Prictive analytics is a branch of analytics that identifies the likelihood of future outcomes bas on historical data. The goal is to provide the best assessment of what will happen in the future. Basically, prictive analytics answers the question “What will happen? The value of prictive analytics lies in enabling business enterprises to proactively anticipate business outcomes. Behaviors, and events to better plan and respond. Given its stochastic nature, having a reliable prictive analytics model is always a challenge during implementation. Here are 10 key tips to improve the reliability of prictive analytics models.
Define a strong business case with
The nine-blocker – three business levers (revenue, cost, and risk) and three russia whatsapp number data data levers (operations, compliance, and performance management). Accept the fact that prictive analytics is about probabilities and not absolute certainties. A 100% accurate prictive analytics model doesn’t exist.
Formulate a practical prictive analytics model with the right dependent and independent variables. Apply domain expertise to incorporate the confound and the controll variables.
Remember, data comes from the
Manage assumptions in the model as assumptions play a they can also be used critical role in ensuring the reliability of the prictive analytics models. For example, the data us in the model is bas on the business process, and it assumes the same business process and the conditions will continue to remain in place in the future as well. Also, data us to drive the model is assum atb directory to be representative of the population free from significant errors and biases.
Have quality data from stable business processes on the dependent and independent variables in the prictive analytic model. business process. If the process is bad or unstable, or if the people who run the process are untrain, then the data quality will also be bad. Also, once the data is collect, prepare the data by removing anomalies and duplicates, update missing data values, or any other measurement errors, that impact the quality of data.