EXPLORING THE CAVERN OF DATA GOVERNANCE

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EXPLORING THE CAVERN OF DATA

GOVERNANCE

AUGUST 2013

Planning and Information Office | SIBI

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Data Management Overview

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Definitions: Data Management & Data Governance

The exercise of authority and control (planning,

monitoring, and enforcement) over the management of

data assets.

(*)

3

Data Management

The planning, execution and oversight of policies, practices

and projects that acquire, control, protect, deliver, and

enhance the value of data and information assets.

(*)

Data Governance

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Data Governance Challenges – Key reasons for Failure

(*)

Data Governance Overview

Data

Governance

Challenges

Failure to

Execute

Lack of knowledge and Understanding by Senior

Management (i.e. skills requirements, strategic outcomes, process improvement) leads to a failure to execute.

Lack of

Ownership

Ownership, responsibility and accountability not

assigned.

Lack of

Awareness

Executives and key

stakeholders of data

management

capabilities have a lack

of knowledge and

awareness of DG.

Lack of

Accountability

Accountability not

assigned to each

process

Task is

overwhelming

DG is too big for any

one person to

accomplish.

Adequate resources

are not assigned.

(*)_{Adapted from 2011 Baseline Consulting Group, Inc.}

- Training - Education - Communications - Workshops - Assign sponsor - DG Forums - Personal development plans - KPIs - Education - Best practices - Bench marking - Leverage other successes - RACI - Data stewards - Personal development plans - KPIs - Pilot projects - Series of manageable projects

- Identify key areas of concern - Split the tasks - Identify and assign

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Data Governance Strategy

5 What is Data Governance for the University Develop processes Identify a key initiative as a Pilot Define KPIs as measures for success Educate and engage stakeholders Document improvements and processes Communicate success

SUCCESSFUL

DATA

GOVERNANCE

Managing Expectations

• Develop DG vision statement in line with University’s strategic vision • Define DG • Scope DG with context of University • Define Data Governance Framework • Define DG organisation • Define roles and

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Data Governance vs. Data Management

Data Governance

(Organisation and Activities)

Strategy

Organisation and roles

Deliverables and standards

Projects and services

Issues management

Creating guiding principles

Data asset valuation

Data Management

(Execution)

Data profiling

Data quality monitoring

Data cleansing

Semantic rules

Data enrichment

Business rules creation &

maintenance

Enterprise data modeling

Metadata definition

Business glossary definition

Data archival

Backup and Recovery

Authentication

• Provide Guidance

• Create & Implement

Deliverables

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Data Management Overview

7

(DMBOK) Data Management Functions

•Analysis •Measurement •Improvement •Architecture •Integration •Control •Delivery

•Acquisition & Storage •Backup & Recovery •Content Management •Retrieval

•Retention

•Architecture •Implementation •Training & Support •Monitoring and Tuning

•Acquisition •Recovery •Tuning •Retention •Purging

•External Codes & Internal Codes •Customer Data

•Product Data

•Dimension Management •Enterprise Data Modelling •Value Chain Analysis

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Data Management Overview

›

Data Governance

›

Data Security Management – Data Visibility

›

Data Quality and Data Profiling

›

Master Data Management

›

Metadata Management & Business Glossary

Current focus for SIBI

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Data Management Overview

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DMBOK – 7 Environmental Elements

People

Process

Technology

• Organisation & Culture

• Roles & Responsibilities

• Goals & Principles

• Activities

• Deliverables

• Practices & Techniques

• Technology

Provide a consistent way to describe and strategically plan each function

_Technology

Roles & Responsibilities Goals & Principles

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Data Management Overview

DMBOK – 7 Environmental Elements

›

Goals & Principles

– The directional business goals of each function and the fundamental principles that guide performance

of each function.

›

Activities - Each function is further decomposed into lower level activities (tasks and steps)

›

Deliverables - The information and physical databases and documents created as interim and final outputs of each function.

Some are considered essential, some are generally recommended, and others are optional depending on circumstances.

›

Roles and Responsibilities - The business and IT roles involved in performing and supervising the function and the specific

responsibilities of each role in that function. Many roles will participate in multiple functions.

›

Practices & Procedures - Common and popular methods and techniques used to perform the processes and produce the

deliverables. Risks and issues management.

›

Technology - Categories of supporting technology (primarily software tools), standards and protocols, product selection

criteria and common learning curves..

›

Organisation and Culture - These issues might include:

-

Reporting Structures, Teamwork and Group Dynamics

-

Budgeting and Related Resource Allocation Issues

-

Authority & Empowerment

-

Shared Values, Beliefs, Expectations & Attitudes

-

Change Management Recommendations

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Data Governance Overview

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Data Governance – University Organisation & Culture

• Support the DGC, by implementing and

refining the data ownership, data stewardship and data custodian roles throughout the University.

• Provide Subject Matter Expert (SME) knowledge and support to the data governance strategy

• Own the data governance strategy • Promote, endorse and approve the development and enhancement of the data governance management framework

Data Owners Management Group (DOMG)

Data Modellers Database Administrators Data Stewards Data Integration Specialists Data Quality Specialists Supported by: Information / Data Architect

Data Governance Committee (DGC)

Organisation

• Operating model

• Arbiters & escalations points

• Data Governance organisation members • Roles & Responsibilities

• Terms of Reference

• Data ownership and responsibility Deans of Faculties and Directors of

Professional services Units, e.g. Finance, Research, HR, ICT

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University Principles and Goals (

)

Data

Management

Principles

Trusted

Valued

Shared

Re-used

Managed

Governed

Data Management Overview

Trusted. We trust in our information. Access to and use of data

will promote trust and confidence through adherence to relevant

Data Governance Policies and procedures, privacy, confidentiality

and security requirements.

Valued. Data is valued as a strategic resource and an asset. As a

result, data and information will be of high quality, accurate,

relevant, timely and support confident business decisions.

Shared. Information and data is accessible, transparent and

available to be shared as part of the University’s sharing of

information obligations to; the community, staff, students,

researchers and alumni.

Re-Used. Data and information should be obtained from a single

authoritative source. Data and information is collected in a

consistent manner and is available to be used for different

purposes with confidence.

Managed. Data and information is managed throughout its

lifecycle and is compliant. Information Management Procedures

and practices are standardised and applied across the University

and apply to all involved in the data management lifecycle.

Governed. Data and information is governed in accordance with

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13

Deliverables, Activities, Practices & Techniques

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Data Management Overview

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DMBOK Functions

•Analysis •Measurement •Improvement •Architecture •Integration •Control •Delivery

•Acquisition & Storage •Backup & Recovery •Content Management •Retrieval

•Retention

•Architecture •Implementation •Training & Support •Monitoring and Tuning

•Acquisition •Recovery •Tuning •Retention •Purging

•External Codes & Internal Codes •Customer Data

•Product Data

•Dimension Management •Enterprise Data Modelling •Value Chain Analysis

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Data Quality Management

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Definition

Planning, implementation and control activities that apply quality

management techniques to measure, assess, improve and ensure the

fitness of data for use.*

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Communication

Princi

p

les

Organisation & Culture

Roles and Responsibilities

Data Quality Management Framework – HR Pilot

Accuracy

Completeness

Integrity

Timeliness

Validity

Consistency

Issues Log

Risk Matrix

Critical success

factors

Authority &

Empowerment

Information

Compliance

Data Privacy

Govt.

Legislation

Internal

Audit

Roles

Forums

Data Custodian Data Owner Sponsor Data Steward SIBI Program Board BOG

Expectations &

Attitudes

Pilot group

structure

Change

Management

Technology: Data Profiling (Informatica), Data cleansing (IDQ-Informatica)

University

of Sydney

Vision

Goals

*** Develop vision for Data Quality Mgmt. and for Pilot with HR data. (workshop)

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Data Quality Management

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Data Quality Dimensions

• Does the data accurately represent reality or a verifiable

source?

Accuracy

• Is all necessary data present?

Completeness

• Are all data elements consistently defined and

understood?

Consistency

• Is the structure of data and relationships among entities

and attributes maintained consistently?

Integrity

• Is data available when needed?

Timeliness

• Do data values fall within acceptable ranges defined by

the business?

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Data Quality Methodology - Roadmap

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2. Define DQ

Requirements

Activities

Deliverables

Technology

3. Profile,

Analyse &

Assess DQ

IDE – Informatica

Data Profiling tool

Baseline

Updated

Issue Log

Scorecard

Report

IDQ – Informatica

Data Quality tool

Recommend

Actions

Actions:

- Training / education / comms - Business Processes

Improvement (SOPs) - Data Validation (data entry

process)

Data Issue

Log

Enables data profiling and analysis with the flexibility to filter and drill down on specific records for better detection of problems.

4/5.Define

DQ metrics &

Business rules

Enables architects and developers to discover and access all data sources, to improve the process of analyzing, profiling, validating, and cleansing data.