Ronald Rodríguez, Senior Software Engineer at Prodigious, a global production platform of the Publicis Groupe, explains that predictive models are based on the analysis of historical data to make predictions of behavior in the future. Within organizations, predictive analytics is used to support decision making.
More and more companies from different economic sectors deeply understand the need to implement good practices and tools for data analysis as part of Digital Transformation processes. In recent months, consumers’ behavior has changed and part of their purchasing decisions are made taking into account the information acquired in ‘an immediate moment’, and even without taking into account the past.
For the adoption of practices and tools, a recognition of the needs and level of data management maturity of each organization is carried out. Today, one of the data sources with the greatest growth in analysis is that of the digital market; Basically, all the information collected about the behavior of a user on a website, become inputs that help to better understand the needs of the consumer and also for companies to reorganize the website in a more dynamic way. These inputs in turn are used for various tasks within the business, one of these is predictive analysis.
Predictive analytics is developed using statistical collection, preparation and evaluation techniques. To this process, Machine Learning (ML) and Artificial Intelligence (AI) techniques are added to generate value from the data. While it is common to wait for assessment documents to consult, there are more and more software applications that are geared towards delivering predictive models to solve various demand response needs.
The application of predictive models is also benefiting the oil industry. In this case, the companies, through sensors located in the machinery and in the terminals, collect amounts of information that allow establishing the opportune moment to carry out preventive maintenance on the equipment. Here the data analysis becomes relevant because maintenance carried out on time prevents the machine from failing, a fact that generates economic savings for companies compared to having production stopped for hours.
Regardless of the context in which these predictive models are applied, it must be clear what you want to forecast and the information that is required for this. Recently, events such as the pandemic have dealt a heavy blow to predictive models. Before the coronavirus, the collection of historical information was essential, after this event consumption habits changed and vary daily, changing the conception of the value of data.
When determining which data is relevant or not for these models, it is important to establish those qualities focused on the success of a business. For example, understanding the facts that achieved the conversion of a user, why a sale was achieved, factors around why the user finally clicked on a button, elements associated with user behavior (in cases like this, the historical data collection).
When mentioning what is the perception of organizations in front of these predictive models, we find that at the beginning, each company has created its hypothesis of why something is successful. However, when companies, like Prodigious, show that from real data that first hypothesis can be validated or refuted, the perception about the importance of making decisions based on data analysis changes completely, showing that high impact factors can generate changes in the user experience.
Although at first there is some resistance, later, depending on the results, the perception changes. There is also some skepticism when seeing things that will be disruptive to the business. The recommendation is not to start with such drastic changes.
From the point of view of the public sector, it is necessary that governments begin to base their decisions on these predictive models, in order to give greater confidence to the people and react in time to possible problems. This type of model works well when properly implemented. In addition, there needs to be a culture of ownership of change to carry it out.
