data quality presentation oct 2006 23092006
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TRANSCRIPT
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.
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