With the digitalization of processes and the integration of the IoT, a network operator receives a colossal amount of information to cross-check and analyze. Problem: not all of them are decisive, or even essential. It is therefore essential to be able to sort and analyze data automatically, to keep only those that are essential to understanding the context and making a good decision. To do this, artificial intelligence annotation tool can be a valuable aid in data contextualization.
Why Contextualize Data?
Contextualizing the data means determining whether it is decisive in responding to a specific problem. An essential mission for network managers, who are often confronted with particularly large volumes of data.
Indeed, the data to be contextualized can come from sensors, technical reference or maintenance, and are often stored in a buffer zone, called Data Lake. The challenge is then to effectively process this data to manage network maintenance operations in near real time. To achieve this, we speak of the “3 V” challenge: it is a question of processing the volume, the variety of data, and of optimizing the velocity with which it can be analyzed.
Problem: not only are the volumes to be processed by network managers massive, but the human brain also has its limits, and is only able to integrate a limited number of parameters simultaneously. Hence the interest of involving artificial intelligence (AI) in the sorting and analysis process, thanks to machine learning.
What Is the Role for Artificial Intelligence?
As you will have understood, machine learning methods make it possible to sort or filter the data that is really useful to network managers, and therefore to save precious time. But these must be finely configured.
These methods are most of the time based on so-called “supervised” learning: the artificial intelligence algorithm trains on a set of “labeled” data, that is to say to which we attribute, for example, a variable indicating whether the data is to be transmitted or not. The algorithm will thus be able to modify its behavior to make it coincide with this reference label (or label).
Artificial intelligence therefore makes it possible not only to contextualize the data in a relevant way, but also to mimic the behavior of an expert. This involves enriching the data concerned with any additional sources to facilitate decision-making, in a more precise and rapid manner.
How To Properly Parameter Artificial Intelligence?
The machine builds its learning on a bias introduced by the human into the algorithm. Care must therefore be taken to remain as objective as possible when labeling the data, to prevent the bias from negatively impacting the processing of the data. This objectivity can only be achieved if the algorithm programmer has very good business knowledge.
If we decide, for example, to transform a numerical variable (such as a temperature, a raw number) into a categorical variable whose values are classes (hot, cold, lukewarm), we introduce a bias. The contours of the classes will necessarily be subjective values. It is therefore advisable to leave the value in numerical form to avoid any misinterpretation.
The CRISP-DM methodology is acclaimed in this context. This is based on a cyclical process in several phases:
- understanding of the business, essential for designing the most suitable solution and determining the indicators that will make it possible to verify the contribution of the approach;
- understanding the data available, their life cycle, how they can respond to the problem;
- data cleaning, to correct any inconsistencies and make the dataset consumable by a machine learning algorithm;
- The analysis of the efficiency of the algorithm, thanks to the indicators initially defined. If this is satisfactory, we can then industrialize and deploy the solution more widely.
The strength of this methodology is that the process on which it is based is not strictly directed. We can thus return to the previous step if necessary, which allows flexible and adaptable management of the research process.
Artificial intelligence makes it possible to contextualize the data effectively, provided that it is well trained. Its major asset: Its ability to deal with complex situations with many factors, often difficult to understand for a human. The Data scientist therefore has a key role in this process: he will have to select the additional sources that will make it possible to enrich the data, to ensure a relevant response to the problem.