Bridging the gap - Data governance & Business Intelligence

Kate Carruthers

Classification: Public

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Agenda

• Context

• Data warehousing & BI

• Data & Information Governance

• Engagement

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Context

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UNSW

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Source: UNSW Annual Report 2015

Global context

“Every budget is an IT budget. Every company is an IT company. Every business leader is becoming a digital leader. Every person is becoming a technology company. We are entering the era of the Digital Industrial Economy.”

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Peter Sondergaard, Gartner

Context

• Staff and students increasingly expect to access resources anywhere and any time

• Facing a digital transformation underpinned by information and data

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Global & local context

• Shifting external environment

• Potential changes to government funding for both teaching and research

• Economic uncertainty in key international markets

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Team Role

• Statutory & Government reporting

• Student load planning

• Student surveys including teaching & course evaluation surveys

• Staff surveys

• Provision of strategic and operational data via UNSW data warehouse

• Information and data governance

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Planning & Surveys

• Setup continuous improvement program

• Optimise approval process

• Improve reporting

• Schedule management – coordination of resources across UNSW

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Original brief

Business Intelligence

• Business Intelligence – Foundation of UNSW BI – Operational reporting and data analysis – Standard and ad hoc reports

• Enterprise Data Warehouse • Enhanced management information and internal reporting • Enhanced statistical and analytic resources for data analysis and

interpretation – easy to read dashboards – simple reports & alerts that can assist staff – useful information for decision making related to student

enrolment trends, retention rates etc.

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Original brief

Data & Information Governance

• Re-establish Data Governance Steering Committee

• Iterative, incremental approach, using in-flight business projects

• Iterative UNSW Business Glossary • Iterative technical processes and models • Common UNSW terms through the UNSW Business Glossary

– HR terms – Student terms

• Common technical models to built consistency in all business systems

• Translation processes - old terms into new terms

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Original brief

Challenge

Enable UNSW to have the right strategic information at the right time to assist in dealing with and managing change, future planning and external impacts

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Data Warehousing & Business Intelligence

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The beginning…

• Existing data warehouse • Enterprise data

warehouse project not delivering

• Tactical and reactive approaches

• Ad-hoc delivery • Ageing and unstable data

warehouse infrastructure • No documentation • No data or information

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Strategic business insight capability

• Provide actionable insights for business leaders

• Enable leaders to understand their business operations

• Build predictive models to enable strategic planning

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Revitalise the platform

• Implement stable and scalable platform for business intelligence

• Onboard critical data sets

• Review existing reporting from customer perspective

• Implement capability to support scorecards and dashboards

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Technology

• Move to new platforms such as cloud

• Decouple technology components to enable agility and flexible approaches

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Foundations

Business glossary

Business metrics

Tools

Data sources

Data quality

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Architecture and Services

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Heterogeneous data sources

Internal External

BRIDG as data broker = Data as a Service

SAS Tableau Calumo QlikView Power BI

HR Student Learning

Management Etc.

ABS BOM

Social media Etc.

Technology

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Azure

Student Load Planning

On premise

UNSW Network

AWS

Information Hub SAS BI &

Data Warehouse

Data & Information Governance

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The beginning…

• Tactical and reactive approaches

• Ad-hoc delivery

• No finalised documentation

• No data or information governance

• No set roles or responsibilities

• No policies or procedures

• Not linked to IT Security

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Ensure that the institution has the right information to support key strategic

initiatives

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Data Quality Management

Data Warehouse, Business Intelligence

& Big Data

Reference & Master Data Management

Data Architecture & Modelling

Data Governance

DATA & INFORMATION GOVERNANCE

• Appropriate use • Business value • Information meaning

• Data transparency • Data lineage • Data Quality

Information Governance Data Governance

• Data Security • Change Impact • Service Levels

• Information Life–cycle • Information Ownership • Privacy

Definition

"Data governance is the organization and implementation of policies, procedures, structure, roles, and responsibilities which outline an enforce rules of engagement, decision rights, and accountabilities for the effective management of information assets."

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Source: John Ladley, Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program, 2012

Baseline Principles

• Data & information governance

– is a business driven activity

– is a framework to enable the business to better manage information and data quality

• No data or information governance activities will be undertaken without business buy-in and leadership

• Decision making rights need to be determined

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The brief

1. Implement an institution-wide data and information strategy, including data governance, control, and policy development

2. Include information protection, information and data governance, and data quality processes, and data life cycle management

3. Work collaboratively across the institution to enable the exploitation of data assets to create business value

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The 4 dimensions Framework:

• provides enterprise wide roles and responsibilities to be accountable for decisions related to data assets

• establishes policies & procedures to manage the data assets

• provides tools for managing operational data tasks

UNSW Data Governance Framework focuses on the oversight, guidance and quality of enterprise data assets enabled through People, Policies, Procedures and Tools

1

Policies are high level statements that provide context for strategic decisions

relating to the data assets

People can be members of UNSW governance bodies, which hold the authority for decision

relating to data assets

Tools are pre-prepared objects that support people carrying out procedures

Procedures are specific instructions designed to ensure policy is followed and

outcomes are measurable

Workflow for Approval

Checklists

Issues Register

Data Profiling

Data Sharing

Data Reporting

Regulatory Compliance

Data Asset Prioritisation

Data Exchange Agreements

Data Process Flow

Data Integration

Data Security

Strategic Drivers

Dim

en

sio

ns

Enterprise Oversight of Data

Enterprise Guidance on Data

Enterprise Quality of Data

Performance Metrics

Policies Procedures Tools

Data Executives

Data Owners

Data Stewards

People

Data Creators/ Data Specialists

1 2 3 4

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Links with Legal, Privacy, Risk, IT & Info Sec

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Information literacy

Data driven improvements

Policies & Standards

Information Quality

Privacy, Compliance,

Security

Architecture, Integration

Establish Decision Rights

Stewardship Assess Risk &

Define Controls Consistent Data

Definitions

Adapted from University of Wisconsin Data Governance Framework

Data & Information Governance Model

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Policy Framework

Coordinating Committees

• Data Governance Steering Committee • Business Intelligence Steering Committee • Information Security Steering Group

Data Ownership & Management

• Data Areas • Data Executives • Data Owners • Data Stewards

• Data Governance Policy • Data Classification Standard • Data Handling Guidelines • Information Security Management System

Data Classification – the classification process will involve appropriate risk assessment

Highly Sensitive

Sensitive

Data that if breached owing to accidental or malicious activity would have a high impact on the University’s activities and objectives.

Data that if breached owing to accidental or malicious activity would have a medium impact on the University’s activities and objectives.

Data that if breached owing to accidental or malicious activity would have a low impact on the University’s activities and objectives.

Data that if breached owing to accidental or malicious activity would have an insignificant impact on the University’s activities and objectives.

Private

Public

High

Medium

Low

• Student zID’s, passwords, UNSW IT systems login

• Student personal records and admission applications

• Faculty/staff employment applications, personnel files, benefits, salary, birth date, personal contact information

• Unpublished research data (at data owner's discretion) • Non-public UNSW contracts, policies and policy manuals • UNSW internal memos and email, non-public reports, budgets, plans or financial information

• Information authorized to be available on or through UNSW website without zID authentication

• Job postings, public research data, staff details, policy or procedure manuals etc.

• Public, available campus maps

Classify the Data Risk Assessment Business Decision

As per the approved Data Classification Standard

As per the UNSW IT Risk Management Policy

As per agreed Data Governance Roles and Responsibilities

Data Management and Operations

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Identify the Data Owner Identify the Information

Assets Assess data risks

Apply data classification to the Information Asset

Apply the controls Data classification process:

Technology

• Adopting Collibra Data Governance Centre

• Starting with business glossaries

• Moving toward reference data

• Business case for Master Data Management

• Integration tools on agenda for next year

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Key Factors in Success

• Work with the willing

• Data & Information Governance

–must be a good fit for each specific Data Area and the business operations it supports

–must be developed collaboratively with the stakeholders

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There is no single ‘right’ answer for how to do it – the process needs to align to the business needs

Engagement

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Cross-functional collaboration

Business

IT BRIDG

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Engagement

• Strong customer engagement – Business Advisory and Reference Groups established

• Important role for IT

• Need to build partnerships

• Used agile methods

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People

• Getting the right mix of skills and institutional knowledge

• Not growing the team too fast

• Building culture and relationships

• Developing technical capability

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What we’ve learned so far

1. Build slowly – don’t rush

2. Bring the customers along too

3. Culture drives strategy

4. Agile approaches work

5. Collaboration matters

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Thank-you

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Kate Carruthers

k.carruthers@unsw.edu.au