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How do you want your data served?


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The role of Data Virtualisation in
your EIM Strategy
Christopher Bradley, IPL
                                                         Intelligent Business
chris.bradley@ipl.com
  1
Presenter
     Chris Bradley
     Head of Business Consulting
     chris.bradley@ipl.com
     +44 1225 475000
                                                           Use this layout for a title
         @InfoRacer


         My blog: Information Management, Life & Petrol
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         http://infomanagementlifeandpetrol.blogspot.com
                                                           picture.




                                                                                   Intelligent Business
2
The role of Data Virtualisation in your EIM strategy
The role of Data Virtualisation in your EIM strategy
The role of Data Virtualisation in your EIM strategy
The role of Data Virtualisation in your EIM strategy
Introduction & Agenda

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7
                             I           Intelligent Business
Chris Bradley Summary:     Chris Bradley Recent speaking engagements:                                                DAMA UK & BCS Data Management Group:, June 11th 2009; London,
                           DAMA International (DAMA / Wilshire), March   5th -8th 2007, Boston, MA                   “Evolve or Die - Data Modelling is not just for DBMS’s”
30 years Information                                                                                                 BPM Europe: (IRM), September 2009, London: ½ day workshop
                           “Data as a service”
Management experience      “Panel of Data Modelling experts”                                                         “An introduction to Data and the BPMN”
                           CDi_MDM Summit (IRM UK), April 30 – May 2nd 2007, London,                                 Data Migration Matters: October 1st 2009, London,
MOD, Volvo, Thorn EMI,     “A Data Architecture for Data Governance”
                                                                                                                     “Designing for Success”
Coopers & Lybrand, IPL     DAMA UK: June 15th 2007, London,                                                          Data Management & Information Management Europe: (DAMA / IRM), November 2-5
                                                                                                                     2009, London,
                           “Data Modelling – Where did it all go wrong?”
                                                                                                                     “Modelling is NOT just for DBMS’s anymore”
Sample Clients: BP,        Data Governance Conference, (Debtech / Wilshire) June 25 -28, 2007, San Francisco, CA,
                                                                                                                     “Meet the Metadata Professional Organisation”
Enterprise Oil, Statoil,   “Data Architecture for Governance – case study”
                           IPL & Embarcadero seminar series: (Bristol, London, Manchester, Edinburgh), October       Enterprise Data World International: (DAMA / Wilshire), March 14th – 19th 2010, San
Exxon Mobil, Audit         2007,                                                                                     Francisco CA,
Commission, MoD, Merrill   “Data Modelling – Where did it all go wrong?”                                             “How to communicate with the business using high level models”
                                                                                                                     IPL & DataFlux Seminar Series: (IPL/DataFlux), March 26th 2010, Bath, UK. “The
Lynch, Barclays, DoD,      DQ/IM & DAMA Europe (IRM London), November 2007,
                                                                                                                     Information Advantage – Exploiting Information Management For The Business”
Imperial Tobacco, GSK ….   “Data Modelling as a service”
                           Data Governance Conference: (Debtech / Wilshire) Florida, December 2007,                  BeyeNETWORK Webinar: (CA/BeyeNETWORK), March 31st 2010, Webinar.
                           “Data Governance 2.0”                                                                     “Communicating with the Business through high level data models”
Experience: Data           DAMA International: (DAMA / Wilshire), March 16th – 21st 2008, San Diego, CA.             Enterprise Architecture Europe: (IRM), June 16th – 18th 2010, London: ½ day
Governance, Master Data    “Modelling for SoA”                                                                       workshop
                                                                                                                     “The Evolution of Enterprise Data Modelling”
Management, Enterprise     “XML amd data models”
                                                                                                                     ECIM Exploration & Production: September 13th 15th 2010, Haugesund, Norway:
Information Management     DAMA International: (DAMA / Wilshire), March 16th – 21st 2008, San Diego, CA.
                           “Establishing Data Modelling as a Service in BP”                                          “Information Challenges and Solutions”
                           BPM Europe: (IRM), September 2008, London:                                                Information Management in Pharmaceuticals: September 15th 2010, London,
Author & conference        “BPMN for Dummies”                                                                        “Clinical Information Management – Are we the cobblers children?”
speaker                    DAMA Europe: (IRM / DAMA), November 2008, London,                                         BPM Europe: (IRM), September 27th – 29th 2010, London, “Learning to Love BPMN 2.0”
                           “BPMN for Dummies”                                                                        DAMA Scandinavia: October 26th-27th 2010, Stockholm, “Incorporating ERP Systems
CDMP(Master), CBIP,        “Data Modelling as a service”                                                             into your overall Models & Information Architecture”
                           Data Governance Europe Sysmposia: (IRM / Debtech; London), February 2009,                 Data Management & Information Management Europe: (DAMA / IRM), November
Prince2, APM                                                                                                         2010, London, “How do you get a Business person to read a Data Model?
                           “Data Governance Challenges in a Major Multi National”
                           Webinar series: (Embarcadero Technologies & IPL), Oct 2008 – Feb 2009,                    Data Governance & MDM Europe: (DAMA / IRM), March 2011, London,
Director DAMA UK & MPO                                                                                               “Clinical Information Data Governance”
                           “The New Formula for Success – Moving Data Modelling beyond the Database”
                           Data Rage 2009: March 17-19 2009,                                                         Enterprise Data World International: (DAMA / Wilshire), April 2011, Chicago IL,
BeyeNetwork Expert         “Evolve or Die – Modelling is not just for DBMS’s anymore”                                “How do you want yours served? – the role of Data Virtualisation and Open Source BI”
Channel Author             “Data Modelling as a service”
“Information Asset         Enterprise Data World International: (DAMA / Wilshire), April 5th -12th 2009, Tampa FL,
Management”                “Exploiting Models for effective SAP implementations”
                           Chairing panel of experts “Keeping modelling relevant”
                           Panel of experts “Issues in information internationalisation”
                                                                                                                                                              October 1st 2009
                           “Modelling is not just for RDBMS’s”
                           DAMA UK & BCS Data Management Group:, June 11th 2009; London,
                                                                                                                                                              The Kings Fund
                                                                                                                                                                  London
                                                                                                                                                                                  Intelligent Business
            8              “Evolve or Die - Data Modelling is not just for DBMS’s”
Chris Bradley Summary:     Chris Bradley Recent publications:
30 years Information       Database Marketing Magazine, February 2009, “Preventing a Data Disaster”
Management experience      http://content.yudu.com/A12pnb/DMfeb09/resources/30.htm

MOD, Volvo, Thorn EMI,     Data Modelling For The Business – A Handbook for aligning the business with IT using high-level data models;
Coopers & Lybrand, IPL     Technics Publishing; ISBN 978-0-9771400-7-7;
                           http://www.amazon.com/Data-Modeling-Business-Handbook-High-
Sample Clients: BP,        Level/dp/0977140075/ref=sr_1_4?ie=UTF8&s=books&qid=1235660979&sr=1-4
Enterprise Oil, Statoil,   BeyeNETWORK “Chris Bradley Expert Channel” Information Asset Management
Exxon Mobil, Audit         http://www.b-eye-network.co.uk/channels/1554/
Commission, MoD, Merrill
                           Article “Data Modelling is NOT just for DBMS’s” (July 2009)
Lynch, Barclays, DoD,
                           http://www.b-eye-network.co.uk/channels/1554/view/10748 and (August 2009)
Imperial Tobacco, GSK ….
                           http://www.b-eye-network.co.uk/view/10986
Experience: Data           Article: Information Management Deficiency Syndrome (September 2009)
Governance, Master Data    http://www.b-eye-network.co.uk/channels/1554/view/11216/
Management, Enterprise     Article: Drowning in spreadsheets (September 2009)
Information Management     http://www.b-eye-network.co.uk/channels/1554/view/11482/
Author & conference        Article “Seven deadly sins of data modelling” (October 2009)
speaker                    http://www.b-eye-network.co.uk/view/11481
                           Article “How do you want yours served (data that is)” (December 2009)
CDMP(Master), CBIP,
                           http://www.b-eye-network.co.uk/
Prince2, APM
                           Article “How Do You Want Your Data Served?” Conspectus Magazine (February 2010)
Director DAMA UK & MPO     Article “10 easy steps to evaluate Data Modelling tools” Information Management, (March 2010)
BeyeNetwork Expert         Article “Big Data, Same Problems” TechTarget (July 2011)
Channel Author             http://searchdatamanagement.techtarget.co.uk/news/2240039201/Round-table-The-value-of-big-data
“Information Asset
Management”                                                                          October 1st 2009
                                                                                     The Kings Fund
                                                                                         London



                                                                                                                                          Intelligent Business
            9
Agenda
1. An Enterprise Information Management Framework
2. What is Data Virtualisation?
3. 5 ways where EII / Data Virtualisation can add value to
   Data Warehousing
4. 6 key considerations when deciding upon Data
   migration and take on (ETL vs EII or both?)
5. Information Management issues in the BI world.
6. IM Certification & Competencies                   Intelligent Business
  10
1. IPL’s Information Architecture Framework
Architecture:                                                                   Framework:
                                     Goals
Orderly arrangement                Principles     Purpose                       Components of
and structure for                                                               the Architecture
assets
                            Governance Planning               People


                                  Lifecycle        Services             Process
                      Quality
                                 Management     Infrastructure
                                                                                  Structure
                 Models / Taxonomy            Catalog / Meta data

                                                                                       Data
                                                 Structured                           Types
                                  Transaction                    Unstructured
   Master Data      MI/BI Data                   Technical
                                     Data                            Data
                                                    Data                                  Intelligent Business
   11
Information Architecture Framework Components
 1. Goals / Principles                                                                 Goals
 2. Governance                                                                       Principles
                                                                                                   1

 3. Planning                                                                  Governance Planning
       (Information Asset Strategy and Roadmap)                                            2                3
 4. Information Quality Process                                         Quality
                                                                                    Lifecycle             Services
                                                                                   Management          Infrastructure
 5. Life Cycle Management                                                    4                 5                        6
    Processes                                                      Models / Taxonomy               Catalog / Meta data
 6. Services Infrastructure                                                            7                                       8

       (Data Integration, Distribution, etc)                                                           Structured
                                                                                    Transaction                         Unstructured
                                                     Master Data      MI/BI Data                       Technical
                                                                                       Data                                 Data
 7. Information Models                                        9                                           Data

        (includes Information relationship models)

 8. Information Catalog / Meta                           9. Master Data Management
       Data Services                                                                                                               Intelligent Business
  12
Information Architecture is one of the four
components of the overall Enterprise Architecture
                                                   Business strategy,
                                 Business          Organization, and
                                                   Core business processes
                                Architecture

                                             Applications
                          Information        Architecture        ERP, etc
      Enterprise Data     Architecture
  Model & Catalog, etc.

                                         Technology
                                         Architecture
                                                             Desktop, network,
                                                             Data centre strategy
                                                                             Intelligent Business
    13
Turning data into Business wisdom
     Data
      10,000 feet
     Information
      Your current altitude is 10,000 feet
     Knowledge
      There is a mountain ahead, peak of 12,000 feet
     Wisdom
      Climb immediately to 15,000 feet
                                                       Intelligent Business
14
Now – That should clear up a few things around here!




                                                         Businesses NEED a
                                                       common vocabulary for
                                                          communication


                                                                        Intelligent Business
        15
2. What is Data Virtualisation?

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A primer .....


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                                    I           Intelligent Business
Virtualise




                  Intelligent Business
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Genres of Virtualisation
                               Data Virtualisation
                                                                                     Abstracts data
                                                                                       from location
                                                                                      and complexity

                       RDBMS              Data                 Web
           Packages                     Warehouses                        Excel
                                                              Services


                             Storage Virtualisation
                                                                                    Abstracts logical
                                                                                      storage from
                                                                                     physical storage

         Disk 1            Disk 2                    Disk 3              Disk 4


                      Application / Server Virtualisation                           Abstracts logical
                                                                                      apps & servers
                                                                                       from physical
                                                                                      apps & servers
                                                                                           Intelligent Business
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      Application 1     Application 2                Server 1            Server 2
Key Purpose of Virtualisation
Overcome (mask) Complexity
     Hardware
     Software
                Improve Agility
                 New solutions
                 Existing solutions
                                      Reduce Costs
                                       Operating
                                       New development
                                                 Intelligent Business
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Data Virtualisation in a Nutshell
             BI, MI and                   Portals and                                          Enterprise
                                                                   Custom Apps
             Reporting                    Dashboards                                            Search




                                               Star         SQL                          Web Services




                                    Virtual                                        Virtual              Relational
                                  Data Marts            Shareable Data           Operational              Views
Data Model                                                 Services              Data Stores




                                                                                                                     Intelligent Business
   20                                    Legacy              Packages            RDBMS             Web
                          Files         Mainframes                                                Services
What are the Business challenges DV addresses?
                                                      Mergers &
                                                      Acquisitions


 Business                              Cost Savings
Challenges     Sales Growth                                    Risk Reduction




Business
Solutions
                         Complexity                     Disparity
   Data
              Location                 Performance                   Completeness
Integration
Challenge                     Security, Quality, Governance


  Data
 Sources


                                                                                    Intelligent Business
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What DV Does


           Data Virtualisation




                                 Intelligent Business
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Typical Data Integration Architectures
                    BI Tools/Apps.           Master Data Mgmt.        Operational Apps.      Inter-enterprise
Common Design, Admin.,



                         Physical Movement and            Abstraction / Virtual           Synchronization
                          Consolidation (ETL,                Consolidation                and Propagation
                                  CDC)                     (Data Federation)                (Messaging)
    Governance




                                                             Common Metadata
                                                           Common Connectivity




                         Pace of Business change & requirement for agility demands that                     Intelligent Business
         23              organizations support multiple styles of data integration
How DV differs
            Physical Movement and   Abstraction / Virtual       Synchronization
             Consolidation (ETL,       Consolidation            and Propagation
                     CDC)            (Data Federation)            (Messaging)




Middle-
               ETL         CDC         Data Virtualization           EAI / ESB
 ware

Purpose     DB  DB     DB  DB       DB  Application       Application  Application
                          Event                                       Event
Attribute   Scheduled                    On Demand
                          Driven                                      Driven

                                                                                   Intelligent Business
      24
How DV Works – Example Scenario
1) I need to build an
    application that
    looks like this…


2) The view or data
   service needs to
    look like this…


     3) And the data
     comes from these
         sources…
                            Intelligent Business
25
Traditional Integration with ETL and Data Warehouses

Traditional Approach
1.    Design entire DW schema
2.    Develop ETL
3.    Refresh on batch basis
4.    Application gets data from
      DW
Issues
      Slow development cycle
      Replicated data
      Batch latencies
      Physical stores overhead
                                                     Intelligent Business
     26
Data Virtualisation design
Design Steps
1. Discover data
2. Model individual view/service
3. Validate view/service

                                           Data model layer
Benefits
   Faster time to solution
   Easy to learn and use tools
   Extensible / reusable objects
   Conform data to a standard data model                      Intelligent Business
   27
Data Virtualisation Production
Production Steps
1. Application invokes
   request
2. Optimized data access
   and retrieval (single query)
                                  Optimizer
3. Deliver data to application

Benefits
   Less replication
   High performance
   Up-to-the-minute data
                                              Intelligent Business
  28
Data Virtualisation Production with Caching
Production Steps
1.        Cache essential data
2.        Application invokes request
3.        Optimized data access and
          retrieval (leveraging cached
          data)
                                         Optimizer   Cache
4.        Deliver data to application

Benefits
      Removes network constraints
      7-24 availability
      Optimal performance
                                                        Intelligent Business
     29
3. Five example usage patterns

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Where Data Virtualisation can
add value to Data Warehousing

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                                            I           Intelligent Business
Prototyping Data Warehouse Development
In traditional DW development,
time taken for schema changes,
adding new data sources and
providing data federation are often
considerable.
Use DV to prototype a development
environment rapidly building
a virtual DW rather than
a physical one.
Reports, dashboards and
so on can be built on the
virtual DW.
After prototyping the physical DW
can be introduced if the
usage merits.

                                Packages   Databases   Files   XML   Intelligent Business
      31
Enriching the DW ETL Process
                  Frequently new data sources particularly from ERPs are required
                  in the DW.
                  Often the ETL lacks data access capabilities to complex sources.
                  Tight processing windows may require access, aggregation &
                  federation activities to be performed prior to the ETL process.
                  Powerful data access capabilities of EII provide rich access and
                  federation capabilities which can present virtual views to the ETL
DW                process which continues as though using a simpler data source.




                                                                       Intelligent Business
 32
Federating
            Data
            Warehouses
           Many organisations have more than
           one DW
           Is the Information in each DW

DW    DW   completely discrete?
           Data Virtualisation provides powerful
           options to federate multiple DW’s by
           creating an integrated view across
           them.
           This has particular relevance in
           providing rapid cross warehouse
           views following a merger or
           acquisition.
                                  Intelligent Business
 33
DW
       Extension
      Business Users Require Data From
      Outside the Data Warehouse so they
      can meet reporting and operational
      needs.

DW    Historical data from the warehouse
      and up-to-the-minute data from
      transaction systems or operational
      data stores is required.
      Summarized data from the warehouse
      and drill-down detail from transaction
      systems or operational data stores is
      required.
      Data Virtualisation can Extend Existing
      Data Warehouses quickly and easily to
      work around the fact that key data
      users need resides outside the
      consolidated data warehouse.
                              Intelligent Business
 34
Complete
                  Master
                  Data View
Master   MDM applications alone cannot fully support
         all requirements as data exists outside of MDM

Data     hub.
         Complementary data integration solutions are
Hub      needed to deal with data maintained outside of
         MDM hubs often in complex, disparate data
         silos.
         DV can extend the Master Data and provide a
         complete 360o view by using master data from
         the hub as the foreign key to quickly and easily
         federate master data with additional
         transactional and historical data to get a
         complete single view of master data.

                                         Intelligent Business
   35
4. Data migration
and take on
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                          picture.

6 key considerations:
ETL vs EII /DV or both?

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                                      I           Intelligent Business
Some Migration Considerations
     What data have we got?
      E-discovery
      Data owners vs. users
     What other data do we require?
      Source model vs target model
     Move all the data or leave some in place?
      Do we use EII vs ETL (or even both)


                                                 Intelligent Business
37
EII or ETL?
1. Will the data be replicated in
both the DW and the Operational
System?
                                  • Will data will need to be updated
                                    in one or both locations?
                                  • If data is physically in two locations
                                    beware of regulatory &
                                    compliance issues (e.g. SoX, HIPPA,
                                    BASEL2, FDA etc)
                                                                 Intelligent Business
   38
EII or ETL?
2. Data Governance



                         • Is the data only to be managed in
                           the originating Operational
                           System?
                         • What is the certainty that DW will
                           be a reporting DW only
                           (vs Operational DW)?        Intelligent Business
  39
EII or ETL?
3. Currency of the data, i.e. Does it
need to be up to the minute?

                                 • How up to date are the data
                                   requirements of the DW?
                                 • Is there a need to see the
                                   operational data?

                                                                Intelligent Business
   40
EII or ETL?
4. Time to solution i.e. how
quickly is the solution required?

                                • Immediate requirement?
                                • Confirmed users & usage? Vs..
                                • ..Flexible, emerging requirements?


                                                            Intelligent Business
  41
EII or ETL?
5. What is the life expectancy of
source system(s)?

                                • Are the source systems likely to be
                                  retired?
                                • Will new systems be
                                  commissioned?
                                • Are new sources required?
                                                              Intelligent Business
  42
EII or ETL?
6. Need for historical / summary /
aggregate data

                               • How much historical data is
                                 required in the DW solution?
                               • How much aggregated / summary
                                 data is required in the DW
                                 solution?
                                                           Intelligent Business
  43
5. BI &
Information
Management      Use this layout for a title
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   Maybe        picture.
spreadsheets
aren’t such a
good solution
  after all!                            Intelligent Business
  44
Effective IM IS crucial today
Higher volumes of data generated by organisations
      Information is all pervasive – if you don’t have a strategy to manage it, you
      will certainly drown in it
Proliferation of data-centric systems
      ERP, CRM, ECM…
Greater demand for reliable information
      Accurate business intelligence is vital to gain competitive advantage, support
      planning/resourcing and monitor key business functions
Tighter regulatory compliance
      Far more responsibility now placed on organisations to ensure they store,
      manage, audit and protect their data (SoX, BASEL, SOLVENCY2, HIPPA, FDA ...)
Business change is no longer optional – it’s inevitable
      Mergers/acquisitions, market forces, technological advances…
                                                                                       Intelligent Business
45
The role of Data Virtualisation in your EIM strategy
Excel, BI and IM !
     Several users within a business are adept at manipulating
     large data extracts in Excel
     Easily derive new fields
     Pivot data
     Aggregate data
     Produce charts and dashboards.
     “All good”, you might say?

                                                             Intelligent Business
47
Excel, BI and IM !
     A “new” copy of the source data is now in your spreadsheet
     You are now (unwittingly) a data steward!
     What are the rules & calculations for derivations?
     Where does the additional data come from?
     Charts / graphs potentially disconnected
     from data
     Distribution leading to data duplication
     & amendment
     What’s the lineage & provenance of the data now?       Intelligent Business
48
A Happy Path?
     Go back to the source
     Avoid “Cottage Industry” reporting
     Record metadata regarding the extract and
     don’t change its values
     If you must correct data, correct at source
     Ensure calculations make sense and are
     properly annotated and tested
     Clearly label distributed versions vs originals.
     Identify versions
     Don’t re-issue your local copy of the source data - redirect any data
     requests to the source                                                  Intelligent Business
49
6. Certification &
Competencies
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                     picture.




                                             Intelligent Business
50
What is CDMP?

     CDMP stands for “Certified Data Management Professional”
     It is the only non-proprietary, widely recognized data
     management certification.
     The certification program was jointly constructed by DAMA
     International (DAMA) and the Institute for Certification of
     Computer Professionals (ICCP).
     DAMA owns the CDMP certification, and ICCP administers
     and delivers exams, provides all record keeping.
                                                            Intelligent Business
51
Why do I need it?
            “Certification, in itself, is not a goal, but Professionalism is.”
                Dr. Paul M. Pair, ICCP Fellow

                               Credential
                                                                                         Increase in Salary
                               Company Requirement                                       Credibility within Organisation
                               Professional Growth                                       Credibility with Customers
                               Self Evaluation                                           Greater Self Esteem
                               Financial Reward                                          Solve Problems Quicker
                               Other



     Why People Certify                                  Primary Achievement Resulting
                                                               from Certification




                                                                                                         Intelligent Business
                          Source: ICCP Research Study (Athabasca University))
52
Which Specialty Exam?




                             Intelligent Business
53
IPL’s Information Management Framework
                                           Goals
                                         Principles
                                                       1


                                  Governance Planning
                                               2                3


                                        Lifecycle          Infrastructure
                            Quality
                                       Management             Services
                                 4                 5                        6


                       Models / Taxonomy               Catalog / Meta data

                                           7                                       8

                                                           Structured
                                        Transaction                         Unstructured
         Master Data      MI/BI Data                       Technical
                                           Data                                 Data
                                                              Data
                   9              10
                                                                                           Intelligent Business
  54
Maturity Model – Information Governance                                                                                     2



       Level 1 - Initial    Level 2 - Repeatable     Level 3 - Defined      Level 4 - Managed       Level 5 - Optimised

 No clear data             Data Ownership          Defined Data           Data Ownership           Data Ownership
 ownership assigned.       Model does not exist.   Ownership Model        Model is                 Model has been
 Data Owners, if any,      Owners                  exists. Ownership      implemented for the      extended such that
 evolve on their own       commissioned in the     Model is loosely       key data entities.       the majority of data
 during project            short-term for          applied to key data    Collaboration            assets are under
 rollouts (i.e. self       specific projects &     entities. Limited      between                  active stewardship.
 appointed data            initiatives. Often      collaboration. Not     stakeholders in place.   Effective governance
 owners). No standard      department or silo      fully 'bought in' to   Governance process       process employed by
 tools or                  focused leading to      data ownership at an   regularly reviews this   stakeholders &
 documentation             ownership by “Data      enterprise level.      model and its            stewards. Well
 available for use         Teams” or “Super                               application, updating    defined standards
 across the whole          Users” that manage                             and improving as         adopted.
 enterprise.               “all” data.                                    needed. Benefits
                                                                          begin to be realised.


                                                                                                               Intelligent Business
  55
Maturity Model – Quality                                                                                                       4



      Level 1 - Initial    Level 2 - Repeatable        Level 3 - Defined      Level 4 - Managed       Level 5 - Optimised

 Limited awareness        The quality of few        Quality measures        Data quality is          The measurement of
 within the enterprise    data sources is           have been defined       measured for all key     data quality is
 of the importance of     measured in an ad         for some key data       data sources on a        embedded in many
 information quality.     hoc manner. A             sources. Specific       regular basis. Quality   business processes
 Very few, if any,        number of different       tools adopted to        metrics information      across the enterprise.
 processes in place to    tools used to             measure quality with    is published via         Data quality issues
 measure quality of       measure quality. The      some standards in       dashboards etc.          addressed through
 information. Data is     activity is driven by a   place. The processes    Active management        the data ownership
 often not trusted by     projects or               for measuring quality   of data issues           model. Data quality
 business users.          departments.              are applied at          through the data         issues fed back to be
                          Limited                   consistent intervals.   ownership model          fixed at source.
                          understanding of          Data issues are         ensures issues are
                          good versus bad           addressed where         often resolved.
                          quality. Identified       critical.               Quality
                          issues are not                                    considerations baked
                          consistently                                      into the SDLC.
                          managed.                                                                                Intelligent Business
 56
Maturity Model – Master Data                                                                                               9



      Level 1 - Initial    Level 2 - Repeatable      Level 3 - Defined       Level 4 - Managed     Level 5 - Optimised

 Limited awareness of     The impact of master     Definition of an        A complete MDM         A full integrated
 MDM. Master Data         data issues gain         MDM strategy is in      strategy has been      MDM hub exists and
 domains have not         recognition within       progress. Master        defined and adopted.   has been adopted
 been defined across      the enterprise.          data domains have       MDM joined up with     across the enterprise
 the enterprise. Silo     Limited scope for        been identified.        data governance and    for all key master
 based approach to        managing master          Several domains are     data quality           data domains. The
 data models means        data due to lack of      targeted for            initiatives. Robust    hub controls access
 multiple definitions     Data Ownership           delivering master       business rules         to master data
 of potential master      Model. Project or        data to specific        defined for master     entities. Many
 data entities, such as   department based         applications or         data domains. Data     applications access
 customer, exist.         initiatives attempt to   projects. Differing     cleansing and          the MDM Hub
                          understand the           products may be         standardisation        through a service
                          enterprise's master      adopted in these        performed in the       layer. Business users
                          data. No MDM             silos for MDM. Senior   MDM hub. Specific      are fully responsible
                          strategy defined.        management support      products adopted for   for master data.
                                                   for MDM grows.          MDM. Master data
                                                                           models defined.                    Intelligent Business
 57
As-Is

                           IM Principles
                             5
              Business
                             4             Data Governance
            Intelligence
                             3
     Master Data             2                   IM Planning
     Management              1
                             0                                  As-Is
       Catalog &
                                                 Data Quality
       Metadata


             Models &                      IM Lifecycle
             Taxonomy                      Management
                           Integration &
                                                                  Intelligent Business
58                             Access
Summary
ben.braine@ipl.com




                           Use this layout for a title
                           with a horizontally
                           striped picture.




  59
                                       I           Intelligent Business
Summary
Data Virtualisation opens
up a brave new world
For data migration,
ETL isn’t “the only way”
Effective Information
Management is crucial

                             Intelligent Business
60
Contact details
      Chris Bradley
      Business Consulting Director
      Chris.Bradley@ipl.com
      +44 1225 475000
                                                             @InfoRacer




     My blog: Information Management, Life & Petrol
     http://infomanagementlifeandpetrol.blogspot.com   Intelligent Business
61
Further information:
Articles including:
    • Seven deadly sins of data modelling
    • The IT Credibility Crunch
    • Information Management Deficiency Syndrome
    • Modelling is not just for DBMS’s
    • Data mining - where’s my hard hat?
    • Master data mix-ups
    • Drowning in spreadsheets
    • Why bother with a semantic layer?
    • Business Intelligence in a cold climate
    • Data Management is everybody's business
    • Information superstition
Download from:
http://bc.ipl.com/                                 Intelligent Business
    62

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The role of Data Virtualisation in your EIM strategy

  • 1. How do you want your data served? Use this layout for a title with a horizontally striped picture. The role of Data Virtualisation in your EIM Strategy Christopher Bradley, IPL Intelligent Business chris.bradley@ipl.com 1
  • 2. Presenter Chris Bradley Head of Business Consulting chris.bradley@ipl.com +44 1225 475000 Use this layout for a title @InfoRacer My blog: Information Management, Life & Petrol with a vertically striped http://infomanagementlifeandpetrol.blogspot.com picture. Intelligent Business 2
  • 7. Introduction & Agenda Use this layout for a title with a horizontally striped picture. 7 I Intelligent Business
  • 8. Chris Bradley Summary: Chris Bradley Recent speaking engagements: DAMA UK & BCS Data Management Group:, June 11th 2009; London, DAMA International (DAMA / Wilshire), March 5th -8th 2007, Boston, MA “Evolve or Die - Data Modelling is not just for DBMS’s” 30 years Information BPM Europe: (IRM), September 2009, London: ½ day workshop “Data as a service” Management experience “Panel of Data Modelling experts” “An introduction to Data and the BPMN” CDi_MDM Summit (IRM UK), April 30 – May 2nd 2007, London, Data Migration Matters: October 1st 2009, London, MOD, Volvo, Thorn EMI, “A Data Architecture for Data Governance” “Designing for Success” Coopers & Lybrand, IPL DAMA UK: June 15th 2007, London, Data Management & Information Management Europe: (DAMA / IRM), November 2-5 2009, London, “Data Modelling – Where did it all go wrong?” “Modelling is NOT just for DBMS’s anymore” Sample Clients: BP, Data Governance Conference, (Debtech / Wilshire) June 25 -28, 2007, San Francisco, CA, “Meet the Metadata Professional Organisation” Enterprise Oil, Statoil, “Data Architecture for Governance – case study” IPL & Embarcadero seminar series: (Bristol, London, Manchester, Edinburgh), October Enterprise Data World International: (DAMA / Wilshire), March 14th – 19th 2010, San Exxon Mobil, Audit 2007, Francisco CA, Commission, MoD, Merrill “Data Modelling – Where did it all go wrong?” “How to communicate with the business using high level models” IPL & DataFlux Seminar Series: (IPL/DataFlux), March 26th 2010, Bath, UK. “The Lynch, Barclays, DoD, DQ/IM & DAMA Europe (IRM London), November 2007, Information Advantage – Exploiting Information Management For The Business” Imperial Tobacco, GSK …. “Data Modelling as a service” Data Governance Conference: (Debtech / Wilshire) Florida, December 2007, BeyeNETWORK Webinar: (CA/BeyeNETWORK), March 31st 2010, Webinar. “Data Governance 2.0” “Communicating with the Business through high level data models” Experience: Data DAMA International: (DAMA / Wilshire), March 16th – 21st 2008, San Diego, CA. Enterprise Architecture Europe: (IRM), June 16th – 18th 2010, London: ½ day Governance, Master Data “Modelling for SoA” workshop “The Evolution of Enterprise Data Modelling” Management, Enterprise “XML amd data models” ECIM Exploration & Production: September 13th 15th 2010, Haugesund, Norway: Information Management DAMA International: (DAMA / Wilshire), March 16th – 21st 2008, San Diego, CA. “Establishing Data Modelling as a Service in BP” “Information Challenges and Solutions” BPM Europe: (IRM), September 2008, London: Information Management in Pharmaceuticals: September 15th 2010, London, Author & conference “BPMN for Dummies” “Clinical Information Management – Are we the cobblers children?” speaker DAMA Europe: (IRM / DAMA), November 2008, London, BPM Europe: (IRM), September 27th – 29th 2010, London, “Learning to Love BPMN 2.0” “BPMN for Dummies” DAMA Scandinavia: October 26th-27th 2010, Stockholm, “Incorporating ERP Systems CDMP(Master), CBIP, “Data Modelling as a service” into your overall Models & Information Architecture” Data Governance Europe Sysmposia: (IRM / Debtech; London), February 2009, Data Management & Information Management Europe: (DAMA / IRM), November Prince2, APM 2010, London, “How do you get a Business person to read a Data Model? “Data Governance Challenges in a Major Multi National” Webinar series: (Embarcadero Technologies & IPL), Oct 2008 – Feb 2009, Data Governance & MDM Europe: (DAMA / IRM), March 2011, London, Director DAMA UK & MPO “Clinical Information Data Governance” “The New Formula for Success – Moving Data Modelling beyond the Database” Data Rage 2009: March 17-19 2009, Enterprise Data World International: (DAMA / Wilshire), April 2011, Chicago IL, BeyeNetwork Expert “Evolve or Die – Modelling is not just for DBMS’s anymore” “How do you want yours served? – the role of Data Virtualisation and Open Source BI” Channel Author “Data Modelling as a service” “Information Asset Enterprise Data World International: (DAMA / Wilshire), April 5th -12th 2009, Tampa FL, Management” “Exploiting Models for effective SAP implementations” Chairing panel of experts “Keeping modelling relevant” Panel of experts “Issues in information internationalisation” October 1st 2009 “Modelling is not just for RDBMS’s” DAMA UK & BCS Data Management Group:, June 11th 2009; London, The Kings Fund London Intelligent Business 8 “Evolve or Die - Data Modelling is not just for DBMS’s”
  • 9. Chris Bradley Summary: Chris Bradley Recent publications: 30 years Information Database Marketing Magazine, February 2009, “Preventing a Data Disaster” Management experience http://content.yudu.com/A12pnb/DMfeb09/resources/30.htm MOD, Volvo, Thorn EMI, Data Modelling For The Business – A Handbook for aligning the business with IT using high-level data models; Coopers & Lybrand, IPL Technics Publishing; ISBN 978-0-9771400-7-7; http://www.amazon.com/Data-Modeling-Business-Handbook-High- Sample Clients: BP, Level/dp/0977140075/ref=sr_1_4?ie=UTF8&s=books&qid=1235660979&sr=1-4 Enterprise Oil, Statoil, BeyeNETWORK “Chris Bradley Expert Channel” Information Asset Management Exxon Mobil, Audit http://www.b-eye-network.co.uk/channels/1554/ Commission, MoD, Merrill Article “Data Modelling is NOT just for DBMS’s” (July 2009) Lynch, Barclays, DoD, http://www.b-eye-network.co.uk/channels/1554/view/10748 and (August 2009) Imperial Tobacco, GSK …. http://www.b-eye-network.co.uk/view/10986 Experience: Data Article: Information Management Deficiency Syndrome (September 2009) Governance, Master Data http://www.b-eye-network.co.uk/channels/1554/view/11216/ Management, Enterprise Article: Drowning in spreadsheets (September 2009) Information Management http://www.b-eye-network.co.uk/channels/1554/view/11482/ Author & conference Article “Seven deadly sins of data modelling” (October 2009) speaker http://www.b-eye-network.co.uk/view/11481 Article “How do you want yours served (data that is)” (December 2009) CDMP(Master), CBIP, http://www.b-eye-network.co.uk/ Prince2, APM Article “How Do You Want Your Data Served?” Conspectus Magazine (February 2010) Director DAMA UK & MPO Article “10 easy steps to evaluate Data Modelling tools” Information Management, (March 2010) BeyeNetwork Expert Article “Big Data, Same Problems” TechTarget (July 2011) Channel Author http://searchdatamanagement.techtarget.co.uk/news/2240039201/Round-table-The-value-of-big-data “Information Asset Management” October 1st 2009 The Kings Fund London Intelligent Business 9
  • 10. Agenda 1. An Enterprise Information Management Framework 2. What is Data Virtualisation? 3. 5 ways where EII / Data Virtualisation can add value to Data Warehousing 4. 6 key considerations when deciding upon Data migration and take on (ETL vs EII or both?) 5. Information Management issues in the BI world. 6. IM Certification & Competencies Intelligent Business 10
  • 11. 1. IPL’s Information Architecture Framework Architecture: Framework: Goals Orderly arrangement Principles Purpose Components of and structure for the Architecture assets Governance Planning People Lifecycle Services Process Quality Management Infrastructure Structure Models / Taxonomy Catalog / Meta data Data Structured Types Transaction Unstructured Master Data MI/BI Data Technical Data Data Data Intelligent Business 11
  • 12. Information Architecture Framework Components 1. Goals / Principles Goals 2. Governance Principles 1 3. Planning Governance Planning (Information Asset Strategy and Roadmap) 2 3 4. Information Quality Process Quality Lifecycle Services Management Infrastructure 5. Life Cycle Management 4 5 6 Processes Models / Taxonomy Catalog / Meta data 6. Services Infrastructure 7 8 (Data Integration, Distribution, etc) Structured Transaction Unstructured Master Data MI/BI Data Technical Data Data 7. Information Models 9 Data (includes Information relationship models) 8. Information Catalog / Meta 9. Master Data Management Data Services Intelligent Business 12
  • 13. Information Architecture is one of the four components of the overall Enterprise Architecture Business strategy, Business Organization, and Core business processes Architecture Applications Information Architecture ERP, etc Enterprise Data Architecture Model & Catalog, etc. Technology Architecture Desktop, network, Data centre strategy Intelligent Business 13
  • 14. Turning data into Business wisdom Data 10,000 feet Information Your current altitude is 10,000 feet Knowledge There is a mountain ahead, peak of 12,000 feet Wisdom Climb immediately to 15,000 feet Intelligent Business 14
  • 15. Now – That should clear up a few things around here! Businesses NEED a common vocabulary for communication Intelligent Business 15
  • 16. 2. What is Data Virtualisation? Use this layout for a title with a horizontally striped picture. A primer ..... 16 I Intelligent Business
  • 17. Virtualise Intelligent Business 17
  • 18. Genres of Virtualisation Data Virtualisation Abstracts data from location and complexity RDBMS Data Web Packages Warehouses Excel Services Storage Virtualisation Abstracts logical storage from physical storage Disk 1 Disk 2 Disk 3 Disk 4 Application / Server Virtualisation Abstracts logical apps & servers from physical apps & servers Intelligent Business 18 Application 1 Application 2 Server 1 Server 2
  • 19. Key Purpose of Virtualisation Overcome (mask) Complexity Hardware Software Improve Agility New solutions Existing solutions Reduce Costs Operating New development Intelligent Business 19
  • 20. Data Virtualisation in a Nutshell BI, MI and Portals and Enterprise Custom Apps Reporting Dashboards Search Star SQL Web Services Virtual Virtual Relational Data Marts Shareable Data Operational Views Data Model Services Data Stores Intelligent Business 20 Legacy Packages RDBMS Web Files Mainframes Services
  • 21. What are the Business challenges DV addresses? Mergers & Acquisitions Business Cost Savings Challenges Sales Growth Risk Reduction Business Solutions Complexity Disparity Data Location Performance Completeness Integration Challenge Security, Quality, Governance Data Sources Intelligent Business 21
  • 22. What DV Does Data Virtualisation Intelligent Business 22
  • 23. Typical Data Integration Architectures BI Tools/Apps. Master Data Mgmt. Operational Apps. Inter-enterprise Common Design, Admin., Physical Movement and Abstraction / Virtual Synchronization Consolidation (ETL, Consolidation and Propagation CDC) (Data Federation) (Messaging) Governance Common Metadata Common Connectivity Pace of Business change & requirement for agility demands that Intelligent Business 23 organizations support multiple styles of data integration
  • 24. How DV differs Physical Movement and Abstraction / Virtual Synchronization Consolidation (ETL, Consolidation and Propagation CDC) (Data Federation) (Messaging) Middle- ETL CDC Data Virtualization EAI / ESB ware Purpose DB  DB DB  DB DB  Application Application  Application Event Event Attribute Scheduled On Demand Driven Driven Intelligent Business 24
  • 25. How DV Works – Example Scenario 1) I need to build an application that looks like this… 2) The view or data service needs to look like this… 3) And the data comes from these sources… Intelligent Business 25
  • 26. Traditional Integration with ETL and Data Warehouses Traditional Approach 1. Design entire DW schema 2. Develop ETL 3. Refresh on batch basis 4. Application gets data from DW Issues Slow development cycle Replicated data Batch latencies Physical stores overhead Intelligent Business 26
  • 27. Data Virtualisation design Design Steps 1. Discover data 2. Model individual view/service 3. Validate view/service Data model layer Benefits Faster time to solution Easy to learn and use tools Extensible / reusable objects Conform data to a standard data model Intelligent Business 27
  • 28. Data Virtualisation Production Production Steps 1. Application invokes request 2. Optimized data access and retrieval (single query) Optimizer 3. Deliver data to application Benefits Less replication High performance Up-to-the-minute data Intelligent Business 28
  • 29. Data Virtualisation Production with Caching Production Steps 1. Cache essential data 2. Application invokes request 3. Optimized data access and retrieval (leveraging cached data) Optimizer Cache 4. Deliver data to application Benefits Removes network constraints 7-24 availability Optimal performance Intelligent Business 29
  • 30. 3. Five example usage patterns Use this layout for a title with a horizontally striped picture. Where Data Virtualisation can add value to Data Warehousing 30 I Intelligent Business
  • 31. Prototyping Data Warehouse Development In traditional DW development, time taken for schema changes, adding new data sources and providing data federation are often considerable. Use DV to prototype a development environment rapidly building a virtual DW rather than a physical one. Reports, dashboards and so on can be built on the virtual DW. After prototyping the physical DW can be introduced if the usage merits. Packages Databases Files XML Intelligent Business 31
  • 32. Enriching the DW ETL Process Frequently new data sources particularly from ERPs are required in the DW. Often the ETL lacks data access capabilities to complex sources. Tight processing windows may require access, aggregation & federation activities to be performed prior to the ETL process. Powerful data access capabilities of EII provide rich access and federation capabilities which can present virtual views to the ETL DW process which continues as though using a simpler data source. Intelligent Business 32
  • 33. Federating Data Warehouses Many organisations have more than one DW Is the Information in each DW DW DW completely discrete? Data Virtualisation provides powerful options to federate multiple DW’s by creating an integrated view across them. This has particular relevance in providing rapid cross warehouse views following a merger or acquisition. Intelligent Business 33
  • 34. DW Extension Business Users Require Data From Outside the Data Warehouse so they can meet reporting and operational needs. DW Historical data from the warehouse and up-to-the-minute data from transaction systems or operational data stores is required. Summarized data from the warehouse and drill-down detail from transaction systems or operational data stores is required. Data Virtualisation can Extend Existing Data Warehouses quickly and easily to work around the fact that key data users need resides outside the consolidated data warehouse. Intelligent Business 34
  • 35. Complete Master Data View Master MDM applications alone cannot fully support all requirements as data exists outside of MDM Data hub. Complementary data integration solutions are Hub needed to deal with data maintained outside of MDM hubs often in complex, disparate data silos. DV can extend the Master Data and provide a complete 360o view by using master data from the hub as the foreign key to quickly and easily federate master data with additional transactional and historical data to get a complete single view of master data. Intelligent Business 35
  • 36. 4. Data migration and take on Use this layout for a title with a vertically striped picture. 6 key considerations: ETL vs EII /DV or both? 36 I Intelligent Business
  • 37. Some Migration Considerations What data have we got? E-discovery Data owners vs. users What other data do we require? Source model vs target model Move all the data or leave some in place? Do we use EII vs ETL (or even both) Intelligent Business 37
  • 38. EII or ETL? 1. Will the data be replicated in both the DW and the Operational System? • Will data will need to be updated in one or both locations? • If data is physically in two locations beware of regulatory & compliance issues (e.g. SoX, HIPPA, BASEL2, FDA etc) Intelligent Business 38
  • 39. EII or ETL? 2. Data Governance • Is the data only to be managed in the originating Operational System? • What is the certainty that DW will be a reporting DW only (vs Operational DW)? Intelligent Business 39
  • 40. EII or ETL? 3. Currency of the data, i.e. Does it need to be up to the minute? • How up to date are the data requirements of the DW? • Is there a need to see the operational data? Intelligent Business 40
  • 41. EII or ETL? 4. Time to solution i.e. how quickly is the solution required? • Immediate requirement? • Confirmed users & usage? Vs.. • ..Flexible, emerging requirements? Intelligent Business 41
  • 42. EII or ETL? 5. What is the life expectancy of source system(s)? • Are the source systems likely to be retired? • Will new systems be commissioned? • Are new sources required? Intelligent Business 42
  • 43. EII or ETL? 6. Need for historical / summary / aggregate data • How much historical data is required in the DW solution? • How much aggregated / summary data is required in the DW solution? Intelligent Business 43
  • 44. 5. BI & Information Management Use this layout for a title with a vertically striped Maybe picture. spreadsheets aren’t such a good solution after all! Intelligent Business 44
  • 45. Effective IM IS crucial today Higher volumes of data generated by organisations Information is all pervasive – if you don’t have a strategy to manage it, you will certainly drown in it Proliferation of data-centric systems ERP, CRM, ECM… Greater demand for reliable information Accurate business intelligence is vital to gain competitive advantage, support planning/resourcing and monitor key business functions Tighter regulatory compliance Far more responsibility now placed on organisations to ensure they store, manage, audit and protect their data (SoX, BASEL, SOLVENCY2, HIPPA, FDA ...) Business change is no longer optional – it’s inevitable Mergers/acquisitions, market forces, technological advances… Intelligent Business 45
  • 47. Excel, BI and IM ! Several users within a business are adept at manipulating large data extracts in Excel Easily derive new fields Pivot data Aggregate data Produce charts and dashboards. “All good”, you might say? Intelligent Business 47
  • 48. Excel, BI and IM ! A “new” copy of the source data is now in your spreadsheet You are now (unwittingly) a data steward! What are the rules & calculations for derivations? Where does the additional data come from? Charts / graphs potentially disconnected from data Distribution leading to data duplication & amendment What’s the lineage & provenance of the data now? Intelligent Business 48
  • 49. A Happy Path? Go back to the source Avoid “Cottage Industry” reporting Record metadata regarding the extract and don’t change its values If you must correct data, correct at source Ensure calculations make sense and are properly annotated and tested Clearly label distributed versions vs originals. Identify versions Don’t re-issue your local copy of the source data - redirect any data requests to the source Intelligent Business 49
  • 50. 6. Certification & Competencies Use this layout for a title with a vertically striped picture. Intelligent Business 50
  • 51. What is CDMP? CDMP stands for “Certified Data Management Professional” It is the only non-proprietary, widely recognized data management certification. The certification program was jointly constructed by DAMA International (DAMA) and the Institute for Certification of Computer Professionals (ICCP). DAMA owns the CDMP certification, and ICCP administers and delivers exams, provides all record keeping. Intelligent Business 51
  • 52. Why do I need it? “Certification, in itself, is not a goal, but Professionalism is.” Dr. Paul M. Pair, ICCP Fellow Credential Increase in Salary Company Requirement Credibility within Organisation Professional Growth Credibility with Customers Self Evaluation Greater Self Esteem Financial Reward Solve Problems Quicker Other Why People Certify Primary Achievement Resulting from Certification Intelligent Business Source: ICCP Research Study (Athabasca University)) 52
  • 53. Which Specialty Exam? Intelligent Business 53
  • 54. IPL’s Information Management Framework Goals Principles 1 Governance Planning 2 3 Lifecycle Infrastructure Quality Management Services 4 5 6 Models / Taxonomy Catalog / Meta data 7 8 Structured Transaction Unstructured Master Data MI/BI Data Technical Data Data Data 9 10 Intelligent Business 54
  • 55. Maturity Model – Information Governance 2 Level 1 - Initial Level 2 - Repeatable Level 3 - Defined Level 4 - Managed Level 5 - Optimised No clear data Data Ownership Defined Data Data Ownership Data Ownership ownership assigned. Model does not exist. Ownership Model Model is Model has been Data Owners, if any, Owners exists. Ownership implemented for the extended such that evolve on their own commissioned in the Model is loosely key data entities. the majority of data during project short-term for applied to key data Collaboration assets are under rollouts (i.e. self specific projects & entities. Limited between active stewardship. appointed data initiatives. Often collaboration. Not stakeholders in place. Effective governance owners). No standard department or silo fully 'bought in' to Governance process process employed by tools or focused leading to data ownership at an regularly reviews this stakeholders & documentation ownership by “Data enterprise level. model and its stewards. Well available for use Teams” or “Super application, updating defined standards across the whole Users” that manage and improving as adopted. enterprise. “all” data. needed. Benefits begin to be realised. Intelligent Business 55
  • 56. Maturity Model – Quality 4 Level 1 - Initial Level 2 - Repeatable Level 3 - Defined Level 4 - Managed Level 5 - Optimised Limited awareness The quality of few Quality measures Data quality is The measurement of within the enterprise data sources is have been defined measured for all key data quality is of the importance of measured in an ad for some key data data sources on a embedded in many information quality. hoc manner. A sources. Specific regular basis. Quality business processes Very few, if any, number of different tools adopted to metrics information across the enterprise. processes in place to tools used to measure quality with is published via Data quality issues measure quality of measure quality. The some standards in dashboards etc. addressed through information. Data is activity is driven by a place. The processes Active management the data ownership often not trusted by projects or for measuring quality of data issues model. Data quality business users. departments. are applied at through the data issues fed back to be Limited consistent intervals. ownership model fixed at source. understanding of Data issues are ensures issues are good versus bad addressed where often resolved. quality. Identified critical. Quality issues are not considerations baked consistently into the SDLC. managed. Intelligent Business 56
  • 57. Maturity Model – Master Data 9 Level 1 - Initial Level 2 - Repeatable Level 3 - Defined Level 4 - Managed Level 5 - Optimised Limited awareness of The impact of master Definition of an A complete MDM A full integrated MDM. Master Data data issues gain MDM strategy is in strategy has been MDM hub exists and domains have not recognition within progress. Master defined and adopted. has been adopted been defined across the enterprise. data domains have MDM joined up with across the enterprise the enterprise. Silo Limited scope for been identified. data governance and for all key master based approach to managing master Several domains are data quality data domains. The data models means data due to lack of targeted for initiatives. Robust hub controls access multiple definitions Data Ownership delivering master business rules to master data of potential master Model. Project or data to specific defined for master entities. Many data entities, such as department based applications or data domains. Data applications access customer, exist. initiatives attempt to projects. Differing cleansing and the MDM Hub understand the products may be standardisation through a service enterprise's master adopted in these performed in the layer. Business users data. No MDM silos for MDM. Senior MDM hub. Specific are fully responsible strategy defined. management support products adopted for for master data. for MDM grows. MDM. Master data models defined. Intelligent Business 57
  • 58. As-Is IM Principles 5 Business 4 Data Governance Intelligence 3 Master Data 2 IM Planning Management 1 0 As-Is Catalog & Data Quality Metadata Models & IM Lifecycle Taxonomy Management Integration & Intelligent Business 58 Access
  • 59. Summary ben.braine@ipl.com Use this layout for a title with a horizontally striped picture. 59 I Intelligent Business
  • 60. Summary Data Virtualisation opens up a brave new world For data migration, ETL isn’t “the only way” Effective Information Management is crucial Intelligent Business 60
  • 61. Contact details Chris Bradley Business Consulting Director Chris.Bradley@ipl.com +44 1225 475000 @InfoRacer My blog: Information Management, Life & Petrol http://infomanagementlifeandpetrol.blogspot.com Intelligent Business 61
  • 62. Further information: Articles including: • Seven deadly sins of data modelling • The IT Credibility Crunch • Information Management Deficiency Syndrome • Modelling is not just for DBMS’s • Data mining - where’s my hard hat? • Master data mix-ups • Drowning in spreadsheets • Why bother with a semantic layer? • Business Intelligence in a cold climate • Data Management is everybody's business • Information superstition Download from: http://bc.ipl.com/ Intelligent Business 62