Predictive analytics is used to predict a future state or outcome by applying different statistical techniques to data. Some examples of results of the application of predictive analytics are: predictions of demand, consumer behaviour or machine maintenance needs.
Among the analytical techniques used for predictive analysis we find the famous Machine Learning. The advantage of Machine Learning is its ability to identify causal relationships in large, sometimes unstructured, data sets without the need for programming to detect these patterns. Other statistical methods used for predictive analytics, such as regression analysis, time series and cluster analysis, are more traditional but proven techniques.
Machine Learning combined with traditional statistical methods form a solid basis for forecasting and prediction in various sectors, provided these techniques are applied correctly and high-quality data or data sources are used. Predictive analytics can turn data sets into a great source of value for companies.
Examples of the application of predictive analytics
Now let’s look at some examples of the application of Predictive Analytics and Machine Learning:
- Demand forecasting: predicting demand based on historical data.
- Recruitment need: determine future recruitment need based on predictive models.
- Banking and insurance fraud: use machine learning algorithms to identify fraud and exceptions.
- Cross-selling: identify the cross-selling potential and needs of each customer.
- Predictive maintenance: train algorithms to detect machine or equipment breakdowns before they occur.
- Consumer behaviour: predict and understand buying patterns and propensity, and create real-time personalised offers.
- Risk: identify risks based on historical and real-time data to avoid defaults or determine eligibility.
Implementation of Predictive Analytics and Machine Learning
The implementation of a Predictive Analytics system requires a disciplined and structured approach. The phases for a good implementation of the system are:
- Definition of business objectives and KPI’s analysis and preparation.
- Data analysis and preparation.
- Selection, testing, training and deployment of the chosen analytical techniques.
- Definition and implementation of the correct IT architecture.
- Implementation of data management and data governance strategies.
At decide4AI we are experts in the development and implementation of Predictive Analytics and Machine Learning techniques. For over 12 years we have been supporting companies in their Digital Transformation processes and helping them in all phases of the process of strategy, definition and implementation of intelligent solutions.
Are you interested in implementing a Predictive Analytics or Machine Learning model in your organization?
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