View individual use cases from a large B2B organization, mid-size financial institution, and a scientific data repository. See the plan and outcome from all case studies.

1. KINGLAND.COM
TOPIC
Data Management Maturity Models
How do we Realize the Benefits?
PRESENTED TO:
Webinar
August 4, 2016
Discover the Confidence of Knowing.

2. Today’s Agenda
Agenda Topics
Scoping use of the Models
Case Study 1; Large B2B use
Case Study 2; Mid-size financial institution
Case Study 3; Small, scientific data repository
Webinar series summary
Introduction to Data Management Maturity Models
Data Management
2

3. Scoping your Use
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4. 4
Data Management
Prerequisites for Trusted Data
• Complete with all appropriate attributes
• In conformance with defined standards
• Cover all expected/required records
• Consistent with required formats and definitions
• Accurate with affinity to prescribed requirements
• Timely according to the needs of the user
• Known to be authoritative
If these are required, then a mature program is necessary to achieve it
For data to be trusted it must be….

5. 5
Capability and Maturity Models
Measuring Data Management Maturity
DMMSM
Kingland is the only firm currently certified to consult on both models
• Both models provide guidance for data management
program
• DMM extends to operational guidance
• Can start with either model, depending on your
circumstances
• Several considerations on model choice and methods of
use
• Go-it alone
• Training (strongly recommended)
• Education or certification?
• Workshops
• Objectives and deliverables tailored to
organizational context and needs
• Assessments
• Affirmation gap analysis, or evidence based
• Consulting and checkpoints
• Standard cadence
• Milestone based

6. 6
Scoping use of the Models
Prerequisites for use of the Models
• Awareness of data and organizational scope
• Identified data management priorities tied to business objectives – agreed to by stakeholders
• Senior organizational commitment
• Organizational recognition and acceptance that this is a journey of change
• Allocation of funding and resources
• Agreement on priorities
• Recognition of expected cultural change
• Understanding and consensus on what you want to get from use of the model
• Program implementation guidance
• Operational data management guidance
• Gap Analysis
• Benchmark
• Comparison to peers
If you do not have these things, do not pass go

7. Case Study Examples
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8. 8
Case Study 1
Case Study #1 - Situation
• Data governance activities had begun across the
organization in the year prior
• Belief was that the organization had moderately mature
data management for priority domains, rolling out to
others
• The organization had recently undergone a major data
quality assessment and cleansing operation on
customer data
• Clear evidence existed of poor data quality
• Duplicate data
• Missing Data
• Inconsistent data
Global B2B technology (hardware/software) manufacturer

9. 9
Case Study 1
Case Study #1 – The Plan
• Objectives
• Perform evidence-based assessment
• Identify current state of data management
• Educate stakeholders with common understanding on Model expectations
• Establish objective baseline from which to measure progress on identified gaps
• Develop specific recommendations based on current state, relative of organizational
objective for data quality improvements
• Method
• On-site workshop of training combined with affirmation-based assessment
• Scoping of workshop identified customer data as domain used as proxy for how data was
managed
• Identified data flows, lines of business and stakeholders
• Scoped limited artifact review
• Scheduled 5-days on-site, with hour-hour scheduling of targeted sessions and
stakeholders - Schedule produced 3 weeks in advance
• Off-site analysis and report development

10. Activity Characterizations
Case Study 1
10
Fully Implemented (FI, 100)
Sufficient artifacts and/or affirmations are present
and judged to be adequate to demonstrate practice
implementation and no weaknesses noted
Largely Implemented (LI, 75)
Sufficient artifacts and/or affirmations are present
and judged to be adequate to demonstrate practice
implementation and one or more weaknesses are
noted
Partially Implemented (PI, 50)
Some evidence is present to suggest some
aspects of the practice is implemented and several
weaknesses are noted.
Improvement In Progress (IP,
25)
Evidence exists to indicate that initiatives have
begun on practice implementation, but activities are
still at the beginning stages and not yet fully
deployed
Not Yet (NY, 0)
Evidence indicates that the practice has not yet
been initiated

11. Case Study #1 - Results
Case Study #Case Study 1
11
• Clear identification of weaknesses in data operations and quality
controls
• Identified gaps in governance, guidance and standards contributed
to operational and quality deficiencies
• Major deficiencies in process management facilitated inconsistent
performance

12. 12
Case Study 1
Case Study #1 – The Outcome
• Leadership had clear, precise knowledge of current state across the data management
continuum
• Identified specific gaps in their governance program that were predominately the root of the
quality problems
• Lack of centralized, common guidance
• Weakly defined and ill-communicated strategy
• Near complete lack of ecosystem controls
• Adjusted organizational priorities and funding initiatives
• Continued need for data cleansing
• Delayed data profiling/assessment tool procurement
• Shifted resources to develop stronger corporate governance structure and data domain
stewardship
• Initiated efforts for earlier quality controls in the data lifecycle

13. 13
Case Study 2
Case Study #2 - Situation
• The firm had made a decision to deploy a corporate
data governance program the year prior, with an
objective to bring all data under an enterprise data
management group
• Prior work had been to identify the 3 priority data
domains, establish broad strategy to migrate those
domains and associated processes into new MDM
repositories, and begun to socialize the concepts with
stakeholders
• The EDM group believed they were at the early
formative stages and wanted to ensure their program
would be designed and implemented appropriately to
fulfill objectives behind the decision
Mid size Financial Institution (~$500 billion assets)

14. 14
Case Study 2
Case Study #2 – The Plan
• Objectives
• Establish a foundation to allow for reduced time to onboard subsequent data domains
• Ensure the data governance team understood the needs of business stakeholders, data
operations and IT to ensure they were positioned for success
• Ensure the program definition was complete before they begin program implementation
• Method
• Series of training sessions on mature data management practices for context to guide
content of governance guidance
• Conduct targeted assessment of current state of data governance program to identify
necessary areas of focus
• Leverage the depth of DMM for understanding operational aspects, and current state,
then use DCAM for guidance on program development and implementation
• 3 days on-site for education and assessment activities and off-site analysis of findings and
report development

15. 15
Case Study 2
Case Study #2 – Results
• Found that the EDM team had some level of
identified work or implementation associated with
nearly 75% of the expected activities of a data
governance program
• Level of implementation within specific areas
indicated strong progress already in place for
activities related to
• Business Case and Funding
• Data Requirements Definition
• Provider Management
• Risk and Configuration Management
• Data Management Strategy (early stage)

16. 16
Case Study 2
Case Study #2 – The Outcome
• Leadership had been leveraging industry lessons related to governance programs and been focusing on areas
they believed were necessary for their stage
• Validated efforts made regarding business case, funding and early stage strategy
• Surprised management on level of consistency and implementation for data requirements definition and
provider management across the 3 data domains
• Domain stewards had ‘seen the light’ early and made significant forward progress
• Identified specific gaps related to strategy
• Team generally understood needs and priorities and was working on many aspects, but no real
documented strategy existed
• Quality strategy not defined
• No communications plan defined
• Leadership and team had much better context and understanding of what was needed to move forward, and
now positioned for success on self-guided implementation leveraging the training received, the DCAM and
results of the gap analysis

17. 17
Case Study 3
Case Study #3 - Situation
• 15-person team responsible for data repository of
scientific data from contributing scientists in a
particular geo-science domain
• To-date, >8000 data sets from around the globe
(since 2006)
• >2000 contributors
• Several original team members were close to
retirement, including leadership
• Several members were new to the team in the past 3
years
• Primary role was curation of data with complete and
appropriate metadata
Small, focused scientific data repository

18. Metadata is Important Because…
Case Study 3
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Context matters

19. 19
Case Study 3
Case Study #3 – The Plan
• Objectives
• Obtain an objective view of existing activities
• Ensure the team had common, shared understanding of data management needs for the
data to meet future expectations of the science community
• Objectives and policies around open, discoverable data for sharing
• Bringing long-tail data into discoverable repositories
• Support for growing trend of need for science to cross boundary domains
• Ensure the activities performed were hardened and institutionalized
• Method
• Combined training and guided assessment 3-day workshop
• Off-site analysis and report development
• Detailed findings of current posture, identified strengths and gaps, specific
recommendations, executive summary for sharing with others

20. Research and Curation Data Lifecycle
Case Study 3
20
Planning
Funding
Collect/Acquir
e
Process/Mode
l
Analyze
Preserve
Publish/Prese
nt
Research
Lifecycle
Planning
Acquire/Receiv
e
Provide for
discovery &
reuse
Preserve
Availability
Curation
Lifecycle
Archive

21. 21
Case Study 3
Case Study #3 – The Outcome
• A fair amount of interpretation was required due to disconnection of traditional data lifecycle
components – breaks the mold of traditional definition for ‘organizational’ nature of data
management
• Recognized that while at first glance it may appear certain model expectations did not apply,
that in context they actually did.
• Found how some of the early stage data management activities of the scientists needed
more and better guidance through enhanced communications related to the data
management continuum
• Generally very sound execution of activities, though weak on documented processes, and data
governance/program leadership was informal and ‘understood’
• Team members initially adverse to process and other specified documentation became
strong advocates by the end
• Leadership and team at-large obtained clear, prioritized recommendations for strengthening
activities to better serve the scientific community and help ensure continued and improved
operations
• Executive report received very positive reviews by NSF for objective insight into the operations

22. Series Summary
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23. 23
Series Summary
Webinar Series Summary
• Regulatory demands and internal business requirements have undeniable needs for data that is precisely defined and fit
for purpose by the users
• Organizations are finding that as they apply more structured and formal methods to measuring data quality, that the
quality is worse than initially assumed
• Higher level data management activities such as master data management and analytics struggle and often fail to meet
organizational objectives without strong data governance and organizational data management programs
• Data management programs must have organizational authority to cause change across the company, and have on-going
sustainment funding
• Historically low information and guidance to implement and manage a mature data management program has changed
with the release of DMM and DCAM
• The models are different, but share a common objective related to organizational implementation of a mature data
management program, with added operational guidance in the DMM
• Multiple methods exist to leverage the models, but it does require an informed, deliberate consideration of a number of
variables
• The need for guidance contained in the models and the ability to leverage them has been proven to be independent of
industry, size or complexity of the organization – there is strong value to all
• You can’t get there unless you commit to try

24. 24
Data Management
Mature Data Management Program Success Matrix
With these you will
achieve…
…this
Operational
Control
Environment
Funded
Implementation
Confusion
Data Quality
Strategy
Funded
Implementation
Dissatisfactio
n
Data Quality
Strategy
Operational
Control
Environment
Data
Management
Strategy
Funded
Implementation
Exasperation
Governance
Structure
Operational
Control
Environment
Frustration
Data Quality
Strategy
Governance
Structure
Operational
Control
Environment
Funded
Implementation
Inconsistenc
y
Data Quality
Strategy
Governance
Structure
Data
Management
Strategy
Governance
Structure
Data Quality
Strategy
Funded
Implementation
Operational
Control
Environment
Data
Management
Strategy
Data
Management
Strategy
Data
Management
Strategy
Governance
Structure
Operational
Control
Environment
Funded
Implementation
Data Fit for
Purpose
Data Quality
Strategy
Data
Management
Strategy
Governance
Structure

25. KINGLAND.COM
jeff.gorball@kingland.com
25
For more information on data governance and maturity –
http://www.Kingland.com/data-maturity-overview

26. 26
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