent with the policies and goals of the organization. There are several factors that contribute to data quality, including:
- Accuracy: Data is accurate when it is free from errors and represents the true value or state of the data.
- Completeness: Data is complete when it includes all of the necessary elements and is free from missing or incomplete values.
- Timeliness: Data is timely when it is available when it is needed and is not outdated.
- Consistency: Data is consistent when it is free from conflicting values or interpretations, and is used in a way that is consistent with the policies and goals of the organization.
Data Privacy
- use Zero trust architecture.
- Storing Data on cloud how to maintain the privacy encrypt the data and keep the keys secured.
Data Security
Governance and Compliance
Master Data Management (MDM) System
Address the needs to have clean data round a key operational data entity such as customer and product. These systems become a single point of reference for other systems (e.g. order system). There are a number of architectural approaches and patterns to provision a quality single version of the truth with respect to key business entities:
https://s-bennett.com/2013/05/03/building-a-master-data-management-mdm-system/
https://s-bennett.com/2013/05/31/mdm-patterns/
- Virtual (aka Registry)
- Centralized
- Consolidated
- Hybrid (aka Co-Existence)