If a model is to yield a result, it needs data. Data are described by a data model, sometimes very simple (‘year’); sometimes more complex (energy consumption by type of energy, consumer category, geographical area, month, year and other attributes); and sometimes more difficult to ascertain (unstructured text, for example – by keyword?). However, the nature of the data model has an impact on the model itself, including the speed at which results can be calculated and the ease with which it can be modified. Here are some of the more common data modeling traps to avoid.
Data modeling without tears: perils & pitfalls to avoid
Sean Salleh
Sean Salleh is a data scientist with experience in guiding marketing strategy from building marketing mix models, forecasting models, scenario planning models, and algorithms. He is passionate about consumer technologies and resource management. He has master's degrees in Operations Research from University of California Irvine and Mathematics from Northeastern University.
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