Adopting the DMBOK - Information Resource Management ...

Adopting the DMBOK
Mike Beauchamp
Member of the TELUS team
Enterprise Data World
16 March 2010

Agenda
• The Birth of a DMO at TELUS
• TELUS DMO Functions
• DMO Guidance
• DMBOK functions and TELUS Priorities
• Adoption Obstacles
• Future Considerations
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Enterprise Data World 2010 – Adopting the DMBOK

The DMO is Born

Timeline
• Late 2008 – Data Management team created as a
‘mashup’ of DA, DBA and BI groups.
• Early 2009 – DMBOK published by DAMA. Review of
TELUS Data Strategy and Data Management functions
conducted by Data Management team.
• April 2009 – EDW 2009, TELUS purchases a copy of
DMBOK.
• Jun 2009 –DAMA DMBOK adopted as the framework
of reference for a TELUS Data Management
Organization (DMO)
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Data Management Organization (DMO)
• Making TELUS ‘Smarter’
• ‘Smarter’ means: ensuring TELUS has the
information it needs to operate, thrive and compete
• More focus on:
• Understanding our current assets
• Cleaning up the “pollution”
• Data Governance (long term, enterprise view) –
preventing further “pollution”
• Getting information into the hands of decision makers
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Enterprise Data World 2010 – Adopting the DMBOK

Data Governance
• Governance is not the same as Management, but
should co-ordinate management activities.
• Governance occurs through successful execution of
co-ordinated management functions and aligned
processes.
• How mature is our current Governance? Where
should we be as a target?
• What are the functional priorities?
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Enterprise Data World 2010 – Adopting the DMBOK

Data Governance Maturity Model
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Enterprise Data World 2010 – Adopting the DMBOK
From “The Data Asset”, Tony Fisher, Wiley and Sons, 2009

TELUS
DMO Functions

Organizational Review
• DA, DBA, and BI functions are mature and
formalized as separate teams, but need to become
integrated under a Data Management mandate.
• What does that process model look like? What other
functions need to be included?
• How do we organize the DMO to functionally
integrate what we are currently doing with what we
need to add to the functional mix?
• Is there something published that we can adopt and
adapt to?
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Enterprise Data World 2010 – Adopting the DMBOK

TELUS DMO Functions – at creation
• DA, DBA, and BI functions considered core competencies of the
new TELUS DMO
• Master Data Management, Metadata Management, and Data
Quality dispersed across many areas, but understood to be
‘developing’ competencies of the TELUS DMO.
• Content Management not clearly understood as a DMO priority,
but seen as the logical place to manage metadata and taxonomies
for unstructured and semi-structured content.
• DA team to continue in a ‘dotted line’ relationship with IT
Architecture to maintain alignment with Application and
Technology domains.
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DMO Guidance

Burton Group
12
• The Data Management Organization: Key to Effective
Data Management v 1.0, 4 May 2009, Author(s): Noreen
Kendle
• Data Management is viewed as a governing body,
responsible for Modeling activities, and connected to
other Data related IT functions
• The Burton model involves Data Architecture, Policies,
Practices, and Processes and is used to support and
protect an organization’s data assets
• The TELUS DMO also has direct responsibility for
Business Intelligence and Database Management. The
Burton model is useful for Data Architecture and
Strategy, but a broader Functional framework is needed
• See also: 2010 Planning Guide: Data Management
Strategies v 1.0, 3 September 2009, Author(s): Lyn
Robison
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Forrester
• Forrester provides a rich perspective for Data Management functions
such as Master Data Management, Data Warehousing/Business
Intelligence, Data Quality and Enterprise Data Integration.
• An overall framework for Data Management, however, doesn’t
appear to exist.
• For Data Governance, see “Data Governance: What Works And
What Doesn‘t by Rob Karel with J. Paul Kirby, Boris Evelson ,
September 10, 2007”
• Seriously consider joining the Information and Knowledge
Management Professional channel.
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Gartner
• The Gartner Data Management and Integration Vendor Guide, April
2009, Regina Casonato, Mark A. Beyer, Ted Friedman
• Limited to Structured Data Assets
• Provides guidance to the large Vendor landscape in four areas:
• Data Quality Tools
• Data Integration Tools
• Database Management Systems
• Data Warehouse DBMS’s
• Also has a list of “Cool” vendors
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Enter the DMBOK
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DMBOK Activities
• Appendix 9 of the DMBOK lists all of the Data Management activities by
Management Function
• I think John’s comment was “This is exactly what I’ve been looking for!”
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DMBOK and
TELUS Priorities

TELUS DMO and DMBOK
• 9 separate management functions
surrounding Data Governance
• Currently covered by TELUS DMO: Data
Architecture, Data Development, Database
Operations, Data Warehousing/Business
Intelligence
• Data Security managed in partnership with
Corporate Security
• Areas for DMO organizational enhancement:
Data Quality, Metadata, and MDM
• Still not sure what to do about Document and
Content Management, but everything else
seems to fit
• Do our activities match DMBOK activities?
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Self Assessment
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Metadata Management at TELUS
• The Data Management Organization (or DMO) at TELUS, is the group responsible for
managing the Data Assets of the enterprise. The Metadata associated with this
governance of Data Assets should also be managed by us and integrated with
appropriate discipline.
• Metadata the DMO manages reasonably well:
• Data 'at rest' (published) and where movement is managed
• Inclusive of Database specifications, data models, business intelligence
frameworks and components, data mapping and transformation
• Metadata for which the DMO needs to develop management processes:
• KPI's, Common Business Vocabulary, Reporting, Operational Metadata for DW-
BIM, MDM, Data Quality functions, Business Process Workflow, SOA,
• Things we don’t currently manage (although we should participate and govern):
• ERP, Configuration Management, Content Management
• Things where the lines are blurred
• BPM, Unstructured data, embedded metadata (data truly 'in motion')
• Web 2.0, Mashups, Enterprise Search, Text Analytics
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TELUS Metadata Strategy
• “A metadata strategy is a statement of direction in Metadata
Management by the Enterprise” – DAMA-DMBOK
• Metadata Management has three basic enterprise organizational models
1)Centralized: All metadata is captured, managed and disseminated from a
corporate repository. Sometimes called the 'uber' repository.
2)Federated: Metadata is managed within defined 'domains' or 'zones'. Governance
mechanisms across the zones are defined and a Metadata Integration Plan is
developed to manage metadata movement between the federated domains.
3)Localized: All metadata locations are responsible for managing and governing
their own metadata independent of each other. Interface agreements are
developed on a discovery basis as the needs of IT projects execute.
• TELUS currently operates under a Localized model, but desires to
operate under a Federated model. This Federated model is not a
DMBOK model
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Federation and DMBOK Metadata Architecture
• The DMBOK describes three approaches to Metadata Architecture (section 11)
1. Centralized – use of a single Metadata Repository that contains copies of
Metadata from identified, relevant sources for the domain under management
2. Distributed – no persistent repository. A metadata retrieval engine uses metadata
virtualization technologies to access metadata from local sources in real time.
3. Hybrid – persistent repository used for “user-added” metadata as enhancements
to metadata are retrieved from source in real time through virtualization.
• The DMBOK does not specifically define a federated approach to Metadata
Management with respect to the organization of Metadata Management Domains
• Within the TELUS DMO Domain the Centralized Metadata Architecture as described
in section 11, will apply (as opposed to Distributed or Hybrid). Should this be an
Enterprise approach applying to all other domains? (recommended).
• Should support “Repository to Repository” co-operation with the DMO Domain playing
the role of an Integration Hub for the Enterprise.
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Data Quality
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Data Quality Process
• Data Quality Processes are usually a variation of the
Deming model for Quality improvement (Plan, Do,
Study, Act). The DMBOK proposes a similar
“Deming-like” Data Quality cycle
• Plan - for the assessment of the current state and
identify key metrics for measuring data quality.
• Deploy - processes for measuring and improving
the quality of data.
• Monitor - and measure the levels in relation to the
defined business expectations.
• Act - to resolve any identified issues to improve data
quality and better meet business expectations.
• Larry English has a new book out. “Information
Quality Applied”, Oct 2009, Wiley
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Gartner - on Data Quality
• TELUS has existing relationships with
numerous vendors including all of the ones
found in the upper right portion of this ‘magic
quadrant’
• If those relationships don’t currently include
appropriate ‘tooling’ for Data Quality, what
would it take to include it?
• An enhanced relationship with SAS now
includes DataFlux Enterprise Data Quality
offerings for use in improving Data Quality at
TELUS.
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DataFlux
• Data Profiling and Discovery – uncovers the structure, completeness and
suitability of data within data resources, cataloging and analyzing metadata to
discover metadata relationships. dfPower Profile is the DataFlux data profiling
tool, dfPower Explorer is the DataFlux tool for metadata analysis.
• Real-Time Data Quality - create data services that can standardize, correct and
integrate data as it arrives in your IT systems. The DataFlux Integration Server
allows you to extend business rules for data quality and data integration
throughout the IT environment. dfPower Quality provides matching technology,
transformation routines and identification logic.
• Data Enrichment - dfPower Verify gives you the functionality to create
complete and usable address information, and enrich that data with geographic,
demographic, product hierarchy or other internal or external data sources.
• Data Monitoring - dfPower Monitor can provide real-time business rules
monitoring for your data, detecting when data exceeds pre-set limits and
allowing you to immediately recognize and correct issues before the quality of
your data declines.
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DataFlux Methodology
• DataFlux has a published Analyze-Improve-
Control methodology that leverages their
Integration Platform. Also looks very Deming-ish.
• Profiling – The Analyze bit
• Quality – Step 1 of Improve
• Integration – Step 2 of Improve
• Enrichment – Step 3 of Improve
• Monitoring – The Control bit
• This methodology is being adopted and tailored
for TELUS use as part of our DQM pilot in 2010.
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DQM and MDM
• “Data quality and MDM are inextricably linked, because the net purpose
of MDM initiatives is to deliver a single source of truth on one or more
master data domains containing accurate, complete, timely and
consistent data. Without early, systematic attention to high levels of data
quality (plus the right data quality tools and solid data governance to
resolve the issues that inevitably come up) your master data hub will
simply be a fast, automated way to shoot yourself in the foot.” – Dan Power,
March 2009, Information Management Magazine
• MDM initiatives and their resulting Data Stores (MDM hubs) at TELUS
require Data Quality processes to be successful. Currently, this is not
co-ordinated under a Data Quality Management function of the DMO.
This is an undesirable gap in Data Governance. A critical success factor
for a DMO Data Quality program is the ability to close this gap
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Obstacles

Economic Meltdown
• 2009 was not an easy year to expand into new
functional areas. Timelines needed some adjustment
• Do we really need to buy new tools or can our
existing infrastructure and technology choices be
used for growth areas?
• Look at re-negotiating deals and strengthening
current vendor relationships
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Organizational Impact
• It is harder to Govern that which you do not also
Manage.
• TELUS has 3 VP’s of Systems Development
• Business Units have considerable autonomy. SaaS is
becoming a mega-trend.
• A “Solutions for Data Management” team doesn’t exist.
Is this an evolution of the SBI team or a ‘matrix’ of
many teams?
• It is likely to be both. See bullet 1
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Funding
• TELUS prioritizes projects that have high ROI. Funding
pool is reduced considerably by mandatory items and
market priorities.
• Making TELUS smarter will require funded effort. The
business case for DMO initiatives will be analyzed.
• Data Management Solutions may not attain the priority
and visibility needed to obtain desired funding.
• Infrastructure Improvement vs. new Capital Asset vs.
Operational Expense
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Future
Considerations

Data Federation
• Look to other sources (Forrester’s “Information Fabric” for
example) for guidance managing solutions and technologies
that use Data Federation and Data Virtualization
• Building Data Services for a SOA delivery will also likely
require use of these technologies and some form of Data
Integration Management
• SaaS, Hosted Solutions and “BI in the Cloud” will accelerate
the need.
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Data Integration Management
• There is no distinct function for this in the DMBOK. It is
assumed that a landscape of Transactional Data, Master
Data Stores, and Reference Data Stores is transformed into
integrated data when moved to a Data Warehouse in a
managed fashion.
• Data Warehousing (and ERP for that matter) creates
integrated data through the population of a homogenized
data store.
• Most enterprises, however, must deal with a dispersed
application landscape of heterogeneous data stores
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Semantic Technologies
• The ‘Cool’ vendors on Gartner’s 2009 DM&I list
• Think of this as Metadata on steroids
• There are some interesting presentations here at EDW. I
intend to educate myself further.
• How will we add this to the mix? Perhaps as an extension
of Metadata Management, as part of the missing Data
Integration Management function or as something new.
• Maybe all three.
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