What it is and why it matters
Predictive analytics combines techniques from statistics, data mining and machine learning to find meaning from large amounts of data and help you foretell the future. Whether you’re in marketing, compliance, customer service, operations or any other business unit, your data can show where you are – and predict where you’re going.
How do organizations approach predictive analytics? The key stages for an analytical life cycle include:
- Analytical data preparation – Access, aggregate, clean and prepare your data for optimal results.
- Visualization and exploration – Explore all data to identify relevant variables, trends and relationships.
- Statistical analysis – Use everything from simple descriptive statistics to complex Bayesian analysis to quantify uncertainty, make inferences and drive decisions.
- Predictive modeling – Build the predictive model using statistical, data mining or text mining algorithms, including the critical capability of transforming and selecting key variables.
- Model deployment – Apply the new champion model, once validated and approved, to new data.
- Model management and monitoring – Continously examine model performance to make sure it's up-to-date and delivering valid results.