Systematic Preventionof. Business Intelligence. Project Failures. Dr. Ilias Ortega Senior Consultant. TDWI Conference Munich June 17 to
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1 Systematic Preventionof Business Intelligence Project Failures Dr. Ilias Ortega Senior Consultant Business Intelligence TDWI Conference Munich June 17 to BASEL BERN LAUSANNE ZÜRICH DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. HAMBURG MÜNCHEN STUTTGART WIEN
2 Contents 1. Why Investigate Business Intelligence Project Failures? 2. Consequences of BI and IT Project Failures 3. Integrated Framework for Quality Improvement in BI Projects 4. Statistical Analysis of BI Project Failures 5. Case Study Research of BI Project Failures 6. Case Study: Multi-Million-Dollar Enterprise Data Warehouse 7. Methodological Analysis 8. Summary 2
3 Why Investigate BI Project Failures? Business Intelligence (BI) project failures point to problems in theory and practice Therefore, they are useful for identifying areas where improvement measures are required Each BI project failure provides information that may be used to prevent similar failures Therefore, the systematic investigation of BI project failures should be encouraged These investigations identify errors to be avoided, and thus contribute to increase the quality of future BI projects Each BI project failure points to a gap either in theory or practice and thereby fosters innovations 3
4 Consequences of BI and IT Project Failures Business Intelligence (BI) projects fail at an alarmingly high rate: according to IT market research firm Gartner, between 70 and 80% of BI projects fail. According to research carried out in 2012 on IT projects by McKinsey and the BT Centre for Major Programme Management at the University of Oxford, IT projects with budgets > 15 million in 2010 USD had: 66% average cost overrun 33% average schedule overrun 17% average benefits shortfall 4
5 Integrated Framework for Quality Improvement in BI Projects 1. Problem Diagnosis 1.a Statistical Analysis 2. Problem Selection 1.b Case Study Research 3. Objectives 5. Methodological Analysis 4. Solutions 6. Action Plan 7. Verification of Action Plan 9. Change Management 8. Implementation 10. Control Results 5
6 Statistical Analysis of BI Project Failures Purpose: Identify areas where improvements are required and discover patterns (what, when, and where) in BI project failures Shortcomings: Large data samples, biased data, and data accessibility 6
7 Pareto Chart of IT Project Failures 70% 60% 50% 40% 30% 20% 10% 0% Unmet schedules Budget overruns Higher-thanexpected maintenance costs Percentual share of IT project failures Insufficient business value and ROI Dynamic Markets survey of 800 IT managers 7
8 Recursive Pareto Analyses to Reach the desired Degree of Detail... Recursion 1 Recursion 2 Recursion 3 8
9 Ishikawa Diagram for BI Project Failures Requirements Planning and Control Documentation Factors contributing to BI project failures Communication Design Changes Stakeholder Commitment 9
10 Case Study Research of BI Project Failures Purpose: Answer qualitative questions and determine contextual effects concerning the why of BI project failures Shortcomings: Non-representative samples, biased data, costs of analysis, and data accessibility 10
11 Case Study: Multi-Million-Dollar Enterprise Data Warehouse Major problems: Reduction of project scope Delays and missed deadlines Insufficient acceptance from business departments Lack of management support 11
12 Case Study: Multi-Million-Dollar Enterprise Data Warehouse Problem causes (1/2): Incomplete, unclear, and not sufficiently detailed requirements Lack of planning and control Overly optimistic planning Lack of adequate documentation, e.g. no document available giving a reliable overview of project progress Design changes leading to costly implementation changes Insufficient communication between project team and business departments Insufficient communication between project management and team members. Team members lacking a good overview of the project tasks 12
13 Case Study: Multi-Million-Dollar Enterprise Data Warehouse Problem causes (2/2): Insufficient communication between project team members, e.g. design changes communicated late to developers Low commitment from project stakeholders to project success due to absence of binding requirements Lack of team cohesion due to absence of minimal social interaction such as common coffee breaks, lunches, etc. Absence of acceptance procedure due to inadequate requirement documents Personnel issues, e.g. dominant role of an individual team member 13
14 Case Study: Multi-Million-Dollar Enterprise Data Warehouse Recommended solution measures (1/2): Detailed requirements Pragmatic planning Continuous control Manageable project scope Milestones Concise documentation Ongoing communication Forceful change request management Comprehensive test management 14
15 Case Study: Multi-Million-Dollar Enterprise Data Warehouse Recommended solution measures (2/2): Value-oriented introduction Stepwise acceptance Knowledge exchange Flexibility Prototypes and pilot projects Reviews Monitoring 15
16 Methodological Analysis Purpose: Transfer effective safety management methods from riskconscious industrial sectors to BI projects Example: Incident Reporting Systems in aviation, spacetravel, surgery, etc. 16
17 Incident Definition: any situation in which an ongoing sequence of events was prevented from developing further and hence preventing the occurrence of potentially serious (safety related) consequences. 17
18 "Iceberg"-Models: Incidents occur more often than Accidents Accident Incident Exceedence Normal Operations 18
19 Benefits of Incident Reporting Systems Large data samples Economical analysis Frequent simultaneous identification of problems and their solutions 19
20 Summary An integrated framework for quality improvementin BI projects has been proposed. The framework implies that: Improvement of the quality of BI projects is a collaborative, organized effort At least three systematic approaches (statistical analysis, case study research, and methodological analysis) lead to improved quality in BI projects 20
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Appendix F Tri-Borough Business Intelligence Pilot Service Delivery Principles Contents Introduction context, about the Principles, using the Principles Service Delivery Principles: 1. Statements of intent