DataData Architecture and GovernanceArchitecture and Governance

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Prepared by Runganan WankundeePrepared by Runganan Wankundee

Prepared by Runganan W.

Data Architecture and GovernanceTeam 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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Team Role and Responsibility

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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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IT Process, Risk and Control Framework

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Information and Data ManagementInformation and Data Management

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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 AnalysisData 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

1

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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4

6

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Data ArchitectureData Architecture

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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.

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

Data GovernanceData Governance

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?“การกาํหนดและบังคับใช้ กฎ กตกิา มารยาท เกี ยวกับงานด้านข้อมูลในองค์กร” หรือ เรียกว่าเป็นขั )นตอนการร่างและบังคับใช้ “ธรรมนูญเกี ยวกับข้อมูล”

• 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?“การกาํหนดและบังคับใช้ กฎ กตกิา มารยาท เกี ยวกับงานด้านข้อมูลในองค์กร” หรือ เรียกว่าเป็นขั )นตอนการร่างและบังคับใช้ “ธรรมนูญเกี ยวกับข้อมูล”

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Data Governance Maturity

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The Pillars of Data Governance

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Data Quality ManagementData Quality Management

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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.

Prepared by Runganan W.

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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DWH & BI ManagementDWH & BI Management

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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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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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Framework

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Meta Data ManagementMeta Data Management

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Meta Data

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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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Master Data ManagementMaster Data Management

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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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“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

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Thank you

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