Data Governance: From speed dating to lifelong partnership
RungananW-DA&DG 201701 V2.0
1. DataData Architecture and GovernanceArchitecture and Governance
VV 22..00VV 22..00
Prepared by Runganan WankundeePrepared by Runganan Wankundee
Prepared by Runganan W.
2. Data Architecture and Governance
Team structure
Data Architecture
and Governance
EIM
• Data governance
• Data quality
• Business glossary
• Master data management
• EDW Data modeler
• Data Steward
• Meta data management
Data Governance Data Modeler
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4. Team Mission & Vision
Team Mission
• Proactively define/align rules.
• React to and resolve issues arising from non-compliance with rules
• Ensure that the highest quality data is delivered via company-wide data governance strategy for
the purpose of improving the efficiency, increasing the profitability and lowering the risk of the
business units we serve.
• To undertake a leadership role in the creation, implementation and oversight of the enterprise-wide
information and data management goals, standards, practices and processes aligned with the goals
of the organization
• To provide expert advice and support in relation to all aspects of Information and Data Governance• To provide expert advice and support in relation to all aspects of Information and Data Governance
including Data Ownership, Data Protection, Data Privacy, Information Usage, Classification and
Retention
Team Vision
Information is treated as an enterprise-wide asset and is readily available to support decision-making
and informed action. Effective use and protection of information in which Data is governed and
leveraged as a unique corporate asset. Promoted enterprise data warehouse as single source of truth
and be the value for business wide.
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6. Information and Data ManagementInformation and Data Management
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7. Information and Data Management
Data management is an administrative process that includes acquiring, validating, storing, protecting,
and processing required data to ensure the accessibility, reliability, and timeliness of the data for its
users
Data Architecture
• Enterprise Data Modeling
• Value Chain Analysis
Data Quality Management
• Quality Req. Specification
• Quality Profiling & Analysis
• Quality improvement
• Quality Dashboard
Metadata Management
• Architecture & Standard
• Capture & Integration
• Repository
Database Operation Management
• Acquisition
• Recovery
• Tuning
• Retention
Data Development
• Analysis
• Data Modeling
• Database Design
• Implementation
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3
5
Data Governance
• Role & Organizations
• DG Framework
• Data Strategy
• Policies & Standards
• Data issue management
• Data Retention Management
Data Security Management
• Data Privacy Standards
• Confidentiality Classification
• Password Practices and Policy
• User Group & Admin Privilege
•User Access ManagementReference & Master Data Management
• Data Integration Architecture
• Master Data Management (Internal &
External)
• Customer Data Integration
• Product Data Integration
DWH & BI Management
• DWH/BI Architecture (Framework)
• DWH Logical Data Model
• BI Technology and Implementation
• BI Training & Support
• BI Monitoring (SLA & Quality) & Tuning
• Repository
• Query & Reporting
• Distribution and Delivery
Document& Content Management
• Acquisition & Storage Planning
• Backup & Recovery
• Electronic Document Management
• Information Content Management
• Retrieval
• Retention
• Retention
• Purging
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Prepared by Runganan W.
9. What is Data architecture?
Data architecture is a set of master blueprint designed to align information assets
with business strategy, and to guide the integration, quality improvement and
effective delivery of data.
Define how the data will be stored, consumed, integrated and managed by different
data entities and IT systems
Oversee the mapping of data sources, data movement, interfaces, and analytics, with
the goal of ensuring data quality
Data architecture is a set of master blueprint designed to align information assets
with business strategy, and to guide the integration, quality improvement and
effective delivery of data.
Define how the data will be stored, consumed, integrated and managed by different
data entities and IT systems
Oversee the mapping of data sources, data movement, interfaces, and analytics, with
the goal of ensuring data qualitythe goal of ensuring data qualitythe goal of ensuring data quality
Prepared by Runganan W.
10. Data Architecture Roadmap
Foundation Y1
Customer master
Data governance
- DG committee
Y2
360 Customer view
Y3
Big data analytic
Event base marketing
- DG committee
- DG Guideline
- Data quality
Data architecture
- As is
- Should be
New EDW
- Data modeler
360 Customer view
Promote EDW as a single source of truth
Master and reference data
- Complete customer master
- Product master
Business glossary
Analytic culture
Integrate Subsidiary data
12. What is Data governance
Data governance is a set of processes that ensures that important data assets
are formally managed throughout the enterprise. 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.
Data governance is Tools, policies and processes to:
• Improve data quality and reduce data redundancy
• Protect sensitive data
Data governance is a set of processes that ensures that important data assets
are formally managed throughout the enterprise. 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.
Data governance is Tools, policies and processes to:
• Improve data quality and reduce data redundancy
• Protect sensitive data• Protect sensitive data
• Ensure data and IT compliance with federal and state regulations
• Encourage use of data, correctly
• Platform for robust data analytics
Data Governance คืออะไร?
“การกําหนดและบังคับใช้ กฎ กติกา มารยาท เกียวกับงานด้านข้อมูลในองค์กร” หรือ เรียกว่าเป็นขั)นตอนการร่าง
และบังคับใช้ “ธรรมนูญเกียวกับข้อมูล”
• Protect sensitive data
• Ensure data and IT compliance with federal and state regulations
• Encourage use of data, correctly
• Platform for robust data analytics
Data Governance คืออะไร?
“การกําหนดและบังคับใช้ กฎ กติกา มารยาท เกียวกับงานด้านข้อมูลในองค์กร” หรือ เรียกว่าเป็นขั)นตอนการร่าง
และบังคับใช้ “ธรรมนูญเกียวกับข้อมูล”
Prepared by Runganan W.
16. Data quality is about having data that is “fit for purpose.”
Benefits
• Accuracy in reporting and business decisions
• Time and cost savings by removing redundant data storage and reduced time
spent on manual data reconciliation
• Build trust in your data
Data quality is about having data that is “fit for purpose.”
Benefits
• Accuracy in reporting and business decisions
• Time and cost savings by removing redundant data storage and reduced time
spent on manual data reconciliation
• Build trust in your data
Data Quality
Prepared by Runganan W.
18. Example Data Quality Monitoring
Quality score Quality level
>=99.9% A
>= 95% B
>= 90% C
< 90% D
Measurement Condition
Customer
Birth date Age between 1-100
Citizen ID Vallidate with check digit rule
Mobile phone Valideate with mobile phone format
Gender Must be M and F
Occupation Value must be in occupation list
Address Address is correct
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19. DWH & BI ManagementDWH & BI Management
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20. Data Warehouse
Data Warehouse is a system used for reporting and data analysis, and is
considered a core component of business intelligence. DWs are central repositories of
integrated data from one or more disparate sources. They store current and historical data
and are used for creating analytical reports for knowledge workers throughout the
enterprise
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21. Business Intelligence
Business Intelligence are the set of strategies, processes, applications, data,
products, technologies and technical architectures which are used to support the collection,
analysis, presentation and dissemination of business information. BI technologies provide
historical, current and predictive views of business operations.
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25. Benefits
• Consistent understanding of data definitions
• Traceability of data transformations
• Reduced data redundancy
• Save time and effort of tracking down data or reconciling duplicated
Benefits
• Consistent understanding of data definitions
• Traceability of data transformations
• Reduced data redundancy
• Save time and effort of tracking down data or reconciling duplicated
Meta Data
• Save time and effort of tracking down data or reconciling duplicated
data
• Ability to identify ahead of time possible consequences and impacts of
any changes to processes, storage, applications or reports.
• Save time and effort of tracking down data or reconciling duplicated
data
• Ability to identify ahead of time possible consequences and impacts of
any changes to processes, storage, applications or reports.
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27. Master Data Management
master data management (MDM) comprises the processes,
governance, policies, standards and tools that consistently
define and manage the critical data of an organization to
provide a single point of reference
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28. “Masterdata”
The critical data used across the organization by multiple divisions
“Masterdata management”
Process and policies to achieve consistent master data, which is
“Masterdata”
The critical data used across the organization by multiple divisions
“Masterdata management”
Process and policies to achieve consistent master data, which is
Master Data Management
Process and policies to achieve consistent master data, which is
managed centrally
Benefits
Single source of all Masterdata, managed centrally and
disseminated
Process and policies to achieve consistent master data, which is
managed centrally
Benefits
Single source of all Masterdata, managed centrally and
disseminated
Prepared by Runganan W.
