Presented By Peter Aiken, Ph.D. Data Governance Strategies “If you don't know where you are going, any road will get you there.” - Lewis Carroll Copyright 2016 by Data Blueprint Slide # 1 Peter Aiken, Ph.D. • 30+ years in data management • Repeated international recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS (vcu.edu) • DAMA International (dama.org) • 9 books and dozens of articles • Experienced w/ 500+ data management practices • Multi-year immersions: – US DoD (DISA/Army/Marines/DLA) – Nokia – Deutsche Bank – Wells Fargo – Walmart – … • DAMA International President 2009-2013 • DAMA International Achievement Award 2001 (with Dr. E. F. "Ted" Codd • DAMA International Community Award 2005 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. The Case for the Chief Data Officer Recasting the C-Suite to Leverage Your Most Valuable Asset Peter Aiken and Michael Gorman 2 Copyright 2016 by Data Blueprint Slide #
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Presented By Peter Aiken, Ph.D.
Data Governance Strategies
“If you don't know where you are going, any road will get you there.” - Lewis Carroll
Copyright 2016 by Data Blueprint Slide # 1
Peter Aiken, Ph.D.• 30+ years in data management • Repeated international recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS (vcu.edu)
• DAMA International (dama.org) • 9 books and dozens of articles • Experienced w/ 500+ data
management practices • Multi-year immersions:
– US DoD (DISA/Army/Marines/DLA) – Nokia – Deutsche Bank – Wells Fargo – Walmart – …
• DAMA International President 2009-2013
• DAMA International Achievement Award 2001 (with Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’sMost Important Asset.
The Case for theChief Data OfficerRecasting the C-Suite to LeverageYour Most Valuable Asset
Peter Aiken andMichael Gorman
2Copyright 2016 by Data Blueprint Slide #
We believe ...
Data Assets
Financial Assets
RealEstate Assets
Inventory Assets
Non-depletable
Available for subsequent
use
Can be used up
Can be used up
Non-degrading √ √ Can degrade
over timeCan degrade
over time
Durable Non-taxed √ √
Strategic Asset √ √ √ √
• Today, data is the most powerful, yet underutilized and poorly managed organizational asset
• Data is your – Sole – Non-depletable – Non-degrading – Durable – Strategic
• Asset – Data is the new oil! – Data is the new (s)oil! – Data is the new chocolate!
• Our mission is to unlock business value by – Strengthening your data management capabilities – Providing tailored solutions, and – Building lasting partnerships
3Copyright 2016 by Data Blueprint Slide #
Asset: A resource controlled by the organization as a result of past events or transactions and from which future economic benefits are expected to flow [Wikipedia]
Data Assets Win!
Welcome: Data Governance Strategies• Date: April 12, 2016 • Time: 2:00 PM ET • Presented by: Peter Aiken, PhD • The data governance function exercises authority and control
over the management of your mission critical data assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
• Learning Objectives – Understanding why data governance can be tricky for most organizations – Steps for improving data governance within your organization – Guiding principles & lessons learned – Understanding foundational data governance concepts based on the
DAMA DMBOK
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Managing Data with Guidance?
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Lewis in front of the cummins safe
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!
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Beth Jacobs abruptly resigned in March
These decisions have consequences!
Why is Data Governance important?• Cost organizations
millions each year in – Productivity – Redundant and siloed
efforts – Poorly thought out
hardware and software purchases
– Delayed decision making using inadequate information
– Reactive instead of proactive initiatives
– 20-40% of IT spending can be reduced through better data governance
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Largely Ineffective Investments
• Approximately, 10% percent of organizations achieve parity and (potential positive returns) on their investments
• Only 30% of investments achieve tangible returns at all
• Seventy percent of organizations have very small or no tangible return on their investments
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The DAMA Guide to the Data Management Body of Knowledge
• Published by DAMA International – The professional
association for Data Managers (40 chapters worldwide)
• DM BoK organized around – Primary data
management functions focused around data delivery to the organization
• Current use derived from military • "a pattern in a stream of decisions" [Henry Mintzberg]
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Strategy in Action: Napoleon defeats a larger enemy
• Question?
– How to I defeat the competition when their forces are bigger than mine?
• Answer:
– Divide and conquer!
– “a pattern in a stream of decisions”
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– “a pattern in a stream of decisions”
Strategy in Action: Napoleon defeats a larger enemy
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Wayne Gretzky’sDefinition of Strategy
He skates to where he thinks the puck will be ...
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Corporate Governance• "Corporate governance - which
can be defined narrowly as the relationship of a company to its shareholders or, more broadly, as its relationship to society….", Financial Times, 1997.
• "Corporate governance is about promoting corporate fairness, transparency and accountability" James Wolfensohn, World
Bank, President Financial Times, June 1999. • “Corporate governance deals
with the ways in which suppliers of finance to corporations assure themselves of getting a return on their investment”,The Journal of Finance, Shleifer and Vishny, 1997.
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Definition of IT GovernanceIT Governance: • "putting structure around how organizations align IT strategy with business strategy,
ensuring that companies stay on track to achieve their strategies and goals, and implementing good ways to measure IT’s performance.
• It makes sure that all stakeholders’ interests are taken into account and that processesprovide measurable results.
• An IT governance framework should answer some key questions, such as how the IT department is functioning overall, what key metrics management needs and what return IT is giving back to the business from the investment it’s making." CIO Magazine (May 2007)
IT Governance Institute, five areas of focus: • Strategic Alignment • Value Delivery • Resource Management • Risk Management • Performance Measures
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Strategy is Difficult to Perceive at the IT Project Level
• If they exist ...
• A singular organizational strategy and set of goals/objectives ...
• Are not perceived as such at the project level and ...
• What does exist is confused, inaccurate, and incomplete
• IT projects do not well reflect organizational strategy
Organizational
Strategy
Set of Organizational
Goals/Objectives
Organizational IT
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Division/Group/Project
Data Strategy in Context
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OrganizationalStrategy
IT Strategy
Data Strategy
OrganizationalStrategy
IT Strategy
Data Strategy
This is wrong!
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OrganizationalStrategy
IT Strategy
Data Strategy
OrganizationalStrategy
IT Strategy
This is correct …
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Data Strategy
No clear connection exists between to business priorities and IT initiatives
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Grow expenses slower than
sales
Grow operating income faster
than sales
Pass on savings
Drive efficiency with technology
Leverage scale globally
Leverage expertise
Deploy new formats
Grow productivity of existing assets
Attract new members
Expand into new channels
Enter new markets
Make acquisitions
Produce significant free
cash flow
Drive ROI performance
Deliver greater shareholder
value
Cus
tom
er
Per
spec
tive Open new
stores
Develop new, innovative formats
Appeal to new demographics
Integrate shopping
experience
Develop new, innovative formats
Remain relevant to all
customers
Increase "Green" Image
Inte
rnal
P
ersp
ectiv
e
Create competitive advantages
Improve use of information
Strengthen supply chain
Improve Associate
productivity
Making acquisitions
Increase benefit from our global expertise
Present consistent view and
experience
Integrate channels Match staffing
to store needs Increase sell through
Fina
ncia
l P
ersp
ectiv
e Reduce expenses
Inventory Management
Human and Intell. Capital investment
Manage new facilities
Improve Sales and margin by facilities
Increased member-base
revenues
Revenue growth Cash flow Return on
Capital
Walmart Strategy Map
See more uniform brand and retail experience
Leverage Growth Return
Gross Margin Improvement
CE
O P
ersp
ectiv
e
Attract more customers & have customer purchasing more
Associate Productivity
Customer Insights
Human Capital Corp. Reputation Acquisition Strategic Planning
Real estate CRM CRM
Analytic and reporting processes
Corporate Reputation - Risk Management, Compliance, Marketing, IT and Data Governance
Corporate Processes
Corporate Data
Inventory Mgmt
Tran
sfor
mat
ion
Por
tfol
io
Supply Chain
Multi ChannelMerchant Tools Supply Chain
Strategic Initiatives
AcctingSales
Transactional Processing
Logistics Associate Locations and Codes
Item
Customer Suppliers
Retail Planning
( Alignment Gap )
Adapted from John Ladley
Supplemental: CMMI Data Strategy ElementsThe data management strategy defines the overall framework of the program. A data management strategy typically includes: • A vision statement, which includes core operating principles;
goals and objectives; priorities, based on a synthesis of factors important to the organization, such as business value, degree of support for strategic initiatives, level of effort, and dependencies
• Program scope – including both key business areas (e.g. Customer Accounts); data management priorities (e.g. Data Quality); and key data sets (e.g. Customer Master Data)
• Business benefits – The selected data management framework and how it will be used – High-level roles and responsibilities – Governance needs – Description of the approach used to develop the data management program – Compliance approach and measures – High-level sequence plan (roadmap).
25Copyright 2016 by Data Blueprint Slide #
Data Governance Strategies• Strategy
– Term of Recent Usage – Context: Organizational -> IT -> Data – Difficult Choices
• Data Governance – What is it? – Why is it important? – Requirements for Effective Data Governance
• Data Governance Components – Frameworks – Building Blocks – Checklists – Worst Practices
• Data Governance Strategy in Action (Storytelling) • Take Aways/References/Q&A
26Copyright 2016 by Data Blueprint Slide #
Tweeting now: #dataed
7 Data Governance Definitions• The formal orchestration of people, process, and technology to enable an
organization to leverage data as an enterprise asset. - The MDM Institute • A convergence of data quality, data management, business process
management, and risk management surrounding the handling of data in an organization – Wikipedia
• A system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods – Data Governance Institute
• The execution and enforcement of authority over the management of data assets and the performance of data functions – KiK Consulting
• A quality control discipline for assessing, managing, using, improving, monitoring, maintaining, and protecting organizational information – IBM Data Governance Council
• Data governance is the formulation of policy to optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions – Sunil Soares
• The exercise of authority and control over the management of data assets – DM BoK
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The
File
Nam
ing
Con
vent
ion
Com
mitt
ee's
Out
put
Organizational Data Governance Purpose Statement• What does data governance
mean to my organization? – Managing data with guidance
– Getting some individuals (whose opinions matter)
– To form a body (needs a formal purpose/authority)
– Who will advocate/evangelize for (not dictate, enforce, rule)
– Increasing scope and rigor of
– Data-centric development practices
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Use Their Language ...
• Getting access to data around here is like that Catherine Zeta Jones scene where she is having to get thru all those lasers …
Supplemental: Data Governance Activities• Understand Strategic
Enterprise Data Needs • Develop and Maintain
the Data Strategy • Establish Data Professional
Roles and Organizations • Identify and Appoint
Data Stewards • Establish Data Governance and Stewardship Organizations • Develop and Approve Data Policies, Standards, and
Procedures • Review and Approve Data Architecture • Plan and Sponsor Data Management Projects and Services • Estimate Data Asset Value and Associated Costs
Data Governance Institute• A system of ideas for guiding analyses • A means of organizing project data • Data integration priorities decision making framework • A means of assessing progress
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http://www.datagovernance.com/
KiK Consulting• A system of ideas for guiding analyses • A means of organizing project data • Data integration priorities decision making framework • A means of assessing progress
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http://www.kikconsulting.com/
IBM Data Governance Council• A system of ideas for guiding analyses • A means of organizing project data • Data integration priorities decision making framework • A means of assessing progress
Elements of Effective Data Governance• A system of ideas for guiding analyses • A means of organizing project data • Data integration priorities decision making framework • A means of assessing progress
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See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/ data-governance.html.
Baseline Consulting (sas.com)
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American College Personnel Association
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Supplemental: NASCIO DG Implementation Process
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Supplemental: Data Governance Checklist✓ Decision-Making Authority
✓ Standard Policies and Procedures
✓ Data Inventories
✓ Data Content Management
✓ Data Records Management
✓ Data Quality
✓ Data Access
✓ Data Security and Risk Management
52Copyright 2016 by Data Blueprint Slide #
Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
Supplemental: Data Governance Checklist• The Privacy Technical Assistance Center
has published a new checklist “to assist stakeholder organizations, such as state and local education agencies, with establishing and maintaining a successful data governance program to help ensure the individual privacy and confidentiality of education records.”
• The five page paper offers a number of suggestions for implementing a successful data governance program that can be applied to a variety of business models beyond education.
• For more information, please visit the Privacy Technical Assistance Center: http://ed.gov/ptac
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Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
Supplemental: NASCIO Scorecard
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Supplemental: 10 DG Worst Practices1. Buy-in but not Committing:
Business vs. IT 2. Ready, Fire, Aim 3. Trying to Solve World Hunger or
Boil the Ocean 4. The Goldilocks Syndrome 5. Committee Overload 6. Failure to Implement 7. Not Dealing with Change
PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’sMost Important Asset.
When our organizations transform to a data-centric approach, we begin to measure success differently than we did before—same project, same process, but with different measures that include: • asking if our data is correct; • valuing data more than valuing "on time and within budget;" • valuing correct data more than correct process; and • auditing data rather than project documents. - Linda Bevolo
Data-Centric Development
Original articulation from Doug Bagley @ Walmart
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ITProjects
Data/Information
Strategy
• In support of strategy, the organization develops specific, shared data-based goals/objectives
• These organizational data goals/objectives drive the development of specific IT projects with an eye to organization-wide usage
• Advantages of this approach: – Data/information assets are developed from an
organization-wide perspective
– Systems support organizational data needs and compliment organizational process flows
– Maximum data/information reuse
• Telemetric data2005-07-17-srm-003.jpg
Why management doesn't need to understand metadata - Link business objectives to technical capabilities
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healthcare.gov• 55 Contractors! • 6 weeks from launch and
requirements not finalized • "Anyone who has written a line of
code or built a system from the ground-up cannot be surprised or even mildly concerned that Healthcare.gov did not work out of the gate," Standish Group International Chairman Jim Johnson said in a recent podcast.
• "The real news would have been if it actually did work. The very fact that most of it did work at all is a success in itself."
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• "It was pretty obvious from the first look that the system hadn't been designed to work right," says Marty Abbott. "Any single thing that slowed down would slow everything down."
• Software programmed to access data using traditional technologies
• Data components incorporated "big data technologies"http://www.slate.com/articles/technology/bitwise/2013/10/problems_with_healthcare_gov_cronyism_bad_management_and_too_many_cooks.html
Form
aliz
ing
the
Rol
e of
U.S
. Arm
y
IT G
over
nanc
e/C
ompl
ianc
e
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Suicide Mitigation
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Data Mapping
12
Mental illness
Deployments
Work History
Soldier Legal Issues
Abuse
Suicide Analysis
FAPDMSS G1 DMDC CID
Data objects complete?
All sources identified?
Best source for each object?
How reconcile differences between sources?
MDR
Suicide Mitigation
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Senior Army Official• A very heavy dose of
management support • Any questions as to future
data ownership, "they should make an appointment to speak directly with me!"
• Empower the team – The conversation turned from "can this be
done?" to "how are we going to accomplish this?"
– Mistakes along the way would be tolerated – Implement a workable solution in prototype form
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Communication Patterns• \
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Source: The Challenge and the Promise: Strengthening the Force, Preventing Suicide and Saving Lives - The Final Report of the Department of Defense Task Force on the Prevention of Suicide by Members of the Armed Forces - August 2010
Vocabulary is Important-Tank, Tanks, Tankers, Tanked
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How one inventory item proliferates data throughout the chain
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555 Subassemblies & subcomponents
17,659 Repair parts or Consumables
System 1:18,214 Total items
75 Attributes/ item1,366,050 Total attributes
System 2 47 Total items
15+ Attributes/item720 Total attributes
System 3 16,594 Total items 73 Attributes/item
1,211,362 Total attributes
System 4 8,535 Total items
16 Attributes/item136,560 Total attributes
System 5 15,959 Total items
22 Attributes/item351,098 Total attributes
Total for the five systems show above:59,350 Items
179 Unique attributes3,065,790 values
Business Implications• National Stock Number (NSN)
Discrepancies – If NSNs in LUAF, GABF, and RTLS are
not present in the MHIF, these records cannot be updated in SASSY
– Additional overhead is created to correct data before performing the real maintenance of records
• Serial Number Duplication – If multiple items are assigned the same
serial number in RTLS, the traceability of those items is severely impacted
– Approximately $531 million of SAC 3 items have duplicated serial numbers
• On-Hand Quantity Discrepancies – If the LUAF O/H QTY and number of items serialized in RTLS conflict, there can
be no clear answer as to how many items a unit actually has on-hand – Approximately $5 billion of equipment does not tie out between the LUAF and
RTLS
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Barclays Excel Spreadsheet Horror• Barclays preparing to buy Lehman’s
Brothers assets. • 179 dodgy Lehman’s contracts were
almost accidentally purchased by Barclays because of an Excel spreadsheet reformatting error
• A first-year associate reformatted an Excel contracts spreadsheet – Predictably, this work was done long after
normal business hours, just after 11:30 p.m...
• The Lehman/Barclays sale closed on September 22nd
• the 179 contracts were marked as “hidden” in Excel, and those entries became “un-hidden” when when globally reformatting the document …
• … and the sale closed …
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CLUMSY typing cost a Japanese bank at least £128 million and staff their Christmas bonuses yesterday, after a trader mistakenly sold 600,000 more shares than he should have. The trader at Mizuho Securities, who has not been named, fell foul of what is known in financial circles as “fat finger syndrome” where a dealer types incorrect details into his computer. He wanted to sell one share in a new telecoms company called J Com, for 600,000 yen (about £3,000).
Possibly the Worst Data Governance ExampleMizuho Securities Mizuho Securities • Wanted to sell 1 share
for 600,000 yen • Sold 600,000 shares
for 1 yen • $347 million loss • In-house system did
not have limit checking • Tokyo stock exchange
system did not have limit checking ...
• … and doesn't allow order cancellations
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Data Governance Strategies• Strategy
– Term of Recent Usage – Context: Organizational -> IT -> Data – Difficult Choices
• Data Governance – What is it? – Why is it important? – Requirements for Effective Data Governance
• Data Governance Components – Frameworks – Building Blocks – Checklists – Worst Practices
• Data Governance Strategy in Action (Storytelling) • Take Aways/References/Q&A
76Copyright 2016 by Data Blueprint Slide #
Tweeting now: #dataed
Maslow's Hierarchy of Needs
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You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present
greaterrisk(with thanks to Tom DeMarco)
Data Management Practices Hierarchy
Advanced Data
Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA
Foundational Data Management Practices
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
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Take Aways• Need for DG is increasing
– Increase in data volume – Lack of practice improvement
• DG is a new discipline – Must conform to constraints – No one best way
• DG must be driven by a data strategy complimenting organizational strategy
• Comparing DG frameworks can be useful
• DG directs data management efforts
• The language of DG is metadata
• Process improvement can improve DG practices
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Data Governance Council Hotel
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81Copyright 2016 by Data Blueprint Slide #
PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’sMost Important Asset.
Supplemental: Data Governance Checklist• Decision-Making Authority
– Assign appropriate levels of authority to data stewards – Proactively define scope and limitations of that authority
• Standard Policies and Procedures – Adopt and enforce clear policies and procedures in a written data
stewardship plan to ensure that everyone understands the importance of data quality and security
– Helps to motivate and empower staff to implement DG
• Data Inventories – Conduct inventory of all data that require protection – Maintain up-to-date inventory of all sensitive records and data systems – Classify data by sensitivity to identify focus areas for security efforts
• Data Content Management – Closely manage data content to justify the collection of sensitive data,
optimize data management processes and ensure compliance with federal, state, and local regulations
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Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
Supplemental: Data Governance Checklist, cont’d• Data Records Management
– Specify appropriate managerial and user activities related to handling data to provide data stewards and users with appropriate tools for complying with an organization’s security policies
• Data Quality – Ensure that data are accurate, relevant, timely, and complete for their intended
purposes – Key to maintaining high quality data is a proactive approach to DG that requires
establishing and regularly updating strategies for preventing, detecting, and correcting errors and misuses of data
• Data Access – Define and assign differentiated levels of data access to individuals based on their
roles and responsibilities – This is critical to prevent unauthorized access and minimize risk of data breaches
• Data Security and Risk Management – Ensure the security of sensitive and personally identifiable data and mitigate the
risks of unauthorized disclosure of these data – Top priority for effective data governance plan
83Copyright 2016 by Data Blueprint Slide #
Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
Supplemental: 10 DG Worst Practices in Detail
1. Buy-in but not Committing: Business vs. IT
– Business needs to do more – Data governance tasks need
to recognized as priority – Without a real business-resource commitment, data governance
takes a backseat and will never be implemented effectively
(business representatives from across enterprise) and separate governance working group (data stewards)
– Problem: Often get the timing wrong: Panels are formed and people are assigned BEFORE they really understand the scope of the data governance and participants’ roles and responsibilities
– Prematurely organize management framework and realize you need a do-over = Guaranteed way to stall DG initiative
84Copyright 2016 by Data Blueprint Slide #
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
Supplemental: 10 DG Worst Practices in Detail3. Trying to Solve World Hunger or Boil the Ocean
• Trap 1: Trying to solve all organizational data problems in initial project phase
• Trap 2: Starting with biggest data problems (highly political issues) • Almost impossible to establish a DG program while tacking data problems
that have taken years to build up • Instead: “Think globally and act locally”: break data problems down into
incremental deliverables • “Too big too fast” = Recipe for disaster
4. The Goldilocks Syndrome • Encountering things that are either one
extreme or another • Either the program is too high-level and
substantive issues are never dealt with or it attempts to create definitions and rules for every field and table
• Need to find happy compromise that enables DG initiatives to create real business value
85Copyright 2016 by Data Blueprint Slide #
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
Supplemental: 10 DG Worst Practices in Detail5. Committee Overload
• Good: People of various business units and departments get involved in the governance process
• Bad: more people -> more politics -> more watered down governance responsibilities
• To be successful, limit committee sizes to 6-12 people and ensure that members have decision-making authority
6. Failure to Implement • DG efforts won’t produce any business value if
data definitions, business rules and KPIs are created but not used in any processes
• Governance process needs to be a complete feedback loop in which data is defined, monitored, acted upon, and changed when appropriate
• Also important: Establish ongoing communication about governance to prevent business users going back to old habits
86Copyright 2016 by Data Blueprint Slide #
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
Supplemental: 10 DG Worst Practices in Detail
7.Not Dealing with Change Management • Business and IT processes need to be
changed for enterprise DG to be successful • Need for change management is seldom addressed • Challenges: people/process issues and internal politics
8.Assuming that Technology Alone is the Answer • Purchasing MDM, data integration or data quality
software to support DG programs is not the solution • Combination of vendor hype and high
price tags set high expectations • Internal interactions are what make
or break data governance efforts
87Copyright 2016 by Data Blueprint Slide #
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
Supplemental: 10 DG Worst Practices in Detail
9.Not Building Sustainable and Ongoing Processes • Initial investment in time, money
and people may be accurate • Many organizations don’t establish a budget, resource
commitments or design DG processes with an eye toward sustaining the governance effort for the long term
10.Ignoring “Data Shadow Systems” • Common mistake: focus on “systems
of record” and BI systems, assuming that all important data can be found there
• Often, key information is located in “data shadow systems” scattered through organization
• Don’t ignore such additional deposits of information
88Copyright 2016 by Data Blueprint Slide #
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
ReferencesWebsites
• Data Governance Book
Data Governance Book
Compliance Book
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IT Governance Books
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Questions?
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It’s your turn! Use the chat feature to submit your questions to Peter now.
+ =
Upcoming EventsEnterprise Data World • San Diego • April 17-22 Home Made Jam - Monday evening Establishing the CDO AgendaApril 19, 2016 @ 3:45-4:30 PM PT Mapping Roles and Structure to Organizational Needs for Ongoing SuccessApril 20, 2016 @ 11:00-12:30 PM PT
Addressing the Data Management Brain DrainApril 20, 2016 @ 2:00-2:45 PM PT
May Webinar:Metadata StrategiesMay 10, 2016 @ 2:00 PM ET Sign up here: • www.datablueprint.com/webinar-schedule • www.Dataversity.net
92Copyright 2016 by Data Blueprint Slide #
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