1. Successfully Leveraging Information into a Specific Action Peter McCallum

2. Agenda Introduction Introduction Data Warehouse Benefit Realisation Research Data Warehouse Benefit Realisation Research Key Learning from that Research Key Learning from that Research Real Life Examples of Analytical Based Work Processes Real Life Examples of Analytical Based Work Processes  Automated KPIs in a Retail environment  Plan Optimisation application for a Telco  Intelligent Invoice Analysis in an Airline Summary Summary

3. Introduction Who am I? Who am I?  18 years experience in the IT industry  First 8 years in bespoke software development  “Discovered” Business Intelligence in 1998  Spent the last 10 years working exclusively in BI & data warehousing

4. Introduction (cont’d) Why Business Intelligence? Why Business Intelligence?  BI is one area of IT where real business value can be delivered …. But not always …. But not always  A particularly challenging project made me ask “Why do some BI initiatives deliver far more benefit that others?”

5. Agenda Introduction Introduction Data Warehouse Benefit Realisation Research Data Warehouse Benefit Realisation Research Key Learning from that Research Key Learning from that Research Real Life Examples of Analytical Based Work Processes Real Life Examples of Analytical Based Work Processes  Automated KPIs in a Retail environment  Intelligent Invoice Analysis in an Airline  Segmented call routing in a Telco Summary Summary

6. DW Benefit Realisation Back to school Back to school  Enrolled in MCOM  Bright Futures Enterprise Scholarship Thesis on DW Benefit Realisation Thesis on DW Benefit Realisation  Qualitative approach  Exploratory study  Sample of organisations that had recently implemented a DW  Interviewed Business owners and IT representatives  Data analysed using Grounded Theory

7. DW Benefit Realisation The benefits realised by each of the organisations varied hugely The benefits realised by each of the organisations varied hugely  One organisation recouped their entire investment in just 2 months of operation  Whereas another organisation slashed their planned BI program significantly after realising few benefits Why the difference? Why the difference?

8. Agenda Introduction Introduction Data Warehouse Benefit Realisation Research Data Warehouse Benefit Realisation Research Key Learning from that Research Key Learning from that Research Real Life Examples of Analytical Based Work Processes Real Life Examples of Analytical Based Work Processes  Automated KPIs in a Retail environment  Intelligent Invoice Analysis in an Airline  Segmented call routing in a Telco Summary Summary

9. Research Findings DW Benefit Realisation Analytical Framework DW Benefit Realisation Analytical Framework Environment Project Initiation Organisational Readiness Project Delivery Benefit Monitoring System Use Perceived Benefits

10. Research Findings # 1. Supporting Business Process # 1. Supporting Business Process  “The more that the use of the data warehouse can be embedded into the business process being supported, the more likely it is that benefits will be realised”  “Operational business processes tend to realise the benefits of data warehousing more readily and more quickly than tactical or strategic business processes.”

11. Research Findings # 2 External & Contextual Influences # 2 External & Contextual Influences  Environment  State of Industry  Level of Government Intervention  Organisational Readiness  Existing IT Systems  Information Sharing culture  Analytical competency of users  Project Initiation  Motivation  Project Sponsor

12. Research Findings # 3. Strategies to increase Benefits realised # 3. Strategies to increase Benefits realised  Focus on increasing use of DW  Providing superior training & support  Maintaining high level of business involvement  Communicating with the business  Ensuring data quality is good  Achieving a high level of system quality

13. Agenda Introduction Introduction Data Warehouse Benefit Realisation Research Data Warehouse Benefit Realisation Research Key Learnings from that Research Key Learnings from that Research Real Life Examples of Analytical Based Work Processes Real Life Examples of Analytical Based Work Processes  Automated KPIs in a Retail environment  Plan Optimisation application for a Telco  Intelligent Invoice Analysis in an Airline Summary Summary

14. Automated KPI System Large hardware retail chain Large hardware retail chain Major business improvement program Major business improvement program 12 Store Level KPIs identified 12 Store Level KPIs identified  Year on Year Revenues  Gross Margins  Stock Turnover  Out of Stocks  …

15. Automated KPI System Calculated for each branch on a monthly basis Calculated for each branch on a monthly basis Results made available online via intranet Results made available online via intranet Posters printed and displayed in each branch Posters printed and displayed in each branch Staff were paid bonuses on achievement of KPIs Staff were paid bonuses on achievement of KPIs

16. Automated KPI System Technologies utilised: Technologies utilised:  Conscious decision to leverage technologies already in use  Data sourced from  OLAP Cubes  Oracle Financials  Manually maintained spreadsheets  Microsoft SQL Server  Microsoft Excel  Microsoft IIS & Active Server Pages

17. Automated KPI System Major Benefits Major Benefits  Production & Distribution of KPIs was entirely automated  System became part of the regular monthly reporting process  Branch management & staff responded to the challenge  Significant cost savings were achieved

18. Agenda Introduction Introduction Data Warehouse Benefit Realisation Research Data Warehouse Benefit Realisation Research Key Learnings from that Research Key Learnings from that Research Real Life Examples of Analytical Based Work Processes Real Life Examples of Analytical Based Work Processes  Automated KPIs in a Retail environment  Plan Optimisation application for a Telco  Intelligent Invoice Analysis in an Airline Summary Summary

19. Plan Optimiser Mobile Telecommunications Operator Mobile Telecommunications Operator Committed to launching a new plan Committed to launching a new plan Designed to increase revenue by increasing the footprint within the customer’s organisation Designed to increase revenue by increasing the footprint within the customer’s organisation But unsure of how to accurately determine which existing customers to target But unsure of how to accurately determine which existing customers to target

20. Plan Optimiser Challenge involved: Challenge involved:  Analysing 3 months call usage data for each customer  Approximately 300,000 customers in the target segment  Executing within very tight timeframe All customers were categorised: All customers were categorised:  No change  Or recommended one of 3 variations of the plan (depending on usage)

21. Plan Optimiser Technologies utilised: Technologies utilised:  No choice but to leverage technologies already in use  Data sourced from  EDW – Customer call data  Redbrick Data Warehouse  Hyperion Reporting  Javascript

22. Plan Optimiser Major Benefits Major Benefits  Entire target base analysed prior to launch  Proactively communicated to entire base exactly which plan suited them best  Low cost but effective solution  Ensured call centre was not swamped on launch date  Prevented customers being sold the wrong plan

23. Agenda Introduction Introduction Data Warehouse Benefit Realisation Research Data Warehouse Benefit Realisation Research Key Learnings from that Research Key Learnings from that Research Real Life Examples of Analytical Based Work Processes Real Life Examples of Analytical Based Work Processes  Automated KPIs in a Retail environment  Plan Optimisation application for a Telco  Intelligent Invoice Analysis in an Airline Summary Summary

24. Intelligent Invoice Analysis New Zealand Airline New Zealand Airline Low cost enhancement to the EDW Low cost enhancement to the EDW Automated verification of Frequent Flyer Partner airline’s invoices Automated verification of Frequent Flyer Partner airline’s invoices

25. Intelligent Invoice Analysis Involved automating a series of simple checks Involved automating a series of simple checks  Was the Frequent Flyer one of ours?  Did the airline fly those legs?  Did the airline fly those legs on those dates?  … All invoice lines were categorised: All invoice lines were categorised:  OK  Suspect  Reject

26. Intelligent Invoice Analysis Technologies utilised: Technologies utilised:  Conscious decision to leverage technologies already in use  Data sourced from  EDW – Loyalty data  EDW – Flight Schedule data  Loyalty Partner’s Invoice Detail files  Oracle  Informatica ETL  Hyperion Reporting

27. Intelligent Invoice Analysis Major Benefits Major Benefits  Verification of Partner Invoices was almost entirely automated  System became part of the regular monthly Partner Payments process  Quality of Data from Partners improved markedly  Payback in 3-4 months of operation

28. Agenda Introduction Introduction Data Warehouse Benefit Realisation Research Data Warehouse Benefit Realisation Research Key Learnings from that Research Key Learnings from that Research Real Life Examples of Analytical Based Work Processes Real Life Examples of Analytical Based Work Processes  Automated KPIs in a Retail environment  Intelligent Invoice Analysis in an Airline  Plan Optimisation application for a Telco Summary Summary

29. Summary Supporting the Business Process Supporting the Business Process  “The more that the use of the data warehouse can be embedded into the business process being supported, the more likely it is that benefits will be realised”

30. Summary Supporting the Business Process Supporting the Business Process  “Operational business processes tend to realise the benefits of data warehousing more readily and more quickly than tactical or strategic business processes.”

31. Summary Doesn’t necessarily require new tools or technologies Doesn’t necessarily require new tools or technologies People and process are more important than the technology People and process are more important than the technology Focus on increasing use of the data Focus on increasing use of the data