adopting the dmbok - information resource management...

Adopting the DMBOK

Mike BeauchampMember of the TELUS teamEnterprise Data World16 March 2010

Enterprise Data World 2010 – Adopting the DMBOK2

Agenda

The Birth of a DMO at TELUS

TELUS DMO Functions

DMO Guidance

DMBOK functions and TELUS Priorities

Adoption Obstacles

Future Considerations

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)


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


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?


Data Governance Maturity Model

From “The Data Asset”, Tony Fisher, Wiley and Sons, 2009

TELUSDMO 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?


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.

DMO Guidance


Burton Group12

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


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.


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


Enter the DMBOK


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!”

DMBOK and TELUS Priorities


TELUS DMO and DMBOK9 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?


Self Assessment


Metadata Management at TELUSThe 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


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 models1)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


Federation and DMBOK Metadata ArchitectureThe 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 management2. 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.


Data Quality


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


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.


DataFluxData 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.


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.


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

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


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


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

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.


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


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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