How the Big data is changing from the predictive model to the prescriptive
The past years, Big Data has grown considerably in volume. A fact literally due to the growth of connected devices owned by individuals. A real headache for companies that must learn to analyze this data whose optimization is now turning to the prescriptive model, a new advance in the commercial opportunities of companies.
If Big Data involves many challenges in terms of technical treatment because of the multiplication of sources, it is also a gold mine for companies which want to take decisions related to the trends in their own markets. Indeed, the analysis of data can constitute a strategic help in the decision-making of the companies.
A complex analysis model based on machine learning
Nevertheless, companies have first to master the use of specific technologies in order to exploit this massive information. Those tools make it possible to collect a large amount of data so that it turns into something intelligible. Then, the data scientists can work on it to get a good value of these data. To develop powerful models, the data scientists use the machine learning technologies which make simulations on developed algorithms to which are added diverse settings.
After the data analysis, the data scientist show it in a way which facilitates its comprehension and exploitation for all the services of the company in order to take the most relevant strategic decision.
An evolution of more exhaustive analysis
At first, data analyzes started with descriptive models. This data analysis model is used to produce business reports to better understandable and past facts. Then came the predictive model which help to explain what will happen in the future. To make it possible, this model mix the past data with predictive algorithms based on the marching learning. For instance, this model has often been used to measure the probability that a person buys a certain product. Nevertheless, it seems debatable that this model has a real utility in this case for a business company.
Indeed, the goal of a sales team is to encourage a customer to buy his product through convincing marketing actions. A sales team knowing that a customer is already ready to buy a product from the company does not require customer acquisition work. The main concern of a sales team is rather to predict whether a marketing action soliciting a customer is relevant or not. Being able to predict if a solicitation would make this analysis more valuable for a company which wish to make the difference. A distinction that leads to a data analysis turned to the prescriptive.
Because of the computing power of Big Data platform and artificial intelligence, users are able to go beyond mere prediction to make recommendations. For instance, programs dedicated to public transport are no longer limited today to calculating an approximate travel time, but directly suggests the shortest route. Applying this prescriptive model to the B to B is an opportunity for sales teams to customize their offers to their customers and prospects. Indeed, we can imagine in the near future that the prescriptive algorithms can support business teams and optimize marketing campaigns for all products of a brand. The previous case of a sales team hesitating if the solicitation of customers would be relevant is the perfect example in which the prescriptive analysis could help this arbitration. A model going beyond the future which risks to open up deeper debates on the free will of individuals.