sei dmm-intro1

SEI-DMM (Data Management Maturity Model) v1.0

A Synopsis and a Ready Reckoner

SEI-DMM : Table of Contents

S No. Topic

1. What is DMM?

2. DMM Framework – An Overview

3. DMM Framework - Detailed

4. Process Area Description

5. Functional Capability & Maturity Definition

6. Process Areas of Category 1 - DM Strategy

7. Process Areas of Category 2 – Data Governance

8. Process Areas of Category 3 – Data Quality

9. Process Areas of Category 4 – Data Operations

10. Process Areas of Category 5 – Platform and Architecture

11. Supporting Processes

12. Infrastructure Support Practices

What is DMM ?

Intended as a comprehensive reference model for the state-of-the-practice process improvement

Defines fundamental business processes of Data management and specific capabilities that constitute a gradated path to maturity

Allows organizations to evaluate themselves against documented best practices, determine gaps and improve the management of their data assets across functional lines of business and geographic boundaries

DMM Framework – An Overview

Comprises of 20 Data Management Process Areas across 5 major categories

5 Supporting Process Areas and 3 Levels of Infrastructure Support Practices

Categories

DM Strategy

Data Quality

Data OperationsPlatform & Architecture

Data Governance

Supporting Processes

Infrastructure Support Practices

DMM Framework - Detailed

Category Process Areas

DM Strategy DM Strategy

Communication

DM Function

Business Case

Program Funding

Data Governance

Governance Management

Business Glossary

Metadata Management

Data Quality Data Quality Strategy

Data Profiling

Data Quality Assessment

Data Cleansing


Category Process Areas

Platform & Architecture

Architectural Approach

Architectural Standards

DM Platform

Data Integration

Historical data archiving and retention

Data Operations

Data Requirement Definition

Data Lifecycle Management

Provider Management


Category Process Areas / Practices

Supporting Processes

Measurement and Analysis

Process Management

Process Quality Assurance

Risk Management

Configuration Management

Infrastructure Support Practices

ISP Level 1 – Perform the Functional Practices

ISP Level 2 – Implement a Managed Process

ISP Level 3 – Institutionalize Organizational Standards

Process Area Description

Each PA composed of

Purpose – Why an org. wants to implement processes of this PA

Introductory Notes – What the org. will accomplish if it implements the PA

Goals – What key capabilities of the org. will be when the PA is implemented

Core Questions – Use the questions to quickly evaluate if the PA is achieving the desired results

Related PAs – PAs that provide inputs, consume outputs or provide material support

Functional Practices – What capabilities need to be in place to successfully achieve the intended results for each maturity level 1-5

Example Work Products – Types of work products that would be produced by successful implementation of the PA

Functional Capability and Maturity Definition

Level 1 – PerformedData managed as per the requirement of each project implementation

Level 2 – ManagedAwareness of the importance of Data as a critical infrastructure Asset

Level 3 – DefinedData treated at organizational level as critical to the success of its mission and performance

Level 4 – MeasuredData treated as a competitive advantage

Level 5 – OptimizedData is seen as critical for survival in a dynamic and competitive market

Process Areas in more detailDeep Dive into the Process Areas of each Category

CATEGORY 1 : DM Strategy

DM Strategy – An Overview

DM Strategy

• Focus on development, strengthening and enhancement of Enterprise DM Program• Define DM Vision, Goals & Objectives and align it with Org. business goals and

objectives• Ensure that the relevant stakeholders are aligned to the program’s implementation

Communication

• Importance of Bi-directional Stakeholder Communication• Planned approach to facilitate continued collaboration among stakeholders• Determine types and frequency of program information via multiple channels

DM Function

• Effectively scope, plan and resource DM activities as a sustained, continuous function• Develop Strong Leadership• Inculcate a shared stakeholder approach to DM Roles and Responsibilities

Business

Case

• Helps Org. to frame, justify and gain approval for DM Initiatives based on the scope and plan created for the DM Strategy

Program Funding

• Addresses the development and ongoing justification of funding• Funding model employed for the DM program and its component projects• Provide appropriate funding for phased, sustained DM improvements

PA 1 – DM StrategyAn Effective DM Strategy defines why the Org. is implementing a DM program, explains what the overall program aims to achieve and identifies how the various components of the initiative fit together. A functional DM strategy should be developed collaboratively and approved by all stakeholders.

A Current State Assessment including capability gap analyses and identifying key dependencies provide a foundation for buy-in to the strategy and the corresponding plan for the implementation

The DM Strategy defines the overall framework of the program and usually consists of› A Vision Statement e.g. goals & objectives ; core operating

principles; priorities› Program Scope e.g. Including both key business areas (Customer

accounts) and DM priorities and key data sets› Business Benefits› Selected DM Framework & how it will be used› Major gaps identified in the current state based on a DM

assessment

PA 1 – DM StrategyG

oals 1. Establish,

maintain and follow a DM Strategy approved by all the relevant stakeholders communicated across the org and reflected in architecture, technology and business planning2. Maintain the DM Strategy for all business areas thru data governance3. Develop, moni-tor and measure the plan for guid-ing the DM program imple-mentation

Core

Qu

esti

on

s 1. Do Executive Stakeholders visibly & actively support the DM strategy?2. Is the DM roadmap aligned with business priorities and milestones?3. Is there sufficient understanding and agree-ment among executives and operational, IT and business stakeholders to support a long term sustainable DM program?4. How are projects aligned with the roadmap that guides the imple-mentation of the DM program?5. Are resources in place to architect, design & lead the DM program and train to enable the desired maturity?

Rela

ted

PA

s 1. Business Case PA2. Communications PA3. Architectural Standards PA4. Data Lifecycle Management PA

PA 1 – DM StrategyLevel 1

: P

erf

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ed 1.Docume

nted Business Objectives2. DM Objectives, priorities and scope for a project3. Report on DM outcomes vs Objectives for a project

Level 2

: M

an

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ed 1. DM

objectives and corresponding metrics2.DM scope Definition3. Subject area mapping to functions that create, update and delete data4. Approved list of DM priorities5. DM priorities mapped to business objectives6. Project Prioritization list7. Capability enablement Sequence plan

Level 3

: D

efi

ned 1. DM Stategy

2. List of DM objectives & Priorities3. DM Policies4. Stakeholder participation and approval docs5. DM program scope docu-mentation6. DM strategy sequence plan7. DM program metrics8. DM program Cost benefit analysis9. DM program reviews10. DM Strategy Dashboard

Level 4

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Level 5

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Functional Practice Statements & Example Work Products

1.Metrics based DM Program Reports2. Plan & documentation for monitoring emerging industry or regulatory requirements3. DM Policies

1.External publications & PPTs about best practi-ces at industry2.Comparative analysis reports of best practices

PA 2 – DM CommunicationsG

oals 1. DM Communi-

cation Strategy ensures that the right messages about the program are under-stood by the right people at right time2. Industry or regulatory guidance that impacts data management is promulgated internally in a timely manner3. Stakeholders participate in the development of DM communications

Core

Qu

esti

on

s 1. How are policies, standards and processes for DM promulgated?2. How does the org keep stakeholders informed about DM plans and projects?3. How is bi-directional communication accomplished among business, IT, DM and executive management about DM priorities, approaches and deliverables?

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PA

s 1. DM Strategy PA

PA 2 – DM CommunicationsLevel 1

: P

erf

orm

ed 1.Commu

nications are managed locally e.g. Announcements, emails , meeting notes or web portal

Level 2

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an

ag

ed 1.Communicati

on Policy2.Announcements, emails , meeting notes or web portal3.Communications Strategy4.Communications Examples

Level 3

: D

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ned 1.Communicati

on Policy2.Announcements, emails , meeting notes or web portal3.Communications Effective-ness Metrics4.Communication Plan5. Peer feed-back about communication

Level 4

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Level 5

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1.Changes to DM communication plans linked to communications effectiveness metrics2.Regulatory corres-pondence i.e. responses to inquiries, reports and memos

1.External communications2.Impacted public policies and industry best practices

PA 3 – DM FunctionG

oals

1. Establish and follow role definitions, responsibilities, authorities and accountability to decisions and interactions related to DM.2. Establish the process for executive oversight of data manage-ment to ensure adoption of consistent policies, processes and stds3. Align the DM function to data governance on DM priorities and decisions.4. Develop, evaluate & reward DM mgmt staff

Core

Qu

esti

on

s 1. Is the DM function defined such that it is clear to all relevant stakeholders?2. Is the DM function aligned to DM strategy through metrics and measures?3. What role do executives play in the design and oversight of the DM function?

Rela

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PA

s 1. DM Management PA2. Program Funding PA3. Governance Management PA4. Data Lifecycle Management PA

PA 3 – DM FunctionLevel 1

: P

erf

orm

ed 1.DM

Resourcing and oversight are event-driven e.g. .Project communications, meeting min2.Assignment of data related roles to projects

Level 2

: M

an

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ed 1.Policies

2.Process documents3.Program guides4.Defined Roles and Responsibilities5. Metrics related to the DM function6. Function review notes or report7. Lessons learnt document

Level 3

: D

efi

ned 1.DM function

documentation2.DM Structure3.Training records4.Compliance or audit reports5. Project reports6. Governance oversight plan7. Communi-cation plans and schedules8. Definition of roles, responsi-bilities9. Performance measures

Level 4

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1.DM Structure2.Performance measures3. Record of changes to DM structure

1.Resource plans2.Priorities aligned with strategy3. Strategic decisions and supporting metrics

PA 4 – Business CaseG

oals

1. Obtain executive sponsorship for the DM program2. Stakeholders approve and adopt the business case across lines of business.3. Business cases justify and help to ensure sustainable financing for DM initiatives4. Business cases for DM are comparable to approved business cases for other org-wide investments

Core

Qu

esti

on

s 1. How does the org. determine the level of investment required for the DM program?2. How does the org. decide whether to develop one umbrella business case or multiple, linked business cases?3. What are the success criteria for the business case?4. Who needs to be involved and who needs to approve?5. Does the business case reflect the objectives and priorities of the DM strategy and sequence plan?6. Does the business case reviewed and approved by DM sponsors?

Rela

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PA

s 1. DM Strategy PA2. Program Funding PA3. Governance Management PA4. Data Lifecycle Management PA

PA 4 – Business CaseLevel 1

: P

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ed 1.A

Business Case is developed for project initiatives e.g. 1. Project documentations, meeting min, discussion documents2.Project level business case

Level 2

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1.Business case standard methodology2.DM Business case initiatives3.Documentation or notes of business case approvals and rejections L

evel 3

: D

efi

ned 1.DM business

cases are defined and consumed by all stakeholders2.Approval documentation for DM business cases & DM TCO and methodology3.Cost benefit analysis results for DM4. DM business case perform-ance metrics5. Traceability matrix for DM TCO 6. Process to collect info on DM costs & allocation methodology

Level 4

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1 TCO mgmt reports2.Program change recommendations based on cost metrics3. Infra budgets4. DM TCO metrics5. DM TCO methodology doc.6.DM TCO change recommendation 7. Audit results & Perf scorecard

1.Proposed changes and updates to DM TCO model2.Published industry articles, white papers, conference sessions3. Predictive analysis tools and models

PA 5 – Program FundingG

oals

1. Priorities and criteria for both discretionary and non discretionary investment are established and followed2. Sustainable program funding methods for making cost and benefit allocations, managing expenditures and establishing priorities are defined and followed.3. Program funding reflects business objectives and organizational priorities

Core

Qu

esti

on

s 1. Is there an approved set of investment criteria and priorities for DM?2. How does data governance provide oversight for DM funding?3. Was the program funding approach developed, evaluated and approved by relevant stakeholders?4. Does the funding model reflect the org’s business models, priorities and financial decision processes?5. Are there defined Cost benefit allocation methods, expense mgmt practices and business cases across the org?

Rela

ted

PA

s 1. Business Case PA2. DM Strategy PA3. Governance Management PA

PA 5 – Program FundingLevel 1

: P

erf

orm

ed 1.DM

project budgets2.DM funding approvals3. Funding requests that incl cost benefit analysis L

evel 2

: M

an

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g 1.DM Business cases 2.DM Program funding method3.DM Budget4.Mgmt reports on mapping of DM costs to the overall pgm,business unit and projects5.Governance documentation related to funding

Level 3

: D

efi

ned 1.DM funding

criteria2.Budget planning process3.Metrics to measure investment and funding objectives4. Documented DM funding model5. Reports measuring DM benefits6. Prioritization criteria and mapping to DM strategy

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1 Metrics and analysis of program funding effectiveness

1.Public presentations or white papers about DM funding2.Approved changes to program funding based on predictive analysis3. Presentations, white papers, articles etc


CATEGORY 2 : Data Governance

Data Governance – An Overview

Governance

Management

• Addresses the processes that facilitate collaborative decision making

• Implement effectively the building, sustaining and compliance functions of governance bodies

Business Glossary

• Helps an organization to achieve a common understanding and representation of an expanding compendium of approved business terms

• Prioritize and sequence the development of the business terms, manage their creation and changes over time

Metadata Management

• Provides top-down approach to architecting, planning, populating and managing the metadata repository to fully describe the organization’s data assets

PA 6 – Governance Management

Effective Data Governance management provides oversight, ensures stakeholder collaboration ond facilitates decisions for critical data subject areas. It addresses three basic data governance functions supporting the org’s data assets – building, sustaining and compliance

Building is the creation of new capabilities Sustaining consists of the processes for collaboration,

evaluation and decision making Compliance is instituted and managed to control data

assets Key Functions of data governance are

› Approve the enterprise data strategy, policies & Stds› Define business terms by subject areas› Assign accountabilities and responsibilities› Develop decision rights and change mechanisms› Address regulatory and other external requirements, data security and

access› Enforce Compliance


Goals

all 1. A Process is

established and followed for aligning data governance with business priorities e.g. ongoing evaluation and refinement to address changes in the business like adding new domains and functions2. Data Governance ensures that all the relevant stakeholders are included and roles and resp. are defined clearly.3. Compliance and control mechanisms with appropriate policies, processes and stds followed

Core

Qu

esti

on

s 1. Does it facilitate collaboration and decision making across business & IT functions?2. Does it clearly define responsibilities and accountability for data domains?3. Does it provide a mechanism for definition of priorities and resolution of competing priorities?4. Does it effectively provide a process for defining, escalating and resolving issues?5. How does the executive sponsors support and how are they informed of the efforts ?6. Does the org have a process to review the activities?

Rela

ted

PA

s 1. Data Lifecycle Management PA2. DM Strategy PA3. Data Management Function PA4. Communications PA


Level 1

: P

erf

orm

ed 1.Governa

nce Process Documentation2.Evidence of imple-mented governance proc-esses3. Descri-ption of data governance roles and responsibilities

Level 2

: M

an

ag

ed

1.Data Governance Charter2.Data Governance Charter3.Documented processes and stds incl. decision process, issue resolution and operations4.Roles, responsibilities and accountability matrix5.Meeting Min.

Level 3

: D

efi

ned 1.Executive

level data governance charter2.Org-wide data governance rollout plan3.Metrics to evaluate the effectiveness of data governance4. Adoption of policies and processes5. Training materials 6. Meeting Min.7. Reports of decisions and action items

Level 4

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1 Metrics based analytical performance reports2. Executive reports of governance effectiveness

1.Internal ppt or white papers on data governance model as an industry best practice2.Reports on continuous governance improvements

PA 7 – Business GlossaryG

oals

all 1. The language of

Data is unambiguously aligned with the language of the business.2. The Org has created a comprehensive, approved business glossary.3. The Org. follows the stds for naming, definitions and metadata of business terms.4. Org-wide access to the business glossary leads to a common understanding of business terms.5. Consistent application of business terms as new projects come

Core

Qu

esti

on

s 1. Is there a policy mandating the use of business glossary?2. How are the glossary created, approved, verified and managed?3. Are the business terms referenced in the design of data stores and repositories?4. Does the org perform cross-ref and mapping of specific business terms to standardized ones?5. Is it accessible to all and how is it enhanced and maintained ?6. Is compliance process in place to ensure that BUs and projects correctly apply business terms?

Rela

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PA

s 1. Data Lifecycle Management PA2. Meta Data Management Function PA

PA 7 – Business GlossaryLevel 1

: P

erf

orm

ed 1.Defined

business terms in project documentation2.Business glossary maintained by a BU3. Business terms and logical attribute mapping

Level 2

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1.Business Glossary2.Business Glossary Policy3.Business Glossary management process4.Business Glossary available online5. Business Glossary Compliance process6. Data Requirements documentation using business terms

Level 3

: D

efi

ned 1.Business

Terms Glossary2.Mapping of business terms to attributes to physical data elements3.Business Terms compliance process4. Policy on the use of std business terms5. Business terms metrics6. Compliance monitoring & Business NC and exception report7. Business Glossary update log8. Impact assessment results

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1 Metadata repository – unified business terms glossary integrated with logical and physical data and ref to industry stds2. Metrics and analysis reports3. Published exception reports, impact analyses and remediation plans

1.Business rules and ontologies associated to business terms in automated mechanism2.White papers and case studies

PA 8 - Metadata Management

Metadata is a category of information that identifies, describes, explains and provides content, context, structure and classification related to Org’s data assets and enables effective retrieval, usage and management of these assets

Effective metadata management and the creation of the Org’s metadata catalog facilitates, supports and contributes to achievement of critical data management activities and objectives

Metadata contains 3 Categories› Business Metadata e.g. Taxonomies, Ontologies, business glossaries

and standards› Technical Metadata e.g. Run-time or dynamic metadata like XML,

messaging and config. Information and Design-time or static metadata like physical data models, DDLs, Data Dictionary and ETL scripts

› Operational Metadata e.g. Process metadata like process steps for production and maintenance, data quality measurement and analysis, Business Rules, names of systems, jobs and programs as well as governance , regulatory and other control requirements

PA 8 – Metadata Management

Goals

all 1. Management

appreciates the value of metadata.2. Data governance oversight directs the development and implementation of the metadata strategy, categorization and stds and ensures its adoption and consistent use.3. Contents of the metadata repository span all categories and classification of data assets and reflects the implemented data layer of Org.4. Internal and relevant external stds are incorporated into metadata

Core

Qu

esti

on

s 1. Is the metadata strategy aligned with internal and the selected external stds?2. How is the scope of metadata addressed for inclusion within the metadata repository defined?3. Are all relevant stakeholders involved in defining metadata categories and properties?4. What is the method for developing and evaluating metadata stds and processes?5. What is the method for maintaining the metadata repository?6. Are Roles and Resp. defined for the capture, updating and use of metadata?

Rela

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PA

s 1. Governance Management PA2. Data Management Function PA3. Data Management Lifecycle PA4. Data Requirements Definition PA5. Architecture Approach PA6. Business Glossary PA7. Data Integration PA

PA 8 – Metadata Management

Level 1

:

Perf

orm

ed1.Metadat

a repository or virtual metadata repository

Level 2

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n 1.Metadata Management Policy2.Business Metadata3.Metadata repositories4.Metadata Meta Model5. Metadata governance and publication approval documentation6. Metadata standards7. Audit results8. Metadata change log

Level 3

: D

efi

ned 1.Metadata

management strategy2.Metadata roles and responsibilities3.Repository reports of metadata extensions4. Metadata management org stds5. Metadata meta model diagrams6. Gap analysis results compar-ing imple-mented plat-forms against metadata7. Metadata metrics reports & progress reports

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Functional Practice Statements & Example Work Products1.Documented quant-itative objectives for meta-data2.Comprehensive meta-data repo-sitory reports3.Measurement appro-aches to include statistical and other quantitative techniques4.Process efficiency reports5.Unified metamodel6. Stds and practices

1.Consistent reporting based on std definitions2.Results of analysis of repository information3.Documentation of prediction models4.Define Quantitative objectives5.Impact analysis reports


CATEGORY 3 : Data Quality

Data Quality – An Overview

Data

Quality

Strategy

• Describes activities designed to help the org. develop a defined, approved and integrated plan to ensure that the quality of data meets business needs

Data

Profiling

• Activities that help the organization to assess the data under management against a set of quality objectives which are defined in the Data Quality Strategy

Data

Quality

Assessment

• Activities that help the organization to assess the data under management against a set of quality objectives which are defined in the Data Quality Strategy

Data

Cleansing

• Achieving efficiencies and successful, repeatable processes for Data Cleansing activities reduces effort and lowers costs enabling the org. to assure ‘fit for purpose’ data assets across its data assets and physical data stores

PA 9 – Data Quality StrategyData Quality Strategy defines the goals, objectives and plans for improving data integrity. Data Quality Strategy addresses data store design, business process and aligned to the target data architecture and reduce ROT (redundant, obsolete and trivial) information in the data.

Strategy created based on an analysis of existing quality issues and business objectives for trusted data.

High quality of data is not the result of technologies alone but also the result of continued scrutiny shared and communicated by all the stakeholders

To achieve a data quality culture, an org. should develop a comprehensive measurable strategy applicable across all business units, business processes and applications.

Measurement criteria to be defined for each of the dimensions of quality like› Accuracy› Completeness› Coverage› Conformity› Consistency› Duplication› Integrity› Timeliness

PA 9 – Data Quality StrategyG

oals 1. Data quality

strategy collaboratively developed with lines of business aligned with business goals.2. Priorities and goals translated into actionable criteria.3. Org-wide data quality program defined and roles and responsibilities established to meet program needs.4. Data quality processes are integrated and aligned with the data quality strategy

Core

Qu

esti

on

s 1. Is data quality emphasized in all initiatives involving the data stores?2. How does the org. measure data quality program progress?3. Org unit made responsible for maintaining the data quality strategy and initiatives?4. Is the strategy widely distributed, communicated and promulgated?5. Does it clearly describe objectives, policies and processes?6. Is it integrated with the systems development lifecycle and business process improvement efforts?

Rela

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PA

s 1. Data Requirement Definition PA2. Data Management Strategy PA3. Data Quality Assessment PA4. Data Profiling PA5 Data Cleansing PA

PA 9 – Data Quality StrategyLevel 1

: P

erf

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ed 1. Data

Quality Plans, criteria and rules2. Meeting notes3. Status updates4. Metrics5. Data quality processing documentation6. Rules implemented in Database and S/W documented as requirements

Level 2

:

Man

ag

ed

1.Data Quality Strategy2. Data Quality sequence plan with key milestones identified3. Policies, processes and guidelines L

evel 3

: D

efi

ned 1. Data Quality

strategy approvals2. Data management stds providing criteria and guidelines3. Approved policies and processes4. Approved metrics5. Embedded SDLC data quality processes6. Business rules organized around subject areas7. Standard data quality processes

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Functional Practice Statements & Example Work Products1.Approved changes to the data quality strategy2.Approved changes to policies, processes and metrics 3.Std metrics based on analytical reports of data quality progress4. Approved modifications to the strategy, seq plan, supporting policies, processes and plans

1. PPTs, whitepapers and articles communicating best practices for data quality strategy

PA 10 – Data ProfilingG

oals 1. A standard set of

methods, tools and processes for data profiling is established and followed.2. Produce recommendations for improving the data quality improvements to data assets.3. Physical data representation is factual, understandable and enhances business understanding of the set of data under management.

Core

Qu

esti

on

s 1. Does the org. have a standard method for profiling data?2. Has the org trained or acquired staff resources with expertise in data profiling tools and techniques?3. Does the org. apply statistical models to analyze data profiling reports?4. Do policies and processes specify the criteria for a data store to undergo profiling?5. Is data profiling scheduled based on defined events, considerations or triggers?

Rela

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PA

s 1. Business Glossary PA2. Metadata Management PA3. Architectural Standards PA

PA 10 – Data ProfilingLevel 1

:

Perf

orm

ed1. Data

Profiling reports2. List of data profiling checks

Level 2

: M

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1.Data profiling methodology documentation2. Approved data profiling plan and schedule3. Data profiling findings reports and metrics4. Proposed business rule additions based on data profiling5. Defined skill set and training plan for staff with data quality responsibilities

Level 3

: D

efi

ned 1. Data

profiling stds incl. criteria for processes, stds, best practice criteria, tailoring and reporting formats2. Data profiling methodologies tailored to org stds3. Traceability of data requirements with content4. Metrics5. Data related decisions and rationale6. Std Data profiling tools & baselines7. Business & technical impact analysis

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Functional Practice Statements & Example Work Products1.Documented profiling methodology, best practices and stds2.Reports on profiling results 3.Dashboards,Scorecards or other decision support tools for data quality and data profiling4. Portal displaying data quality models and results used for performance baselines

1.Log of stakeholders’ usage of profiling results2.Control charts showing stabilized processes3.Data profiling improvements included in strategies, programs and reports4.Real time data profiling reports generated on schedule5. Conclusions drawn from data profiling analyses

PA 11 – Data Quality Assessment

Goals 1. Establish &

sustain a business driven function to evaluate and improve the quality of data assets.2. Standardize data quality assessment objectives, targets and thresholds as per industry techniques.3. Establish methods for statistical evaluation of data quality.4. Establish std data quality assesment reporting utilizing scorecards, dashboards and other analytical reports5. Utilize the results and conclusions of data quality assessments

Core

Qu

esti

on

s 1. Are std quality assessment techniques and methods documented and followed?2. How are data quality assessments conducted and are they scheduled or event driven?3. Are std data quality rules developed for core data attributes?4. Are data quality rules engines or assessment tools employed?5. Are the business, technical and cost impacts of data quality issues analyzed and used as input to data quality improvement priorities?

Rela

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PA

s 1. Data Quality PA2. Data Profiling PA3. Metadata Management PA

PA 11 – Data Quality Assessment

Level 1

:

Perf

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ed1. Data

Quality Rules 2. Data Quality Assessment results

Level 2

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1.Documented objectives, targets and thresholds.2. Documented data quality dimensions and attributes3. Metrics for data quality assessments4. Documented analysis of business and technical impacts5. Effort estimates for data quality improvements6. Business stakeholders review data quality assessments & provide recommendations

Level 3

: D

efi

ned 1.

Documented scores, targets and thresholds for each std data quality dimension2. Published and accessible org level data quality rules for approved attributes3. Org level data quality assessment policy4. Std org level data quality assessment processes

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Functional Practice Statements & Example Work Products1.Audit & Control reports2.Data Quality assessment progress reports for improvements3.Data quality confidence surveys

1.Assessment analysis reports2. Process review documentation3. Process improvement proposals and approvals

PA 12 – Data CleansingG

oals 1. A Data Cleansing

strategy has been created and is consistently followed.2. Standard data cleansing processes are established and sustained.3. Data cleansing standards are consistently verified by all stakeholders.

Core

Qu

esti

on

s 1. Does the org have a reusable set of data cleansing processes (automated and manual) to resolve data quality issues?2. Is there a defined process for verifying corrections and assessing effectiveness?3. How does the org cleanse duplicate records?4. Are corrections implemented at the source of capture?5. Are data cleansing followed through to analysis of root causes?6. Does ROI incorporate data cleansing costs?7. Are consistent toolsets used?

Rela

ted

PA

s 1. Data Requirement Definition PA2. Data Quality Assessment PA3. Metadata Management PA4. Provider Management PA

PA 12 – Data CleansingLevel 1

:

Perf

orm

ed1. Data

Cleansing requirements.2. Data Cleansing guidelines.

Level 2

: M

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1.Data Cleansing policy.2. Data Cleansing processing and rules.3. Data Cleansing metrics.4. Data Cleansing plans.5. Data Correction methodologies.6. Data Cleansing issues

Level 3

: D

efi

ned 1. Data change

history log.2. Traceability matrix3. Data cleansing feedback4. RACI matrix for data cleansing governance, activities and rule development5. Data Cleansing results report templates

Level 4

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1.Service level agreements2.Feedback documentation

1.Meeting Min. showing involvement in stds.2. SLAs include cleansing processes and expectations for data providers


CATEGORY 4 : Data Operations

Data Operations – An Overview

Data

Requirements

Definition

• Contains practices which ensure that specifications for data used by a business process satisfy business objectives, are validated by stakeholders, prioritized and well documented through a repeatable process

Data Lifecycle

Management

• Assists an organization to ensure that its data flows are well mapped to business processes through all lifecycle phases

Provider Management

• Describes best practices for data source selection and controlled, bidirectional interactions with internal and external providers

PA 13 – Data Requirement Definition

Data Requirement Definition established the process used to identify, define, prioritize, document and validate the data needed to achieve business objectives. Data Requirements should be stated in business language and reuse the approved and standard business terms as it is essential for effectively sharing data across the organization.

Data Requirements Definition process is an important contributor to the enrichment, validation and creation of business glossary terms and definitions.

Method chosen for defining and documenting data requirements should be in alignment with application lifecycle processes.

Requirement Definition follows an orderly discovery and decomposition process that includes articulation of business concepts and needs.

Business Rules are developed in parallel with the logical design of the applications supporting the destination data store.


Goals 1. Data

requirements definitions consistently satisfy business objectives.2. All relevant stakeholders have a common understanding of data requirements.3. Approved standards are followed for data names, definitions and representations in requirements definitions as appropriate.

Core

Qu

esti

on

s 1. How are business and technical data requirements solicited, captured, adjudicated and verified with stakeholders?2. How are the data requirements mapped to the business objectives?3. How are approved data requirements validated against standard data definitions as well as logical and physical representations?

Rela

ted

PA

s 1. Data Lifecycle Management PA2. Data Profiling PA3. Data Management PA4. Governance Management PA5. Data Management Function PA6. Architectural Standards PA

Level 1

: P

erf

orm

ed 1. Catalog

of business terms and their definitions2. Data Requirements Documentation3. Review Board meeting notes4.Documented stakeholder requirements review decisions

Level 2

: M

an

ag

ed

1.Data Requirements specification document.2.Requirements mapping to business objectives.3.Requirements mapping to data models4. Stakeholder requirements approvals5. Review board notes and decisions.

Level 3

: D

efi

ned 1. Std . Data

requirements template2.Requirements mapping to use case documentation3.Requirements mapping to business processes4. Documented data security and entitlement rules5. Stakeholder or review board consensus documentation

Level 4

: M

easu

red

Level 5

: O

pti

miz

ed


1.Std toolset to maintain mapping and traceability between business and data requirements2.Selection criteria for adoption of industry best practices for the data requirements definition framework

1.Recommendation to improve data requirement processes.2. Decisions to change data requirement processes3. Public ppts, articles and white papers


PA 14 – Data Lifecycle Management

Goals 1. The data lifecycle

pertaining to selected business processes is defined and maintained to reflect changes.2. Business processes are mapped to data flows based on a framework for identifying and prioritizing shared data flows; this mapping extends through the data lifecycle at the attribute level.3. Mapping of data impacts, dependencies and interdependencies are defined and maintained.

Core

Qu

esti

on

s 1. What activities, milestones and products are defined for mapping business processes to the data created and maintained in support of these processes?2. Has the org. established clear roles and responsibilities for creating and maintaining a mapping of business processes to data?3. Are std process modeling methods and tools employed to model and define business processes?4. Does governance have a role in the management and orchestration of business process data needs, mapping and prioritization?

Rela

ted

PA

s 1. Data Requirements Definition PA2. Metadata Management PA3. Governance Management PA

Level 1

: P

erf

orm

ed 1.

Business process to data element mapping, specifying CRUD matrix2.Consumer and producer matrix3. Data Flow diagrams at attrib. level4. List of data sources and attributes for a data set

Level 2

: M

an

ag

ed

1.Data Change management process.2.Governance process for shared data assets and data sets.3.Business process cata-logs & maps to shared attri-butes.4. Data source selection crit-eria5. Mapping between data producers and consumers6. Business process model-ing tools7. Metadata repository8. Data attrib sourcemapping

Level 3

: D

efi

ned

1. Process to Data mapping template2.Data mapping project plan3.DM org. roles &responsibilities4. Change mgmt process for defined data sets5. Lifecycle data mapping of core business processes6. Data maps7. Context diagrams8. Interface, data source and destination change records9. Identified data attrib. sources

Level 4

: M

easu

red

Level 5

: O

pti

miz

ed


1.Metrics documentation and results2.Approved process mapping change requests3.Remediation process4.Remediation Plans

1.Data Dependencies Reports2.Recommendations to improve data lifecycle mgmt processes3. Data lifecycle forecasting reports4.Reports to Sr mgmt based on statistical analysis5.Public ppts,white papers or docs on data lifecycle mgmt process experience

PA 14 – Data Lifecycle Management

PA 15 – Provider Management

Goals 1. Data reqmts for

sourcing, procure-ment and provider mgmt incl. data quality criteria are assessed according to a documented process.2. Selecting, contracting, monito-ring and managing data providers is performed accord-ing to a std data source selection and control process3. Potential sources and providers, incl. their services, data scope, processes and technologies are identified.4. Std SLAs address all bus. Requirements & used to manage data providers

Core

Qu

esti

on

s 1. How are data sour-cing requirements captured, validated & understood?2. Are requirements for data sourcing specific, unambi-guous, driven by business require-ments and feasibly procurable?3. Is there a mecha-nism that ensures business approval of sourcing require-ments?4. How are data attri-butes mapped to data sources and downstream appli-cations? 5. How is the data source selection process managed?6. How are service and content quality from data providers monitored?

Rela

ted

PA

s 1. Data Requirements Definition PA2. Data Quality Strategy PA3. Governance Management PA4. Data Profiling PA5. Data Quality Assessment PA

Level 1

: P

erf

orm

ed 1. Data

sourcing requirements.2.Data source selection criteria.3.Contract Coverage checklist for exter-nal providers4. Data feed evaluation reports5. Agree-ment with internal & external data providers6. Approv-ed vendor invoices

Level 2

: M

an

ag

ed

1.Procurement policies.2.Data source selection criteria.3.Data sourcing requirements.4. Mapping of data require-ments to sources.5. Providers SLAs6. Procurement process7. Data Source Evaluations8. Meeting Min. with data providers

Level 3

: D

efi

ned

1. Std data sourcing process2.SLA template3.SLAs with providers4. Defined Quality criteria for data sourcing5. Defined metrics for measuring data sourcing6. Updates to data sourcing process based on stakeholder feedback & best practices7. Stds, procedures, policies and work flow diag.8. Data provider meeting minutes

Level 4

: M

easu

red

Level 5

: O

pti

miz

ed


1.Performance reports, dashboards, scorecards & heat maps2.Scoring criteria for data providers3.Analytical reports of provider performance4. Recomm-endations for changes to provider SLAs

1.Analytical results2.Data Sourcing performance related recommendations3. Alignment mechanism for data sources to business objectives4.PPTs, articles and white papers

PA 15 – Provider Management


CATEGORY 5 : Platform and Architecture

Platform & Architecture – An Overview

Architectural Approach

• Assists the Org. in developing an approved approach to scope and design a consumable data and technology architecture that stresses on the duplicate data reduction and maximizing data sharing

Architectural Standards

• Addresses the development and approval of standards of data representation, data access and data distribution

DM

Platform

• Emphasizes stakeholder involvement and governance in decisions that affect platform selection and implementation

Data Integration

• Helps the Org. to create and maintain alignment with business needs through design of shared data stores and to establish and enforce standards

Historical Data archiving & Retention

• Addresses versioning, record retention and archiving, ensuring that data satisfies availability needs, business needs and regulatory requirements as applicable

PA 16 – Architectural Approach

Goals 1. The approved

architectural approach is consistent with business needs and Arch. stds.2. The transition plan from the “As-is” to the “To-be” state is consistently monitor-ed to ensure that projects are aligned with long term objectives3. The Arch. approach is approv-ed and adopted by all relevant stake-holders.4. Platform and tech capability decisions are aligned with the arch approach and approved by stakeholders5. Metrics used by Bus. & IT stakeholders

Core

Qu

esti

on

s 1. How does the Org. approach archi-tecting information assets?2. Is the Arch appr-oach consistently followed and adopt-ed by all relevant stake-holders?3. What is the rationalization method used for eliminating duplicate data?4. Does the Org. have an approved data dictionary stack and governance applied to modifications, additions and sun setting? 5. Has the Org. documented and approved the tech capabilities and reqmts to satisfy operational bus. Continuity?

Rela

ted

PA

s 1. Data Management Strategy PA2. Architectural Standards PA3. Governance Management PA4. Data Integration PA5. Data Profiling PA

Level 1

: P

erf

orm

ed 1.Architec

ture design for imple-mentation2.Business and tech. approvals for archi-tecture3.Stakeholder list for architecture appro-vals

Level 2

: M

an

ag

ed

1.Documented approval for architectural designs.2.Approval process for arch design through governance.3.Approved arch utilization.4.Shared data interface traceability map.5.Implementation consistent with approved designs

Level 3

: D

efi

ned

1. Ration-alization reports and decision criteria2.Data related arch approach3. Implement-ation checklists aligned with transition plan4. Evaluation of external & inter-nal stds.5. List of Arch adoption stake-holders and BUs6. Tech req. specifications.7. Arch blue-print compared to the As-Is architecture8. Data Quality profiling reports applied to design

Level 4

: M

easu

red

Level 5

: O

pti

miz

ed


1.Cost Bene-fit analyses2.Quantitative perform-ance criteria & evaluation targets for designed components and archi-tecture3.Statistical models employed to guide arch decisions4.Document-ed limit-ations of current arch approach

1.Modifications to arch approach 2.Prediction model comp-arison report against business objectives3. Stakehold-er feedback 4.PPTs or pub-lications abt the org’s arch approach5. Identified enhanced bus. Capabilities due to enhanced data analysis

PA 16 – Architectural Approach

PA 17 – Architectural Standards

Goals 1. Develop a comp-

rehensive set of data standards aligned with the Arch approach and the DM strategy.2. Institute a sust-ainable standards development and maint. process involving business and IT stakeholders3. Establish effective governance and auditing processes for standards adherence and exceptions4. Define and enforce a data distribution stds for requests and approvals5. Define and enforce approved data access methods across platforms

Core

Qu

esti

on

s 1. What are the categories of stds required for the org’s target data arch and how are they scoped and defined?2. How does the org determine business needs and techno-logy strategy for dev-eloping approved, std data access and governance?3. How are data models approved, maintained and governed?4. Has the Org. defined architect-urally aligned, std data access methods and criteria?5. How does the Org promulgate, audit and enforce standards?

Rela

ted

PA

s 1. Data Management Strategy PA2. Data Quality PA3. Governance Management PA4. Data Management Function PA5. Data Requirements Definition PA6. Business Glossary PA7. Metadata Management PA8. Data Integration PA

Level 1

: P

erf

orm

ed 1.Data

Stds used by projects2.Validation of As-Is data stores against referenc ed stds

Level 2

: M

an

ag

ed

1.A Policy that requires adherence to standards.2.Standards artifacts.3.Standards approval process.4.Standards Change Request process.5.Project references to standards6. Guidance for incorporating stds into design

Level 3

: D

efi

ned

1. Compliance or regulatory reporting stds and require-ments2.Stds develop-ment and modification process3. Standards tailoring guidance4. Standards exception process5. Audit results reports6. Architecture review board meeting notes7. Data Std policies and procedures

Level 4

: M

easu

red

Level 5

: O

pti

miz

ed


1.Standards review docu mentation2. Impact analysis for proposed changes to stds3. Architect-uralstandards complia-nce metrics

1.Engagement with external stds bodies2.Research of emerging technologies3. Proposed stds for future technologies likely to be adopted4. PPTs and other published work related to data stds

PA 17 – Architectural Standards

PA 18 – Data Management Platform

Goals 1. The Platform

satis-fy the approved requirements and architecture.2. Processes exist and are followed for effective platform management to meet business needs3. The Platform is supported by adequately trained and skilled personnel4. The platform provides trusted data

Core

Qu

esti

on

s 1. How are authoritative data sources defined, selected and integrated into particular portions of the platform?2. How does the org address overlapping platforms and data duplication?3. Does the org have a process for making “build versus buy” decisions?4. How does the org address platform scalability, security and resiliency in accordance with? Anticipated growth of data, users and overall complexity?5. What forms of data, data exchange and interfaces are supported by the platform?

Rela

ted

PA

st 1. Data Lifecycle

Management PA2. Data Quality StrategyPA3. Governance Management PA4. Data Management Function PA5. Data Management Strategy PA6. Business Glossary PA7. Metadata Management PA

Level 1

: P

erf

orm

ed 1.Inventor

y of data management platforms and components

Level 2

: M

an

ag

ed

1.Data Management platform documentation2.Approved deployment and conversion and migration plans.3.Documented platform decisions and rationale.4.Documented stakeholder involvement in the design and approval of data manage-ment platform deployment plan

Level 3

: D

efi

ned

1. Document-ation mapping critical data elements to platforms 2.Documents identifying and justifying data duplication3. Platform implementation plan4. Platform architecture designs5. Platform performance data6. SLAs7. Platform metadata

Level 4

: M

easu

red

Level 5

: O

pti

miz

ed


1.Metrics to measure both qualit-ative and quantitative performance of DM platform2. Measure-ment and analysis plans3. Statistical analysis according to the measurement plan4. Approved decisions based on analysis of metrics

1.Causal analysis2.Performance prediction models3. Approvals for improve-ments4. Predicted Vs actual performance analyses5. Approved optimization plans6. Public PPTs and other formal & informal docs related to DM platform

PA 18 – Data Management Platform

PA 19 – Data IntegrationG

oals 1. Establish and

follow a consistent process to ensure ongoing business & technology align-ment for data integration.2. Data Integration is performed utilizing std processes and toolsets that enable compliance with data architecture stds & data quality requirements3. Proactively research and eval-uate integration technologies for application and adoption4. Establish, manage data conversion, transformation and enrichment so that the data is fully processed & meets quality stds

Core

Qu

esti

on

s 1. How are data consolidation needs assessed?2. How is future re-dundancyminimized?3. How does the org consolidate data effectively where redundancy exists?4. Do Data Integ-ration stds exist & are they reviewed, moni-tored, approved & enforced?5. Describe the compliance process-es employed to en-force integration stds?6. How are data quality thresholds & targets applied to sources of data at ingestion, integration?7. Are the processes to identify missing data automated?

Rela

ted

PA

s 1. Architectural Standards PA2. Data Quality Strategy PA3. Data Lifecycle Management PA4. Data Profiling PA5. Metadata Management PA

Level 1

: P

erf

orm

ed 1. Data

Integration scripts

Level 2

: M

an

ag

ed

1.Data Integration standards2.Verification and Validation plans.3.Integration test environ-ments.4.APIs5. Data Integration policy

Level 3

: D

efi

ned

1. Verification & Validation results2.Performance requirements3. Performance metrics and analysis results4. Measures & metrics for continuous improvement in data quality5. Integration method stds6. Data Delivery policy & SLAs7. Integration best practices guidance8. Standard interface specifications9. Integration environment CM process

Level 4

: M

easu

red

Level 5

: O

pti

miz

ed


1.Statistical analysis results2. Data profiling analyses3.Consolidated highly shared data with continuous improvement

1.Quantitative methods2.Performance triggers and thresholds3. Root cause analysis results4. PPTs, white papers or published articles

PA 19 – Data Integration

PA 20 – Historical Data, Retention & Archiving

Goals 1. Historical data is

managed consist-ently leveraging common standards2. Business needs for capturing and storing historical data are met.3. An approved process for deter-mining when and how data should be archived is followed containing defined activity steps4. Data retention periods are consistent with both legal and regulatory requirements.5. Data archives reflect organizational and regulatory requirements.

Core

Qu

esti

on

s 1. What are the arch stds & conventions applied to the structure & mgmt of historical data & how are the corres. Business rules defined and governed?2. How is data retent-ion for the required length of time assured?3. How is the integrity of archived data maintained?4. Is there a consistent approach for the retrieval & integration of archived historical data with current data?5. How is an audit trail for data changes monitored and managed?6.What consider-ations applied when archived data can be deleted?

Rela

ted

PA

s 1. Architectural Standards PA2. Data Requirements Definition PA3. Data Management Function PA4. Governance Management PA

Level 1

: P

erf

orm

ed 1. Backup

Registers for data stores and data archives2.Archiving procedures3. Change log files4. Data retention business rules5. Data archiving or dest-ruction procedures

Level 2

: M

an

ag

ed

1.Data Retention policies2.Restoration testing records.3.Encrypted archives.4.Data Encryption requirements5. Archived data access tests

Level 3

: D

efi

ned

1. Restoration procedure documentation2.Application with access to historical data3.Data logging policy including log retention4. Data archive requirements5. Archive backup and restoration requirements6. Restoration testing records for archived data7. Audit and test records

Level 4

: M

easu

red

Level 5

: O

pti

miz

ed


1.Improvement process2. Process improvement reports and records3.Change management records4. Regulator or stake-holders feedback5. Statistical and other quantitative analysis reports

1.Public PPTs, white papers, articles and other documents communicating processes and experience

PA 20 – Historical Data, Retention & Archiving

Process Areas in more detail

SUPPORTING PROCESSES

INFRASTRUCTURE SUPPORT PRACTICES

Supporting Processes – An Overview

Measurement & Analysis

• Addresses measures and select analytical techniques for identifying strengths and weaknesses in data management processes

Process

Management

• Addresses a usable set of organizational process assets and plans, implements and deploys organizational process improvements informed by the business goals and objectives and the current gaps in the organization’s processes

Process

Quality Assurance

• Provides staff and management with objective insight into process execution and the associated work products

Risk

Management

• Identifies and analyzes potential problems to take appropriate action to ensure objectives can be achievedCo

nfiguration Management

• Addresses the integrity of the operational environment using configuration identification, control, status accounting and audits

SPA 1 – Measurement & Analysis

Purpose & Overview• Purpose of this SPA is to develop and sustain a measurement capability and

analytical techniques to support managing and improving DM activities• Measurement and analysis provides visibility into the performance of the

DM program and involves activities like specifying objectives of measurement; analysis techniques and mechanisms for data collection, storage, reporting & feedback; implementing the above techniques and provide objective results to be used for making informed decisions and take the appropriate action.

• The Integration of measurement and analysis into DM processes supports activities like planning & estimating; tracking actual progress; identifying & resolving issues; integration of remedial actions into the DM program etc

Goals• A set of metrics that measures the

satisfaction of the DM program’s objectives is established and used

• The process of measuring DM capa-bilities and improvements based on defined metrics is established & used

• Org-wide access to DM measure-ments & analysis results

• Stakeholders are kept informed about the status of the DM program

Core Questions• What measures and analyses exist

to determine if DM goals and objectives are being met?

• How does the Org define, measure, analyze and report on DM?

• How are measurements and analyses integrated into DM processes?

SPA 2 – Process Management Purpose & Overview• Purpose of this SPA is to establish and maintain a usable set of org. process

assets and plan, implement and deploy org process improvements informed by the business goals and objectives & the current gaps in the org’s processes

• Org. process assets enable consistent process execution across the org. and provide a basis of cumulative, long-term benefits to the organization

• Improvements to the processes are obtained from various sources like measurement of processes; lessons learned in implementing processes; results of process appraisals; product & service evaluation activities; customer satisfaction evaluations and benchmarking against other org’s processes an d recommendations from other improvement initiatives in the organization.

Goals• The Org operates according to its

set of standard processes• The Org follows defined methods

for maintaining their processes to accommodate changes in business requirement, stds and technology

• Process measures, process assets and examples are maintained in a repository

Core Questions• How are processes, methods,

procedures, policies and standards maintained?

• How is process performance measured?

• How does the org. measure process compliance?

• How does the org. ensure that improvements are identified, pursued, implemented and validated?

SPA 3 – Process Quality Assurance

Purpose & Overview• The Purpose of this SPA is to provide staff and management with objective

insight into process execution and the associated work products• This SPA involves activities like objectively evaluating performed processes

and work products against applicable process descriptions, standards and proced-ures; identifying and documenting NCs; providing feedback to staff and managers on the results of QA activities and ensuring that NC issues are addressed.

• The methods used to perform objective evaluations are formal audits by separate QA organizations; peer reviews; in-depth review of work e.g. desk audits; distributed review of work products and process checks built into the processes such as fail-safe when they are done incorrectly

Goals• Management has visibility into the

quality of the process and products• NC issues are addressed at the

appropriate level• Process and Product quality have

become an embedded discipline at all levels in the organization

Core Questions• Are Process NC issues raised to an

appropriate level?• Are quality issues analyzed for

positive trending?• Do all relevant stakeholders have

visibility into the quality of the process and products?

SPA 4 – Risk Management Purpose & Overview• The Purpose of this SPA is to identify and analyze potential problems in

order to take appropriate action to ensure objectives can be achieved.• Risk Management addresses issues that could endanger achievement of

critical objectives• Effective Risk Management includes early and aggressive risk identification

through collaboration and the involvement of relevant stakeholders• Risk Management consider internal and external, technical and non-

technical sources of risks• Risk Management process involves defining a risk management strategy;

identifying and analyzing risks and handling identified risks i.e. risk mitigation

Goals• The Organization is operating with

an understanding of its current level of risk

• The Organization is pursuing risk mitigation plans to limit the potential damage from identified risks

• Risks are continually identified, analyzed and monitored.

Core Questions• Does the Org. know the amount of

risk it is operating under?• Has the Org. identified and

implemented risk mitigation and contingency plans?

• Does the Org. periodically monitor risks and take appropriate update actions?

SPA 5 – Configuration Management Purpose & Overview• The purpose of this SPA is to establish and maintain the integrity of the

operational environment using configuration identification, control, status accounting and audits

• CM is a partnership between business, data, and IT resources to control the integrity of the products, data stores and interfaces and changes to them.

• CM involves activities like identifying the configuration of the operational environment and data interfaces at given points in time; Controlling and managing data interfaces and the operating environments; Managing changes to data interfaces; Maintaining the integrity of data interfaces and providing accurate status of data interfaces to the end users & customers

Goals

• Maintain the integrity of data as changes occur.

• Define and implement a configuration and release management system.

Core Questions

• How is configuration management implemented and measured?

• How are data changes planned and controlled across the data lifecycle?

ISP - Infrastructure Support Practices

ISP –

Level 1Perform

the Functional Practices

• Ensure that adopting organizational components like project, business unit perform the processes

ISP – Level 2

Implement a Managed Process

• Ensure that adopting organizational components do the following : operate under an org policy; plan the process; provide resources; assign responsibility; train people, manage configurations, identify & involve relevant stakeholders; monitor and control the process; objectively evaluate adherence & review status with Higher management

ISP –

Level 3

Institutionalize Org. Standards

• Ensure that a set of std processes is in place; Org. assets support the use of the std process; elements can be tailored from the standard process to fit unique circumstances; process related experiences are collected to support future use and improvements

SEI-DMM (Data Management Maturity Model) v1.0

Prepared By:

Meena [email protected]

sei dmm-intro1

Data & Analytics