The future was anticipated with digital transformation, making reality what only seen as fiction. At all times people produce data, using social networks, communication platforms or enterprise applications. They are mountains of data from the most varied origins and in different formats.
It is humanly impossible to treat them without the help of technology. Thus, there is enormous potential in support systems capable of dealing with this avalanche of data, unfolding it in patterns, generating information or merely providing feedback for that same system.
The predictive models of big data have worked wonders in the fields of marketing or industrial maintenance, among others. The next step is not only to predict the future but also to submit proposals for the decision to the man.
Big data, internet of things (IoT), data science, machine learning, artificial intelligence … buzzwords follow one another without one knowing what these terms cover, causing confusion. In fact, these different domains intervene at several stages of the data processing chain.
Until then, companies used to manage their own data, structured data, mastered and in a known format. With big data, they open up to new sources, whether information flows generated by connected objects, commentaries left on social networks, browsing on the web or the open. Data. These exogenous data, sometimes unstructured, enrich the existing information assets.
Make The Data Speak More Value
Big data platforms make it possible to collect and make intelligible these data to exotic formats. “Scalable,” they can absorb large volumes of data without knowing in advance their evolution and the value that can be drawn from it. This search for value is the role of the data scientist. He will make the data “speak” by building predictive models able to respond to business challenges.
Take the example of the predictive maintenance of a connected car. Thermal or sensory sensors will return a set of metrics on engine temperature, oil level, tire wear, and other variables. The big data platform collects and structures these raw data. Companies will need to manage massive amounts of data with JD Edwards apps to manage and process massive amounts of data to improve the overall performance of a company’s workflow.
The model designed by the “data scientist” can then apply and deliver its prediction: the risk of failure in the next seven days is 93%. Alert, the dealer, takes the decision or not to contact the owner of the vehicle. Artificial intelligence will automatically trigger the intervention and even guide the mechanic in the repair actions to be performed.
Go Beyond The Basic Forecast
It is no longer a question of delivering only indicators but a proposal for a decision. This shift from the predictive model to the authoritarian model applies to a large number of use cases.
Finally, there is the question of respect for privacy. When anonymization is no longer sufficient, one solution consists in deporting the calculations within the equipment without the information being stored, we then speak of embedded intelligence. New generations of radars will be able to analyze the video in real time to report traffic violations on the road.
