SlideShare a Scribd company logo
1 of 56
Data Governance
IASA is

a   non-profit professional association
 run   by architects
 for   all IT architects
 centrally   governed and locally run
 technology     and vendor agnostic




                                           Th
Information Architecture

   Module 0: Course Intro, Architecture              Module 3: Data Integration
    Fundamentals Introduction: Data,                      Lesson 1: Integrating at the Company Level
    Information and Knowledge                             Lesson 2: Data Characteristics
                                                          Lesson 3: Data Integration at the System level
   Module 1: Information for Business
       Lesson 1 – Information as Strategy            Module 4: Data Quality and Governance
                                                          Lesson 1 – Data and Information Quality
       Lesson 2 – Relating Information to Value
                                                          Lesson 2: Data Compliance
       Lesson 3 – Information Scope and                  Lesson 3 – Data and Information Governance
        Governance
                                                      Module 5: Advanced Information Management
   Module 2: Information Usage                           Lesson 1: Data Warehousing
       Lesson 1 – Who Uses Your Information              Lesson 2: Business Intelligence
       Lesson 2 – How, When, Where and Why               Lesson 3: Data Security and Privacy
        is Information Used                               Lesson 4: Metadata and Taxonomy Management
       Lesson 3 – Form Factors                           Lesson 5: Knowledge Management
       Lesson 4 – Usage Design 1
                                                      Module 6: Architecture throughout the Lifecycle
       Lesson 5 – Quality Attributes for
        Information Architecture
       Lesson 6 – Data Tools and Frameworks
Typical State of Affairs
The Problem
What is data governance?
Governance is Not a Popular Topic
Governance is Overcomplicated
 It   is not about politics
 Itis not about delivering
  verdicts
 Itis not about defining
  solutions
Step 1: Form a Team
Step 2: Establish Lineage
Step 3: Assess Maturity
Step 4: Create Governance Body
Step 5: Define Governance
Step 6: Define Charter




               •Charter
Step 7: Define Scope
Step 8: Create a Plan
Career Path
Iasa Architect Services


   Adopt Iasa Standards – Skills taxonomy, role descriptions,
    compensation models
   Set your goals for value
   Assess your current team – gap analysis
   Implement processes
   Develop and educate people
   Certify employees and vendors
   Build communities
•December 6th – 7th, 2012 // Austin, TX
                                                      •Training: Dec. 3rd – 5th, 2012
                                                    •Certification: Dec. 7th – 9th, 2012
                                                                        •


                                                     •www.iasaworldsummit.org

•Leading Innovation in Architecture. Stay ahead of the technology curve and
           shape the future of architecture in your organization.

        •Connect and share insights with the largest global network of
              technology and enterprise architect practitioners.

     •Attend sessions on the most current breakthroughs, case studies and
     key topics in architecture - presented by a mix of practicing architects
              from top performing businesses and organizations.

 •Participate in pre-conference workshops and training. Maximize your time
  at the summit by taking part in one of six intensive training courses designed
        to provide immediate solutions to benefit your everyday practice.



                                      •Featuring Industry Leading Keynote Speakers:




                         •Sheila Jeffrey             •David Del Giudice                 •Paul Preiss
                    Senior Information Architect      Principal Architect            President, Founder
                         Bank of America                 AstraZeneca                    Iasa Global




                       •David Manning                  •Scott Whitmire                     •Cat Susch
                      IT Enterprise Architect         Enterprise Architect           Enterprise Architect
                     Idaho Power Company                  Nordstrom                       Microsoft
End Module
Data Architecture for Data
                      Governance



                                   November 8, 2012


                           Presented by Malcolm Chisholm Ph.D.
                              mchisholm@refdataportal.com
                                 Telephone 732-687-9283
                                  www.refdataportal.com
                                 www.bizrulesengine.com

© AskGet.com Inc., 2012. All rights reserved
Agenda
    • Introducing Enterprise Architecture (EA)

    • Introducing Data Governance

    • Data Architecture and Data Governance

    • Data Architecture Patterns

    • Data Architecture, Governance, and The Business




© AskGet.com Inc., 2012. All rights reserved
Introducing Enterprise Architecture
                          (EA)




© AskGet.com Inc., 2012. All rights reserved
Need for Enterprise Architecture (EA)
Myriad of Independent Projects in IT           Strategy is Set at High Level
                                                       Here is where we
                                                        want to be in 5
                                                           years…




                                                       CxO’s


                                                     Operationalization

                                               Enterprise      …Others…
                                               Architecture


• EA is responsible for ensuring overall business strategy is implemented
  by IT
© AskGet.com Inc., 2012. All rights reserved
The IT Mindset
  That’s not a clear                                   We have a high-level
  requirement…                                          strategy…
  Tell me what you want me
  to build
  Then I will design it
  Then I will build it
  Then I will turn it over to you
  Then I will walk away




                                     IT        CxO




 • IT people just want to build stuff and hand it off to other people
 • They want business sponsors who will pay for this stuff and tell them
   exactly what is needed
 • Strategy does not match this mindset

© AskGet.com Inc., 2012. All rights reserved
Design without Architecture




                                                                                         Stairway to Ceiling




                        All Design – No Architecture
     Rooms: 160; Doorways: 467; Doors: 950; Fireplaces: 47 (gas, wood,
     coal); Bedrooms: 40
                                                                                         “Circular” Stairway
     Constructed 1884 – 1922 (38 continuous years); Cost: $5.5M
         Blueprints: Never made; Individual rooms sketched out by Sarah   “…staggering amount of creativity, energy, and
              Winchester on paper or other media (e.g. tablecloths)        expense poured into each and every detail”
                                                                            http://www.winchestermysteryhouse.com



     • The Winchester Mystery House
     • Lots of design – but no architecture
     • There is a difference between architecture and design

© AskGet.com Inc., 2012. All rights reserved
Design vs. Architecture
                                                             Changed
                           Source              Integration     Data
                           Mirror                            Capture
    Data
    Flow




                                                DDL


  • Architecture deals with many instances of a component type that
    must interact
  • Design deals with one instance of a component type, without regard
    to interaction
  • E.g. Perspective of Databases : Data Environment (BI or Integration
    Environment in this example)

© AskGet.com Inc., 2012. All rights reserved
Basic EA Taxonomy (“BAIT”)

                                    Enterprise Architecture




       Business                                                      Technology
      Architecture                                                   Architecture


                        Application                   Information
                        Architecture                  Architecture




 •   EA is clearly not a monolithic discipline
 •   It looks more like a collection of dissimilar components
 •   Each component is a competency – so this matters
 •   Yet there is no common agreement
© AskGet.com Inc., 2012. All rights reserved
Introducing Data Governance




© AskGet.com Inc., 2012. All rights reserved
What is Data Governance? Starter Definitions
                               From the Data Governance Institute



    ”The exercise of decision-making and authority for data-related
    matters.”

    ”A system of decision rights and accountabilities for information-
    related processes, executed according to agreed-upon models which
    describe who can take what actions with what information, and when,
    under what circumstances, using what methods.”


                                     www.datagovernance.com




© AskGet.com Inc., 2010. All rights reserved
Data Governance is about Processes
                                           Processes for…

    Deciding who has what rights regarding data
    Making decisions about data
    Implementing decisions about data


                                           In other Words…

    Identifying the processes needed to manage data
    Identifying the actors in these processes
    Designing these processes
    And designing the processes to design these processes




© AskGet.com Inc., 2010. All rights reserved
Data Governance – A Caution
   Data Governance is the design, building, and operation of formal
   business processes to manage the enterprise data resource.




            W R ON G
            The Business has….
            Business Processes
                                                 IT has…
                                               Governance

• The decision rights of actors are defined within the context of these
   business processes
• There is no real distinction between the processes of “data governance”
   and the “business processes” of the business . [Compare HR].
• A distinction that identifies the set of business processes that manage the
   enterprise data resource as “data governance” is valid. [Compare HR].
• Use of the term data governance to further the pretence that IT is
universe
   totally outside of the enterprise is invalid.
• Therefore, the processes of data governance are truly business
© AskGet.com Inc., 2010. All rights reserved
processes
Data Architecture and Data
                             Governance




© AskGet.com Inc., 2012. All rights reserved
Architectural Levels
     Reference
    Architecture
   Assimilate best            Conceptual
      architectural           Architecture
  practices from the
    IT industry. All           Match/select         Logical
 relevant component            architectural      Architecture
      classes and          component classes
      patterns are         and patterns to the   Detailed design of      Physical
      understood.           business needs,         future state        Architecture
                            (including future      architecture.
                                  plans).                              Influencing actual
                                                                            physical
                                                                      implementations to
                                                                             ensure
                                                                       conformance with
                                                                      logical architecture

   A reference architecture is the highest level of architecture. It
   can be undertaken prior to even thinking about the enterprise.
   Its advantages are that it can be very high level and can cover
   the entire enterprise. Useful for thinking about high level data
   layers

© AskGet.com Inc., 2011. All rights reserved
Reference Data Architecture - Example




   • Example of a simple reference data architecture
   • Just data layers – no services or components
    BUT – just having a picture can be problematic
© AskGet.com Inc., 2011. All rights reserved
Need Definitions, Explanations – Not Just Picture

                                                         Layer
Characteristic            Transactional
                                                    Data Warehouse                  Data Mart
                           Application
                                                                             Data structured and
                                                 View of data across the
                     Data produced via the                                   filtered to support
                                                 enterprise. Supports
    Purpose          automation of business
                                                 dissemination, derivation
                                                                             specific information
                     processes                                               needs of small groups of
                                                 of knowledge and history
                                                                             users.
                                                 Derivations (including
                                                                             Data from Warehouse is
                     All base (non-derived)      aggregations) produced
 Data Life Cycle     data originates here        here, and history is
                                                                             transformed to support
                                                                             specific reporting
                                                 inferred
                                                 Extract / Transform /
                     Create / Source / Read /                                Subscribe / Transform /
Data Operations      Update / Delete / Archive
                                                 Load / Derive / Publish /
                                                                             Archive
                                                 Archive

                                                 Subject Oriented /          Information Requirement
  Data Model         Normalized to 3NF           Snowflaked / Conformed      Oriented / Snowflaked /
                                                 Dimensions                  Conformed Dimensions


• Much more is needed than the above
• Definitions are a technical reference; explanations help stakeholders to
  understand the reference architecture
 © AskGet.com Inc., 2011. All rights
            reserved
Subject Area Model




• A Subject Area Model is a taxonomy of the major areas in the enterprise
  that are relevant to data management
• A necessary starting point for MDM
• 1 – A subject area should have stable definitions of entities
• 2 – Production of master data should not cross subject area boundaries
• Identify the major data concepts in each subject area – may will be
  master data entities.
© Askget.com Inc., 2011. All rights reserved
A Subject Area Model is Not Costly



                                               Data Management




•   10-15 subject areas per model is the norm
•   Do not need elaborate data modeling tools
•   Much more about a standardized view of the enterprise
•   Can be done with 1-3 people
•   Gets a lot of bang for the buck
•   Is high-level and important



© Askget.com Inc., 2011. All rights reserved
Example of Use: Establish Vendor Data Profiles
                                       Map Vendor’s      Data Vendor
                                       Data to Subject        1
                                           Areas


                                                         Data Vendor
   Data Management                                            2


      Create &                                           Data Vendor
                                Map Vendors
   Manage Subject                 to Uses
                                                              3
    Area Model(s)

                                                         Data Vendor
                                                              4

                               Map Subject
                              Areas to User              Data Vendor
                              Requirements                    5

   Subject Area Model(s)                         Users

 • A Subject Area Model provides the basis for a common understanding of
   the data vendors provide
 • Will need to go to major entity clusters within Subject Area
© AskGet.com Inc., 2011. All rights reserved
A Subject Area Model is a Taxonomy
                                                       I don’t like your
                                                         definition of
                                                          this subject
         Your model                                           area!
        does not tell
         me what is
       financial data!


                                                         You mean to tell me
                                                          every one of these
                                                             subject areas
                                                           includes access
                                                           security for data!


• People think a taxonomy is a way of unlocking the secrets of the universe
• Taxonomies are a tools – you can have as many as you need to do your
  job
• You can never expect the boundaries of a subject area model to be
  exactly precise and never changing


© Askget.com Inc., 2011. All rights reserved
Create a Metadata Subject Area Model




              The Fundamental Ontology of Metadata for the Enterprise
© Askget.com Inc., 2011. All rights reserved
Metadata Subject Area Model
                                               Advantages
        1. Standardizes the metadata in the enterprise at a high level.

        2. Can be used to prioritize projects and programmes.

        3. Useful in communications.
        4. Shows what metadata is being produced by subject area. This is a high-level
           inventory.
        5. Can distinguish data-related metadata from other data
        6. Within each subject area, definitions should be constant. There may be
           variations across subject areas.
        7. Subject areas are candidates for conceptual models.

                                           Disadvantages
        1. It is a taxonomy and can be argued over. No one taxonomy will satisfy every
           perspective. We use data categorization for that (see later). The Subject Area
           Model should be the most common and natural perspective.
        2. The Subject Area Model often has more things expected of it than it can
           deliver. Managing expectations can be tricky.


      The Subject Area Model is itself Metadata and needs to be managed
© Askget.com Inc., 2011. All rights reserved
Data Architecture Patterns




© AskGet.com Inc., 2012. All rights reserved
What is an Architectural Pattern?
  “Pattern in architecture is the idea of       • An architectural style is a central,
  capturing architectural design ideas            organizing concept for a system.
  as archetypal and reusable                    • An architectural pattern describes a
  descriptions”                                   coarse-grained solution at the level of
  - Christopher Alexander, quoted in Business     subsystems or modules and their
  Model Generation by Osterwalder and             relationships.
  Pigneur (2010)
                                                • A system metaphor is more conceptual
                                                  and it relates more to a real-world
  Antipattern: A pattern that is                  concept over a software engineering
  commonly used, but which is                     concept.
  ineffective or damaging. E.g.                 - Quoted from A Practical Guide to Enterprise
  Analysis Paralysis.                             Architecture by McGovern, Ambler et al, at
                                                http://www.infoq.com/news/2009/02/Architectural-
  - Term created by Andrew Koenig, 1995
                                                  Styles-Patterns


   • Patterns occur a lot in architecture
   • There are also styles, metaphors and antipatterns
   • Seem to have emerged from the application architecture
     competency (e.g. rare to hear of dimensional modeling as a pattern
     or style)

© AskGet.com Inc., 2011. All rights reserved
Example: Farm and Market Pattern




         Traditional MDM Integration Hub



                                               Farm and Market Pattern for MDM


© AskGet.com Inc., 2011. All rights reserved
Six Patterns for Data Hubs: 1 – Publish/Subscribe




   •   Publisher pushes data to hub
   •   Subscriber pulls data from hub
   •   No data integration
   •   Publisher may not know who the subscribers are
   •   Weak governance - may not even be SLA’s

© AskGet.com Inc., 2011. All rights reserved
Six Patterns for Data Hubs: 2 – ODS for Integrated Reporting




   • Supports principle that transaction applications will not do
     operational reporting (adverse affect on performance)
   • Some form of integration
   • Anti-pattern of real-time data warehousing
   • History (changed data capture) not considered

© AskGet.com Inc., 2011. All rights reserved
Six Patterns for Data Hubs: 3 – ODS for Data Warehouses




   •   No reporting in this kind of ODS
   •   Probably evolved from ODS for reporting
   •   Strong integration
   •   Just to feed warehouses
   •   At odds with Data Warehouse that has own staging, integration

© AskGet.com Inc., 2011. All rights reserved
Six Patterns for Data Hubs: 4 – Traditional MDM Hub




   • Master Data only
   • Integration does happen (typically just Trust & Survivorship)
   • Some content management needed – typically to correct data
     quality problems
   • Typically DQ functionality an afterthought
   • Distributes “Golden Copy” Master Data to enterprise

© AskGet.com Inc., 2011. All rights reserved
Six Patterns for Data Hubs: 5 – Message Hub




   •   Messages – real-time or near-real-time - not batch data
   •   Message switch
   •   Command and control for message orchestration
   •   Not just “listening” – does routing
   •   Messages also stored in database


© AskGet.com Inc., 2011. All rights reserved
Six Patterns for Data Hubs: 6 – Integration Hub




   •   Like message hub, but for batch data
   •   Event data equivalent of MDM hub
   •   Just one place where integration done
   •   Both warehouse and transaction applications need integrated data
   •   Removes need for each application to do integration by itself

© AskGet.com Inc., 2011. All rights reserved
Data Architecture, Governance, and
                    The Business




© AskGet.com Inc., 2012. All rights reserved
Another View of EA
           Many Years Ago…                            Today…
   …”the business” knew the business           …it does so much less




• Long ago, people had to know the business rules concerning what they
  did
• These are now in applications, and staff are more concerned with
  knowing how to make the applications work
• Data is an asset, but staff tend to know little about the data
• Loss of business knowledge
© AskGet.com Inc., 2012. All rights reserved
Who Knows The Business?




• The business seems to be more fragmented than long ago (though that is
  difficult to prove)
• Managers who know the business know less of it and are so time-
  constrained they cannot help much on aligning projects to enterprise
  strategy
© AskGet.com Inc., 2012. All rights reserved
Populate EA with Business Experts
           A Different EA Vision:                     IT Tendency:
       Architects + Business Experts           Populate EA with Architects




  •   Business experts provide better relations with rest of business
  •   Keep architects with just being oriented to architecture
  •   Immediate business credibility for EA
  •   They get poached


© AskGet.com Inc., 2012. All rights reserved
Questions and Answers
                 Data Architecture for Data
                       Governance


                                   November 8, 2012


                            Presented by Malcolm Chisholm Ph.D.
                                 Telephone 732-687-9283
                             mchisholm@MasterDataMgmt.com


© AskGet.com Inc., 2012. All rights reserved

More Related Content

What's hot

Building a Data Governance Strategy
Building a Data Governance StrategyBuilding a Data Governance Strategy
Building a Data Governance StrategyAnalytics8
 
Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...
Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...
Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...DATAVERSITY
 
Data Governance Takes a Village (So Why is Everyone Hiding?)
Data Governance Takes a Village (So Why is Everyone Hiding?)Data Governance Takes a Village (So Why is Everyone Hiding?)
Data Governance Takes a Village (So Why is Everyone Hiding?)DATAVERSITY
 
Enterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data ArchitectureEnterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
 
Data Mesh Part 4 Monolith to Mesh
Data Mesh Part 4 Monolith to MeshData Mesh Part 4 Monolith to Mesh
Data Mesh Part 4 Monolith to MeshJeffrey T. Pollock
 
DAS Slides: Master Data Management – Aligning Data, Process, and Governance
DAS Slides: Master Data Management – Aligning Data, Process, and GovernanceDAS Slides: Master Data Management – Aligning Data, Process, and Governance
DAS Slides: Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
 
Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
 
Data Architecture Strategies: Data Architecture for Digital Transformation
Data Architecture Strategies: Data Architecture for Digital TransformationData Architecture Strategies: Data Architecture for Digital Transformation
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
 
Building a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business GoalsBuilding a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
 
Enterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data ArchitectureEnterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
 
Modern Data architecture Design
Modern Data architecture DesignModern Data architecture Design
Modern Data architecture DesignKujambu Murugesan
 
Data Governance
Data GovernanceData Governance
Data GovernanceRob Lux
 
Best Practices in Metadata Management
Best Practices in Metadata ManagementBest Practices in Metadata Management
Best Practices in Metadata ManagementDATAVERSITY
 
Building the Data Lake with Azure Data Factory and Data Lake Analytics
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsBuilding the Data Lake with Azure Data Factory and Data Lake Analytics
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
 
Data Architecture Best Practices for Advanced Analytics
Data Architecture Best Practices for Advanced AnalyticsData Architecture Best Practices for Advanced Analytics
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
 
You Need a Data Catalog. Do You Know Why?
You Need a Data Catalog. Do You Know Why?You Need a Data Catalog. Do You Know Why?
You Need a Data Catalog. Do You Know Why?Precisely
 
Data Architecture - The Foundation for Enterprise Architecture and Governance
Data Architecture - The Foundation for Enterprise Architecture and GovernanceData Architecture - The Foundation for Enterprise Architecture and Governance
Data Architecture - The Foundation for Enterprise Architecture and GovernanceDATAVERSITY
 
Data Modeling & Metadata Management
Data Modeling & Metadata ManagementData Modeling & Metadata Management
Data Modeling & Metadata ManagementDATAVERSITY
 

What's hot (20)

Building a Data Governance Strategy
Building a Data Governance StrategyBuilding a Data Governance Strategy
Building a Data Governance Strategy
 
Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...
Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...
Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...
 
Data Governance Takes a Village (So Why is Everyone Hiding?)
Data Governance Takes a Village (So Why is Everyone Hiding?)Data Governance Takes a Village (So Why is Everyone Hiding?)
Data Governance Takes a Village (So Why is Everyone Hiding?)
 
Enterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data ArchitectureEnterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data Architecture
 
Data Mesh Part 4 Monolith to Mesh
Data Mesh Part 4 Monolith to MeshData Mesh Part 4 Monolith to Mesh
Data Mesh Part 4 Monolith to Mesh
 
DAS Slides: Master Data Management – Aligning Data, Process, and Governance
DAS Slides: Master Data Management – Aligning Data, Process, and GovernanceDAS Slides: Master Data Management – Aligning Data, Process, and Governance
DAS Slides: Master Data Management – Aligning Data, Process, and Governance
 
Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?
 
Data Architecture Strategies: Data Architecture for Digital Transformation
Data Architecture Strategies: Data Architecture for Digital TransformationData Architecture Strategies: Data Architecture for Digital Transformation
Data Architecture Strategies: Data Architecture for Digital Transformation
 
Building a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business GoalsBuilding a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business Goals
 
Enterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data ArchitectureEnterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data Architecture
 
Modern Data architecture Design
Modern Data architecture DesignModern Data architecture Design
Modern Data architecture Design
 
Data Governance
Data GovernanceData Governance
Data Governance
 
8 Steps to Creating a Data Strategy
8 Steps to Creating a Data Strategy8 Steps to Creating a Data Strategy
8 Steps to Creating a Data Strategy
 
Best Practices in Metadata Management
Best Practices in Metadata ManagementBest Practices in Metadata Management
Best Practices in Metadata Management
 
Building the Data Lake with Azure Data Factory and Data Lake Analytics
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsBuilding the Data Lake with Azure Data Factory and Data Lake Analytics
Building the Data Lake with Azure Data Factory and Data Lake Analytics
 
Data Architecture Best Practices for Advanced Analytics
Data Architecture Best Practices for Advanced AnalyticsData Architecture Best Practices for Advanced Analytics
Data Architecture Best Practices for Advanced Analytics
 
You Need a Data Catalog. Do You Know Why?
You Need a Data Catalog. Do You Know Why?You Need a Data Catalog. Do You Know Why?
You Need a Data Catalog. Do You Know Why?
 
Data Architecture - The Foundation for Enterprise Architecture and Governance
Data Architecture - The Foundation for Enterprise Architecture and GovernanceData Architecture - The Foundation for Enterprise Architecture and Governance
Data Architecture - The Foundation for Enterprise Architecture and Governance
 
Webinar Data Mesh - Part 3
Webinar Data Mesh - Part 3Webinar Data Mesh - Part 3
Webinar Data Mesh - Part 3
 
Data Modeling & Metadata Management
Data Modeling & Metadata ManagementData Modeling & Metadata Management
Data Modeling & Metadata Management
 

Similar to Data Architecture for Data Governance

Data-Ed Slides: Data Architecture Strategies - Constructing Your Data Garden
Data-Ed Slides: Data Architecture Strategies - Constructing Your Data GardenData-Ed Slides: Data Architecture Strategies - Constructing Your Data Garden
Data-Ed Slides: Data Architecture Strategies - Constructing Your Data GardenDATAVERSITY
 
Data Architecture Strategies
Data Architecture StrategiesData Architecture Strategies
Data Architecture StrategiesDATAVERSITY
 
All Together Now: A Recipe for Successful Data Governance
All Together Now: A Recipe for Successful Data GovernanceAll Together Now: A Recipe for Successful Data Governance
All Together Now: A Recipe for Successful Data GovernanceInside Analysis
 
DataEd Slides: Data Modeling is Fundamental
DataEd Slides:  Data Modeling is FundamentalDataEd Slides:  Data Modeling is Fundamental
DataEd Slides: Data Modeling is FundamentalDATAVERSITY
 
Why Data Modeling Is Fundamental
Why Data Modeling Is FundamentalWhy Data Modeling Is Fundamental
Why Data Modeling Is FundamentalDATAVERSITY
 
Data-Ed Slides: Data Modeling Strategies - Getting Your Data Ready for the Ca...
Data-Ed Slides: Data Modeling Strategies - Getting Your Data Ready for the Ca...Data-Ed Slides: Data Modeling Strategies - Getting Your Data Ready for the Ca...
Data-Ed Slides: Data Modeling Strategies - Getting Your Data Ready for the Ca...DATAVERSITY
 
Agile & Data Modeling – How Can They Work Together?
Agile & Data Modeling – How Can They Work Together?Agile & Data Modeling – How Can They Work Together?
Agile & Data Modeling – How Can They Work Together?DATAVERSITY
 
Data-Ed Webinar: Data Architecture Requirements
Data-Ed Webinar: Data Architecture RequirementsData-Ed Webinar: Data Architecture Requirements
Data-Ed Webinar: Data Architecture RequirementsDATAVERSITY
 
Data-Ed: Data Architecture Requirements
Data-Ed: Data Architecture Requirements  Data-Ed: Data Architecture Requirements
Data-Ed: Data Architecture Requirements Data Blueprint
 
Data-Ed Webinar: Data Modeling Fundamentals
Data-Ed Webinar: Data Modeling FundamentalsData-Ed Webinar: Data Modeling Fundamentals
Data-Ed Webinar: Data Modeling FundamentalsDATAVERSITY
 
Business Centric Data Modeling
Business Centric Data ModelingBusiness Centric Data Modeling
Business Centric Data ModelingDATAVERSITY
 
An AI Maturity Roadmap for Becoming a Data-Driven Organization
An AI Maturity Roadmap for Becoming a Data-Driven OrganizationAn AI Maturity Roadmap for Becoming a Data-Driven Organization
An AI Maturity Roadmap for Becoming a Data-Driven OrganizationDavid Solomon
 
M365VM - Project Cortex: AI Powered Knowledge Network for the Enterprise
M365VM - Project Cortex: AI Powered Knowledge Network for the EnterpriseM365VM - Project Cortex: AI Powered Knowledge Network for the Enterprise
M365VM - Project Cortex: AI Powered Knowledge Network for the EnterpriseJoel Oleson
 
Fisher Practice Areas 2012
Fisher Practice Areas 2012Fisher Practice Areas 2012
Fisher Practice Areas 2012fish1960
 
Data-Ed Online Webinar: Data Architecture Requirements
Data-Ed Online Webinar: Data Architecture RequirementsData-Ed Online Webinar: Data Architecture Requirements
Data-Ed Online Webinar: Data Architecture RequirementsDATAVERSITY
 
Conceptual vs. Logical vs. Physical Data Modeling
Conceptual vs. Logical vs. Physical Data ModelingConceptual vs. Logical vs. Physical Data Modeling
Conceptual vs. Logical vs. Physical Data ModelingDATAVERSITY
 
Getting Data Quality Right
Getting Data Quality RightGetting Data Quality Right
Getting Data Quality RightDATAVERSITY
 
Enterprise Data Architect Job Description
Enterprise Data Architect Job DescriptionEnterprise Data Architect Job Description
Enterprise Data Architect Job DescriptionLars E Martinsson
 
Data Structures - The Cornerstone of Your Data’s Home
Data Structures - The Cornerstone of Your Data’s HomeData Structures - The Cornerstone of Your Data’s Home
Data Structures - The Cornerstone of Your Data’s HomeDATAVERSITY
 

Similar to Data Architecture for Data Governance (20)

Data-Ed Slides: Data Architecture Strategies - Constructing Your Data Garden
Data-Ed Slides: Data Architecture Strategies - Constructing Your Data GardenData-Ed Slides: Data Architecture Strategies - Constructing Your Data Garden
Data-Ed Slides: Data Architecture Strategies - Constructing Your Data Garden
 
Data Architecture Strategies
Data Architecture StrategiesData Architecture Strategies
Data Architecture Strategies
 
All Together Now: A Recipe for Successful Data Governance
All Together Now: A Recipe for Successful Data GovernanceAll Together Now: A Recipe for Successful Data Governance
All Together Now: A Recipe for Successful Data Governance
 
DataEd Slides: Data Modeling is Fundamental
DataEd Slides:  Data Modeling is FundamentalDataEd Slides:  Data Modeling is Fundamental
DataEd Slides: Data Modeling is Fundamental
 
Why Data Modeling Is Fundamental
Why Data Modeling Is FundamentalWhy Data Modeling Is Fundamental
Why Data Modeling Is Fundamental
 
Data-Ed Slides: Data Modeling Strategies - Getting Your Data Ready for the Ca...
Data-Ed Slides: Data Modeling Strategies - Getting Your Data Ready for the Ca...Data-Ed Slides: Data Modeling Strategies - Getting Your Data Ready for the Ca...
Data-Ed Slides: Data Modeling Strategies - Getting Your Data Ready for the Ca...
 
Agile & Data Modeling – How Can They Work Together?
Agile & Data Modeling – How Can They Work Together?Agile & Data Modeling – How Can They Work Together?
Agile & Data Modeling – How Can They Work Together?
 
Data-Ed Webinar: Data Architecture Requirements
Data-Ed Webinar: Data Architecture RequirementsData-Ed Webinar: Data Architecture Requirements
Data-Ed Webinar: Data Architecture Requirements
 
Data-Ed: Data Architecture Requirements
Data-Ed: Data Architecture Requirements  Data-Ed: Data Architecture Requirements
Data-Ed: Data Architecture Requirements
 
Data-Ed Webinar: Data Modeling Fundamentals
Data-Ed Webinar: Data Modeling FundamentalsData-Ed Webinar: Data Modeling Fundamentals
Data-Ed Webinar: Data Modeling Fundamentals
 
Business Centric Data Modeling
Business Centric Data ModelingBusiness Centric Data Modeling
Business Centric Data Modeling
 
An AI Maturity Roadmap for Becoming a Data-Driven Organization
An AI Maturity Roadmap for Becoming a Data-Driven OrganizationAn AI Maturity Roadmap for Becoming a Data-Driven Organization
An AI Maturity Roadmap for Becoming a Data-Driven Organization
 
Data is not the new snake oil
Data is not the new snake oilData is not the new snake oil
Data is not the new snake oil
 
M365VM - Project Cortex: AI Powered Knowledge Network for the Enterprise
M365VM - Project Cortex: AI Powered Knowledge Network for the EnterpriseM365VM - Project Cortex: AI Powered Knowledge Network for the Enterprise
M365VM - Project Cortex: AI Powered Knowledge Network for the Enterprise
 
Fisher Practice Areas 2012
Fisher Practice Areas 2012Fisher Practice Areas 2012
Fisher Practice Areas 2012
 
Data-Ed Online Webinar: Data Architecture Requirements
Data-Ed Online Webinar: Data Architecture RequirementsData-Ed Online Webinar: Data Architecture Requirements
Data-Ed Online Webinar: Data Architecture Requirements
 
Conceptual vs. Logical vs. Physical Data Modeling
Conceptual vs. Logical vs. Physical Data ModelingConceptual vs. Logical vs. Physical Data Modeling
Conceptual vs. Logical vs. Physical Data Modeling
 
Getting Data Quality Right
Getting Data Quality RightGetting Data Quality Right
Getting Data Quality Right
 
Enterprise Data Architect Job Description
Enterprise Data Architect Job DescriptionEnterprise Data Architect Job Description
Enterprise Data Architect Job Description
 
Data Structures - The Cornerstone of Your Data’s Home
Data Structures - The Cornerstone of Your Data’s HomeData Structures - The Cornerstone of Your Data’s Home
Data Structures - The Cornerstone of Your Data’s Home
 

More from DATAVERSITY

Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...DATAVERSITY
 
Data at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and GovernanceData at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
 
Exploring Levels of Data Literacy
Exploring Levels of Data LiteracyExploring Levels of Data Literacy
Exploring Levels of Data LiteracyDATAVERSITY
 
Make Data Work for You
Make Data Work for YouMake Data Work for You
Make Data Work for YouDATAVERSITY
 
Data Catalogs Are the Answer – What Is the Question?
Data Catalogs Are the Answer – What Is the Question?Data Catalogs Are the Answer – What Is the Question?
Data Catalogs Are the Answer – What Is the Question?DATAVERSITY
 
Data Modeling Fundamentals
Data Modeling FundamentalsData Modeling Fundamentals
Data Modeling FundamentalsDATAVERSITY
 
Showing ROI for Your Analytic Project
Showing ROI for Your Analytic ProjectShowing ROI for Your Analytic Project
Showing ROI for Your Analytic ProjectDATAVERSITY
 
How a Semantic Layer Makes Data Mesh Work at Scale
How a Semantic Layer Makes  Data Mesh Work at ScaleHow a Semantic Layer Makes  Data Mesh Work at Scale
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
 
Is Enterprise Data Literacy Possible?
Is Enterprise Data Literacy Possible?Is Enterprise Data Literacy Possible?
Is Enterprise Data Literacy Possible?DATAVERSITY
 
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
 
Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
 
Data Governance Trends - A Look Backwards and Forwards
Data Governance Trends - A Look Backwards and ForwardsData Governance Trends - A Look Backwards and Forwards
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
 
Data Governance Trends and Best Practices To Implement Today
Data Governance Trends and Best Practices To Implement TodayData Governance Trends and Best Practices To Implement Today
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
 
2023 Trends in Enterprise Analytics
2023 Trends in Enterprise Analytics2023 Trends in Enterprise Analytics
2023 Trends in Enterprise AnalyticsDATAVERSITY
 
Data Strategy Best Practices
Data Strategy Best PracticesData Strategy Best Practices
Data Strategy Best PracticesDATAVERSITY
 
Who Should Own Data Governance – IT or Business?
Who Should Own Data Governance – IT or Business?Who Should Own Data Governance – IT or Business?
Who Should Own Data Governance – IT or Business?DATAVERSITY
 
Data Management Best Practices
Data Management Best PracticesData Management Best Practices
Data Management Best PracticesDATAVERSITY
 
MLOps – Applying DevOps to Competitive Advantage
MLOps – Applying DevOps to Competitive AdvantageMLOps – Applying DevOps to Competitive Advantage
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
 
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...DATAVERSITY
 
Empowering the Data Driven Business with Modern Business Intelligence
Empowering the Data Driven Business with Modern Business IntelligenceEmpowering the Data Driven Business with Modern Business Intelligence
Empowering the Data Driven Business with Modern Business IntelligenceDATAVERSITY
 

More from DATAVERSITY (20)

Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
 
Data at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and GovernanceData at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and Governance
 
Exploring Levels of Data Literacy
Exploring Levels of Data LiteracyExploring Levels of Data Literacy
Exploring Levels of Data Literacy
 
Make Data Work for You
Make Data Work for YouMake Data Work for You
Make Data Work for You
 
Data Catalogs Are the Answer – What Is the Question?
Data Catalogs Are the Answer – What Is the Question?Data Catalogs Are the Answer – What Is the Question?
Data Catalogs Are the Answer – What Is the Question?
 
Data Modeling Fundamentals
Data Modeling FundamentalsData Modeling Fundamentals
Data Modeling Fundamentals
 
Showing ROI for Your Analytic Project
Showing ROI for Your Analytic ProjectShowing ROI for Your Analytic Project
Showing ROI for Your Analytic Project
 
How a Semantic Layer Makes Data Mesh Work at Scale
How a Semantic Layer Makes  Data Mesh Work at ScaleHow a Semantic Layer Makes  Data Mesh Work at Scale
How a Semantic Layer Makes Data Mesh Work at Scale
 
Is Enterprise Data Literacy Possible?
Is Enterprise Data Literacy Possible?Is Enterprise Data Literacy Possible?
Is Enterprise Data Literacy Possible?
 
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
 
Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?
 
Data Governance Trends - A Look Backwards and Forwards
Data Governance Trends - A Look Backwards and ForwardsData Governance Trends - A Look Backwards and Forwards
Data Governance Trends - A Look Backwards and Forwards
 
Data Governance Trends and Best Practices To Implement Today
Data Governance Trends and Best Practices To Implement TodayData Governance Trends and Best Practices To Implement Today
Data Governance Trends and Best Practices To Implement Today
 
2023 Trends in Enterprise Analytics
2023 Trends in Enterprise Analytics2023 Trends in Enterprise Analytics
2023 Trends in Enterprise Analytics
 
Data Strategy Best Practices
Data Strategy Best PracticesData Strategy Best Practices
Data Strategy Best Practices
 
Who Should Own Data Governance – IT or Business?
Who Should Own Data Governance – IT or Business?Who Should Own Data Governance – IT or Business?
Who Should Own Data Governance – IT or Business?
 
Data Management Best Practices
Data Management Best PracticesData Management Best Practices
Data Management Best Practices
 
MLOps – Applying DevOps to Competitive Advantage
MLOps – Applying DevOps to Competitive AdvantageMLOps – Applying DevOps to Competitive Advantage
MLOps – Applying DevOps to Competitive Advantage
 
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...
 
Empowering the Data Driven Business with Modern Business Intelligence
Empowering the Data Driven Business with Modern Business IntelligenceEmpowering the Data Driven Business with Modern Business Intelligence
Empowering the Data Driven Business with Modern Business Intelligence
 

Recently uploaded

My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationRidwan Fadjar
 
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr LapshynFwdays
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsMark Billinghurst
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Patryk Bandurski
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxhariprasad279825
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piececharlottematthew16
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Manik S Magar
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticscarlostorres15106
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024Lorenzo Miniero
 
Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machinePadma Pradeep
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxNavinnSomaal
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsMemoori
 

Recently uploaded (20)

My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 Presentation
 
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptx
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piece
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
 
Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machine
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptx
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
 

Data Architecture for Data Governance

  • 2. IASA is a non-profit professional association  run by architects  for all IT architects  centrally governed and locally run  technology and vendor agnostic Th
  • 3. Information Architecture  Module 0: Course Intro, Architecture  Module 3: Data Integration Fundamentals Introduction: Data,  Lesson 1: Integrating at the Company Level Information and Knowledge  Lesson 2: Data Characteristics  Lesson 3: Data Integration at the System level  Module 1: Information for Business  Lesson 1 – Information as Strategy  Module 4: Data Quality and Governance  Lesson 1 – Data and Information Quality  Lesson 2 – Relating Information to Value  Lesson 2: Data Compliance  Lesson 3 – Information Scope and  Lesson 3 – Data and Information Governance Governance  Module 5: Advanced Information Management  Module 2: Information Usage  Lesson 1: Data Warehousing  Lesson 1 – Who Uses Your Information  Lesson 2: Business Intelligence  Lesson 2 – How, When, Where and Why  Lesson 3: Data Security and Privacy is Information Used  Lesson 4: Metadata and Taxonomy Management  Lesson 3 – Form Factors  Lesson 5: Knowledge Management  Lesson 4 – Usage Design 1  Module 6: Architecture throughout the Lifecycle  Lesson 5 – Quality Attributes for Information Architecture  Lesson 6 – Data Tools and Frameworks
  • 6. What is data governance?
  • 7. Governance is Not a Popular Topic
  • 8. Governance is Overcomplicated  It is not about politics  Itis not about delivering verdicts  Itis not about defining solutions
  • 9. Step 1: Form a Team
  • 10. Step 2: Establish Lineage
  • 11. Step 3: Assess Maturity
  • 12. Step 4: Create Governance Body
  • 13. Step 5: Define Governance
  • 14. Step 6: Define Charter •Charter
  • 15. Step 7: Define Scope
  • 16. Step 8: Create a Plan
  • 18. Iasa Architect Services  Adopt Iasa Standards – Skills taxonomy, role descriptions, compensation models  Set your goals for value  Assess your current team – gap analysis  Implement processes  Develop and educate people  Certify employees and vendors  Build communities
  • 19. •December 6th – 7th, 2012 // Austin, TX •Training: Dec. 3rd – 5th, 2012 •Certification: Dec. 7th – 9th, 2012 • •www.iasaworldsummit.org •Leading Innovation in Architecture. Stay ahead of the technology curve and shape the future of architecture in your organization. •Connect and share insights with the largest global network of technology and enterprise architect practitioners. •Attend sessions on the most current breakthroughs, case studies and key topics in architecture - presented by a mix of practicing architects from top performing businesses and organizations. •Participate in pre-conference workshops and training. Maximize your time at the summit by taking part in one of six intensive training courses designed to provide immediate solutions to benefit your everyday practice. •Featuring Industry Leading Keynote Speakers: •Sheila Jeffrey •David Del Giudice •Paul Preiss Senior Information Architect Principal Architect President, Founder Bank of America AstraZeneca Iasa Global •David Manning •Scott Whitmire •Cat Susch IT Enterprise Architect Enterprise Architect Enterprise Architect Idaho Power Company Nordstrom Microsoft
  • 21. Data Architecture for Data Governance November 8, 2012 Presented by Malcolm Chisholm Ph.D. mchisholm@refdataportal.com Telephone 732-687-9283 www.refdataportal.com www.bizrulesengine.com © AskGet.com Inc., 2012. All rights reserved
  • 22. Agenda • Introducing Enterprise Architecture (EA) • Introducing Data Governance • Data Architecture and Data Governance • Data Architecture Patterns • Data Architecture, Governance, and The Business © AskGet.com Inc., 2012. All rights reserved
  • 23. Introducing Enterprise Architecture (EA) © AskGet.com Inc., 2012. All rights reserved
  • 24. Need for Enterprise Architecture (EA) Myriad of Independent Projects in IT Strategy is Set at High Level Here is where we want to be in 5 years… CxO’s Operationalization Enterprise …Others… Architecture • EA is responsible for ensuring overall business strategy is implemented by IT © AskGet.com Inc., 2012. All rights reserved
  • 25. The IT Mindset That’s not a clear We have a high-level requirement… strategy… Tell me what you want me to build Then I will design it Then I will build it Then I will turn it over to you Then I will walk away IT CxO • IT people just want to build stuff and hand it off to other people • They want business sponsors who will pay for this stuff and tell them exactly what is needed • Strategy does not match this mindset © AskGet.com Inc., 2012. All rights reserved
  • 26. Design without Architecture Stairway to Ceiling All Design – No Architecture Rooms: 160; Doorways: 467; Doors: 950; Fireplaces: 47 (gas, wood, coal); Bedrooms: 40 “Circular” Stairway Constructed 1884 – 1922 (38 continuous years); Cost: $5.5M Blueprints: Never made; Individual rooms sketched out by Sarah “…staggering amount of creativity, energy, and Winchester on paper or other media (e.g. tablecloths) expense poured into each and every detail” http://www.winchestermysteryhouse.com • The Winchester Mystery House • Lots of design – but no architecture • There is a difference between architecture and design © AskGet.com Inc., 2012. All rights reserved
  • 27. Design vs. Architecture Changed Source Integration Data Mirror Capture Data Flow DDL • Architecture deals with many instances of a component type that must interact • Design deals with one instance of a component type, without regard to interaction • E.g. Perspective of Databases : Data Environment (BI or Integration Environment in this example) © AskGet.com Inc., 2012. All rights reserved
  • 28. Basic EA Taxonomy (“BAIT”) Enterprise Architecture Business Technology Architecture Architecture Application Information Architecture Architecture • EA is clearly not a monolithic discipline • It looks more like a collection of dissimilar components • Each component is a competency – so this matters • Yet there is no common agreement © AskGet.com Inc., 2012. All rights reserved
  • 29. Introducing Data Governance © AskGet.com Inc., 2012. All rights reserved
  • 30. What is Data Governance? Starter Definitions From the Data Governance Institute ”The exercise of decision-making and authority for data-related matters.” ”A system of decision rights and accountabilities for information- related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.” www.datagovernance.com © AskGet.com Inc., 2010. All rights reserved
  • 31. Data Governance is about Processes Processes for… Deciding who has what rights regarding data Making decisions about data Implementing decisions about data In other Words… Identifying the processes needed to manage data Identifying the actors in these processes Designing these processes And designing the processes to design these processes © AskGet.com Inc., 2010. All rights reserved
  • 32. Data Governance – A Caution Data Governance is the design, building, and operation of formal business processes to manage the enterprise data resource. W R ON G The Business has…. Business Processes IT has… Governance • The decision rights of actors are defined within the context of these business processes • There is no real distinction between the processes of “data governance” and the “business processes” of the business . [Compare HR]. • A distinction that identifies the set of business processes that manage the enterprise data resource as “data governance” is valid. [Compare HR]. • Use of the term data governance to further the pretence that IT is universe totally outside of the enterprise is invalid. • Therefore, the processes of data governance are truly business © AskGet.com Inc., 2010. All rights reserved processes
  • 33. Data Architecture and Data Governance © AskGet.com Inc., 2012. All rights reserved
  • 34. Architectural Levels Reference Architecture Assimilate best Conceptual architectural Architecture practices from the IT industry. All Match/select Logical relevant component architectural Architecture classes and component classes patterns are and patterns to the Detailed design of Physical understood. business needs, future state Architecture (including future architecture. plans). Influencing actual physical implementations to ensure conformance with logical architecture A reference architecture is the highest level of architecture. It can be undertaken prior to even thinking about the enterprise. Its advantages are that it can be very high level and can cover the entire enterprise. Useful for thinking about high level data layers © AskGet.com Inc., 2011. All rights reserved
  • 35. Reference Data Architecture - Example • Example of a simple reference data architecture • Just data layers – no services or components BUT – just having a picture can be problematic © AskGet.com Inc., 2011. All rights reserved
  • 36. Need Definitions, Explanations – Not Just Picture Layer Characteristic Transactional Data Warehouse Data Mart Application Data structured and View of data across the Data produced via the filtered to support enterprise. Supports Purpose automation of business dissemination, derivation specific information processes needs of small groups of of knowledge and history users. Derivations (including Data from Warehouse is All base (non-derived) aggregations) produced Data Life Cycle data originates here here, and history is transformed to support specific reporting inferred Extract / Transform / Create / Source / Read / Subscribe / Transform / Data Operations Update / Delete / Archive Load / Derive / Publish / Archive Archive Subject Oriented / Information Requirement Data Model Normalized to 3NF Snowflaked / Conformed Oriented / Snowflaked / Dimensions Conformed Dimensions • Much more is needed than the above • Definitions are a technical reference; explanations help stakeholders to understand the reference architecture © AskGet.com Inc., 2011. All rights reserved
  • 37. Subject Area Model • A Subject Area Model is a taxonomy of the major areas in the enterprise that are relevant to data management • A necessary starting point for MDM • 1 – A subject area should have stable definitions of entities • 2 – Production of master data should not cross subject area boundaries • Identify the major data concepts in each subject area – may will be master data entities. © Askget.com Inc., 2011. All rights reserved
  • 38. A Subject Area Model is Not Costly Data Management • 10-15 subject areas per model is the norm • Do not need elaborate data modeling tools • Much more about a standardized view of the enterprise • Can be done with 1-3 people • Gets a lot of bang for the buck • Is high-level and important © Askget.com Inc., 2011. All rights reserved
  • 39. Example of Use: Establish Vendor Data Profiles Map Vendor’s Data Vendor Data to Subject 1 Areas Data Vendor Data Management 2 Create & Data Vendor Map Vendors Manage Subject to Uses 3 Area Model(s) Data Vendor 4 Map Subject Areas to User Data Vendor Requirements 5 Subject Area Model(s) Users • A Subject Area Model provides the basis for a common understanding of the data vendors provide • Will need to go to major entity clusters within Subject Area © AskGet.com Inc., 2011. All rights reserved
  • 40. A Subject Area Model is a Taxonomy I don’t like your definition of this subject Your model area! does not tell me what is financial data! You mean to tell me every one of these subject areas includes access security for data! • People think a taxonomy is a way of unlocking the secrets of the universe • Taxonomies are a tools – you can have as many as you need to do your job • You can never expect the boundaries of a subject area model to be exactly precise and never changing © Askget.com Inc., 2011. All rights reserved
  • 41. Create a Metadata Subject Area Model The Fundamental Ontology of Metadata for the Enterprise © Askget.com Inc., 2011. All rights reserved
  • 42. Metadata Subject Area Model Advantages 1. Standardizes the metadata in the enterprise at a high level. 2. Can be used to prioritize projects and programmes. 3. Useful in communications. 4. Shows what metadata is being produced by subject area. This is a high-level inventory. 5. Can distinguish data-related metadata from other data 6. Within each subject area, definitions should be constant. There may be variations across subject areas. 7. Subject areas are candidates for conceptual models. Disadvantages 1. It is a taxonomy and can be argued over. No one taxonomy will satisfy every perspective. We use data categorization for that (see later). The Subject Area Model should be the most common and natural perspective. 2. The Subject Area Model often has more things expected of it than it can deliver. Managing expectations can be tricky. The Subject Area Model is itself Metadata and needs to be managed © Askget.com Inc., 2011. All rights reserved
  • 43. Data Architecture Patterns © AskGet.com Inc., 2012. All rights reserved
  • 44. What is an Architectural Pattern? “Pattern in architecture is the idea of • An architectural style is a central, capturing architectural design ideas organizing concept for a system. as archetypal and reusable • An architectural pattern describes a descriptions” coarse-grained solution at the level of - Christopher Alexander, quoted in Business subsystems or modules and their Model Generation by Osterwalder and relationships. Pigneur (2010) • A system metaphor is more conceptual and it relates more to a real-world Antipattern: A pattern that is concept over a software engineering commonly used, but which is concept. ineffective or damaging. E.g. - Quoted from A Practical Guide to Enterprise Analysis Paralysis. Architecture by McGovern, Ambler et al, at http://www.infoq.com/news/2009/02/Architectural- - Term created by Andrew Koenig, 1995 Styles-Patterns • Patterns occur a lot in architecture • There are also styles, metaphors and antipatterns • Seem to have emerged from the application architecture competency (e.g. rare to hear of dimensional modeling as a pattern or style) © AskGet.com Inc., 2011. All rights reserved
  • 45. Example: Farm and Market Pattern Traditional MDM Integration Hub Farm and Market Pattern for MDM © AskGet.com Inc., 2011. All rights reserved
  • 46. Six Patterns for Data Hubs: 1 – Publish/Subscribe • Publisher pushes data to hub • Subscriber pulls data from hub • No data integration • Publisher may not know who the subscribers are • Weak governance - may not even be SLA’s © AskGet.com Inc., 2011. All rights reserved
  • 47. Six Patterns for Data Hubs: 2 – ODS for Integrated Reporting • Supports principle that transaction applications will not do operational reporting (adverse affect on performance) • Some form of integration • Anti-pattern of real-time data warehousing • History (changed data capture) not considered © AskGet.com Inc., 2011. All rights reserved
  • 48. Six Patterns for Data Hubs: 3 – ODS for Data Warehouses • No reporting in this kind of ODS • Probably evolved from ODS for reporting • Strong integration • Just to feed warehouses • At odds with Data Warehouse that has own staging, integration © AskGet.com Inc., 2011. All rights reserved
  • 49. Six Patterns for Data Hubs: 4 – Traditional MDM Hub • Master Data only • Integration does happen (typically just Trust & Survivorship) • Some content management needed – typically to correct data quality problems • Typically DQ functionality an afterthought • Distributes “Golden Copy” Master Data to enterprise © AskGet.com Inc., 2011. All rights reserved
  • 50. Six Patterns for Data Hubs: 5 – Message Hub • Messages – real-time or near-real-time - not batch data • Message switch • Command and control for message orchestration • Not just “listening” – does routing • Messages also stored in database © AskGet.com Inc., 2011. All rights reserved
  • 51. Six Patterns for Data Hubs: 6 – Integration Hub • Like message hub, but for batch data • Event data equivalent of MDM hub • Just one place where integration done • Both warehouse and transaction applications need integrated data • Removes need for each application to do integration by itself © AskGet.com Inc., 2011. All rights reserved
  • 52. Data Architecture, Governance, and The Business © AskGet.com Inc., 2012. All rights reserved
  • 53. Another View of EA Many Years Ago… Today… …”the business” knew the business …it does so much less • Long ago, people had to know the business rules concerning what they did • These are now in applications, and staff are more concerned with knowing how to make the applications work • Data is an asset, but staff tend to know little about the data • Loss of business knowledge © AskGet.com Inc., 2012. All rights reserved
  • 54. Who Knows The Business? • The business seems to be more fragmented than long ago (though that is difficult to prove) • Managers who know the business know less of it and are so time- constrained they cannot help much on aligning projects to enterprise strategy © AskGet.com Inc., 2012. All rights reserved
  • 55. Populate EA with Business Experts A Different EA Vision: IT Tendency: Architects + Business Experts Populate EA with Architects • Business experts provide better relations with rest of business • Keep architects with just being oriented to architecture • Immediate business credibility for EA • They get poached © AskGet.com Inc., 2012. All rights reserved
  • 56. Questions and Answers Data Architecture for Data Governance November 8, 2012 Presented by Malcolm Chisholm Ph.D. Telephone 732-687-9283 mchisholm@MasterDataMgmt.com © AskGet.com Inc., 2012. All rights reserved

Editor's Notes

  1. The typical state of affairs you will find in most organizations is as follows: We know we have data Some of the lineage is tracked – but probably out of date There is not a lot of clarity on ownership While the goal may be to have single sources of data – over time ad hoc data services and processes pop up Data quality is unclear or assumed but not guaranteed
  2. The problem is that data is not static. While you may take a snapshot at any point in time it is the truth for that moment in time. Even if you have processes in place to manage the addition of data, change management of master and meta data and a set of guidelines – you can still end up with out of control data systems The root cause of this is the lack of governance, you don’t currently have governance……. See why this is tough problem to solve? The solution prevents the problem but most organizations have no clue where to start or if they do start they may either fail or complete the effort but not have a transition plan to maintain governance once it is established and over time the organization finds itself back in the same or a similar state.
  3. There are a number of things that a Data Governance can do. It all depends on risk, priorities etc. As part of the lineage exercise and purely by formation and communication of the council’s existence – it is possible to start with a backlog of items to be addressed at the first meeting. So first and foremost – define how intake will work, how decisions will be made and how issues will be managed – the basics. Get the structure in place. Then make sure that you not only have a plan for today but that it is a flexible enough plan to be sustained over time. Because just as change is inevitable in data, the need for governance will not go away. Data governance councils will identify goals and priorities for achieving a level of proficiency across the organization which encompasses everything from policies and processes to monitoring and communications. Individuals outside of the governance council may approach the group with problems and findings which lead to investigations, studies and eventually standards for the management and handling of data. These standards will cover anything from data retention periods to best practices. The overall goal of the council is to ensure consistency in the collection, storage and delivery of data to support the business with cost effectiveness as a balancing factor.
  4. If you walk into a room and mention governance, you can easily see that there are not a lot of people that will stick around for the conversation. Unless they have had the epiphany of how governance actually enables business and is not about locking things down and people out. There are concepts which have bad reputations – such as process and governance. These reputations are formed by bad implementations, overly zealous implementations or an inability to sustain the change over time. Worse yet, there are a lot of misconceptions about what governance actually is.
  5. In most cases – governance has been overcomplicated. It is easier to talk about what governance isn’t vs. what governance is ‘ to govern’ is at the root – which translates to: have political authority: to be responsible officially for directing the affairs, policies, and economy of a state, country, or organization control something: to control, regulate, or direct something have influence over something: to have or exercise an influence over something This drives a lot of the perceptions people have about governance. Instead let’s think of governance as having goals such as: defining expectations, granting power, and verifying performance
  6. The first step in the governance lifecycle is to form a team. This team may not end up being the final governing body or governance board. But as you develop the concept of governance you will want to ensure that you have equal representation of all data producing, storing and consuming groups – in other words almost every team of the organization will have representation at the table. As mentioned, this is not necessarily the final governance team. What is important at the formative stage is that you form a team of individuals with working knowledge of the data.
  7. The first action is to catalog the data of the organization. The size of the data does not fall in line with the size of the company. In other words there can be very small companies with only a few employees which process and store petabytes of data a day. So don’t be fooled by looking at the size of the organization into thinking the data will align to that size. Data lineage is an iterative process. The size of the enterprise will determine how deep you go into the processes. Instructor: Flip to next slide to review BPMN process level definitions then return to this slide Choose a level that can be achieved in a short period of time. The goal would be to hit level 3 or 4. But in a larger enterprise you may only be able to hit level 1 or 2. If all you can accomplish in 2-3 weeks is a list of all data repositories with owners, purpose and some basic information about data sources etc. then you have enough to get started. The goal behind capturing lineage is to tie data to a purpose, typically a KPI (Key Performance Indicator). If you gather the KPIs for the company by department and then travel backwards from the end metric the goal is to find all processes and sources which lead to that data. No matter what, the lineage effort should catalogue systems and data. Instructor: Skip ahead past the next slide to the following slide
  8. While the initial team is compiling the data lineage they should also be assessing maturity. Maturity should not be assumed to be consistent across the enterprise nor should you consider that maturity would be consistent across any one lineage for a metric.
  9. By now it is time to form the Governance Body – you can form this body at any point in the process, but for the most part, until there is an overview of the data environment and state of maturity, the Governance Body will mostly sit back and support the discovery process. Let’s call this the Data Governance Council. The Council will be made up of representatives of each functional area of the organization. It is possible that there will be members that were part of the original working team that reside on the Council, or it could be there management.
  10. Instructor: Ask – How do you define governance? Use this as an example of how individuals that make up the council will have their own definition of governance. As you can see – each one of you has different definitions of governance. This will also be true of the members of the council. Instructor: Ask – Why do you have the council define governance? Answers resemble the following: (you are looking for 2 major inputs) Establish a common definition Get all council members aligned to the definition As you can see, whenever you bring a group of people together of varying roles and backgrounds there is a likelihood that their definition of governance will vary. With that being said, it is important to have the council define governance to ensure that everyone has the same expectations. A shared definition of governance will only apply to this council for this organization at this time. Over time the definition may change and should be revisited each year.
  11. The best way to revisit the definition to ensure it is still effective and applicable is to create a charter which includes the definition of governance.
  12. Instructor: Ask – What do you think the scope of a data governance council should be? Answer: this will vary – you are looking for answers which are about the creation of rules or standards but not actually doing or building – typically. Note: If there are a lot of comments about building systems etc. (Ask – is there a risk of conflict of interest if the governance council develops systems vs. governing data?) Scope = focus. You can’t tackle everything – this is actually one of the reasons that governance councils fail – they have unreasonable goals. Each year the council should have a set of goals that are reasonable and can be achieved, followed by stretch goals. It is important to remember that everyone has a ‘day’ job and that the governance council typically gets about 10% of their attention. Image: street_sign_post_questions_400_clr presentermedia.com
  13. Define a set of goals and expectations typically these align to the terms of the council. A reasonable set of commitments for the first year of a council would be to: Establish lineage Establish current level of maturity Identify optimal level of maturity to reach Define roadmap Execute 1 st year actions, etc. The important thing is to review progress regularly and identify when things get off track. Adjust the plan if it takes longer to achieve completion or if the outcome does not meet the expectations of the council. Don’t wait until the end and let things slowly fall apart. The failure of a governance council often comes when success is assumed and not monitored. When defining the roadmap, it is important to think of the goals as being multi-generational. Additionally you will have to break down these goals into actions, accountabilities, responsibilities, constraints and dependencies.