What is the difference between business intelligence and data science?

At the moment, to help the decision-making process and imagine actions that are supposed to lead to the expected success, data science has the wind in its sails. It would certainly be less relevant without relying on a foundation, that of business intelligence.

data science”, which is the ability of an organization to analyze, extract, and format amounts of data in a visual and impactful way. The aim is to identify and present future-oriented trends. A mission led by a data scientist who has to develop leads to answer questions and imagine future hypotheses based on significant data. Data science is known as reactive and it is placed in anticipation.

Know that we must not confuse this science of data with another called «business intelligence» or BI. Again, this involves analysing data from big data in order to help the decision. More than profiling the future, business intelligence focuses on the past. It is a kind of historian’s work that makes it possible to fully understand the past based on data and to draw up a precise description of the past and the present.

Business intelligence experts can provide data scientists with reports on current trends. How? By collecting the raw data and classifying it in a structured database otherwise known as a data warehouse. Once organized, the data are presented in the form of a synthetic dashboard. It is from this that the expert will be able to, for example, check which projects have been successful and analyze their data.

What is Business Intelligence (BI)?

Business intelligence and data science, hand in hand

And if data science is currently on the rise thanks to its ability to carry out foresight, it loses a lot of relevance if it does not rely on the analyzes delivered by BI.

Because, as Victor Hugo rightly pointed out, if “the future is a door, the past is the key”. In other words, BI must remain more than ever the foundation of data science. The latter can then draw inspiration from the existing to carry out its hypotheses.

In both cases, as this available data is part of big data, it is increasingly necessary to use a consistent machinery doped with artificial intelligence (AI) and more precisely the «machine learning». It is machine learning that fills in the data specific to BI so that AI can automate the analysis and allow data science to develop its scenarios. For the organization’s staff to be able to tackle BI autonomously, there are also solutions, for example Microsoft’s Power BI, which can be described as self-directed.service, because there is no need to be an IT expert to process data from big data.

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