When building models we are often confronted with assumptions that evolve over time. In most cases it is important to capture these changes to keep our model relevant. Over the last decade, Business Intelligence solutions have created a culture of self-service IS information.  Given this democratization and decentralized access to data has created many opportunities and pitfalls for business analysts and decision-makers. We are going to outline some opportunities and pitfalls relating to shared modeling and a few strategies to get started.

This post presents the opportunities and challenges stemming from moving towards a distributed modeling paradigm in the organization. Also presented is a high-level integrated predictive/collaborative planning process.

Prof Frank H Knight (1921) proposed that "risk" is randomness with knowable probabilities, and "uncertainty" is randomness with unknowable probabilities. However, risk and uncertainty both share features with randomness. The illustration below explains the relationship of the concepts better than words...

Source: Knight, F H (2002/1921), Risk, Uncertainty and Profit, Washington, DC: BeardBooks.


 A brief review of Oracle and Microsoft database/application integration with Excel and Crystal Ball for comprehensive modeling

 Quick overview of the parts of an optimization model in MS Excel.

When using tools such as Excel, Crystal Ball or ModelRisk, it is very important to be able to translate a mental model to a mathematical one. Let me illustrate, when you think about your business, you often will think of abstract notions such as profit or margins. These are mental constructs because their are no physical representations of profit or margins (except a pile of cash) only mathematical ones.