Business Intelligence is a technology that allows a business user to view and analyze data in order to share information with other users. A fairly typical case: the dashboard that the sales manager consults to identify trends in turnover, which products have the best results.
In a very simple way, we can say that Artificial Intelligence is the ability to simulate human intelligence through computer technology. We also speak of augmented intelligence because the objective is more to increase human intelligence than to replace it.
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Traditionally, operational staff such as sales representatives, store managers, operational managers, buyers, production line bosses receive a report or dashboard that allows them to access the results that IT managers or analysts have. prepared for them.
The information contained in these reports has been designed to answer recurring questions. If this operational needs information that is not present in the report or the dashboard, he must request it and wait for the return. Thanks to the natural language interrogation techniques offered by certain BI solutions currently available on the market, it is possible that the department manager directly interrogates the system and quickly obtains the information he is looking for (provided that the data is present upstream).
As a result, it is no longer necessary for the Financial Director to ask the management controller for the trend on a given cost item: he asks the question himself without IT expertise or knowledge of the data model.
Via augmented intelligence, the BI system will apply algorithms on the data. These algorithms will calculate the correlations between indicators and characterize the data. Thanks to this increased intelligence, the system offers the user additional avenues of analysis.
Let us take the example of a user who asks for the performance of a given sector of activity at a given period. The system can, from there, offer him to see the performance of this sector of activity by customer profile, for example.
Augmented intelligence also materializes on suggestions for data representations. This type of graph or visualization will be more or less relevant depending on the request.
Here, we are talking about a “machine learning” learning system. The more the user interacts with the data, the more the system offers relevant correlations.
The simplest example: on a sales trend curve, the system projects the months to come, taking into account the trends of the past.
It is not always possible for a user to query the right dataset to meet their need. The user may need to enrich his dashboard with data that has not been prepared upstream.
A simple example: the BI system is plugged into historical data and the user has the budget data in another file. Augmented intelligence is there to speed up the junction between past data and budget data to calculate budget hit rate faster.
AI in BI simplifies the process of cleaning and preparing data. With automated preparation, you can go from making data available to use it in minutes rather than hours or days.
Gartner has predicted that, by 2021, 75% of pre-made reports will be replaced or enhanced with augmented intelligence. In this global race in the data economy, thanks to the detection of trends and correlations in data and by suggesting additional avenues of analysis, augmented intelligence in BI saves time and increases productivity. and potentially identify problems before they arise.
In conclusion, Augmented Intelligence in a BI solution frees up more time for analysis and reflection, time previously devoted to the preparation of static reports or the implementation of data in the BI solution.
The future of software engineering is Deep Learning, which understands the incredible power of AI in BI. Over the next decade, it will be everywhere, in everything we use. In some ways, that future is already here. AI is now a key differentiator between businesses.
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