Right now, to help with the decision and imagine actions that are supposed to lead to the expected success, data science is on the rise. It would certainly be less relevant without relying on a foundation, that of business intelligence.
One of these data sciences analyzes past information, the other makes predictions for the future. In other words, these two skills are closely linked but certain differences allow us to better understand the role of each of these disciplines.
Definition n ° 1: what is data science?
Futura has already painted a portrait of a fashionable phenomenon: the ” data science », that is to say the ability of an organization to analyze, extract and format quantities of data to stage them in a visual and impactful way. It is about identifying and presenting trends oriented towards the future. A mission led by a data scientist who must develop avenues to answer questions and imagine future hypotheses based on significant data. Data science is known to be reactive and it places itself in anticipation.
In summary, here is the role of the data scientist:
- it processes data from multiple sources at high speed;
- it understands, analyzes and models this data set;
- it also provides decision support tools, for example algorithms which will allow certain tasks to be authorized;
- its role is part of the overall development of the company and it helps it to progress and prosper.
In other words, the indicators created by data science will prove to be invaluable for managers who will rely on these prediction models to anticipate the future. This is particularly relevant in a very competitive business sector where each decision-making is essential for the sustainability of a company or a group.
Definition n ° 2: what is business intelligence?
Moreover, this data science should not be confused with another called “business intelligence” or BI. Here again, it is a question of analyzing the data coming from big data in order to help in the decision. More than profiling the future, the business intelligence rather focuses on the past. It is a kind of historian’s work that allows us to understand the past well by relying on data and to draw up a precise description of the past and the present.
This practice encompasses a large number of tools and methodologies that are sometimes specific to companies.
- It uses data already stored. At this stage of the work, no prediction is made, it is above all the collection and classification of information.
- This involves preparing the data for subsequent analysis by a team of analysts.
Missed opportunities, successful strategies, market trends based on seasonality or a specific commercial technique… All of this information that can emerge from this introspection to make essential decisions.
Business intelligence experts can provide data scientists reports on current trends. How? ‘Or’ What ? By collecting the raw data and classifying it in a structured database otherwise known as a “data warehouse”. Once organized, the data is presented in the form of a synthetic dashboard. It is from this that the expert will be able, for example, to check which projects have been successful and to analyze their data.
This video explains how BI delivers relevant, reliable information to the right people at the right time to make relevant decisions faster. © Hitachi Solutions Canada
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 what already exists to carry out its hypotheses.
So that the staff of the organization can tackle BI independently, there are also solutions, for example Power BI from Microsoft, which can be described as self-service, because you don’t have to be an IT expert to process big data.
These two strategic areas which are useful in many fields of activity such as IT, finance, commerce… The tools offered by data science will give advice to the different hierarchical levels of a company to make the best use of this knowledge. .
Machine learning, at the center of this ecosystem
In both cases, as these available data are based on big data, it is necessary to call more and more on a substantial machinery doped with artificial intelligence (IA) and more precisely to “machine learning”. It is also machine learning that swallows up the data specific to BI so that the AI can automate the analysis and allow data science to develop its scenarios.
Let us take a few moments to define the machine learning, a complex notion that can lead to confusion. To put it simply, this is a technology that learns through experience and not through an immutable computer program. Performance is constantly evolving and each new data enriches this flow. Data mining and statistics allow, among other things, to make predictions or trends (also called insights).
In the finance industry, for example, machine learning can uncover fraudulent transactions or offer tailor-made banking products to users. Another example, in the field of health, this technology offers the possibility of diagnosing certain diseases more quickly by cross-checking similar data.
Article produced in partnership with the DataScientest teams
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