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TOPIC

Data Management Maturity Models How do we Realize the Benefits?PRESENTED TO:

Webinar

August 4, 2016

Discover the Confidence of Knowing.

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Today’s Agenda

Agenda TopicsScoping use of the ModelsCase Study 1; Large B2B useCase Study 2; Mid-size financial institutionCase Study 3; Small, scientific data repositoryWebinar series summary

Introduction to Data Management Maturity Models

Data Management

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Scoping your Use

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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….

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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

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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

Case Study Examples

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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

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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

Activity Characterizations

Case Study 1

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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

Case Study #1 - Results

Case Study #Case Study 1

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• 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

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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

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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)

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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

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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)

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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

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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

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Metadata is Important Because…

Case Study 3

Context matters

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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

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Research and Curation Data Lifecycle

Case Study 3

Planning

Funding

Collect/Acquire

Process/ModelAnalyze

Preserve

Publish/Present

Research Lifecycle

Planning

Acquire/Receive

Provide for discovery &

reusePreserve

Availability

Curation Lifecycle

Archive

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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

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Series Summary

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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

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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 DissatisfactionData Quality

Strategy

Operational Control

Environment

Data Management

Strategy

Funded Implementation ExasperationGovernance

Structure

Operational Control

Environment

FrustrationData Quality Strategy

Governance Structure

Operational Control

Environment

Funded Implementation InconsistencyData Quality

StrategyGovernance

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

KINGLAND.COM25

jeff.gorball@kingland.com

For more information on data governance and maturity – http://www.Kingland.com/data-maturity-overview

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