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Introduction to MDM
William El Kaim
Oct. 2016 - V 2.0
This Presentation is part of the
Enterprise Architecture Digital Codex
http://www.eacodex.com/Copyright © William El Kaim ...
Plan
Introduction to Data Governance
• Introduction to Data Quality
• Introduction to MDM
• MDM Delivery Model
• MDM Archi...
The Data Management Context
• Large Global Organizations with a multitude of business processes and
systems to process tra...
The Quest for Trusted Data
• Trusted data are data used by business stakeholders to support their
processes or decisions w...
So What Exactly Is Data Governance?
• Data governance is a set of processes ensuring that important data assets
are formal...
The Responsibilities of Data Stewards
• Stewards should be considered data subject-matter experts for their
respective bus...
Data Stewardship Metrics
Objective Dimensions
• Accuracy
• Whether the data values being held reflect the properties of th...
Data Stewardship Metrics
Subjective Dimensions
• Believability
• The degree to which users of the data believe and trust i...
Data Governance Framework Example
Copyright © William El Kaim 2016 11Source: SAS
The Role Of Technology In Data Governance
• Data profiling and data quality software supports data stewards in:
• Profilin...
Synthesis
• Data is the “raw material” used everywhere
• Data Stewardship is the recognition that data is a resource that ...
Plan
• Introduction to Data Governance
Introduction to Data Quality
• Introduction to MDM
• MDM Delivery Model
• MDM Archi...
Data Quality: What Is Being Measured?
Timeliness. While more of an operational quality metric, timeliness
addresses whethe...
Data Quality: What Is Being Measured?
Consistency and standardization. Delivering data that
doesn’t conform to defined for...
Data Quality Software Supports Trusted Data
• Data quality software (DQS) provides the technology enabler for
implementing...
Data Quality Solutions Market
Copyright © William El Kaim 2016 18
Plan
• Introduction to Data Governance
• Introduction to Data Quality
Introduction to MDM
• MDM Delivery Model
• MDM Archi...
What is MDM?
• Definition
• A business capability enabling an organization to first identify trusted master data and
then ...
Spreadsheet Effect!
R&D Operations Sales Marketing Procurement Finance & HR
Data Warehouse Finance Human Resources Sales O...
Why MDM Is Complex?
Copyright © William El Kaim 2016 22
USA
App
MD
Data Warehouse Finance Human Resources Sales & Marketin...
Plan
• Introduction to Data Governance
• Introduction to Data Quality
• Introduction to MDM
MDM Delivery Model
• MDM Archi...
MDM Delivery Model
Issues
• MDM as a business capability has been difficult to achieve due to:
• The complexity of integra...
MDM Delivery Model
Analytical MDM
• Analytical MDM, focuses on providing a one directional business view of
information th...
MDM Delivery Model
Operational MDM
• Operational MDM
• Focuses on consolidating data from disparate upstream data sources ...
Source: May 16, 2008, “Trends 2008: Master Data Management”
MDM Maturity Model
Forrester
Copyright © William El Kaim 2016 ...
MDM Maturity Model
Gartner
Copyright © William El Kaim 2016 28
Plan
• Introduction to Data Governance
• Introduction to Data Quality
• Introduction to MDM
• MDM Delivery Model
MDM Archi...
MDM Ecosystem
• Do not Confuse Delivery Methods with MDM Technology Options
Copyright © William El Kaim 2016 30
Introducing the MDM Ecosystem
• The MDM ecosystem consists of upstream, downstream and core
components . . .
• The MDM eco...
Introducing the MDM Ecosystem
Source: October 2, 2008, “It’s Time To Invest In Upstream Data Quality”
Forrester report
Cop...
MDM ecosystem is complex
Source: Forrester, April 28, 2008, “Making MDM And SOA Better Together”Copyright © William El Kai...
Breadth of Data Impacts Architecture
Copyright © William El Kaim 2016 34
Architectural Approach to MDM
Copyright © William El Kaim 2016 35
Architectural Approach to MDM
Copyright © William El Kaim 2016 36
Architectural Approach to MDM
Gartner Vision
Copyright © William El Kaim 2016 37
Data Integration Problem Space for MDM
Copyright © William El Kaim 2016 38
Integration Services for MDM
Copyright © William El Kaim 2016 39
Plan
• Introduction to Data Governance
• Introduction to Data Quality
• Introduction to MDM
• MDM Delivery Model
• MDM Arc...
Criteria For Identifying Master Data
• A data is a Reference/Master data(1) if
• It is duplicated inside several systems
•...
Usual Information System Architecture
• Complexity of data mapping
• Difficulty for managing referential integrity rules c...
Improvement #1
• Reduction and simplification of data mapping treatments
• Management of referential integrity rules that ...
Improvement #2
• Reference/Master data administration (a.k.a. governance) is unified
• The MDM’s user interface is used fo...
Who Is Responsible For Updating?
It Depends Of The IS Strategy And IT Ability
Copyright © William El Kaim 2016 45
Address
...
Who Is Responsible For Updating?
It Depends Of The IS Strategy And IT Ability
CRM
Address
MDM
New address
Address
Other sy...
Who Is Responsible For Updating?
It Depends Of The IS Strategy And IT Ability
ERP
Product
MDM
Product
Other systems
Push t...
Why now ?
• End-of-life of existing systems growing old with difficulty due to several
successive software layers added du...
Example – Carlson Hospitality
Copyright © William El Kaim 2016 49
Example – Carlson Hospitality
Copyright © William El Kaim 2016 50
Example – Carlson Hospitality
Copyright © William El Kaim 2016 51
The CDI profile hub consists of a database and services ...
Plan
• Introduction to Data Governance
• Introduction to Data Quality
• Introduction to MDM
• MDM Delivery Model
• MDM Arc...
MDM Project Mgt.
Data Governance is Key
Copyright © William El Kaim 2016 53
MDM Project Mgt.
Strong Program Management Is Critical
• Key program management skills include:
• Defining and executing c...
MDM project Mgt.
Copyright © William El Kaim 2016 55
MDM project Mgt.
Copyright © William El Kaim 2016 56
Data Stewardship Responsibilities
• Document and implement business-naming standards.
• Creates and maintains business met...
The Questions That Must Be Asked!
• Existing tools for managing reference/master data
• Direct SQL, Excel, Specific tools,...
First Project Possible Scope
• Objectives
• Acquiring MDM Modeling procedures in an operational way
• Using governance fea...
First Project Metrics
• Duration from 4 up to 6 weeks
• From the Modelling to roll-out in
production and utilization by us...
CIM Modelling Lifecycle
Workshops to build up the
semantic data architecture
relying on Domains of
business objects
Tools ...
CIM Modelling Lifecycle
• Iterative (bottom->up)
• Incremental Modeling with frequent
loading in the MDM for validating vi...
Plan
• Introduction to Data Governance
• Introduction to Data Quality
• Introduction to MDM
• MDM Delivery Model
• MDM Arc...
Evolution of Data Awareness
Copyright © William El Kaim 2016 64Source: SAS
Master Data Synthesis
Copyright © William El Kaim 2016 65
Common Barriers Hindering MDM Success
• Considering MDM as purely a technology initiative
• Assuming that dirty data is ju...
Recommendations
• Consider data quality strategies that support enterprise demands:
• Prioritize your data quality objecti...
The 7 Building Blocks of MDM
Copyright © William El Kaim 2016 68
The 7 Building Blocks of MDM
Copyright © William El Kaim 2016 69
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Introduction to Master Data Management

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An introduction to data quality, data governance and master data management.

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Introduction to Master Data Management

  1. 1. Introduction to MDM William El Kaim Oct. 2016 - V 2.0
  2. 2. This Presentation is part of the Enterprise Architecture Digital Codex http://www.eacodex.com/Copyright © William El Kaim 2016 2
  3. 3. Plan Introduction to Data Governance • Introduction to Data Quality • Introduction to MDM • MDM Delivery Model • MDM Architecture • Master Data Value • MDM project Mgt. • Conclusion Copyright © William El Kaim 2016 3
  4. 4. The Data Management Context • Large Global Organizations with a multitude of business processes and systems to process transactions are often faced with the challenge of not having a “Single Source of Truth” for their Master Data. • Systems Architecture and Data Architecture objectives appear to be divergent and tactical rather than cohesive and strategic • Data is an enterprise asset used to make strategic business decisions • Very often accuracy, completeness, accessibility and security of data prevents effective business decision making • 80% of data in Transactions is Master and Reference Data! • Organizations are naturally endowed with isolated pools of data that are not optimally leveraged for the sum of the parts to result in the whole Copyright © William El Kaim 2016 4
  5. 5. The Quest for Trusted Data • Trusted data are data used by business stakeholders to support their processes or decisions with no reservations as to its relevance, freshness, accuracy, integrity, and other previously agreed upon definitions of quality • In order to deliver trusted data it is required to: • Ensure data quality through appropriate processes and best practices • Break traditional functional and IT silos to share data across the enterprise. • Introduce the right tools and platform • Unfortunatelly • Ignoring the need for trusted data is common until the lack of it impacts your business. Copyright © William El Kaim 2016 5
  6. 6. So What Exactly Is Data Governance? • Data governance is a set of processes ensuring that important data assets are formally managed throughout the enterprise. • It formalizes the “fiduciary” duty for the management of data assets critical to its success. • Data governance ensures that data can be trusted and that people can be made accountable for any adverse event that happens because of low data quality. • It is about putting people in charge of fixing and preventing issues with data so that the enterprise can become more efficient. • Data governance also describes an evolutionary process for a company, altering the company’s way of thinking and setting up the processes to handle information so that it may be utilized by the entire organization. • It’s about using technology when necessary in many forms to help aid the process. Copyright © William El Kaim 2016 6
  7. 7. The Responsibilities of Data Stewards • Stewards should be considered data subject-matter experts for their respective business functions and processes. • Stewards are responsible for guiding the effort, not necessarily executing it themselves. • Stewards have other roles and responsibilities and therefore cannot effect significant change on their own. • Their roles as stewards should be to guide and influence others in implementing the changes necessary to improve data quality. They should be viewed as the leaders of the data quality improvement effort, not necessarily the "doers.“ • Stewards should define and monitor quality measures to justify the program but also must have specific goals for data quality improvement. • Stewards must be accountable • Stewardship should be based on manageable subsets of data. Copyright © William El Kaim 2016 8
  8. 8. Data Stewardship Metrics Objective Dimensions • Accuracy • Whether the data values being held reflect the properties of the real-world object or an event that the data is intended to model. • Consistency • Whether the values of attributes managed or presented in multiple locations are the same. • Existence • Whether a value is being held for a particular attribute. • Integrity • Whether all expected relationships between data in multiple data stores, tables and files are intact. • Validity • Whether the values held fall within the allowable domain of values established for an attribute. Copyright © William El Kaim 2016 9
  9. 9. Data Stewardship Metrics Subjective Dimensions • Believability • The degree to which users of the data believe and trust it. • Interpretability • The degree of ease with which data is consumed and understood. • Relevance • The degree to which the data supports and furthers the goals and objectives of users, processes and the organization. • Timeliness • The degree to which the latency of data delivery matches the needs of the consuming individuals or processes. Copyright © William El Kaim 2016 10
  10. 10. Data Governance Framework Example Copyright © William El Kaim 2016 11Source: SAS
  11. 11. The Role Of Technology In Data Governance • Data profiling and data quality software supports data stewards in: • Profiling and analyzing source data. • Defining and capturing standard definitions. • Standardizing lists of values. • Defining and implementing cleansing, standardization, validation, enrichment, and matching; and merging business rules for automatic data quality validation and remediation. • Defining and implementing exception rule parameters where manual intervention is required. Copyright © William El Kaim 2016 12 Data governance is not an IT project: It is a business strategy that can be optimized with the appropriate use of enabling technologies.
  12. 12. Synthesis • Data is the “raw material” used everywhere • Data Stewardship is the recognition that data is a resource that needs to be managed. • It involves recognizing the criticality of the data quality and making stewardship of it a jointly shared responsibility of the business and IT. • Data Stewardship is one of the key enablers of turning data into information that can be used for strategic advantage. • There has been a quality revolution that has redefined quality from being an optional characteristic to a basic requirement for both goods and services. • When the level of data quality is equal among the competition, the competitive battle lines are drawn in other areas. • However, organizations have been redefining the role of data and data quality causing data to be in the heart of the competition. Copyright © William El Kaim 2016 13
  13. 13. Plan • Introduction to Data Governance Introduction to Data Quality • Introduction to MDM • MDM Delivery Model • MDM Architecture • Master Data Value • MDM project Mgt. • Conclusion Copyright © William El Kaim 2016 14
  14. 14. Data Quality: What Is Being Measured? Timeliness. While more of an operational quality metric, timeliness addresses whether the delivery of data from one environment to another meets user expectations. ? Copyright © William El Kaim 2016 15 Accuracy. Data must be consistent with the intended goal. Completeness. Having missing or invalid data leads to problems. Integrity. Not having the expected relationships between multiple data sets intact presents data integrity issues. Hierarchal Relationship Accuracy. Parent-child relationships can be overlooked, leading to data quality issues.
  15. 15. Data Quality: What Is Being Measured? Consistency and standardization. Delivering data that doesn’t conform to defined formats and standards can lead to chaos. Copyright © William El Kaim 2016 16 Third-party enrichment. Not all data exists inside the enterprise and often must be appended with third-party information. Freshness. A different metric than timeliness, freshness focuses on the age of the data, which may have varying levels of usefulness depending on its type. Uniqueness. While data will be scattered throughout the enterprise, not all of it should be considered unique.
  16. 16. Data Quality Software Supports Trusted Data • Data quality software (DQS) provides the technology enabler for implementing many of the data quality rules and processes defined through your data governance efforts. Copyright © William El Kaim 2016 17
  17. 17. Data Quality Solutions Market Copyright © William El Kaim 2016 18
  18. 18. Plan • Introduction to Data Governance • Introduction to Data Quality Introduction to MDM • MDM Delivery Model • MDM Architecture • Master Data Value • MDM project Mgt. • Conclusion Copyright © William El Kaim 2016 19
  19. 19. What is MDM? • Definition • A business capability enabling an organization to first identify trusted master data and then leverage master data to improve business processes and decisions. • Identify trusted master data • MDM defines and/or derives the most trusted and unique “version” of important enterprise data (e.g., vendor, customer, product, employee, asset, material, location, etc.). • Leverage master data to improve business processes and decisions • MDM incorporates this master version of the data within functional business processes (sales, marketing, finance, support, etc.) that will provide direct benefit to employees, customers, partners, or other relevant stakeholders within an organization. • Master data alone provides little value • Hence, anticipation of how the data will be consumed by other applications or systems within the context of a business process provides the most value. • Master data management begins where data quality software leaves off! • MDM is a business capability enabled through the integration of multiple technologies and business processes. Copyright © William El Kaim 2016 20
  20. 20. Spreadsheet Effect! R&D Operations Sales Marketing Procurement Finance & HR Data Warehouse Finance Human Resources Sales Operations Silo Effect! Shared Data by Businesses & Systems Copyright © William El Kaim 2016 21 Partners Products Items Services Customers Channels Pricing Locations Stores Organization Employees Suppliers Assets Finance Accounts Codes Hierarchies
  21. 21. Why MDM Is Complex? Copyright © William El Kaim 2016 22 USA App MD Data Warehouse Finance Human Resources Sales & Marketing Operations BW SAP App CRM App Products Accounts Accounts Products Employees Org Customers Products Items Locations ID Account num Name Invoicing .../... ID Label Description Price Promotions .../... ID Hierarchies Markets .../... Master Data are dispersed and redundant Where is the thruth ? No integrity between data silos Version 1 Version 1.1 Version 2 Version 1 Version 3 Lots of life cycles Not in sync! Europe App MD en_US fr_FR de_DE
  22. 22. Plan • Introduction to Data Governance • Introduction to Data Quality • Introduction to MDM MDM Delivery Model • MDM Architecture • Master Data Value • MDM project Mgt. • Conclusion Copyright © William El Kaim 2016 23
  23. 23. MDM Delivery Model Issues • MDM as a business capability has been difficult to achieve due to: • The complexity of integration and architecture alternatives, • A lack of data governance and business ownership, • Existing processes that impede the capture of high-quality data • Prohibitive implementation costs paired with poor scoping and prioritization. • As an added bit of irony, this solution that helps to enable a single version of the truth does not itself boast a single version of the truth regarding its own market definition. Copyright © William El Kaim 2016 24
  24. 24. MDM Delivery Model Analytical MDM • Analytical MDM, focuses on providing a one directional business view of information through version-controlled hierarchy management and dimensional modeling capabilities • For example, product families, sales channels, and sales regions are common views managed in these environments. • Many customers begin their MDM journey with analytical MDM • Analytical MDM is easier to tackle and is a recommended first step because it is primarily about the data. • Because its one directional nature introduces much less risk and complexity than attempting to bi-directionally synchronize master data with critical production application. • Analytical MDM usually corresponds with the third level of Forrester’s MDM Maturity Model. Copyright © William El Kaim 2016 25
  25. 25. MDM Delivery Model Operational MDM • Operational MDM • Focuses on consolidating data from disparate upstream data sources into a reconciled analytical environment (usually a data warehouse or operational data store) for reporting and analysis. • Bi-directionally synchronizes trusted master data in real time, across heterogeneous information environments. • Requires the much more challenging need to synchronize business processes as well as data. • Operational MDM typically corresponds with levels four and five of the MDM maturity model. Copyright © William El Kaim 2016 26
  26. 26. Source: May 16, 2008, “Trends 2008: Master Data Management” MDM Maturity Model Forrester Copyright © William El Kaim 2016 27 Analytical MDM Operational MDM Operational MDM
  27. 27. MDM Maturity Model Gartner Copyright © William El Kaim 2016 28
  28. 28. Plan • Introduction to Data Governance • Introduction to Data Quality • Introduction to MDM • MDM Delivery Model MDM Architecture • Master Data Value • MDM project Mgt. • Conclusion Copyright © William El Kaim 2016 29
  29. 29. MDM Ecosystem • Do not Confuse Delivery Methods with MDM Technology Options Copyright © William El Kaim 2016 30
  30. 30. Introducing the MDM Ecosystem • The MDM ecosystem consists of upstream, downstream and core components . . . • The MDM ecosystem includes: • Sources. Source systems capture the raw materials (data) used to build the master record. • Centralized data management factories. Technologies and processes to collect, standardize, consolidate, aggregate, and apply business rules leading to the finished product (master data). • Business processes, systems, and access tools. Package and deliver master data to support contextual business consumption. • Transportation systems. Information integration technologies ensure data seamlessly navigates through these components. Copyright © William El Kaim 2016 31
  31. 31. Introducing the MDM Ecosystem Source: October 2, 2008, “It’s Time To Invest In Upstream Data Quality” Forrester report Copyright © William El Kaim 2016 32 Without effective governance, upstream business processes pollute downstream data requirements
  32. 32. MDM ecosystem is complex Source: Forrester, April 28, 2008, “Making MDM And SOA Better Together”Copyright © William El Kaim 2016 33
  33. 33. Breadth of Data Impacts Architecture Copyright © William El Kaim 2016 34
  34. 34. Architectural Approach to MDM Copyright © William El Kaim 2016 35
  35. 35. Architectural Approach to MDM Copyright © William El Kaim 2016 36
  36. 36. Architectural Approach to MDM Gartner Vision Copyright © William El Kaim 2016 37
  37. 37. Data Integration Problem Space for MDM Copyright © William El Kaim 2016 38
  38. 38. Integration Services for MDM Copyright © William El Kaim 2016 39
  39. 39. Plan • Introduction to Data Governance • Introduction to Data Quality • Introduction to MDM • MDM Delivery Model • MDM Architecture Master Data Value • MDM project Mgt. • Conclusion Copyright © William El Kaim 2016 40
  40. 40. Criteria For Identifying Master Data • A data is a Reference/Master data(1) if • It is duplicated inside several systems • Common examples: Customer address, Organization, Product description, etc. • It is keyed before being used by transactional systems • Common examples: table of labels for products by regions, technical and functional parameterization, etc. • Systems generate and handle many reference/master data because of • Several data duplications inside many functional and technical silos • Several configuration and parameterization tools such as • Excel spreadsheets, direct SQL coding, specific in-house tools, parameterization tools bring by software packages, etc. Copyright © William El Kaim 2016 41 (1) Similar terms in the context of this presentation. In some articles and surveys “Reference Data” is used for code-labels data and “Master Data” is used for business and more complicated data (structrures, life-cycles)
  41. 41. Usual Information System Architecture • Complexity of data mapping • Difficulty for managing referential integrity rules connecting data across several systems • Duplication of business rules for validating data • Lack of auditability and traceability regarding the use of data Copyright © William El Kaim 2016 42  Updating data in a point- to-point mode between systems without a pivot format (a.k.a. Common Information Model) The MDM tackles those drawbacks SystemSystem SystemSystem SystemSystem SystemSystem Format #1 Format #2 Format #3 Format #4 mapping mapping mapping MIDDLEWARE – ETL, EAI, ESB (point-to-point mode) Propagation of data updating across systems
  42. 42. Improvement #1 • Reduction and simplification of data mapping treatments • Management of referential integrity rules that connect data across systems • Unification of business rules for validating data • Auditability and traceability regarding the use of data Copyright © William El Kaim 2016 43  A Common Information Model (CIM) is required and modeled with help from a suitable method  The CIM is a shared model SystemSystem SystemSystem SystemSystem SystemSystem Format #1 Format #2 Format #3 Format #4 MDM mapping Common Information Model mapping Propagation of data updating across systems MIDDLEWARE – ETL, EAI, ESB Storage with the Common Information Model (allowing for a better traceability) Reference data administration (data governance) Convergence
  43. 43. Improvement #2 • Reference/Master data administration (a.k.a. governance) is unified • The MDM’s user interface is used for data feeding, version management, comparison and merge of versions, deployment of versions, querying of data, traceability, reporting, etc. Copyright © William El Kaim 2016 44 SystemSystem SystemSystem SystemSystem System MDM Common Information Model Feeding of data depending on execution contexts: versions and variants (organization, channel, regions, etc.) MIDDLEWARE – ETL, EAI, ESB Direct reading of the MDM Propagation of values This system is overhauled and takes advantage of direct access to MDM mapping ACTIVE GOVERNANCE OF REFERENCE DATA Governance
  44. 44. Who Is Responsible For Updating? It Depends Of The IS Strategy And IT Ability Copyright © William El Kaim 2016 45 Address CRM Address MDM Checks, cleans… Ok, Ko, Result… Address Other systems Push the data 1 2 3 5 The Address should be associated with a state so as to indicate its validity Updating + COMMIT 4 Updating depending on the result
  45. 45. Who Is Responsible For Updating? It Depends Of The IS Strategy And IT Ability CRM Address MDM New address Address Other systems Push the data 1 3 5 In a theorical world relying strongly on SOA the Address shouldn’t be recorded in the CRM (stage 4) since a service interaction with the MDM allows for getting the Address Push the data Address 2 Updating + COMMIT 4 Updating + COMMIT synchronization Copyright © William El Kaim 2016 46
  46. 46. Who Is Responsible For Updating? It Depends Of The IS Strategy And IT Ability ERP Product MDM Product Other systems Push the data 2 2 Updating with additional transactional data Product 1 Updating Master Data Copyright © William El Kaim 2016 47
  47. 47. Why now ? • End-of-life of existing systems growing old with difficulty due to several successive software layers added during last years • So many functional and technical silos • Retirement of some key business users and IT specialists • Loss of business and IT knowledge regarding existing assets • Lack of documentation • Loss of Modelling knowledge • Useless complexity of maintenance due to the lack of IT alignment with the business • MDM comes into play not only for increasing data quality! • Misunderstanding it is a risk that will reduce benefits of MDM • Let’s take an insurance industry example (next slides) Copyright © William El Kaim 2016 48
  48. 48. Example – Carlson Hospitality Copyright © William El Kaim 2016 49
  49. 49. Example – Carlson Hospitality Copyright © William El Kaim 2016 50
  50. 50. Example – Carlson Hospitality Copyright © William El Kaim 2016 51 The CDI profile hub consists of a database and services that are provided in real-time or batch
  51. 51. Plan • Introduction to Data Governance • Introduction to Data Quality • Introduction to MDM • MDM Delivery Model • MDM Architecture • Master Data Value MDM project Mgt. • Conclusion Copyright © William El Kaim 2016 52
  52. 52. MDM Project Mgt. Data Governance is Key Copyright © William El Kaim 2016 53
  53. 53. MDM Project Mgt. Strong Program Management Is Critical • Key program management skills include: • Defining and executing change management strategies. • Clearly defining roles and responsibilities. • Data stewardship training. • Rapid issue resolution by executive steering committees. • Strategic communications planning • Data Stewardship has, as its main objective, the management of the enterprises’ data assets • to facilitate a common understanding and acceptance of the data. • The purpose of doing this is to maximize the business return on the investment made in the data resources. • The expected results are improved reusability, accessibility and quality of the data. Copyright © William El Kaim 2016 54
  54. 54. MDM project Mgt. Copyright © William El Kaim 2016 55
  55. 55. MDM project Mgt. Copyright © William El Kaim 2016 56
  56. 56. Data Stewardship Responsibilities • Document and implement business-naming standards. • Creates and maintains business metadata definitions for business users • Develop consistent data definitions and data aliases. • Document standard calculations and derivations. • Document the business rules related to the data - for example, edit and validation rules. • Monitor development efforts for adherence to standards. • Ensure ownership and responsibility for the maintenance of data quality standards. • Looks for common data problems, finds ways to solve problems • Performs duplicate suspect processing of guest profile data (merges, unmerges) • Sends defects back to data owners or source that created bad data • Uses metrics to check the quality of the data and data process Copyright © William El Kaim 2016 57
  57. 57. The Questions That Must Be Asked! • Existing tools for managing reference/master data • Direct SQL, Excel, Specific tools, ERP configuration, etc. • Execution environments and related life-cycles • Often many parameters with different values depending on those environments: test, UAT (User Acceptance Test), training, run-time, etc. • Existing processes for data integration • EAI, ESB, ETL treatments • The level of maturity in Common Information Model • Does it exist? Via an Operational Data Store ? • The lack of data quality • Duplication, wrong values, errors when using data due to lack of business documentation • The lack of IT alignment with business • Taking into account external constraints stemming from business regulation (SOX, Basel II, Solvency II...) Copyright © William El Kaim 2016 58
  58. 58. First Project Possible Scope • Objectives • Acquiring MDM Modeling procedures in an operational way • Using governance features brought by the MDM tool • Version management, variant management, permission management, approval processes, etc. • By avoiding • Staying in the scope of the IT department only • Governance features must be used by business users not only by IT specialists • Building up data models that will not be reusable • Cautious with quick-win approach. We prefer to adopt an approach that fosters sustainable results • Being too conceptual • Fostering an iterative approach by validating models with help from the MDM’s User Interface. It requires a Model-driven approach Copyright © William El Kaim 2016 59
  59. 59. First Project Metrics • Duration from 4 up to 6 weeks • From the Modelling to roll-out in production and utilization by users • Less than 100 data localized within 3 Business Objects (BO) • Less than 5 referential integrity constraints between these BO • 1 Business Object = a set of entities tightly coupled in term of semantic (coarse grained object) • 2 synchronizations between MDM and systems • It is more secure if an infrastructure such as EAI/ESB/ETL is already available • Use of a suitable MDM that encourages a rapid implementation by parameterization rather than a rigid lifecycle software development • This is the case for Orchestra Networks tool Copyright © William El Kaim 2016 60
  60. 60. CIM Modelling Lifecycle Workshops to build up the semantic data architecture relying on Domains of business objects Tools for automatic analysis of existing databases to help the Modeling of the semantic data => HELP WHEN NEEDED BUT NO MORE! Progressive Modeling by business objects UI of the MDM is used to support data validation Automatic loading Help for validating data models Top-Down approach (Data Enterprise Architecture) Re-engineering Bottom-up Iterative cycle MDM Prod. MDM MDM Test MDM Data Enterprise Architecture Relies on the Data Enterprise Architecture N N+1 Copyright © William El Kaim 2016 61
  61. 61. CIM Modelling Lifecycle • Iterative (bottom->up) • Incremental Modeling with frequent loading in the MDM for validating via the MDM’s User Interface • A MDM tool that allows for automatic loading from data models is required=> Model-driven MDM • Avoiding the tunnel effect • Allowing a data validation by users with help from the MDM’s UI • Risks are taken due to modifications of data models during cycles. It involves data migration between the successive versions of data models • Enterprise Architecture (top->down) • A global effort to build up an global data architecture (a data map) • Better stability and upgradeability of data models • Mastering data Modelling relying on EA and business architecture is required • management at the level of the enterprise is needed (global act) • The launching phase IS COMPLEX • it requires strong competency in EA and data Modelling • After the launching phase data Modelling relies on the global data architecture. • Then the iterative life-cycle can be started in a secure way Copyright © William El Kaim 2016 62
  62. 62. Plan • Introduction to Data Governance • Introduction to Data Quality • Introduction to MDM • MDM Delivery Model • MDM Architecture • Master Data Value • MDM project Mgt. Conclusion Copyright © William El Kaim 2016 63
  63. 63. Evolution of Data Awareness Copyright © William El Kaim 2016 64Source: SAS
  64. 64. Master Data Synthesis Copyright © William El Kaim 2016 65
  65. 65. Common Barriers Hindering MDM Success • Considering MDM as purely a technology initiative • Assuming that dirty data is just an IT problem • Managing the vast complexity of multiple data domains without proper techniques, including common data models, integration APIs, and Web- service-enabled features • Lacking focus on data governance, prioritization, people, and process • Underestimating the level of executive sponsorship required for success • Ineffectively prioritizing funding and managing costs Copyright © William El Kaim 2016 66
  66. 66. Recommendations • Consider data quality strategies that support enterprise demands: • Prioritize your data quality objectives by focusing on data elements supporting your most business-critical processes. • Get started with project-based data quality. • Ride the coattails of cross-enterprise data management initiatives. • Adopt data governance to allow you to evolve from project-based DQ to enterprise-class MDM. • Master Data will become the focal point in the SOA architecture ‘battle’ • Application Independent MDM solutions will provide a richer context for an SOA than Application Specific approaches (e.g., SAP, Oracle) Copyright © William El Kaim 2016 67
  67. 67. The 7 Building Blocks of MDM Copyright © William El Kaim 2016 68
  68. 68. The 7 Building Blocks of MDM Copyright © William El Kaim 2016 69
  69. 69. Twitter http://www.twitter.com/welkaim SlideShare http://www.slideshare.net/welkaim EA Digital Codex http://www.eacodex.com/ Linkedin http://fr.linkedin.com/in/williamelkaim Claudine O'Sullivan Copyright © William El Kaim 2016 70

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