Developing a Data Management Strategy Using CMMI Data Maturity Model

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Developing a Data Management

Strategy Using CMMI Data Maturity

Model

Dr. Sanjay Shirude,

Ph.D., PMP, CDMP, CBIP

ACCEL B I

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Dr. Sanjay Shirude,

PH.D., PMP, CDMP, CBIP, CMDM

Dr. Sanjay Shirude has +20 years of experience in management of design, development, and deployment of enterprise data management systems. Dr. Shirude has significant expertise simplifying business IT integration by collecting and translating business requirements and objectives for application

development, quality control, performance reporting, budgeting, and resource management into technical specifications and process management. As a data management expert, His technical expertise extends into data governance, business case analysis, business intelligence, SOA, and cloud computing. His experience covers Agile, scrum, and SDLC waterfall methodologies; with roles as a program manager, scrum master, product owner, analyst, trainer, and mentor.

• PhD Management [Information Systems] • MS Management Science

• MS Statistics, Pune University Pune, India

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CMMI – Worldwide Process Improvement

CMMI Quick Stats:

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CMMI Model Portfolio

Establish, manage,

and deliver services

Product development

/ software

engineering

Acquire and integrate

products / supply

chain

Workforce

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Data Management Maturity (DMM)

SM

_Model

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What’s in the Model?



25 Process Areas

• Purpose – Introduction - Goals

– Questions - Capability Level

Criteria – Work Products

• Policies – Processes –

Standards – Governance –

Metrics – Enabling Technology

– Implementation Tips



300+ Practice Statements



300+ Work Products

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Why the DMM

SM

is Useful

Collaborative Influence



The CxO’s best friend

• Lines of business forge a shared

perspective

• Lines of business understand

current strengths and weaknesses

• Lines of business understand their

roles

• Reveals critical needs for the data

management program

• Winning hearts and minds

-m

otivates all parties to collaborate

for improvements

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DMM

SM

Structure

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DMM Process Areas

Data Management Strategy

Name Description

Data Management Strategy

Data Management Strategy Goals, objectives, principles, business value, prioritization, metrics, and sequence plan for the data management program

Communications Communications strategy for data management initiatives and mechanisms, ensures business, IT, and data management stakeholders are aligned with bi-directional feedback

Data Management Function Structure of data management organization, responsibilities and accountability, interaction model, staffing for data

management resources, executive oversight

Business Case Decision rationale for determining what data management initiatives should be funded based on benefits to the

organization and financial considerations

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Do I need a Data Management Strategy?



Benefits

• Business Alignment

• Shared Vision

• Enhanced Collaboration

• Path Forward

• Sustained program support

• Optimal resource allocation

• Fosters top-down informed

decisions



Success Factors

• Secure active participation of all

relevant stakeholder, especially the

business

• Ensure visible and active executive

sponsorship

• Determining which business process

drives the DMS

• Agree on Prioritizations criteria and

method

• Broad-based approval

• High level sequence plan – not too

detailed

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Data Management Strategy – Purpose, Definition, Goals



Purpose

• Defines the vision, goals, and objectives for the data management program,

and ensure that all relevant stakeholders are aligned on priorities and the

program implementation and management



Definition

• Rationale for the data management program, which defines the aims of the

program, identifies the components of the initiative and describes how they

fit together



Goals

• Establish maintain and follow a DMS that aligned with organizational

strategy approved by all stakeholders, communicated across the

organization and reflected in architecture, technology and business

planning.

• Maintains the DMS including goals, objective, priorities and scope for all

business areas through data governance program.

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DMM Process Areas

Data Governance and Data Quality

Data Governance

Governance Management Structure of data governance, governance processes and leadership, metrics development and monitoring

Business Glossary Creation, change management, and compliance for terms, definitions, and properties

Metadata Management Strategy, classification, capture, integration, and accessibility of business, technical, process, and operational metadata

Data Quality

Data Quality Strategy Plan and initiatives for the data quality program, aligned with business objectives and impacts

Data Profiling Analysis of semantic data content in physical data stores for meaning and defect detection

Data Quality Assessment Assessment and improvement of data quality, business rules and known issues analysis, measuring impact and costs

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DMM Process Areas

Platform & Architecture and Data Operations

Platform & Architecture

Architectural Approach Architectural strategy, frameworks, and standards for implementation planning

Architectural Standards Data standards for representation, access, and distribution

Data Management Platform Technology and capability platforms selection for data distribution and integration into consuming applications

Data Integration Integration and reconciliation of data from multiple sources into target destinations, standards and best practices, data quality processes at point of entry

Historical Data, Archiving and Retention

Management of historical data, archiving, and retention requirements

Data Operations

Data Requirements Definition Process and standards for developing, prioritizing, evaluating, and validating data requirements

Data Lifecycle Management Mapping of data to business processes as data flows from one process to another

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DMM Process Areas

Supporting Processes

Adapted from CMMI

Measurement and Analysis Establishing and reporting metrics and statistics for each process area within the data management program, supports managing to performance milestones

Process Management Management and enforcement of policies, processes, and standards, from creation to dissemination to sun-setting

Process Quality Assurance Evaluation and audit to ensure quality execution in all data management process areas

Risk Management Identifying, categorizing, managing and mitigating business and technical risks for the data management program

Configuration Management Establishing and maintaining the integrity of data

management artifacts and products, and management of releases

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Measurement = Confidence



Activity-focused and

evidence-based evaluation of the data

management program



Allows organizations to gauge their

data management achievements

against peers



Fuels enthusiasm and funding for

improvement initiatives



Enhances an organization’s

reputation – quality and progress

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Guided Navigation to Lasting Solutions

-The Data Management Maturity Model



Reference model framework of fundamental data management

capabilities



Measurement instrument for organizations to evaluate

capability maturity, identify gaps, and incorporate guidelines for

improvements



From contributions of many experts, DMM was structured and

crafted to leverage the strengths and proven approach of CMMI



Conducted DMM Assessments for: Microsoft Corporation;

Fannie Mae; Federal Reserve System Statistics Function; Ontario

Teachers Pension Plan; and Freddie Mac.,Securities and

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Infrastructure Support Practices = Maturity

Level 2 - Institutionalize as a Managed

Process



Establish an Organizational Policy



Plan the Process



Provide Resources



Assign Responsibility



Train People



Manage Configurations



Identify and Involve Relevant Stakeholders



Monitor and Control the Process



Objectively Evaluate Adherence



Review Status with Higher Level Management

Level 3 - Institutionalize Organizational

Standards



Establish Standards



Provide Assets that Support the Use

of the Standard Process



Plan and Monitor the Process Using a

Defined Process



Collect Process-Related Experiences

to Support Future Use

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Independent Process Areas

• Every organization performs data

management disciplines

• What is emphasized is what grows –

changing priorities

• Can become piecemeal – focus on

highest pain, not root causes

• DMM Process Areas were designed

to stand alone for evaluation

• Reflects real-world organizations

• Simplifies the data management landscape

for all parties

• Because “everything is connected”

relationships are indicated

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What the DMM is Not



Not a compendium of all data

management knowledge



Does not address every topic and

sub-topic that’s important

• 35+ years of evolution

• Foundational thinkers

• Talented vendors

• Wealth of collective experience

• Fully mature industry practices.



Too much specificity = 1000+ pages



Not a cookbook



Doesn’t identify the “one best way”

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You Are What You DO



Model emphasizes behavior –

• Creating effective, repeatable processes

• Leveraging and extending across the organization



Activities result in work products

• Processes, standards, guidelines, templates, policies, etc.

• Reuse and extension = maximum value



Non-prescriptive – technology, architectural

approaches, organizational structures, etc.



Too much specificity = 1000+ pages = overwhelming

and forces organization into non-optimal solutions

Reuse

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How the DMM

SM

helps the DM Professional

“Help me to help you” – platform for your customers – conveys

roles, shared concepts, complexity, connectedness

Provides an integrated 360 degree view - energizes collaboration, increased involvement of lines of business

Actionable and implementable initiatives, grounded in business strategy and organization’s imperatives

Enhances business cases for funding of rapid achievements Qualifications – the “A Team” for the global standard