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Integrating Modeling and Analytics into Decision Making

The main objective behind the uses of modeling and analytics is to improve business performance in the chosen domains (sales, marketing, risk, etc.) by making better decisions. Intuitive and experience-based decisions are appropriate in many circumstances, but even in those cases the decisions are more likely to be successful if they are made within an analytical framework to track results and inform future iterations.

For Bridgeforce, analytics are essential to providing consulting services, but we are also very much aware of the limitations. We have seen harm done by decisions that could have been better informed by available data if the right analytical framework had been in place and in some cases, even greater harm done by reliance upon analyses or models that were fundamentally flawed.

Therefore, we see the most effective use of analytics is when it involves both the art and science: the tools generally involve strong science, while figuring out which ones to trust is sometimes an art. Good analytics begin with access to internal and/or external data and ends with applications of findings for business profitability and growth. Intermediate steps (at a high level) consist of collection, cleaning, and archiving of business data; use of statistical tools to analyze the data; and formulation and execution of business strategy.

Download the full white paper Integrating Modeling and Analytics into Decision Making to learn about how true analytics is more than archiving business data and preparing reports and how to steer business through modeling and analytics as well as awareness of their limitations.

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