Predictive analytics is used to predict a future state or outcome by applying different statistical techniques on data. Examples of the outcomes of applying predictive analytics are predictions of demand, consumer behaviour and machine maintenance needs.
Machine learning is a one of the techniques used for predicitive analytics. The advantage of machine learning is the capability to identify causal relationships in large, sometimes unstructured, data sets without the need to be explicitly programmed to detect these patterns. Other statistical methods such as regression analysis, time series and clustering analysis are more traditional techniques but proven to be powerful. Machine learning combined with traditional statistical methods is a robust basis to make forecasts and predictions in a variety of industries, provided these techniques are applied correctly and high quality data or data sources are used. Predictive analytics may turn previous unused datasets into valuable business drivers.
Application
Examples
Demand
forecasting
Predict demand based on
historical data, adjust.
Hiring Need
Determine hiring need based on predictive models. Both for short term workforce scheduling and for planning longer term hiring campaigns.
Insurance and
banking fraud
Use Machine Learning algorithms trained to identify fraud and exceptions.
Cross selling
Identify cross sell potential and hidden customer needs.
Maintenance avoidance
Train algorithms to detect machine or equipment breakdowns before they actually occur.
Consumer Behaviour
Predict and understand buying patterns and propensity to buy and create in real time personalized offerings.
Risk
Identify risks based on historical data and real time data to prevent defaults or to determine eligibility.
Demand
forecasting
Predict demand based on
historical data, adjust.
Hiring Need
Determine hiring need based on predictive models. Both for short term workforce scheduling and for planning longer term hiring campaigns.
Insurance and
banking fraud
Use Machine Learning algorithms trained to identify fraud and exceptions.
Cross selling
Identify cross sell potential and hidden customer needs.
Risk
Identify risks based on historical data and real time data to prevent defaults or to determine eligibility.
Consumer Behaviour
Predict and understand buying patterns and propensity to buy and create in real time personalized offerings.
Maintenance avoidance
Train algorithms to detect machine or equipment breakdowns before they actually occur.
Decide4AI
Implementing predictive analytics requires a disciplined and structured approach. Decide4AI
supports organisations in all phases of the process.
Defining business aligned goals
and KPI’s
Data analysis and preparation
Selecting, testing, training and deploying the right techniques
Defining and implementing the right IT architecture
Implementing data management and governance strategies
Defining business aligned goals
and KPI’s
Data analysis and preparation
Selecting, testing, training and deploying the right techniques
Defining and implementing the right IT architecture
