Predictive analytics is a branch of analytics that identifies the likelihood of future outcomes based on historical data. The goal is to provide the best assessment of what will happen in the future. Basically, predictive analytics answers the question “What will happen?” The value of predictive 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 predictive analytics model is always a challenge during implementation. Here are 10 key tips to improve the reliability of predictive analytics models.

1. Define a strong business case with the nine-blocker – three business levers (revenue, cost, and risk) and three data levers (operations, compliance, and performance management). Accept the fact that predictive analytics is about probabilities and not absolute certainties. A 100% accurate predictive analytics model doesn’t exist.
2. Formulate a practical predictive analytics model with the right dependent and independent variables. Apply domain expertise to incorporate the confound and the controlled variables.
3. Manage assumptions in the model as assumptions play a critical role in ensuring the reliability of the predictive analytics models. For example, the data used in the model is based 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 used to drive the model is assumed to be representative of the population free from significant errors and biases.
4. Have quality data from stable business processes on the dependent and independent variables in the predictive analytic model. Remember, data comes from the business process. If the process is bad or unstable, or if the people who run the process are untrained, then the data quality will also be bad. Also, once the data is collected, prepare the data by removing anomalies and duplicates, update missing data values, or any other measurement errors, that impact the quality of data.
5. Do not predict for a long-time horizon; keep the time horizon for six to 18 months. The “cone of uncertainty” is a concept used to describe the increasing uncertainty in predicting the future as we move further away from the present moment. 
6. Leverage the most granular data for training and testing the predictive analytics algorithm. Use aggregate data for managing the outputs, as aggregate data will follow the law of averages and the outcomes will “even out” or balance any deviation from a presumed average.
7. Don’t rely on just one algorithm for prediction. Use multiple algorithms to solve the problem. Use the concept of Ensemble models. Ensemble models combine multiple individual algorithms such as regression, decision trees, neural networks, support vector machines (SVM), and so on to create a more powerful and accurate predictive model that can handle the complexities of real-world environment/data.
8. Apply data splitting and cross-validation techniques to train and test the predictive analytics model. Split the available data into training, validation, and test sets to properly assess model performance before deployment. Also, take care of overfitting and underfitting for having the right statistical fit in the model by applying concepts such as multicollinearity, principal component analysis (PCA), and more.
9. Constantly assess and validate the model performance for any model and data drifts using KPIs like MAE, MSE, RMSE, P-value, precision, recall, F1-score, ROC-AUC, etc.
10. Last but not least, (predictive) data and analytics is not a substitute or a replacement for your common sense and business knowledge. You need both experience and data to make good predictions and decisions. 

Overall, predictive analytics makes predictions about future outcomes and then uses those predictions to improve decision-making. No matter the industry or the business function, predictive analytics can provide the insights needed to better plan and respond. It could be predicting the asset failure for an oil and gas firm or detecting frauds in a bank or predicting the revenue in retail stores. Predictive analytics models can proactively anticipate business outcomes to better plan and respond and ultimately improve business performance.