www.certussolutions.com

Context is Everything Using policies, data quality and stewardship to govern information

Ashwin Sinha Practice Manager – Information Management Ashwin.Sinha@certussolutions.com Date: August 2013

• Information Governance

• Business Glossary

• University example

• Context of Data Quality

• Data Quality Framework

• Certus in University

• Questions

Agenda

Six Steps to Governance

1. Set your Goals - the core statements that guide the operation and development of the

information supply chain.

2. Define Your Metrics - the set of measurements used to assess the ongoing effectiveness of

the program and associated governance processes.

3. Make Decisions - the organisational structure and changing ideological model to analyse and

make policy decisions.

4. Communicate Policy - the tools, skills and techniques used to communicate policy decisions to

the organisation.

5. Measure Outcomes – Compare policy results with goals, inputs, decision models, and

communication to provide constant feedback on policy effectiveness.

6. Audit results – the tool you use to benchmark everything.

Information Governance – Key areas

Information Governance – Operational

Planning Design

Taxonomy Identify

Stewards Glossary Training

Monitor Quality and Compliance

Scoping

Business Terms

Data Quality

Security and Classification

MDM

Define Framework

Profiling Assess Issues

Implement Rules

Implement Scorecard

Planning

Planning Define Process

Publish Dictionary

Publish Policies

Pilot MDM Profiling

Reference Data Management

Import Policies

Import Rules

Connect Rules to Terms

Implement Metrics

Implement Scorecard

Business Case

Charter Organisation

Structure Deploy

Stewards Reference

Architecture Governance

Import Terms

EDW Planning Define Process

Scope Industry Model

Customise Industry Model

Deploy Model to Glossary

Stewardship and Review

Deploy KPI Monitoring

BAU

Definition of a Glossary

• What is a Glossary?

• Most Glossaries are labels and descriptions stored in PDF, Word or HTML format.

• There is a lack of consistency, ownership and re-use across these Glossaries. They are expensive to build and maintain.

Glossary for Universities

• Student ?

• EFTSL – Equivalent Full Time Student Load. That is, a full time student with 24 subject units will have an EFTSL of 1, and a half time student will have an EFTSL of 0.5

• An EFTSL - or Equivalent Full Time Student Load - is a representation of the amount of load a student would have when studying full time for one year. At Curtin 200 credit points equates to 1.0 EFTSL.

• The measure used to determine a student's enrolled load. One 'EFTSL' is the amount of student load determined by the University to be equal to a full-time load for one student for one year, and is expressed at the University as 36 units.

More on same term

At the University of Sydney:

• 1 EFTSL is equivalent to 48 credit points (1 year of full-time study)

How do I find out the EFTSL of a unit of study?

• The EFSTL a unit of study can be determined by dividing the unit credit point value by 48

Can we have a consistent meaning of above terms always, in absence of context?

Why Use a Glossary?

Central Nervous System

Data Integration

Data Quality

Master Data Management

Big Data Analytics

Information Lifecycle

Management

Privacy & Security

Policies Glossary

• The Central Nervous System receives information from all parts of the body and coordinates activity.

Giving Context to Information

• Under a traditional documentation and development approach artefacts decline in value

• In a Metadata Driven architecture Business Glossary and data lineage becomes more valuable over time.

Contain inaccuracies

Become Stale Get Lost

Somewhere Represent old

standards

Retains SME Knowledge

Shared across projects

Reflects the real world

Drives Change Management

DQM – Why?

Benefits of undertaking a dedicated data quality management (DQM) initiative as part of the Information Management programme include:

– Engagement on DQM as a strategic issue throughout the enterprise

– Cost-effective actions, targeted at the most critical issue

– Supports treating information as a strategic asset that supports business process

execution

– Measurable outcomes. Providing context to Data Quality issues.

– Sustainability for the long term

– Assurance that business value & benefit meets the initial business case

– Clear roadmap for future progress

Data Quality Framework

Information Analyzer

Data Rule and Exception

Management

Data Cleansing

And Mastering

Data Transformation

validate

cleanse &

enrich

“master”

assess &

discover define

objectives

report monitor /

track

Data Profiling

And Analysis

Exception Reporting

Business Glossary

Policies

Data Quality Scorecard

• Mobilising People, Processes and Tools to improve the quality of information

1

4

Improving Data Quality Safeguards Data Quality Scorecards are part of a framework to reduce risk:

• The Data Quality Scorecard is a measure of problems, the overall Information Management framework of Business Glossary,

Metadata reporting and exception handling provides the support for handling these problems.

• A Scorecard is the ongoing audit that data is safe to use and the safeguard against complacency.

Data Lineage: where did the problem

occur?

Stewardship: who is responsible for this

data?

Business Glossary: what do we know about this data?

Exception Management: what do we do about

this problem?

DQM – How (Scalable Approach)

Scope

• Define DQM and boundaries

• Ensure stakeholder buy-in

• Define data domains

• Identify DQ Issues

Measure

• Establish DQ analysis environment

• Profile data

• Capture business rules

• Root cause analysis

• Quantify business impacts

• Qualitative

• Quantitative

• Agree action plan

Action

• Establish DQ resolution environment

• Address DQ issues

• People

• Process

• Technology

• Establish DQ prevention activity

• People

• Process

• Technology

Operate

• Embed DQM as BAU

• Extend and evolve DQM across organisation

• Extend and evolve as part of approach to Information Management

DQM – How (Scalable Approach)

DQM – Keys to Success

There are many factors to ensure the success of establishing and operating a DQM capability:

– DQM needs to be clearly linked to business objectives with business sponsorship

– Clearly articulated business impacts both quantitative as well as qualitative

– Clearly understand root causes and costs to remediate / prevent

• People, Process and Technology

– Start with high value and low cost to demonstrate value and ensure ‘right-sized’

approach

– Define KPI’s that leverage D.A.T.A.

• Digestible,

• Actionable

• Timely

• Auditable

Certus provides the framework

What rule sets are available? A combined set of over 250 data quality rules and over 200 Business Glossary terms for Education sector developed and provided by Certus and IBM is available to kick start a project. Examples of the rules available include:

• Party/person rules - validation of an actual person's name, gender and age. Some examples of this are reasonableness checks on whether name is in a valid format, age range is valid, etc.

• Address rules - validation of the various parts that make up the address, and reference checks of cities and post codes.

• Email and web rules - validate the format of email addresses, email domain names, host names, URLs and IP addresses.

• Field format rules- A large number of generic field format validation rules that cover a range of different field types such as indicators, codes, dates, numeric formats, etc.

Certus DQF Overview

Data Quality Framework

The artefacts within the Data Quality Framework provide the client with a top down understanding of the implementation of the Data Quality Process. The Framework gives you an understanding of the roles, responsibilities, tasks and processes involved in the implementation.

The DQF Hierarchy Tree defines the high level steps required for the deployment of the framework. These steps then break down into supporting artefacts at the median level, providing the client with:

– Business Process Diagram for each main level in the Hierarchy tree.

– RACI document which details the tasks and technologies involved in each level of the hierarchy tree and roles and responsibilities across each task.

– Project Plan template

Technical artefacts are also included that detail each step involved in the process.

Certus DQF Framework

DQF Framework Components

• Level 1 CDQF Hierarchy Tree - Top level processes involved in the framework.

• Level 2 Detailed artefacts supporting the top level processes

• RACI Document Roles – An outline of the roles required within your organisation for this initiative. Responsibilities – responsibilities outlined and assigned by role. Technologies – technology components required in each step.

• Project Plan The project plan provides a template for deployment covering each task within the Framework broken down by duration and role involved.

• Business Process Method Map

Provides a visual representation of the flow of tasks throughout each business process.

DQF Technical Components

DQF Data Model • The DQF data model provides the underlying store for the metadata that is

transferred throughout the framework.

Framework Integration Components The components below make up the technical plumbing of the Framework.

• ETL (IBM DataStage)

• Database Scripts (DB2, Oracle, SQL Server)

• Operating System Script (Linux, Windows)

Certus DQ Dashboard and Active Reporting Solution • High Level Dashboard

• Reporting that gives you the ability to drill down into the high level metrics.

• Available for iPad for mobility

Data Rule Sets • Data rules sets are available to assist in the development of your own internal data

rules. They give you a head start with common rules found in separate data sets i.e. Name and Address.

Educational Experience

Who we’ve been working with...

James Cook University

NSW Board of Studies

Christchurch Polytechnic

Institute of Technology

Edith Cowan University

Deakin University

Griffith University

Massey University

Ministry of Education

NSW Department of Education & Communities

NSW TAFE

Open University

Study Group International

Southern Cross University

University of Auckland

University of South Australia

University of Technology Sydney

University of Wollongong

Two examples

James Cook University

As part of Information to Analytics project

Certus team has worked with JCU to establish

an information management framework, BI

reporting and Budgeting & Planning. Solution. It

also addresses some of the early Information

Governance requirements.

Open University

The key development work was on student

enrolments and the cost of running particular

courses. It was enrolment-driven, looking at

the individual subject units, and the student

profiles which may contribute to additional

course requirements.

What we do

ASSET MANAGEMENT

INFORMATION MANAGEMENT

INFRASTRUCTURE SERVICES

MIDDLEWARE SOLUTIONS

COLLABORATION SERVICES

WEB SOLUTIONS

SUPPORT SERVICES

LICENSING

Enhance operational efficiency and optimise management processes in your asset intensive business

Create, utilise and maintain trusted information assets with IM strategies and solutions

Maintain system performance and unleash productivity with reliable, on-demand business infrastructure

Build enterprise applications and remove information silos for improved business agility

Connect people, share information and empower your organisation to work smarter

Drive improved business performance with unique user-centred design methodologies

Subscribe to flexible IBM software support and access expert technical resources to maintain operational efficiency

Extract maximum value from your IBM software with effective, compliant licence management

BUSINESS ANALYTICS

ENTERPRISE ARCHITECTURE & SDLC

Align corporate strategy with business process, information, applications and technology

Deliver complete, consistent and accurate information to decision-makers for improved business performance

Thanks & Questions

FAQ

• How dependent is the solution on customer having key components of IBM InfoSphere and IBM Cognos? Can the solution be ported to other technologies with a limited effort?

To realise the full benefits of the solution the customer would need to be an IBM Information Server and Cognos user.

The framework is also useful to clients who wish to implement a DQ project on separate technology. The architectural and project management components give the customer a generic framework to implement there project.

FAQ

• How flexible is the DQ rule creation and editing for future maintenance?

The data rules that are created for the project are easily edited from the IA interface.

• What sort of support would be needed to keep the solution running?

Once the technical components of the project are productionised a number of roles will have to be filled to ensure an ongoing data quality improvement. A technical resource to monitor the execution and deal with issues that arise. A business resource from each area to ensure the data quality results are monitored and initiatives are taken to ensure the improvement of that data over time.

• What will justify the ROI business case for a customer buying this?

In our experience full implementation of the Data Quality Framework would save the customer up to 12 months of development effort getting it off the ground from scratch.

FAQ

• What the pre-requisites on software, hardware and discovery to finish this activity?

At a generic level the framework is a tool to manage and plan a data quality project.

At the full technical level the Framework requires IBM Information Server Components;

IBM Information Analyzer V8.7 min

IBM Datastage V8.7 min

IBM Business Glossary V8.7 min

IBM Cognos User licences V10.1 min

• How do we ensure that the solution is flexible for customers with different data quality needs and different types of data?

The framework has been generically designed it provides a structure to implement over an industry. It is not s rigid structure or formulated from an industry specific point of view.