Business analysis, coupled with the science of analytics, is an important topic in today’s business environment. The proliferation of data production in every facet of an operation has created massive amounts of information; but what happens after the data is mined and decisions need to be made? That’s where predictive modeling comes in; using analytics to create a statistical model of future behavior. If you build an accurate model of your customer’s behavior, you’ll be able to stay ahead of the curve.
A predictive model is a combination of any number of variables, or predictors, that influence behavior. Because models are data-driven, they can be applied to any operation, industry, or function. Typical predictors in a production environment could be supplier lead times, or a product’s shelf-life. A marketing department might focus on a consumer’s purchase habits, and demographic information like age or gender. The applications and opportunities are endless. Modeling is used in applications for capacity planning, information technology, and disaster recovery planning, among many others. A predictive model can be developed anywhere that data is collected.
The role of the business analyst is to discover and determine the relevant predictors, collect the data, and develop the model. In many cases, this is an ongoing process where the model is updated as new data is collected. For example, a changes in material suppliers could affect production time, requiring model validation. Models can be simple or complex, depending upon the number of predictors and their relationships to each other.
A business analyst, even with a predictive model, is not a fortune teller. Their job is to understand the climate, and tell you what might happen, based on the data and predictors being used.
Remember, running a business successfully does not need to be complicated. Keep it simple!
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