Prescriptive Analytics

When knowing what will probably happen, a next logical step is to define a set of actions and analyse the probability that an action will result in the required effect. The predictive analysis is extended with a means of comparing the consequences of actions based on a predictive result.

If the best action is automatically invoked, e.g. by using an event processing environment, your company has reached a dynamic adaptation level without human interference.
Your organization is really becoming agile when this automated Prescriptive Analytics level is reached.

Monitoring the actions is needed to determine if the models in use are still valid or should be fine tuned or altered.
With our data scientists we will help you to design the statistical models and will monitor and improve these models during use.

If actions are automatically invoked, e.g. by using an event processing environment, your company has reached a dynamic adaptation level without human interference.

Your organization is really becoming agile when this automated Prescriptive Analytics level is reached.

Data Sources

To be able to predict, the scope of data sources, should be a subject of investigation. Do we have all the required data? Could we use data from external sources and in real-time?Using moving data in streaming analytics will open all kind of new solutions.

Prescriptive analytics will almost always be followed by an automatically initiated follow up. Based on the probability of the predicted outcome of actions the best action is automatically executed.

Models and Monitoring

Predictive models have been defined in a cooperation between business analysts and data scientist. You need to understand the business to be able to collect the right data and define models using this data.Prescriptive models will give feedback on pre-defined actions and calculate the probability that a specific effect will be realised. This will be the input for the decision makers.

Our Analytics architects will organize a monitoring loop in order to fine tune and adapt the prescriptive models.

From Predictive to Prescriptive Analytics

The combination of predictive and prescriptive analytics will help you achieve both efficiency and effectiveness. For example, predictive analytics will help you understand the drivers behind customer buying patterns to anticipate the products customers want. Prescriptive analytics will help you optimize scheduling, production, inventory and supply chain design to deliver what they want in the most optimized way.

From Prescriptive to Streaming Analytics

With prescriptive analytics next actions are evaluated and best action will probably be executed.It makes no sense to automatically execute the best action if there is no need for urgence. Urgence originates when moving data needs to be analyzed in real-time and used in the decision making. Moving data in streaming analytics will open all kind of new solutions. The location of a customer in combination with historical behavior and store inventories are input for promoting specific articles.

Know your customer and act within seconds!

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