Module 1 Data Governance and Stewardship Core Concepts1.pptx

Data Governance and
Stewardship
Module 1: Data Governance and Stewardship Core Concepts

Module 1: Data Governance
and Stewardship Core
Concepts
Unit 1.1. Data Management and Data Governance
Unit 1.1.1. Data Management and Other Related Functions
Unit 1.1.2. Differences between Data Management and Data Governance
Unit 1.2 Data Governance Business Drivers and Additional Concepts
Unit 1.2.1. Possible Data Governance Business Drivers
Unit 1.2.2. Additional Data Governance Concepts
Module Summary
The first module in this course provides definitions for data governance and data management, the
differences between the two, possible business drivers and other concepts such as a maturity model and
treating data as an asset.

Unit 1.1. Data Management and Data
Governance
• Focus
• In this unit, we define Data Management and Data Governance.
We also look at differences between managing data vs.
governing data.

Unit 1.1.1. Data Management and
Other Related Functions
• Objective 1.1.1
• Define Data Management, Enterprise Information
Management and Data Architecture.

Unit 1.1.1. Data Management and Other
Related Functions - Learning Activity 1
• In understanding the differences between managing data and governing data, we have to look at
• what needs to be governed
• and where does governance occur.
• The DAMA-DMBOK2, p. 19, defines Data Management as the
“development, execution and supervision of plans, policies, programs and practices that deliver, control,
protect, and enhance the value of data and information assets throughout their lifecycle.”
• The DAMA-DMBOK goes on to say that the mission of the Data Management function is to meet and
exceed the information needs of all the stakeholders in the enterprise in terms of information
availability, security, and quality.
• Data Governance is one of ten functions of Data Management to ensure that data is managed.

• The DAMA-DMBOK discusses Data Management in terms of many other names such as Information Management, Enterprise
Information Management or Information Asset Management. However, it considers these names to be synonymous, and uses
Data Management consistently.
• However, clarification is needed to determine whether Data Management is a localized function or an enterprise function. Putting
the word Enterprise in front of Information Management provides the clarity that it is an enterprise-level program (EIM).
EIM is a set of business processes, disciplines and practices used to manage the information created from the organization’s data.
The goal is to provide information as a formal business asset that is
• secure,
• easily accessible,
• meaningful,
• accurate
• and timely.
Data Governance is one of the disciplines in EIM.

• Another function where Data Governance activities may occur is in Data Architecture or in Enterprise Architecture. The DAMA-
DMBOK defines Data Architecture as
“Defining the data needs of the enterprise, and designing the master blueprints to meet those needs. This function includes the
development and maintenance of enterprise data architecture, within the context of all enterprise architecture, and its connection
with the application system solutions and projects that implement enterprise architecture.”
• Data Governance is not called out in this definition, but what this definition discusses is the development of the picture or
roadmap of the information management environment, components and interactions.
• Often, organizations place Data Governance, possibly along with IT Governance, in an Enterprise Architecture group to govern
these roadmaps and expressions, and to make sure these visions are carried out in future systems development efforts.
• In these cases, Enterprise Architecture, which includes Data Architecture, may report to IT, the business or somewhere in-
between.

• Read Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program, 2019 by John
Ladley, Chapter 2, "Introduction," and Chapter 3, pages 15-21.
• Read DAMA-DMBOK, Chapter 2, "Data Management Overview," pages 17 - 25.
• For further information:
• 7 Steps to a Successful Enterprise Information Management Program by Shannon Kempe / September 19, 2013
• How does Data Management relate to EIM?
Enterprise Information Management (EIM) is really about effective Data Management. It is often used as a formal
nomenclature within organizations to clarify a set of specific Data Management practices they are undertaking.
• What would be governed in Data Architecture?
Data architecture consists of models, policies, rules, and standards that govern which data is collected and how it is
stored, arranged, integrated, and put to use in data systems and in organizations.

7 Steps to a Successful Enterprise Information
Management Program
• According to Jennings, Enterprise Information Management (EIM) is
the,
“framework of interdependent disciplines required to turn data into
consistent and accurate information to be fully leveraged across the
organization, by business and technology users, to improve an
organization’s performance.”

Management Program
1. Develop or customize an EIM framework
2. Conduct a baseline current state assessment and cultural discovery
3. Understand direction for EIM within the organization and program
duration by preparing a maturity assessment and roadmap
• There are various sample stages of EIM maturity that businesses can use to
determine their own current state. They are:
 Non-existent
 Developing
 Planned
 Measured
 Enhancing

Management Program
4. Involve key EIM stakeholders in all strategic discussions and align
the program goal with strategic initiatives
5. Build awareness of the EIM programs on all levels – communication
and socialization
6. Gain an understanding of resource needs and prepare for
education, training, and resource acquisition
7. Determine the standards for measuring success of the program
• Business value
• Acceptability and compliance

Some methods for measuring Success:
• Money:
• Does a program help a business save money?
• Is it bringing in additional cash flow?
• Customer satisfaction:
• Has customer satisfaction improved as a result of implementation of a new EIM program?
• Quality:
• Has the quality of a business’s data and metadata improved as a result of EIM?
• Product or service development:
• Is the EIM program bringing in additional business?
• Intellectual capital:
• Has the program increased the knowledge level of personnel?
• Strategic relationships:
• Have any new relationships formed as a result of EIM?
• Employee attraction and retention:
• Has employee turnover changed as a result of the new program?
• Sustainability:
• Can the organization keep the program going over a long-term period?

Unit 1.1.2. Data Management and
Other Related Functions
• Objective 1.1.2
• Define data management and governance differences.

• Data Management is not the same as Data Governance. If we look at
the terms management and governance, we find the following:
• Management comprises planning, organizing, staffing, leading or directing,
and controlling an organization or initiative to accomplish a goal.
• Governance is relating to consistent management, cohesive policies,
guidance, processes and decision-rights for a given area of responsibility.
• The DAMA-DMBOK, V 2 p. 67, defines
“Data governance as the exercise of authority and control (planning,
monitoring, and enforcement) over the management of data assets.
The data governance function guides how all other data management
functions are performed.”

• Data Governance consists of a governing body, which directs the management of
data aspects in an organization.
• It is the governing body that oversees the overall Data Management function of
an organization.
• Data Governance resides at the center of the DMBOK Framework 'Wheel'
because it touches all aspects of Data Management
(see the DAMA-DMBOK Data Management Functional Framework diagram.)
• This area defines the controls, policies, processes, and rules for Data
Management.
• This is similar to what auditors do with the verification of compliance to
standards and defining new controls and standards as required.
• Data Governance sets the right policy and procedures for ensuring that things are
done in a proper way – that data and information are managed properly.

DMBOK 2 Framework
'Wheel’ ‘Evolved Wheel'

• Data Management is all about doing things in the proper way.
• Data Management has the responsibility to implement any policies,
procedures or systems of Data Governance.
• There should be a separation of duties between the people involved
in Data Management and those who perform Data Governance
activities.

• Read Data Governance: How to Design, Deploy, and Sustain an
Effective Data Governance Program, 2019 by John Ladley, Chapter 3,
'Data Management' pages 16-23..
For further information:
• Data Management vs. Data Governance: Improving Organizational
Data Strategy By Michelle Knight on December 12, 2017
• Why should there be a separation of duties between Data
Management and Data Governance?
• How are Data Governance and Data Management set up in your
organization?

• Discuss the Governance V diagram on page 21 of Ladley’s book. Is it
useful to you?
• In terms of EIM, Information Management and Data Governance,
describe the supply chain metaphor?
• In the simplest terms, data governance establishes policies
and procedures around data, while data management
enacts those policies and procedures to compile and use
that data for decision-making.

Unit 1.2 Data Governance Business
Drivers and Additional Concepts
• Focus
• In this unit, we look at possible business drivers that determine Data
Governance focus areas, and additional concepts that are important
to Data Governance.

Unit 1.2.1. Possible Data Governance
Business Drivers
• Objective 1.2.1
• Identify possible Data Governance business drivers that determine
focus of a Data Governance program.

Business Drivers - Learning Activity 1
• The need for Data Governance can be triggered by different business
drivers.
• The type of data being governed can differ,
• but the data governance program operates the same way,
• i.e. create rules, resolve issues and conflicts, and provide ongoing services.
• General business drivers can include:
• The need to have cross-functional leadership body to support enterprise systems
development with data architecture and models - with the focus being policy,
standards and/or strategy
• The need to make routine collaborative decisions on data but do not know all the
stakeholders or how to assemble them – a focus here is management alignment
• Concerns about regulatory compliance, contractual compliance, or compliance with
internal requirements or security controls - with the focus being privacy, compliance
and/or security

• Other types of business drivers can include growing revenues and reducing
costs.
• Growing revenue from the Data Governance perspective is to provide accurate data
to understand the customers or products, and provide more robust control for
managing the relevant data.
• It could include creation of information products or assets to make new sales, or utilization of
data to achieve new business capabilities.
• Reducing costs includes reducing duplicate data, its processes, and errors in data.
• Specific business solutions or programs can provide Data Governance
business drivers such as:
• The need to have cross-functional decision-making and accountabilities for a major
system acquisition, development or update – with the focus being architecture,
integration and/or analysis.
• Master Data Management (MDM) is an example of a process to create, integrate,
maintain and use categories of master and reference data across the enterprise.
• MDM identifies and / or develops “golden” records of truth for shareable data
through the Data Governance program.

• Having poor quality data that needs remediation – the focus in data quality
to understand what purpose, action or context is involved and how this
data should be measured.
• Data quality is a common problem that is addressed in many Data
Governance programs.
• The need to have information readily available and accessible for decision
making and to achieve organizational goals – the focus here is the data
warehousing and business intelligence environment.
• Besides good data quality, data for analytics need to be defined and
standardized.
• The BI environment needs governing to avoid shadow IT activities, e.g.
growth of spreadsheets and MS access databases for operational end user
systems.

Business Drivers- Learning Activity 2
• Read Data Governance: How to Design, Deploy, and Sustain an Effective Data
Governance Program, by John Ladley, Chapter 3, "Solutions," pages 23-26.
• DAMA-DMBOK V2 Chapter 4 Section 1.1 Business Drivers , pages 70-71.
• For further information
• "3 Data Governance Challenges Today's Companies Face" Lisa Morgan is a
freelance writer who covers Big Data and BI for Information Week.
• "Data Governance - Proving Value: How exactly does data governance make a
difference? By Nancy Couture, CIO | APRIL 29, 2019 06:01 AM PT
• What are some other types of business drivers for Data Governance? Customer
Loyalty, Employee Churn / turnover
• What are the business drivers and focus areas of your internal Data Governance
program?

Unit 1.2.2. Additional Data Governance
Concepts
• Objective 1.2.2
• Discuss additional Data Governance concepts such as
principles, policies, Information Management Maturity Model,
and treating data/information as an asset.

Concepts - Learning Activity 1
• Two concepts important to Data Governance are principles and policies.
• Principles are statements or beliefs or philosophy. They guide performance of each
function in the DAMA-DMBOK.
• A policy is a codification of principles.
• A standard is a type of policy.
• The DAMA-DMBOK defines a data policy as “short statements of management intent and
fundamental rules governing the creation, acquisition, integrity, security, quality, and use of data and
information.”
• One way to assess an organization’s current state for EIM and Data Governance
is to use a maturity model.
• There are several adapted to Data / Information Management and EIM.
• Basically, they adapt the Carnegie Mellon’s CMMI maturity model to data
management or EIM.
• Five maturity levels are mapped to process performance, technology support and
quality and predictability of results.
• The levels are (1) initial, (2) repeatable, (3) defined, (4) managed, and (5)
optimized.

• Once current state is accessed, the desired state can be
achieved through a framework for arranging activities and
managing expectations.
• What is important to Data Governance is the concept of treating
data or information as an asset.
• Assets are resources with recognized value that are captured
and used with careful control and investments.
• Data and information are now recognized as enterprise assets
that need to be standardized, tracked, assessed value and
assigned accountabilities.

• Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program, 2019 by John
Ladley, Chapter 3, pages 26-31.
• For further information:
• Data as an asset:Defining and implementing a data strategy by Max Duhe/Matt Gracie/Chris Maroon/Tess
Webre | Deloitte Insights, February 25, 2019
• Data Governance Program Effectiveness by the Numbers By Amber Lee Dennis, DataVersity on September 6,
2017
• The Difference Between Data Governance and IT Governance by CINDY NG, Varonis UPDATED: 1/17/2018
• Defining the Differences Between Information Governance, IT Governance, & Data Governance by Eshan
Gholami, November 27, 2016, LinkedIn
• CMMI, “Capability Maturity Model Integration V2.0,” Overview, highlights.
• Where should Data Governance report in an organization? Many organizations position data governance under
the Chief Financial Officer (CFO). Other organizations position data governance under the Chief Risk Officer
(CRO) or the Chief Operational Officer (COO).
• What stage is your organization in an Information Management Maturity Model?

Module 1: Data Governance and
Stewardship Core Concepts
Module Summary
• Data Management is the “business function of planning for, controlling and
delivering data and information assets.
• Data Governance is the exercise of authority and control (planning, monitoring,
and enforcement) over the management of data assets.
They are not the same concepts and their duties should be kept separated.
• Business drivers that give a Data Governance program a focus include
compliance, management alignment, and policies, standards and strategy.
• Solutions that trigger Data Governance include Data Quality, Data Analytics and
MDM.
• Principles and policies are elements of a Data Governance program.
• An Information Management Maturity Model provides organizations a method for
assessment of their EIM / Data Governance program.
• The idea of treating data / information as an asset is an important one for Data
Governance.

1) Contributing to standardized data definitions
could be an activity of what type of Data
Governance focus?
• Select one:
• a. Policies, Compliance, Security
• b. Data Quality
• c. Management Alignment
• d. Privacy, Compliance, Security

Contributing to standardized data definitions could
be an activity of what type of Data Governance
focus?
• Select one:
• a. Policies, Compliance, Security
• b. Data Quality
• d. Privacy, Compliance, Security

2) What group does the managing of
information?
• Select one:
• a. Data Architecture
• b. Information Management
• c. Data Warehousing
• d. Data Governance

What group does the managing of
information?
• Select one:
• b. Information Management
• c. Data Warehousing

3) What function has processes that ensure that
important data assets are formally managed
throughout the enterprise?
• Select one:
• a. Data Management
• b. Quality Control
• c. Data Architecture

What function has processes that ensure that
important data assets are formally managed
throughout the enterprise?
• Select one:
• b. Quality Control
• c. Data Architecture

4) What group designs the rules that
information is managed by?
• What group designs the rules that information is managed by?
• Select one:
• b. Data Architecture
• c. Data Governance
• d. Enterprise Information Management

What group designs the rules that
information is managed by?
• What group designs the rules that information is managed by?
• Select one:
• b. Data Architecture
• c. Data Governance
• d. Enterprise Information Management

5) Departmental data is found in what stage of the
Information Management Maturity Model?
• Select one:
• a. Initial
• b. Repeatable
• c. Defined
• d. Managed

Departmental data is found in what stage of the
Information Management Maturity Model?
• Select one:
• a. Initial
• b. Repeatable
• c. Defined
• d. Managed

6) Which of the following is NOT a Generally
Accepted Information Principle?
• Select one:
• a. Going Concern
• b. Due Diligence
• c. Understanding
• d. Real Value

Which of the following is NOT a Generally
Accepted Information Principle?
• Select one:
• a. Going Concern
• b. Due Diligence
• c. Understanding
• d. Real Value

7) What type of Data Governance focus might
identify sensitive data across systems?
• Select one:
• a. Data Quality
• b. Privacy, Compliance, Security
• d. Policies, Standards, Strategy

What type of Data Governance focus might
identify sensitive data across systems?
• Select one:
• a. Data Quality
• b. Privacy, Compliance, Security

8) What function represents the day-to-day data
activity done to achieve information asset
management?
• Select one:
• b. Data Governance
• c. Enterprise Information Management
• d. Data Management Correct

What function represents the day-to-day data
activity done to achieve information asset
management?
• Select one:
• b. Data Governance
• c. Enterprise Information Management
• d. Data Management

9) What type of Data Governance focus might
ensure consistent data definitions?
• Select one:
• a. Data Quality
• b. Architecture

What type of Data Governance focus might
ensure consistent data definitions?
• Select one:
• a. Data Quality
• b. Architecture

10) Measuring the value of data and data-related
efforts could be an activity of what type of Data
Governance focus?
• Select one:
• a. Management Alignment
• b. Policies, Standards, Strategy
• c. Privacy, Compliance, Security
• d. Data Quality

Measuring the value of data and data-related
efforts could be an activity of what type of Data
Governance focus?
• Select one:
• a. Management Alignment
• b. Policies, Standards, Strategy
• c. Privacy, Compliance, Security
• d. Data Quality

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