Intro to Data Governance - COPAS

Report
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?
“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
According to IDC:
In 2011 we created 1.8 zetabytes (or 1.8
trillion GBs) of information
- enough data to fill 57.5 billion 32GB Apple iPads
- enough iPads to 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.
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
“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
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 and Oil & Gas
Planning
Disposing
Producing
Drilling
Completing
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
 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?
Data
 “…representation of facts as text, numbers, graphics,
images, sound or video… Facts are captured, stored,
and expressed as data.” (Mosley, 2010, p.2)
Definition
Format
Timeframe
Relevance
Patterns & Trends
Relationships
Assumptions
Knowledge
Information
Data
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
People
Policies
Technology
Related Terminology
Data Management
Master Data Management (MDM)
Data Quality (DQ)
Business Intelligence (BI)
Data Stewardship
Accountability
Responsibility
Data Management
“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)
 Managing information as a recognized and formal
asset. (Ladley, 2012, p.11)
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
Data Life
Cycle
Ladley, 2012, p.10
Master Data Management
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)
Single Source of the TRUTH!
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
“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)
 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)
 Using information to achieve organizational goals.
(Ladley, 2012, p.15)
Business Intelligence (BI)
 BI is a capability that enables data-driven decision making.
Gathering
Data
Analyzing
Data
REDUCE LATENCY
Making
Decisions
BI and Analytics Platforms
Dashboards
I believe you. It’s important.
Now what?!
Data Governance Maturity
Undisciplined
 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…
…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
Reactive
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 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 subpar data before it causes problems
Proactive
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 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
Governed
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
Executive Steering:
Plan & Guide
Advisory Council:
Manage
Working Team:
Day-to-day
Strategic
Approves
Tactical
Operational
Defines
Enforces
Data Stewardship
Stewardship is:
The management or care of something.
(www.vocabulary.com/dictionary/stewardship)
The activity or job of protecting and being
responsible for something.
(http://www.merriam-
webster.com/dictionary/stewardship)
Some examples specific to Data:
Definition and classification
Root cause analysis
Monitoring usage
Accountability vs. Responsibility
 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)
Responsibility can be delegated – Accountability cannot!
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 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.
Q&A
Sources & Resources
 Business Intelligence. (2013). Gartner.com. Retrieved April 30,2014 from
http://www.gartner.com/it-glossary/business-intelligence-bi/.
 Business Intelligence. (n.d.). Wikipedia. Retrieved April 13, 2014 from
http://en.wikipedia.org/wiki/Business_intelligence.
 Earley, S. (2011). The DAMA Dictionary of Data Management (2nd ed.).
Bradley Beach, NJ: Technics Publications.
 EMC. (2014). EMC.com. Retrieved April 30, 2014 from
http://www.emc.com/leadership/digital-universe/2014iview/high-valuedata.htm.
 Ladley, J. (2012). Data governance how to design, deploy and sustain
an effective data governance program. Waltham, MA: Morgan
Kaufmann.
 Master Data. (n.d.) IMB.com. Retrieved April 30, 2014 from http://www01.ibm.com/software/data/master-data-management/overview.htm.
 Maturity Model. (n.d.) SAS.com. Retrieved May 1, 2014 from
http://www.sas.com/offices/NA/canada/lp/DIDQ/DataFlux.pdf.
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 http://www.merriam-webster.com/dictionary/stewardship.
 Stewardship. (n.d.) Vocabulary.com. Retrieved April 13, 2014 from
www.vocabulary.com/dictionary/stewardship.
 TDWI Data Governance Fundamentals. (2010).
 Udeh, E. (2012). Big Data and the E&P organization. ETLsolutions.com.
Retrieved April 26, 2014 from http://www.etlsolutions.com/big-data-andthe-ep-organization/.

similar documents