The Model Enterprise: A Blueprint for Enterprise Data Governance
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The document provides an overview of master data management (MDM) as a key component of data governance, emphasizing its importance in maintaining the accuracy and consistency of data assets within an organization. It details the characteristics and complexities of master data, governance processes, and the role of data models and metadata in supporting effective data governance. Additionally, the importance of ongoing improvement and collaboration in MDM practices is highlighted.
The Model Enterprise: A Blueprint for Enterprise Data Governance
- 3. Governance • Carrots & Sticks • Control Points • Pragmatism • Durability • Balancing Act • Transparency • Enforceability • Chinese Handcuffs
- 4. 1© 2017 IDERA, Inc. All rights reserved. THE MODEL ENTERPRISE: A BLUEPRINT FOR ENTERPRISE DATA GOVERNANCE MAY 2, 2017 Ron Huizenga Senior Product Manager, Enterprise Architecture & Modeling @DataAviator
- 5. 2© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 2© 2017 IDERA, Inc. All rights reserved. AGENDA § Governance Overview § Definitions § Master Data § Data lineage & life cycle § Master Data Management (MDM) § Importance of Data Models § Data quality Data Governance Data Architecture Management Data Development Database Operations Management Data Security Management Reference & Master Data Management Data Warehousing & Business Intelligence Management Document & Content Management Metadata Management Data Quality Management
- 6. 3© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 3© 2017 IDERA, Inc. All rights reserved. ER/STUDIO ENTERPRISE TEAM EDITION 2016+ ER/Studio Software Architect ER/Studio Business Architect ER/Studio Repository & Team Server ER/Studio Data Architect
- 7. 4© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 4© 2017 IDERA, Inc. All rights reserved. DMBOK: DEFINITIONS § Data Governance • The exercise of authority, control and shared decision making (planning, monitoring and enforcement) over the management of data assets. § Master Data • Synonymous with reference data. The data that provides the context for transaction data. It includes the details (definitions and identifiers) of internal and external objects involved in business transactions. Includes data about customers, products, employees, vendors, and controlled domains (code values). § Master Data Management • Processes that ensure that reference data is kept up to date and coordinated across an enterprise. The organization, management and distribution of corporately adjudicated data with widespread use in the organization.
- 8. 5© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 5© 2017 IDERA, Inc. All rights reserved. MASTER DATA CLASSIFICATION CONSIDERATIONS § Behavior § Life Cycle § Complexity § Value § Volatility § Reuse
- 9. 6© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 6© 2017 IDERA, Inc. All rights reserved. MASTER DATA - BEHAVIOR § Can be described by the way it interacts with other data § Master data is almost always involved with transactional data § Often a noun/verb relationship between the master data item and the transaction • Master data are the nouns • Customer • Product • Transactional data capture the verbs • Customer places order • Product sold on order § Same type of relationships are shared between facts and dimensions in a data warehouse • Master data are the dimensions • Transactions are the facts
- 10. 7© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 7© 2017 IDERA, Inc. All rights reserved. MASTER DATA - LIFECYCLE § Describes how a master data element is created, read, updated, deleted (CRUD) § Many factors come into play • Business rules • Business processes • Applications § There may be more than 1 way a particular master data element is created § Need to model: • Business process • Data lineage • Data flow • Integration • Include Extract Transform and Load (ETL) for data warehouse/data marts and staging areas
- 11. 8© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 8© 2017 IDERA, Inc. All rights reserved. BUSINESS PROCESS & DATA CRUD
- 12. 9© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 9© 2017 IDERA, Inc. All rights reserved. MASTER DATA – COMPLEXITY, VALUE § Complexity • Very simple entities are rarely a challenge to manage • The less complex an element, the less likely the need to manage change • Likely not master data elements • Possibly reference data − States/Provinces − Units of measure − Classification references § Value • Value and complexity interact • The higher value a data element is to an organization the more likely it will be considered master data
- 13. 10© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 10© 2017 IDERA, Inc. All rights reserved. MASTER DATA - VOLATILITY § Level of change in characteristics describing a master data element • Frequent change = high volatility • Infrequent change = low volatility § Sometimes referred to as stability • Frequent change = unstable • Infrequent change = stable
- 14. 11© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 11© 2017 IDERA, Inc. All rights reserved. MASTER DATA - REUSE § Master data elements are often shared across a number of systems § Can lead to inconsistency and errors • Multiple systems • Which is the “version of truth” • Spreadsheets • Private data stores § An error in master data can cause errors in • All the transactions that use it • All the applications that use it • All reports and analytics that use it § This is one of the primary reasons for “Master Data Management”
- 15. 12© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 12© 2017 IDERA, Inc. All rights reserved. WHAT IS MASTER DATA MANAGEMENT? § The processes, tools and technology required to create and maintain consistent and accurate lists of master data § Includes both creating and maintaining master data § Often requires fundamental changes in business process § Not just a technological problem § Some of the most difficult issues are more political than technical § Organization wide MDM may be difficult • Many organizations begin with critical, high value elements • Grow and expand § MDM is not a project • Ongoing • Continuous improvement
- 16. 13© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 13© 2017 IDERA, Inc. All rights reserved. MDM ACTIVITIES § Identify sources of master data § Identify the producers and consumers of the master data § Collect and analyze metadata about for your master data § Appoint data stewards § Implement a data-governance program and council § Develop the master-data model § Choose a toolset § Design the infrastructure § Generate and test the master data § Modify the producing and consuming systems § Be sure to incorporate versioning and auditing
- 17. 14© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 14© 2017 IDERA, Inc. All rights reserved. IMPORTANCE OF DATA MODELS § Full Specification • Logical • Physical § Persistence Boundaries • Business Data Objects § Descriptive metadata • Names • Definitions (data dictionary) • Notes § Implementation characteristics • Data types • Keys • Indexes • Views § Business Rules • Relationships (referential constraints) • Value Restrictions (constraints) § Security Classifications + Rules § Governance Metadata • Master Data Management classes • Data Quality classifications • Retention policies
- 18. 15© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 15© 2017 IDERA, Inc. All rights reserved. DATA DICTIONARY – METADATA EXTENSIONS
- 19. 16© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 16© 2017 IDERA, Inc. All rights reserved. ER/STUDIO – METADATA ATTACHMENT SETUP
- 20. 17© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 17© 2017 IDERA, Inc. All rights reserved. UNIVERSAL MAPPINGS
- 21. 18© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 18© 2017 IDERA, Inc. All rights reserved. UNIVERSAL MAPPINGS § Ability to link “like” or related objects • Within same model file • Across separate model files § Entity/Table level § Attribute/Column level
- 22. 19© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 19© 2017 IDERA, Inc. All rights reserved. SUMMARY § Master Data Management is a critical aspect of Data Governance § Master Data Characteristics • Behavior • Lifecycle • Complexity • Volatility • Reuse § MDM is an ongoing, continuous improvement discipline, not a project § Data models & metadata constitute the blueprint for data governance § Mapping the processes that utilize the data is imperative to defining the data life cycle § Achieving data maturity is a journey
- 23. 20© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 20© 2016 IDERA, Inc. All rights reserved. THANKS! Any questions? You can find me at: [email protected] @DataAviator
- 24. The Governance Game Robin Bloor, PhD
- 25. Big Data Means Big Governance The analytical opportunity of BIG DATA is clear – there are already many profitable uses Nevertheless, all data needs to be GOVERNED
- 26. The Data Governance Challenge Data Sources Metadata Management Data meaning Data compliance Data provenance & lineage Data cleansing Data security Data audit record Data life-cycle mgt Data Governance is a perpetual process
- 27. The Growth of Compliance u International – GRC (Governance, Risk, Compliance) – ISO (standards) u US Government: – SOX – GLBA – HIPAA – FISMA – FERPA u Europe – GDPR (Data protection laws) with variances – New: The right to be forgotten
- 28. The Full Data Lake Picture Data Cleansing Data Security Ingest Metadata Mgt Real-Time Apps Transform & Aggregate Search & Query BI, Visual'n & Analytics Other Apps Data Lake Mgt Data Governance DATA LAKE To Databases Data Marts Other Apps Archive Life Cycle Mgt Extracts Servers, Desktops, Mobile, Network Devices, Embedded Chips, RFID, IoT, The Cloud, Oses, VMs, Log Files, Sys Mgt Apps, ESBs, Web Services, SaaS, Business Apps, Office Apps, BI Apps, Workflow, Data Streams, Social...
- 29. The Need For Data Modeling & MDM
- 30. Points To Note u The more complex the data universe the more you need a model. u In theory it is a view of the data universe. In practice it is part of it. u Beginning: Modeling is top-down and bottom up. You build in both directions u It is not and never can be a project. It is an on- going activity.
- 31. The Net Net Because IT and data management is evolving so quickly, governance and data modeling must also evolve quickly
- 32. u Agile modeling clearly requires effective collaboration between all data users at every level. How does your technology help with cultural issues? u Which data stores and databases do you support aside form the usual relational sources? (Hadoop, NoSQL, unstructured, etc.)offer for NoSQL databases? u How do you accommodate the IoT?
- 33. u If you do not do MDM already, how do you start and what are the immediate business benefits? u Do you model data flows (consider, for example, real-time analytics)? u Where do you see current/future competition emerging from in the modeling or governance market?