Star Track Express, an Australian express freight company founded in 1974, has evolved through a joint venture and mergers, making it the largest provider in Australia with a high success rate in deliveries. The document discusses the challenges and needs for real-time data processing and operational intelligence, emphasizing the transition to an active data warehouse that can handle large volumes of data with low latency for operational and analytical purposes. Recent implementations have demonstrated the effectiveness of this new architecture, achieving 100% uptime and efficiency in query responses.

1. Active Data WarehousingCan you have your cake and eat it too ?Jeff MonicoGeneral Manager Information Systems

2. AgendaINTRODUCING STAR TRACK EXPRESS

3. THE ‘REAL TIME ENTERPRISE’

4. THE TRIGGER FOR CHANGE

5. ACTIVE DATA WAREHOUSING

6. THE RESULTS

7. OPERATIONAL INTELLIGENCE

8. QUESTIONSCompany OverviewAustralian owned and operated Express Freight Company.

9. Founded in 1974 by Greg Poche. Greg’s influence and core guiding principles are very much part of the business ethos today.

10. December 2003 saw Australia Post and Qantas form a joint venture to acquire Star Track Express from its founder.

11. On the 18th May 2011 the retail division of Australian Air Express was merged into Star Track Express to create Australia’s largest door-to-door express service provider.

12. 4,500+ Employees, 3,750+ Vehicles (Vans, Trucks, Prime Movers, Trailers, Forklifts).

13. Each day over 400,000 items of freight are picked up and delivered, with an industry leading ~99% success rate.Core Services

14. How we collect and use dataBusiness activity has a very short life cycle, with data being collected and used at many points during that lifecycle……18:3018:4019:0016:45DATA COLLECTION (SCANS)TIME EVENT DESCRIPTION16:45 Freight picked up18:30 Freight unloaded at Sortation Depot18:40 Freight sorted at Sortation Depot into Lane 2119:00 Freight loaded onto interstate trailer M20005:00 Freight unloaded at Sortation Depot05:35 Freight sorted at Sortation Depot onto Centre Wishbone07:00 Freight loaded onto delivery truck ABC12309:30 Freight successfully delivered09:30 Consignment Note signed by Bob.09:30DATA USAGE05:0005:3507:00

15. Problems we faceLarge Volumes of DataProducing millions or records per day which are needed for operational and analytical queries.Fast Response TimesUsers demand sub second response times on operational queries.Near Real Time LoadsReal time business needs real time data from a variety of sources. Latency of more then a minute or two is an issue.

16. Large Data SetsExtending the data being collected from financial, to analytical, to transactional created exponentially larger data sets……..

17. Low LatencyData comes from multiple sources, from inside and outside of local networks and needs to be available with very low latency………OLTP SQL ServerWireless IPDelivery Event??File TransferMainframe (RMS)Sortation EventEDW

18. Varied Query Response Needs<1s<1s<10s<1m

19. Data capturedIntelligence deliveredAction takenLow latency, fast response times and effective BI together helps accelerate decisions and create business value…..Business eventValueTimeMissed OpportunitySituationGained OpportunityTwo Trucks are dispatched to same location for delivery

20. Customer is curious and a bit frustrated as to why 2 trucks are needed…