A Brief History of the Future of Business Intelligence

Business intelligence twenty years. Business Objects software company helped democratize business intelligence in the 1990s – before it was taken over by German software giant SAP in 2007. Since then, many tech players have been revamping business intelligence by renaming it Data Discovery (Qliktech), Data Discovery (SAS), Analytics (Tableau, IBM, Oracle), Data Stories (Jolicharts), etc.

However, business intelligence, which is still full of innovation potential, is undergoing profound turmoil in the form we know it. In fact, in the past, it was mainly descriptive: that is, depending on the dataset to which it was attached, business intelligence tools came to show which sample data they were assigned to. logically and syntactically. For main use: preparing reports. However, the huge market (with a scoop of a hundred billion) of meta-analysis is today threatened with obsolescence by a wave of a new kind: that of actors based on the winning trio:

from U.S Basic cloud computing that has become a utility Like electricity or highways, the revolution here is that startups have unlimited computing power from their creation. These startups invent and manipulate huge amounts of data, previously available only to giant corporations that were too petrified to innovate in anything;

– Open data traffic or rather “Third Party Data Disclosure”, which goes beyond Open Gov to reach businesses and even civil society. They are all rediscovering the concept of the ecosystem by providing micro-services that expose data to third parties, for a fee or not: this is the API Economy and Social Networks, or rather GAFA, is keen to play a leading role;

– Artificial intelligencewhich revisits 30-year-old statistical algorithms, such as random tree forests or neural networks, based on the computing capabilities of the now-affordable cloud, but also on popular third-party data, and finally on the Cartesian purpose of surveying the world through analogy, Because by placing sensors everywhere (smartphones, tablets, IoT, …) we allow the machine to learn faster, or constantly improve its relevance.

Hadoop minotaur . has been dismantled

We can say without pretending that assembling this triptych paints what big data is. As we finally crack the Hadoop minotaur, the contenders to reinvent BI were able to rely on the components of the deceased beast to resume the march forward by inventing predictive analysis. The latter consists simply of having the tools to predict what the data will be like in a few seconds, a few minutes, a few hours, a few days, a few months. It’s the legend of the Trojan prince Helena, but in fact: from the intersection of internal signals with past and current external signals, the machine was able to get closer to what the future will be with less risk of error. to be calculated. Predictive analysis succeeds in descriptive analysis by making it a giant step forward!

Let’s get to work: predictive analysis makes it possible for a point-of-sale manager to anticipate the traffic in his store, to adjust for his consultant’s needs; Predictive analysis makes it possible to know how many employees will be on sick leave the next day, by analyzing the date, calendar, data on the spread of epidemics in the regions of interest, etc. ; In short, predictive analysis will soon be ubiquitous, which is a good thing, because it really tickles our intuition, without us being able, by building the laws of nature, to guarantee the result: predictive analysis makes us grow in our decision-making functions.

Descriptive analytics goes beyond predictive analytics

Meta-analytics goes further than predictive analytics: it not only informs the decision-making process, but it works automatically. Thus, a restaurateur whose AI expects a hundred wrappers to see his delivery of fresh produce is automatically adjusted to reduce food waste and thus conserve natural resources at the same time with its operating margins. Or, AI oversees networks of sensors in buildings to automatically control equipment in order to increase user comfort while optimizing energy and fluid expenditure. Meta-analytics is here today: all the ingredients are on the workbench and thousands, millions of organizations write recipes for them every day. Who writes it? These are the data scientists, you know, the ones our companies are obsessed with because they’re better programmers than statisticians, better mathematicians than developers.

Cognitive analysis denotes the direction in which machines and humans interact in symbiosis. To illustrate, let’s take the case of deliveries between an autonomous vehicle and its driver: Research shows that this phase is complex, for example because the driver’s senses and muscles are numb, so returning the loop puts the crew and surrounding vehicles at risk in the event that an emergency maneuver is needed for example. Let’s be clear: We are not yet at cognitive intelligence. Remember, however, that cognitive analysis aims to make a machine able to understand and adapt its behavior – in our case – not only to behavior, but also to human feelings, feelings and emotions. And in France, we have the ecosystem and the economic, political and technological dynamics needed to move the future of business intelligence, i.e. cognitive analysis, from the science fiction novel to the fantastic eight in the evening news report. We did this for descriptive analysis.

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