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Data Analytics vs Business Intelligence: what are the differences?

We often hear about Data Analytics and Business Intelligence (BI), but it's not always easy to really understand what makes them different, is it? These two approaches each have their own role, and knowing how to differentiate between them can really help a business to make better use of its data. So let's take a look at how they complement each other and why it's worth taking a closer look!

Business Intelligence: a global view of the company

The main aim of Business Intelligence is to analyse past data to provide a clear and detailed view of the current state of a business. By structuring and visualising data in the form of reports and dashboards, BI helps decision-makers to monitor performance and analyse historical trends in an accessible way. It answers the 'what' and 'when' questions to optimise day-to-day operations.
For example, a sales company can track its sales by region or by product to adapt its strategy according to the data observed. BI, therefore, is mainly used to monitor key performance indicators and give managers an overview of the business.

Data Analytics: understand, predict and act

Data Analytics goes a step further by analysing data in depth in order to draw out insights and identify trends or underlying relationships. It encompasses several sub-disciplines such as analysis descriptiveanalysis predictive and analysis prescriptive. Unlike BI, which focuses on the past, Data Analytics helps answer the questions "why?" and "what's going to happen?"
For example, a company can use predictive techniques to anticipate future demand and adapt its stocks. This exploratory approach uses advanced modelling and machine learning techniques to identify causes and optimise strategies. In this respect, it is more advanced than BI in its exploration of data.

Key differences between BI and Data Analytics

Although their tools and methods may sometimes overlap, BI and Data Analytics have very distinct objectives:
Objective: BI is designed to provide an overview of the business and is aimed at managers and decision-makers. Data Analytics, on the other hand, aims to uncover detailed insights, and is often used by analysts and data scientists.

Technology: BI uses accessible tools such as Tableau or Power BI for rapid data visualisation, while Data Analytics relies on the same software but also more advanced software (Python, R) and statistical methods for complex analyses.

Complementarity and business applications

Although different, BI and Data Analytics are complementary. By combining the two, a company can gain an overview with BI, then delve deeper into certain aspects with Data Analytics. Data Analytics to better understand the underlying dynamics and anticipate future developments.
In healthcare, for example, BI helps monitor occupancy rates, while Data Analytics can identify risk factors in certain patients. In finance, BI monitors performance, while Data Analytics helps to detect potential fraud. In this way, each area uses these two approaches to optimise its decision-making.

In conclusion

In conclusion, Business Intelligence and Data Analytics are two distinct but complementary approaches. BI offers a global view of past and current data for immediate decisions, while Data Analytics explores in depth to identify insights and predict the future. So if you really want to make the most of your data, combine these two approaches! Together, they can enrich your decisions and take your business to the next level.