Quack Chat: Diving into Data Governance

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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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Q&AJoin the discussion: http://community.idera.com/

You can find me at:[email protected]

@DataAviator

Quack Chat: Diving into Data Governance

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