14 Feb 2022

AI-based analytics

The artificial intelligence It is the combination of computer science with mathematical and statistical models to develop complex algorithms that aim to simulate human intelligence.. To program this artificial intelligence, we teach you how to act in front of all the possible scenarios in which you can find yourself. In the same way that we learn and evolve over time, artificial intelligence is analyzing patterns and learning and improving its behavior.

Analysis types

Implementing AI-based analytics in our company allows us to analyze large amounts of data at high speed and with very little margin for error.. AI-based analytics, o business analytics, consists of transforming data into information in order to perform the following types of analysis.

  • descriptive analysis: analysis of what happened. With data aggregation methods (data aggregation) and data mining (data mining) find patterns and trends. An example is the typical annual report of expenses and profits in a company.
  • diagnostic analysis: analysis of what has caused a certain event. Taking into account the reasons that have caused a certain event in the past, makes you an analysis of what reasons have caused the new event to be analyzed.
  • predictive analytics: probability-based analysis of what may happen in the future. To do it, uses machine learning techniques to collect data and tries to fill the data we don't yet have with its best prediction. It also uses the previously seen data mining method together with statistical models. This analysis can detect opportunities to grow the company or anticipate problems.
  • prescriptive analytics: This analysis goes one step further than the previous one.. Instead of showing us the data you have obtained and analyzed, tells us what is the best action to take against the problem encountered. Combine the two previous analyzes and analyze what would happen if we make certain decisions or others in the face of a problem. Taking into account the objectives and limitations of the company, informs you of the possible repercussions of each action to consider.

Barriers to Implementation

To implement an analytics based on AI we must take some factors into consideration. A virtual intelligence machine works with a high volume of data constantly captured and processed, concept known as big data. This data comes from many different sources and from all areas of the business., so we need to make sure we have a good filtering system and data storage to avoid inconsistency errors. Also, We must ensure that we are complying with the data protection policies in force today.

On the other hand, it is also important to work with the right resources. You need one infrastructure large and secure enough to store your data, like the cloud. And finally, a skilled labor for the task is essential. Skills from many fields are needed, as statistics, programming languages, etc. and few professionals are able to master them all.

Today's networks are complex, scalable and dynamic, translating into a greater number of problems and alerts. And the volume of data to be processed is increasing. AI-based analytics will greatly help network teams, and yet today only a quarter of businesses are implementing it. Cisco's AI-powered analytics app (integrated in Cisco DNA Center) reduces “events” or problems in a 99%.

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