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COPAS Education Day Intro to Data Governance Jennifer Major, CPA. May 6, 2014. Agenda. Data Governance Why Bother? Definition Related Terminology Data Governance Organization & Culture. Why Bother?.

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  • Data Governance
    • Why Bother?
    • Definition
  • Related Terminology
  • Data Governance Organization & Culture
why bother

Why Bother?

“Organizations that do not understand the overwhelming importance of managing data and information as tangible assets in the new economy will not survive.”

Tom Peters, 2001 (author of In Search of Excellence)

big data

Big Data

  • - enough data to fill 57.5 billion 32GB Apple iPads
  • - enough iPadsto build a Great iPad Wall of China twice as tall as the original
  • The world’s information now doubles!! about every year and a half.

According to IDC:

  • In 2011 we created 1.8 zetabytes(or 1.8 trillion GBs) of information
big data1

Big Data

In every minute of every day:

- 204 million email messages- 4 million Google searches-  72 hours of new YouTube videos- 2.46M bits of content shared on Facebook- More than 277,000 tweets- 216,000 Instagram posts

rapidly changing technology

Rapidly Changing Technology

“Enterprises are finding new sources of data, new ways to analyze data, new ways to apply the analysis to the business, and new revenues for themselves as a result. They are using new approaches, moving from descriptive to predictive and prescriptive analytics and doing data analysis in real-time. They are also increasingly adopting self-service business intelligence and analytics, giving executives and frontline workers easy-to-use software tools for data discovery and timely decision-making.” (EMC, 2014)




more data more problems

More Data More Problems

  • Dissatisfaction with Data Quality
  • Data Access Issues
  • Data Delivery Issues
  • Inconsistent Systems of Record
  • Too Many Sources
  • Lost or Disorganized Data

Blosser, Data Governance at Chevron GOM: A Case Study, May 2013.


Business asset planning and optimization, which highlights more extensive use of data to plan and optimize the performance of business assets across the entire life cycle of that asset


Digital oil fields, which focuses on the exploding use of digital sensors in oil fields (and other plant and equipment), providing massive volumes of operational data that enable optimization in near real time

data governance mitigates

Data Governance Mitigates

  • Improved Data Quality
  • Improved Data Access
  • Improved Data Delivery
  • Clearly Defined Systems of Record
  • Consistent Uses of Data
  • Organized Data
the benefits of data governance

The Benefits of Data Governance

  • Revenue increase
    • improved finding efficiency and better operational results from better informed decision makers.
  • Risk reductions
    • sustaining licenses to operate, maintaining the value of data assets, avoiding unintended data loss, disclosure or damage to reputation.
  • Acquisition cost reduction
    • reducing the cost of data acquisition, e.g. by properly and securely archiving expensive data sources such as seismic, can prevent costs associated with reacquiring such data and can yield tens of millions of dollars per year from such optimization
  • OPEX optimization
    • reducing the current cost of data management by standardizing technologies and formalizing the organizational capabilities.

(Udeh, Big Data and the E&P organization, 03/13/2014)

what is data

What is Data?

Patterns & Trends







  • Data
    • “…representation of facts as text, numbers, graphics, images, sound or video… Facts are captured, stored, and expressed as data.”(Mosley, 2010, p.2)
what is data governance

What is Data Governance?

  • “…the organization and implementation of policies, procedures, structure, roles, and responsibilities which outline and enforce rules of engagement, decision rights, and accountabilities for the effective management of information assets” (Ladley, 2012, p.11)
  • “Data governance refers to people and organizational capability, processes and controls, and technology and architecture” (Blosser, 2013, p.5)
data governance components
Data Governance Components

Document Business Processes

Identify Business Rules & Data Rules

Define ‘Acceptable’ Data

Data Stewards

Data Custodians

Quality Analysts

DQ Programmers

DQ Software



related terminology

Related Terminology

  • Data Management
  • Master Data Management (MDM)
  • Data Quality (DQ)
  • Business Intelligence (BI)
  • Data Stewardship
  • Accountability
  • Responsibility
data management

Data Management

  • Managing information as a recognized and formal asset. (Ladley, 2012, p.11)
  • “The business function that develops and executes plans, policies, practices, and projects that acquire, control, deliver, and enhance the value of data and information” (Earley, 2011, p.78)
how is that different than data governance

How is that different than Data Governance?

  • Data Management is the hands on doing
    • Manages data within ‘guidance’
  • Data Governance is making sure data is managed properly
    • Develops ‘guidance’ aka Rules & Policies
    • Ensures “the doing” is done by the rules
the governance v

The Governance V

Data Life Cycle

Ladley, 2012, p.10

master data management

Master Data Management

Single Source of the TRUTH!

According to DAMA, MDM is “Processes that ensure that reference data is kept up to date and coordinated across an enterprise. The organization, management, and distribution of corporately adjudicated data with widespread use in the organization” (Earley, 2011, p.163)

master data

Master Data

Master Data is common data about customers, suppliers, partners, products, materials, accounts and other critical “entities,”that is commonly stored and replicated across IT systems. Master Data is the high-value, core information used to support critical business processes across the enterprise. (IBM, 2014)

data quality

Data Quality

  • “The degree to which data is accurate, complete, timely, consistent with all requirements and business rules, and relevant for a given use” (Ladley, 2012, p.14)
  • “’Bad Data’ does not just appear, and is almost always corrected by a change in processes or habits.” (Ibid)
business intelligence bi

Business Intelligence (BI)

  • Using information to achieve organizational goals. (Ladley, 2012, p.15)
  • Set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes. (Wikipedia)
  • An umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance. (Gartner)
business intelligence bi1

Business Intelligence (BI)

  • BI is a capability that enables data-driven decision making.

Gathering Data

Analyzing Data

Making Decisions

Reduce Latency



  • 33% of Companies at this stage
    • Few defined rules and policies regarding DQ and integration
    • Redundant data in different sources, formats, and records
    • Little or no executive-level insight into the costs of bad or poorly-integrated data
movin on up

Movin’ on Up…

…To the Reactive Stage

  • Assess data maturity across enterprise
  • Establish objectives for Data Governance
  • Identify size and scope of governance efforts
  • Identify critical data assets for governance


  • 45-50% of Companies at this stage
    • Locates and confronts data problems only after they occur
    • Varied levels of data quality
    • Some employees understand the importance of high quality info, but
    • Management support is lacking
movin on up1

Movin’ on Up…

…To the Proactive Stage

  • Create a new strategic vision for DQ
  • Obtain executive support – a high degree
  • Create a Data Governance Team
  • Establish cross functional business rules
  • Implement data monitoring that detects sub-par data before it causes problems


  • Less than10% of companies have reached this level
    • Companies have ability to avoid risk and reduce uncertainty
    • Data moves from commodity to Asset!
    • Data implements MDM around critical Master Data
movin on up2

Movin’ on Up…

…To the Governed stage

  • Create a unified approach for all corporate information
  • Assemble and integrate the DG organization
  • The business controls DM while IT supports
  • Full Business Process Management (BPM) now possible


  • Very few companies operate at this level.
    • Unified DG strategy through enterprise
    • DQ, integration, and synchronization are integral parts of all business processes
    • Organization achieves impressive results from a single, unified view
the dg organization

The DG Organization

Executive Steering: Plan & Guide


Advisory Council: Manage


Working Team: Day-to-day


data stewardship

Data Stewardship

Stewardship is:

  • The management or care of something. (
  • The activity or job of protecting and being responsible for something. (
  • Some examples specific to Data:
    • Definition and classification
    • Root cause analysis
    • Monitoring usage
accountability vs responsibility

Accountability vs. Responsibility

Responsibility can be delegated – Accountability cannot!

  • Responsibility is the obligation incurred by an individual in a specific role to perform the duties of that role, to take actions and produce results that affect the organization’s assets. (TDWI, 2010, p.2-3)
  • Accountability is the individual liability created by use of authority. The condition of being fully answerable for results and achievement of goals. (TDWI, 2010, p.2-3)
culture of data governance

Culture of Data Governance

  • Actually, effective data governance isn't about data at all. Instead, it's about changing how companies view their data.- Jane Griffin, “Data Governance Defined” (2011)
  • Data governance describes an evolutionary process for a company, altering the company’s way of thinking and setting up the processes to handle information so that it may be utilized by the entire organization. - Steve Sarsfield "The Data Governance Imperative” (2009)
data governance elevator pitch

Data Governance Elevator Pitch

Data Governance is ensuring our data is an asset that is properly managed so we can support effective and efficient operations and mitigate risk in order to reach our company goals.

sources resources

Sources & Resources

  • Business Intelligence. (2013). Retrieved April 30,2014 from
  • Business Intelligence. (n.d.). Wikipedia. Retrieved April 13, 2014 from
  • Earley, S. (2011). The DAMA Dictionary of Data Management (2nd ed.). Bradley Beach, NJ: Technics Publications.
  • EMC. (2014). Retrieved April 30, 2014 from
  • Ladley, J. (2012). Data governance how to design, deploy and sustain an effective data governance program. Waltham, MA: Morgan Kaufmann.
  • Master Data. (n.d.) Retrieved April 30, 2014 from
  • Maturity Model. (n.d.) Retrieved May 1, 2014 from
sources resources continued

Sources & Resources continued

  • Mosley, M., Brackett, M., & Earley, S. (2010). The DAMA guide to the data management body of knowledge (DAMA-DMBOK guide) (2nd ed.). Bradley Beach, N.J.: Technics Publications.
  • Stewardship. (n.d.) Merriam-Webster online. Retrieved April 13, 2014 from
  • Stewardship. (n.d.) Retrieved April 13, 2014 from
  • TDWI Data Governance Fundamentals. (2010).
  • Udeh, E. (2012). Big Data and the E&P organization. Retrieved April 26, 2014 from