Data Ed: Best Practices with the DMM

Copyright 2013 by Data Blueprint

Welcome: Data Management Maturity - Achieving Best Practices using DMM

The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization's data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements. Key Takeaways: • Our profession is advancing its knowledge and has a wide

spread basis for partnerships• New industry assessment standard is based on successful

CMM/CMMI foundation• Clear need for data strategy• A clear and unambiguous call for participation

Date: July 14, 2015Time: 2:00 PM ETPresented by: Melanie Mecca & Peter Aiken

1

Presented by Melanie Mecca & Peter Aiken, Ph.D.

Data Management Maturity

Achieving Best Practices using DMM


Your PresentersMelanie Mecca • CMMI Institute/

Director of Data Management Products and Services

• 30+ years designing and implementing strategies and solutions for private and public sectors

• Architecture/Design experience in:

– Data Management Programs

– Enterprise Data Architecture

– Enterprise Architecture

• DMM primary author from Day 1

Peter Aiken • 30+ years data mgt. • Multiple Int. awards/recognition • Founding Director,

Data Blueprint (datablueprint.com)

• Associate Professor of IS (vcu.edu) • Past, President, DAMA

International (dama.org) • 9 books and dozens of articles • 500+ empirical practice

descriptions • Multi-year immersions w/

organizations as diverse as US DoD, Nokia, Deutsche Bank, Wells Fargo, Walmart, and the Commonwealth of Virginia

7


• Motivation

- Are we satisfied with current performance of DM?

• How did we get here?

- Building on previous research

• What is the Data Management Maturity Model?

- Ever heard of CMM/CMMI?

• How should it be used?

- Use Cases and Value Proposition

• Where to next?

• Q & A?

Outline: Design/Manage Data Structures

8

DMMSM Structure

9

Data Governance

Data Management

Strategy

Data Quality

Data Operations

PlatformArchitecture

SupportingProcesses

‹#›

DMM Primer

• Reference model of foundational data management practices• Measurement instrument to evaluate capabilities and maturity• Answers the question: “How are we doing?”• Guidelines for: “What should we do next?”• Baseline for: Integrated strategy & high-value specific

initiatives / improvements• By CMMI Institute with our Sponsors – Booz Allen

Hamilton, Lockheed Martin, Microsoft, and Kingland Systems - and many contributing experts

!10

‹#›

DMM Themes

• Architecture and technology neutral – applicable to legacy, DW, SOA, unstructured data environments, mainframe-to-Hadoop, etc.

• Industry independent – usable by every organization with data assets, applicable to every industry

• Emphasis on current state – organization is assessed on the implemented data layer and existing DM processes

• Launch collaborative and sustained capability improvement – for the life of the DM program [aka, forever].

If you manage data, the DMM will benefit you

�11


Maslow's Hierarchy of Needs

12

Data Management Practices HierarchyYou can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present

greaterrisk(with thanks to Tom DeMarco)

Advanced Data

Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA

Foundational Data Management Practices

13Copyright 2015 by Data Blueprint Slide #

Data Platform/Architecture

Data Governance Data Quality

Data Operations

Data Management Strategy

Technologies

Capabilities

‹#›

Foundation for Business Results

• Trusted Data – demonstrated and independently measured capability to assure customer confidence in the data assets

• Improved Risk and Analytics Decisions – a comprehensive and measured DM strategy ensures decisions are made based on accurate data

• Cost Reduction/Operational Efficiency – clarity about current and target states supports elimination of redundant data and streamlining of DM processes and data stores

• Regulatory Compliance – independently evaluated and measured DM capabilities to meet regulator requirements and

provide a yardstick within industries.

�14


• Motivation








• Where to next?

• Q & A?

Outline: Data Management Maturity

15


Motivation

• "We want to move our data management program to the next level" – Question: What level are you at now?

• You are currently managing your data, – But, if you can't measure it, – How can you manage it effectively?

• How do you know where to put time, money, and energy so that data management best supports the mission?

"One day Alice came to a fork in the road and saw a Cheshire cat in a tree. Which road do I take? she asked. Where do you want to go? was his response. I don't know, Alice answered. Then, said the cat, it doesn't matter."

Lewis Carroll from Alice in Wonderland

16


DoD Origins• US DoD Reverse Engineering

Program Manager

• We sponsored research at the CMM/SEI asking

– “How can we measure the performance of DoD and our partners?”

– “Go check out what the Navy is up to!”

• SEI responded with an integrated process/data improvement approach

– DoD required SEI to remove the data portion of the approach

– It grew into CMMI/DM BoK, etc.

17


Acknowledgements

0018-9162/07/$25.00 © 2007 IEEE42 Computer P u b l i s h e d b y t h e I E E E C o m p u t e r S o c i e t y

version (changing data into other forms, states, orproducts), or scrubbing (inspecting and manipulat-ing, recoding, or rekeying data to prepare it for sub-sequent use).

• Approximately two-thirds of organizational datamanagers have formal data management training;slightly more than two-thirds of organizations useor plan to apply formal metadata management tech-niques; and slightly fewer than one-half manage theirmetadata using computer-aided software engineer-ing tools and repository technologies.3

When combined with our personal observations, theseresults suggest that most organizations can benefit fromthe application of organization-wide data managementpractices. Failure to manage data as an enterprise-, cor-porate-, or organization-wide asset is costly in terms ofmarket share, profit, strategic opportunity, stock price,and so on. To the extent that world-class organizationshave shown that opportunities can be created throughthe effective use of data, investing in data as the onlyorganizational asset that can’t be depleted should be ofgreat interest.

Increasing data management practice maturity levels can positively impact the

coordination of data flow among organizations, individuals, and systems. Results

from a self-assessment provide a roadmap for improving organizational data

management practices.

Peter Aiken, Virginia Commonwealth University/Institute for Data Research

M. David Allen, Data Blueprint

Burt Parker, Independent consultant

Angela Mattia, J. Sergeant Reynolds Community College

A s increasing amounts of data flow within andbetween organizations, the problems that canresult from poor data management practicesare becoming more apparent. Studies haveshown that such poor practices are widespread.

For example,

• PricewaterhouseCoopers reported that in 2004, onlyone in three organizations were highly confident intheir own data, and only 18 percent were very con-fident in data received from other organizations.Further, just two in five companies have a docu-mented board-approved data strategy (www.pwc.com/extweb/pwcpublications.nsf/docid/15383D6E748A727DCA2571B6002F6EE9).

• Michael Blaha1 and others in the research communityhave cited past organizational data management edu-cation and practices as the cause for poor databasedesign being the norm.

• According to industry pioneer John Zachman,2 orga-nizations typically spend between 20 and 40 percentof their information technology budgets evolving theirdata via migration (changing data locations), con-

Measuring Data ManagementPractice Maturity: A Community’s Self-Assessment

MITRE Corporation: Data Management Maturity Model • Internal research project: Oct ‘94-Sept ‘95 • Based on Software Engineering Institute Capability

Maturity Model (SEI CMMSM) for Software Development Projects

• Key Process Areas (KPAs) parallel SEI CMMSM KPAs, but with data management focus and key practices

• Normative model for data management required; need to: – Understand scope of data management – Organize data management key practices

• Reported as not-done-well by those who do it

18

‹#›

CMMI Institute Overview

• Owned by Carnegie Mellon University

• Formed & evolved from Carnegie Mellon’s Software Engineering Institute (SEI) - a federally funded research and development center (FFRDC)

• Continues to support and provide all CMMI offerings and services delivered over its 20+ year history at the SEI

• Now for-profit, streamlined and focused on responding to business & market requirements

• $10 MM business, 24 full-time employees with dedicated training, partner and certification teams to support the ecosystem

�19


CMMI – Worldwide Process Improvement• Quick Stats:

– Over 10,000 organizations

– 94 countries

– 12 national governments

– 10 languages

– 500 Partners

– 1373 appraisals in 2013

20


Source: Applications Executive Council, Applications Budget, Spend, and Performance Benchmarks: 2005 Member Survey Results, Washington D.C.: Corporate Executive Board 2006, p. 23.

Percentage of Projects on Budget By Process Framework Adoption

…while the same pattern generally holds true for on-time performancePercentage of Projects on Time By Process Framework Adoption

Key Finding: Process Frameworks are not Created EqualWith the exception of CMM and ITIL, use of process-efficiency frameworks does not predict higher on-budget project delivery…

21


CMMI Model Portfolio

22

Establish, Manage, and Deliver Services

Product Development / Software Engineering

Acquire and integrate products / supply chain

Workforce development and management

Rearchitecting to present a more unified/modular offering

‹#›

DMM Drivers and Bio

• Data management is broad and complex = challenging

• An effective DM program requires a planned strategic effort – not a Project, or a separate Program – a lifestyle.

• Organizations needed a comprehensive reference model to precisely evaluate data management capabilities

• DMM unifies understanding and priorities of business, IT, and data management.

• Foundation for collaborative and sustained capability building.

Late 2009 – Gleam in the eye

Jan 2011 – Launch development

Sep 2012 – CMMI Transformation

Apr 2014 – Industry Peer Review

Aug 2014 – DMM 1.0 Released

DMM Timeline

Now–2016 – DMM Ecosystem

�23

Who Wrote It and Why

• Authors with deep knowledge and experience in designing and implementing data management

– Industry skills - MDM, DQ, EDW, BI, SOA, big data, governance, enterprise architecture, data architecture, business and data strategy, platform implementation, business process engineering, business rules, software engineering, etc.

• Consortium approach – proven approaches – Broad practical wisdom - What works – DM experts combined with reference model architects and business

knowledge experts from multiple industries – Extensive discussions resulting in consensus

• We wrote it for all of us – To quickly and accurately measure where we are – To accelerate the journey forward with a clear path

and milestones

24

‹#›

DMM and DMBOK

CMMI Institute and DAMA International are forming a collaborative partnership to:

• Eliminate any confusion between the two tools and highlight their complementarity

• Extend and enhance data management training for organizations and professionals

• Provide benefits to DAMA members (members receive a discount for our public training classes)

• Harmonize DMM and DMBOK offerings as they develop

�25


• Motivation








• Where to next?

• Q & A?


26

‹#›�27

‹#›

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 = maximize value, lower costs, happier staff

• Process Areas were designed to stand alone for evaluation• Reflects real-world organizations• Flexible for multiple purposes

• Whole model• Selected Category(ies)• Specific Process Areas

• Relationships are indicated because operationally, “everything is connected”

�28

One concept for process improvement, others include:

• Norton Stage Theory • TQM • TQdM • TDQM • ISO 9000

and focus on understanding current processes and determining where to make improvements.


DMM Capability Maturity Model Levels

Our DM practices are informal and ad hoc, dependent upon "heroes" and heroic efforts

Performed (1)

Managed (2)

Our DM practices are defined and documented processes performed at

the business unit level

Our DM efforts remain aligned with business strategy using standardized and consistently implemented practices

Defined (3)

Measured (4)

We manage our data as a asset using advantageous data governance practices/structures

Optimized

(5)DM is strategic organizational capability, most importantly we have a process for

improving our DM capabilities

29

‹#›

DMM Capability Levels

Performed

Managed

Defined

Measured

Optimized

Level

1

Level

2

Level

3

Level

4

Level

5

Risk

Quality

Ad hoc

Reuse

Stress

Clarity

�30

‹#›

Capability and Maturity Disambiguation

Capability – “We can do this”• Specific Practices – “We’re doing it well”• Work Products – “We’ve documented the processes we are

following” (processes, work products, guidelines, standards, etc.)

Maturity – “….and we can prove it”• Process Stability – “Take it to the bank”• Ensures Repeatability

• Policy• Training• Quality Assurance, etc.

�31

‹#›

DMM Structure

Core Category

Process Area

Purpose

Introductory Notes

Goal(s) of the Process Area

Core Questions for the Process Area

Functional Practices (Levels 1-5)

rRelated Process Areas

Example Work Products

Infrastructure Support Practices

eExplanatory Model Components Required for Model Compliance

�32

Maintain fit-for-purpose data, efficiently and effectively

DMM℠ Structure of 5 Integrated DM Practice Areas

33


Manage data coherently

Manage data assets professionally

Data architecture implementation

Data lifecycle implementation

Organizational support

DMM Process AreasData Management Strategy

34

Name Description


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 to ensure 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

Data Management Funding Funding justification for the data management program and initiatives, operational and financial metrics

Create, communicate, justify and fund a unifying vision for data management

DMM Process AreasData Governance

35

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

Active organization-wide participation in key initiatives and critical decisions essential for the data assets

DMM Process AreasData Quality

36

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

Data Cleansing Mechanisms to clean data, reporting and tracking of data issues for correction with impact and cost analysis

A business-driven strategy and approach to assess quality, detect defects, and cleanse data

Platform & Architecture

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

Architectural Standards Data standards for representation, access, and distributionData 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

DMM Process AreasPlatform & Architecture

37

A collaborative approach to architecting the target state with appropriate standards, controls, and toolsets

DMM Process AreasData Operations

38

Data Operations

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

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

Provider Management Standardization of data sourcing process, SLAs, and management of data provisioning from internal and external sources

Systematic approach to address business drivers and processes, building knowledge for maximizing data assets

DMM Process AreasSupporting Processes

39

Supporting Processes Adapted from CMMIMeasurement 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

Systematic approach to address business drivers and processes, building knowledge for maximizing data assets


• Motivation








• Where to next?

• Q & A?


40

‹#›

Natural events for employing the DMM

• Use Cases - assess current capabilities before: • Developing or enhancing DM program / strategy• Embarking on a major architecture transformation• Establishing data governance• Expansion / enhancement of analytics • Implementing a data quality program• Implementing a metadata repository• Designing and implementing multi-LOB solutions:

• Master Data Management• Shared Data Services• Enterprise Data Warehouse• Implementing an ERP• Other multi-business line efforts.

Like an Energy audit or an executive physical

�41


Assessment Components

Data Management Practice Areas


DM is practiced as a coherent and coordinated set of activities

Data Quality

Delivery of data is support of organizational objectives – the currency of DM

Data Governance

Designating specific individuals caretakers for certain data

Data Platform/Architecture

Efficient delivery of data via appropriate channels

Data Operations Ensuring reliable access to data

Capability Maturity Model Levels

Examples of practice maturity

1 – PerformedOur DM practices are ad hoc and dependent upon "heroes" and heroic efforts

2 – ManagedWe have DM experience and have the ability to implement disciplined processes

3 – Defined

We have standardized DM practices so that all in the organization can perform it with uniform quality

4 – MeasuredWe manage our DM processes so that the whole organization can follow our standard DM guidance

5 – Optimized We have a process for improving our DM capabilities

42


Industry Focused Results• CMU's Software

Engineering Institute (SEI) Collaboration • Results from hundreds organizations in

various industries including: ✓ Public Companies ✓ State Government Agencies ✓ Federal Government ✓ International Organizations

• Defined industry standard • Steps toward defining data management

"state of the practice"

43


Data Governance

Platform & Architecture

Data Quality

Data Operations

Focus: Implementation

and Access

Focus: Guidance and

Facilitation

Optimized (V)

Measured (IV)

Defined (III)

Managed (II)

Initial (I)

Development guidance

Data Adminstration

Support systems

Asset recovery capability

Development training

0 1 2 3 4 5

Client Industry Competition All Respondents

Data Management Practices Assessment

Challenge

Challenge

Challenge

Data Program Coordination

Organizational Data Integration

Data Stewardship

Data Development

Data Support Operations

44Copyright 2015 by Data Blueprint

High Marks for IFC's Audit


Leadership & Guidance

Asset Creation

Metadata Management

Quality Assurance

Change Management

Data Quality

0 1 2 3 4 5

TRE ISG IFC Industry Benchmarks Overall Benchmarks

1

2

3

4

5

Data

Prog

ram

Coor

dinati

on

Orga

nizati

onal

Data

Integ

ratio

n

Data

Stew

ards

hip

Data

Deve

lopme

nt

Data

Supp

ort O

pera

tions

2007 Maturity Levels 2012 Maturity Levels

Comparison of DM Maturity 2007-2012


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

47

Starting the Journey - DMM Assessment Method

• DMM can be used as a standalone guide• To maximize its value as a catalyst - forging shared perspective and accelerating

the program, our method:– Provides interactive launch collaboration event with broad range of stakeholder– Evaluates capabilities collectively by consensus affirmations– Facilitates unification of factions - everyone has a voice / role – Solicits key business input through supplemental interviews– Verifies capability evaluation with work product reviews (evidence)– Report and executive briefing presents Scoring, Findings, Observations, Strengths,

and targeted specific Recommendations. • In the near future, audit-level rigor will be introduced to serve as a benchmark of

maturity, leveraging the CMMI Appraisal method.

To date, over 200 individuals from business, IT, and data management in early adopter organizations have employed the DMM - practice by practice, work product by work product - to evaluate their capabilities.

�48

‹#›

DMM Assessment SummarySample Organization

�49

‹#›

Next Step Sample – DM Roadmap

Comprehensive and Realistic Roadmap for the Journey

�50

‹#›

Cumulative Benchmark – Multiple organizations

�51

‹#›

Summary - Why Do a DMM Assessment?

• Engage the lines of business through education• Tour de force – learn precisely “How are we doing?”• Clarifies priorities – “What should we do next?”• Industry-wide standard begets confidence• Heals factions and silos – (i.e., improves climate for an

organization-wide program)• Creates:

• Common concepts, perspective, and terminology• A shared vision and purpose

• Baseline for monitoring progress over time

�52

53

Establishing a Common Data Management LanguageData Management Maturity Model

Microsoft

Strategic Enterprise Architecture

Data Management Operat

ions Platform &

Architecture

Data Quality

Data Governance

Data Management Strateg

y

54

CMMI Assessment Recommendations

• Unified effort to maximize data sharing and quality

• Monitor and measure adherence to data standards

• Top-down approach to prioritization • Up-stream error prevention • Common Data Definitions

• Leverage best practices for data archival and retention

• Maximize shared services utilization

• Map key business processes to data

• Leverage Meta Data repository

• Integrate data governance structures • Prioritize policies, processes,

standards, to support corporate initiatives

Microsoft

Strategic Enterprise Architecture

▪ In the world of Devices and Services, Data Management is a pillar of effectiveness

▪ DMM is a key tool to facilitate the Real-Time Enterprise journey

▪ Active participation of cross-functional teams from Business and IT is key for success

▪ Employee education on the importance of data and the impact of data management is a good investment

▪ Build on Strengths!

55

Key Lessons

Microsoft IT Annual Report may be found at: http://aka.ms/itannualreport

Microsoft

How the DMMSM Helps the Organization

56

Gradated path -step-by-step improvements

Unambiguous practice statements for clear understanding

Functional work products to aid implementation

Common language Shared understanding of progress

Acceleration

‹#›

How the DMMSM helps the DM Professional

“Help me to help you” – education for roles, complexity, connectedness

Integrated 360 degree program level view – launches collaboration, increased involvement of lines of business

Actionable and implementable initiatives

Strong support for business cases

Certification path – defined skillset and industry recognition

�57

The DMM Ecosystem

58

‹#›

DMM Ecosystem - Product Suite Overview

• Data Management Maturity Model o Comprehensive document with

descriptions, practice statements and work products

o Enterprise license option

• Assessments o Structured, facilitated working sessions

resulting in detailed current/future state executive report

• Training & Certification o Introductory, Advanced and Expert

courses with associated certifications

• Formal Measurement/Appraisal (2016) o Benchmark measurement and scoring of

capability/maturity level

�59

‹#›

DMM Ecosystem – Training

Results / Assets

Partner Program / Outreach

Certifications

Product Suite

DMM

Training Classes

• Building EDM Capabilities (3 days)

• eLearning Building EDM Capabilities (self-paced, web-based) (10 hours)

• Mastering EDM Capabilities (5 days)

• Enterprise Data Management Expert (5 days)

• Future – EDM Lead Appraiser (5 days)

On-site courses available at your location

�60

‹#›

DMM Ecosystem - Certifications

Certifications: Credentials and Credibility

• Enterprise Data Management Expert (EDME) – Assessing and Launching the DM Journey

• DMM Lead Appraiser (DMM LA) – Benchmarking and Monitoring Improvements

�61

‹#›

DMM Ecosystem – Partner Program

�62

‹#›

DMM Ecosystem – Results and Assets

Results

• Case studies • Best Practice Examples • Benchmarking • Web publication of approved

appraisals

DMM Assets

• Translations (#1 Portuguese) • Seminars (RDA, Governance,

Quality) • DMM Compass • Profiles – Regulatory • Academic Courses • White Papers / Articles

�63


• Motivation








• Where to next?

• Q & A?


64

Top Operations

Job

Top Data Job

65


Top Job

Top IT

Job

Top Marketing

Job

Data Governance Organization

Top Data Job

• Dedicated solely to data asset leveraging • Unconstrained by an IT project mindset • Reporting to the business • There is enough work to justify the function

and not much talent • The CDO provides significant input to the

Top Information Technology Job

• 25 Percent of Large Global Organizations Will Have Appointed Chief Data Officers By 2015 Gartner press release. Gartner website (accessed May 7, 2014). January 30, 2014. http://www.gartner.com/newsroom/ id/2659215?

• By 2020, 60% of CIOs in global organizations will be supplanted by the Chief Digital Officer (CDO) for the delivery of IT-enabled products and digital services (IDC)

The Case for theChief Data OfficerRecasting the C-Suite to LeverageYour Most Valuable Asset

Peter Aiken andMichael Gorman

Top Finance

Job

The "waterfall" development model - creates more, new data siloes

66


SoftwareData

Data is not a Project• Durable asset

– An asset that has a usable life more than one year

• Reasonable project deliverables – 90 day increments – Data evolution is measured in years

• Data – Evolves - it is not created – Significantly more stable

• Readymade data architectural components – Prerequisite to agile development

• Only alternative is to create additional data siloes!

67


Evolving Data is Different than Creating New Systems

68


Common Organizational Data (and corresponding data needs requirements)

New Organizational Capabilities

Systems Development

Activities

Create

Evolve

Future State

(Version +1)

Data evolution is separate from, external to, and precedes system development life cycle activities!

Executive Perspective

• The TDJ’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 - motivates all parties to collaborate for improvements

69

For more information

• Feel free to email me: • [email protected]

• And visit our web site: • http://cmmiinstitute.com/DMM

71

Trends in Data Modeling August 11, 2015 @ 2:00 PM ET/11:00 AM PT

Data Quality Engineering Sepember 8, 2015 @ 2:00 PM ET/11:00 AM PT

Sign up here: www.datablueprint.com/webinar-schedule or www.dataversity.net

Copyright 2013 by Data Blueprint 72

Upcoming Events

10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056

Data Ed: Best Practices with the DMM

Data & Analytics

Transcript of Data Ed: Best Practices with the DMM