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Diving into Data Governance
March 14, 2017Ron HuizengaSenior Product Manager, Enterprise Architecture & Modeling
@DataAviator
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ER/Studio Enterprise Team Edition
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Agenda Governance Overview Definitions Master Data Data lineage & life cycle Master Data Management (MDM) Importance of Data Models Data quality Change Management & Audit Business Glossaries Data Maturity
Data Governance
Data Architectur
e Manageme
nt Data Developme
nt
Database Operations Manageme
nt
Data Security
Management
Reference & Master
Data Manageme
nt
Data Warehousin
g & Business
Intelligence Manageme
nt
Document & Content Manageme
nt
Metadata Manageme
nt
Data Quality
Management
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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.
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Master Data Classification Considerations Behavior Life Cycle Complexity Value Volatility Reuse
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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
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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
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Business Process & Data CRUD
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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
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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
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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”
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Data Lineage Source of truth Chain of custody Transformations
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Data Lineage
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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
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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
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Importance of data models Full Specification
Logical Physical
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
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Data Dictionary – Metadata Extensions
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ER/Studio – Metadata Attachment Setup
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Universal Mappings Ability to link “like” or related objects
Within same model file Across separate model files
Entity/Table level Attribute/Column level
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Universal Mappings
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Data Quality (DMBOK) Data Quality
The degree to which data is accurate, complete, timely, consistent with all requirements and business rules, and relevant for a given use.
Information Quality The degree to which information consistently meets the requirements and
expectations of knowledge workers in performing their jobs. In the context of a specific use, the degree to which information is meet
the requirements and expectations for that use.
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Data Quality Accuracy Timeliness Completeness Consistency Relevance Fitness For Use
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Poor Data Quality Implications Costs a typical company the equivalent of 15% to 20% of revenue
Estimated by US Insurance Data Management Association Low Quality = Low Efficiency It is insidious – most data quality issues are hidden in day to day
work From time to time, a small amount of bad data leads to a disaster of
epic proportions
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Poor data quality isn’t a new problem
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Mitigation Best Practice Adopt the philosophy of prevention Show thought leadership Be accountable at the points of data creation Measure, control, improve Establish data culture
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Change Management Tasks & Requirements
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Change Management & Audit
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Providing Meaning: Business Glossary
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Glossary Integration
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Data MaturityLevel 0 1 2 3 4 5Description None Initial Managed Standardized Advanced OptimizedData Governance None Project Level Program Level Division Level Cross Divisional Enterprise Wide
Master Data Managementno formal master data clasification
Non-integrated master data
Integrated, shared master data repository
Data Management ServicesMaster data stewards
establishedData stewardship
council
Data Integrationad-hoc, point to
point
Reactive, point-to-point interfaces,
some common tools, lack of standards
common integration platform, design
patterns
Middleware utilization: service bus, canonical model, business rules,
repository
Data Excellence Centre (education
and training)
Data Excellence embedded in
corporate culture
Data QualitySilos, scattered data,
inconsistencies accepted
Recognition of inconsistecies but no management plan to
address
Data cleansing at consumption in order to attempt
data quality improvement
Data Quality KPI's and conformance visibility,
some cleansing at source.
Prevention approach to data quality
Full data quality management
practice
BehaviourUnaware /
Denial Chaotic Reactive Stable Proactive Predictive
Continuous ImprovementIntroduction Expanson Transformation
Technology & Infrastructure
Information & Strategic Business
EnablementPrimary IS Focus
HIGH LOWRiskLOW HIGHValue Generation
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Summary Master Data Management and Data Quality are vital aspects 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
Change management and auditability is paramount for compliance Integrated business glossaries provide definition and context Achieving data maturity is a journey
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