The document discusses key aspects of data governance including governance, data stewardship, data quality, and master data management. It provides definitions and descriptions of these terms. For example, it defines data governance as the overall management of the availability, usability, integrity and security of enterprise data. It also notes that data stewardship, data quality, and master data management are pillars of effective data governance. The document then provides more details on each of these concepts.

1. Governance
Stewardship
Quality
Master Data Management
Analytics
Data

2. Author
• Astute corporate resource with 10+ years of corporate experience with emphasis on database management, programming, software
development, testing, web technologies and product improvement for corporations. Combines expert software and database management
expertise with strong qualifications in Software, Data Engineering & Information Management.
Concurrently, manage all the database functions for the current company. Industry experience in Information Technology. Strong
understanding of the complex challenges in Software Development and problem troubleshooting. An expert on identifying and solving
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3. Agenda
• What is Governance
• What is Data Governance
• Pillars of Data Governance
• Data Stewardship
• Data Quality
• Master Data Management
• Use Cases
• What is Data Analytics
By JBH Syed| BSCS | MSDEIM | MCTS | MCITP | OCA | OCP | OCE | SCJP | ITL V3F | COBIT 5F | PRINCE2

4. What is Governance
• We can define the governance a set of rules , control on something
• Set of actions or manner of governing a state or organization
• Governance is all of the processes of governing, whether undertaken by a
government , a market or a network, over a social system including (family,
tribes , formal or informal organization , a territory or across territories) and
whether through the laws, norms, power or language of an organized
society ( Wikipedia )
By JBH Syed| BSCS | MSDEIM | MCTS | MCITP | OCA | OCP | OCE | SCJP | ITL V3F | COBIT 5F | PRINCE2

5. What is Data Governance
• Data governance (DG) is the overall management of the availability,
usability, integrity and security of data used in an enterprise.A sound data
governance program includes a governing body or council, a defined set of
procedures and a plan to execute those procedures
(https://searchdatamanagement.techtarget.com/definition/data-governance)
By JBH Syed| BSCS | MSDEIM | MCTS | MCITP | OCA | OCP | OCE | SCJP | ITL V3F | COBIT 5F | PRINCE2

6. Data Governance
Data Governance is the management of these functional areas
• Data Quality Management
• Data Architecture Management
• Data Security Management
• Data Development
• Data Operations Management
• Master Data Management
• Meta Data Management
• Document and Content Management
• Data Warehousing and BI Management

7. Basics Pillars of Data Governance
• Data Stewardship
• Data Quality
• Master Data Management
• Use Cases
By JBH Syed| BSCS | MSDEIM | MCTS | MCITP | OCA | OCP | OCE | SCJP | ITL V3F | COBIT 5F | PRINCE2

8. Data Stewardship
• An essential trait of the data steward is to be accountable for various
portions of the data.The major objective of such data governance is to
assure data quality in terms of accuracy, accessibility, consistency,
completeness and updating.
• Teams of data stewards typically are formed to guide actual data
governance implementations.These teams may include database
administrator, business analysts and business personnel familiar with
specific aspects of data within the organization. Data stewards work with
individuals positioned in the overall data lifecyle to help ensure data use
conforms to a company's data governance policies
By JBH Syed| BSCS | MSDEIM | MCTS | MCITP | OCA | OCP | OCE | SCJP | ITL V3F | COBIT 5F | PRINCE2

9. Data Quality
• Data quality is the driving force behind most data governance activities.
Accuracy, completeness and consistency across data sources are the
crucial hallmarks of successful initiatives.
• Data scrubbing, also known as data cleansing, is a common element in
the data quality initiative, as it identifies, correlates and removes
duplicated instances of the same data points. Data scrubbing accounts for
the various ways in which, for example, the same customer or product
may be described. Data editors, data mining tools, data differencing
utilities, data linking tools, as well as version control, workflow and
project management systems are included among software types that
help organizations attain better data quality.

10. Master Data Management
• Data governance touches on nearly every aspect of data management, but one
area of data management very closely associated with data governance
processes is master data management (MDM).This is a discipline that
establishes a master reference to ensure consistent use of data across large
organizations.
• Metadata repositories, which hold data about data, are often used in
establishing cross-group reference data in MDM programs. Product and
customer data is a major emphasis of MDM systems. As with data governance
generally, master data management projects can also encounter controversy
within organizations, as different product groups or lines of business in the
company promote different views on how to best present data
By JBH Syed| BSCS | MSDEIM | MCTS | MCITP | OCA | OCP | OCE | SCJP | ITL V3F | COBIT 5F | PRINCE2

11. Use Cases
• Data governance is a particularly important component of mergers and
acquisitions, business process management, legacy modernization,
financial and regulatory compliance, credit risk management, analytics,
business intelligence applications, data warehouses, and data lakes.
• As data uses expand and new technologies emerge, data governance will
gain wider application. Numerous high-profile data breaches have made
data security a more central part of data governance efforts. Calls for data
privacy have also led to the inclusion of data protection and data privacy
audits as part of data governance programs.The European Union's (EU's)
directive concerning General Data Protection Regulation (GDPR) is an
example of a use case for data governance.
By JBH Syed| BSCS | MSDEIM | MCTS | MCITP | OCA | OCP | OCE | SCJP | ITL V3F | COBIT 5F | PRINCE2

12. Data Analytics
• Analytics is the discovery, interpretation, and communication of
meaningful patterns in data. Especially valuable in areas rich with recorded
information, analytics relies on the simultaneous application of statistics,
computer programming and operations research to quantify performance.
( Wikipedia)
By JBH Syed| BSCS | MSDEIM | MCTS | MCITP | OCA | OCP | OCE | SCJP | ITL V3F | COBIT 5F | PRINCE2