Achieving advanced analytics benefits, fast
To articulate the meaning of advanced analytics, you need to understand these two areas:
- Predictive analytics: With Predictive analytics we can address questions that will help us to forecast what is going to happen or more accurately, what’s more likely to happen. We can create forecasts and scenarios based on machine learning models that will help us to determine what’s the likelihood of a new product to be sold, predict which suppliers are more trusted to deliver in time and which clients might not pay in time to have a better cash flow forecast.
- Prescriptive analytics: With Prescriptive analytics we can address more advanced scenarios and answer the most complex business question: Based on historical and external data sources, probabilities and different scenario analysis what would be the best possible business outcome, and what needs to be done in order to achieve this?
To make this concept more tangible, let’s think of a use case where we need to evaluate what would be the most efficient route for a delivery track. This should take into consideration many different variables, such as track size, fuel price, length, different routes, local or regional restrictions, taxes and the type of the delivery. The question below is a real example that a well-known global retailer discovered using prescriptive analytics:
What is the least profitable shipment in our business? The answer, identified using prescriptive analytics, was when the content of the shipment is containing water bottles.
So that is another example that enables a business the flexibility to avoid scenarios which aren’t beneficial and get suggestions how can they replace it by using different factors.