Evaluating the impact and effect of analytics on your data

quality……..the NT Community Health way

ARK data quality conference 2006

Anastasia Govan

Director Whitehorse Strategic Group

October 2006

Overview

1. What is Community Health (CH)

2. CH Knowledge requirements overview

3. DHCS Information Systems

4. CH Information Systems

5. General data environment issues

6. Transforming data into business knowledge in CH

7. Which tools should we use?

8. Analysis and retrieval limitations - the case of the missing venues data

9. Justifying the cost of purchasing tools

1. What is Community Health

Well Womens Cancer Screening

Community & Primary

Care

HearingPlanning & Developme

nt Child

Youth & Family

Director

Customer Service

Assistant Secretary

CEO

Quality and Best Practice

Dept Health & Comm Srvcs (DHCS)

Health Services Division (HSD)

Community Health Branch (CHB)

CHB Work units

OPERATIONAL

MANAGEMENT

EXECUTIVE

Administration

Nursing

Planning & Developme

nt

Hearing/Cancer

Screening

MINISTER

2. Comm Health Knowledge requirements

3. DHCS Information Systems

4. Community Health Information Systems

OPERATIONAL

MANAGEMENT

EXECUTIVE

Nursing

Planning & Devt

Hearing/Cancer

MINISTER

Administration

CCIS Hearsoft Papsmear

Breastscreen

DW

Intranet

Intranet/oh&s/spreadsheets/monthly reportsClient files

Staff files

Risk formsMainframe

Mthly reports

hardcopy

SpreadsheetsAppointments

•Outsourced service providers•Distance•Bandwidth•Disparate databases and KPI’s •Branch organisation•One off queries•No Community Health Universe•Different IT domains•IT driving system development instead of end users•Manual manipulation of data •Lack of business rules•Lack of data dictionary/ontology•Lack of system documentation•Records deleted in operational databases after extract to DW are not deleted in the DW•DW only updated on the 10th of each month from operational databases

5. General data environment issues

Extract

Load

Cleanse

Mine

Present

6. Which tools are most relevant

CCIS

SHILO

Data marts

Business objects

Intranet

7. Which tools should we use?

7. Which tools should we use?

36 respondents – 5.9 mean

Accountants IT

Highest Consistency of Data between Source and

Warehouse 6.69

Consistency of Data between Source and

Warehouse 6.61

Lowest Incremental Update Capability 5.19

Model Integrity 4.67

Average for All 18 Quality Assurance Entities

6.07 5.73

Query Performance 6.06 5.94

Decision Support Capability

6.19 6.22

Ad hoc Query Capability

6.27 5.78

Data Mining Capability

5.71 5.56

Vendryzyk study

7. Which tools should we use?

Intranet management report

8.Transforming data into business knowledge

9. Analysis and retrieval limitations

Rudra & Yeo study

Analysis and retrieval limitations – the case of the incorrect venues

CCIS

SHILO

Data marts

Business objects

Intranet

Ch

ief

Info

rmati

on

O

ffice

r

Business Analyst – Management Reporting

Team

CCIS Manager

Data Warehouse Manager

Director Community Health

10. Justifying the cost of purchasing tools

SubIMG (Planning Team)

Reporting ProjectsWork Program Reviewand HSIMG Priorities

Update

Endorsed

Rejected

HSIMG (Steering Committee)

Strategic Rating(PDM)

Info Div (Systems Group)

Feasibilty Rating(TELOS)

Proposal 3

System Planning

ManagementReports

Production

TestingFunctional

SpecificationsProject Plans

HSDU WorkProgram Schedule

Feasible

Strategic

Y

Y

N

N

Proposal 2Proposal 1

Gilhooly, K. (2005). Dirty Data BLIGHTs the Bottom Line. Computerworld, v 39,pp 23-4

Rudra, A & Yea, E. (1999). Key Issues in Achieving Data Quality and Consistency in Dataamong Large Organisations in Australia. Proceedings of the 32nd Hawaii International Conference on System Sciences, IEEE.

Sen, R; Sen, T; Vendrzyk, V. An Instrument for Assessing Quality of Data Warehouses. Journal of Data Warehousing, Summer 2000, pp. 31-41

Theodoratos, D & Bouzeghou. (2001). Data currency quality satisfaction in the design of a data warehouse. International Journal of Cooperative Information Systems, Vol. 10, No. 3 pp299-326

Vendrzyk, V; Rymysen, D; Sen, A.(2001). How management accountants assess the quality of data warehouses. Management Accounting Quarterly, Spring

Wang, R;Kon, H;Madnick, S.(1993). Data Quality Requirements Analysis and Modeling

Wang, R; Reddy, M; Henry, K. (1992). Toward Quality Data: An Attribute-Based Approach

References

Whitehorse Strategic Group Ltd.

PO Box 2096,Darwin, Northern Territory

Australia 3000.

Level 3, 45 William Street,Melbourne, Victoria

Australia 3000.

Anastasia.govan@acslink.net.au

Whitehorse Strategic Group Ltd. is a management consulting practice with a well established reputation in Government and industry. It is a proud Australian company

with significant international experience. Whitehorse has a broad client base, predominantly from major private companies and the public sector, especially those elements of the public sector undergoing

commercialization or other business change processes. Whitehorse was founded in 1987 by a group of creative individuals who came together with a shared vision to create a new style of strategic consulting. The

principals of Whitehorse come from diverse backgrounds and disciplines, and all have extensive management experience.

Anastasia specialises in Information Architecture and process mapping across Australia and Asia