Business Intelligence: Five Reasons Your Project Estimates Could Be Inaccurate
A perspective from Mohit Sahgal.
As IT budgets dwindle and cost of poor quality concerns rise, the need for accurate work effort estimation has never been more important. With an unglamorous history, Business Intelligence (BI) projects in particular have come under increased scrutiny. There is little tolerance for initiatives that promise much, but deliver well-short of expectations. Today, businesses demand more visibility, transparency and accountability in work effort sizing, solution architecting and delivery.
Accurate estimates – demonstrated by on-time, on-budget delivery – build confidence that other projects, similar in size and complexity, will also be successful. Inaccurate estimates are quickly becoming malpractice in some organizations, wiping-out any chance of future funding from demanding, wary and increasingly cost-conscious executive sponsors.
What are the issues? Reasons for inaccurate estimates fall into one or more of the following categories:
- Generalization, for example “90-day” or “9-month” delivery cycles. These unreliable “predictions” ignore the real factors driving the work effort, staffing, task duration and costing. More challenging is guessing without accounting for assumptions and risks. Even when estimation is difficult, guessing should be avoided.
- Misalignment of the estimating approach with the delivery methodology; that is, sizing the work effort which does not adequately reflect the work required.
- Inappropriate estimating factors sizing the work effort. Hurdles include too many factors, too few factors, incorrect factors, non-existent factors or the tasks themselves are inappropriate.
- Limited use of calibrated estimating models. Unlike Function Point counts and LOC (lines of code) estimation, there are few published standards which account for the myriad of BI design patterns and technologies. Thus, calibration is problematic and the results cannot easily be corroborated with similar project experiences.
- Limited or no estimation competency. Organizations do not inherently spend on estimation improvements or recognize the need for experienced estimation resources.
How can organizations improve? According to Steve McConnell, a recognized software estimation authority, “deceptively simple practices produce surprisingly accurate results”. At Paradigm, we have seen similar approaches work for our clients; by following several practical considerations, your organization will improve the accuracy of project estimates.
Over the next few weeks I will highlight how organizations can improve BI estimation accuracy.
Mr. Mohit Sahgal is the VP of Analytics of Paradigm Technology. He is an accomplished senior executive with decades of management consulting expertise. Mohit has held various leadership positions including Senior Executive at Accenture, Partner at IBM, Partner at Capco, and Executive Director at Ernst & Young LLP.
Mr. Sahgal LinkedIn profile can be found here: www.linkedin.com/in/mohit-sahgal
Paradigm Technology is a strategic consulting company serving the banking, airline, manufacturing, high-tech and retail marketplaces. We utilize innovative business and technology solutions to help clients enable their digital transformation programs, and improve their Analytics, Cloud, Master Data Management, and Project Leadership solution delivery. Paradigm is ready to support you in your organizations journey. For more information about Paradigm Technology, email email@example.com or visit us at www.pt-corp.com.
 McConnell, Steve, 2006, “Software Estimation: Demystifying the Black Art”, Microsoft Press.