Digital intelligence, to move from data to decision

The set of tools and methodologies that, in combining the collection and harnessing of data with the design and use of models and algorithms, facilitates, enriches and supports decision-making. More generally, digital intelligence (DI) can also refer to:

• data analytics, particularly descriptive, predictive and prescriptive, and to a number of more specialized fields such as data science, business intelligence, artificial intelligence, machine learning and operations research;
• the various technological and scientific approaches that power the transition from sources of data (e.g., smart sensors, platforms reliant on the Internet of Things, data warehouses) to the creation of social or economic value and to the platforms enabling design of novel management methods and business models, such as those that underpin the sharing economy.

In the context of data valorization and transfer, digital intelligence is the vital link in the chain connecting the many empowering digital technologies that generate data and true value creation for organizations.

Taken in isolation, data storage and distribution solutions, and sensors, represent a cost, and it is only by exploiting the data they generate (e.g., to make decisions, or create new products or services) that value can be extracted from them. Using digital data, digital intelligence paves the way for interactions between processes and sources of data, two elements that were previously confined to silos. Where such interactions already exist, DI enables automation or acceleration.

In contrast to more focused areas of study such as artificial intelligence, machine learning, deep learning, operations research, statistics, simulation and the like, DI encompasses them all and globally addresses—beyond silos—the diverse challenges of data valorization and those faced by organizations.

Particularities of DI technologies include the following:

1. An abstract, algorithmic component, which is generally poorly understood by non-experts;
2. Development and creation of value that are very closely linked to the specific application context by a data-model-algorithm combination;
3. A continuous dynamic of system scalability (the data and, therefore, the system are scalable, along with the increasing sophistication of models and algorithms);
4. A different, more complex industrialization path than those involved in process-based systems, whose behaviours are often determinist and predictable.

These particularities contrast strongly with other technology sectors (e.g., pharmaceutics and drug discovery) and, given the critical context in which Québec companies must improve both productivity and export capacity, the leveraging of DI technologies must take this into account.