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This open access book presents a set of basic techniques for estimating the benefit of IT development projects and portfolios. It also offers methods for monitoring how much of that estimated benefit is being achieved during projects.
Readers can then use these benefit estimates together with cost estimates to create a benefit/cost index to help them decide which functionalities to send into construction and in what order. This allows them to focus on constructing the functionality that offers the best value for money at an early stage.
Although benefits management involves a wide range of activities in addition to estimation and monitoring, the techniques in this book provides a clear guide to achieving what has always been the goal of project and portfolio stakeholders: developing systems that produce as much usefulness and value as possible for the money invested. The techniques can also help deal with vicarious motives and obstacles that prevent this happening. The book equips readers to recognize when a project budget should not be spent in full and resources be allocated elsewhere in a portfolio instead. It also provides development managers and upper management with common ground as a basis for making informed decisions.
List of contents
1 Business Value Disadvantaged.- 2 Benefit Points - An Overview.- 3 Benefit Points for the Project.- The Best Part of the Story.- 4 Benefit Points for the Portfolio.- 5 Earned Business Value Management.- 6 Agile Uncertainty Assessment for Benefit Points and Size Points.- 7 Benefit and Cost Periodized - Stretching Your Points.- 8 Final Remarks.
About the author
JO ERSKINE HANNAY is a senior scientist at the Norwegian Computing Center in Oslo, an associate professor at the University of Oslo and an adjunct researcher at the EDOS Centre at Simula Metropolitan Center for Digital Engineering. His research interests include benefits management, benefit and cost estimation, expertise and skill acquisition, the effects of human factors on programming, simulation-based training and machine learning. Hannay received a Ph.D. from the Laboratory for Foundations of Computer Science, University of Edinburgh, Scotland.
