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The main differences between BI and Big Data

One of the particularities of the 21st century is the importance given to data analysis, which is at the heart of any company’s management. These are then guided by “data management”, which includes the storage and processing of these data.

As technologies continue to evolve, data flow management is becoming more sophisticated and automated to improve procedures and processing times. All this data is used to govern activities in order to improve the productivity of companies.

The challenge can be more complex than it seems: data must be collected, processed and presented in the right form, to the right recipient, at the right time and on a continuous basis. In this sense, Business Intelligence (BI) and Big Data are essential tools for accessing and analyzing data. Business leaders can then rely on these business analytics technologies to have global insights, build, manage and improve the reliability of their decision-making strategy.

BI and Big Data, essential concepts for any digitalized company

Definition and role of Business Intelligence (BI)

Business Intelligence (BI), also known as “decisional computing”, brings together various processes and tools available to a company. It allows the capture and enhancement of data prior to its processing and distribution.

Their presentation generally takes the form of visual and synoptic dashboards. These dashboards are called “DashBoards” and ensure perfect traceability of data. They also provide an overview of the various processes. BI also includes data mining.

Business Intelligence analyzes and standardizes a considerable amount of data. This process is ultimately used to guide the company’s decision support system, which makes it possible to:

  • Structure operational data;
  • Facilitate the exchange of relevant information;
  • Simplify the application of this information in strategic actions.

Data Business Intelligence is an effective solution to control costs, identify blocking factors and market trends in order to reach set objectives. It is an excellent way to stay ahead of the competition, to optimize operational efficiency and above all to generate future revenues.

Definition and role of Big Data

Literally, Big Data means “massive data” or “megadata”. It includes a significant amount of digital data that a traditional tool could not handle. Big Data includes 3 main categories of data:

  • Structured data;
  • Semi-structured data;
  • Unstructured data.

The profitability of Big Data is largely based on the efficiency of the data analysis. In other words, it’s about a company’s ability to harness Big Data intelligence to derive useful information for its development.

All this can be summarized in a single discipline: “Data Science”. This refers to the use of the “Big Data business” to generate value. This refers to the use of the “Big Data business” to generate value.

data science

Companies are generating, storing and processing more and more data, such as photo and video downloads, click streams on the Internet, comments and messages on social networks, business transactions, customer data, etc. Jobs are becoming specialized in this field and training is becoming necessary.

The more information is collected, the more tedious and costly it becomes. In order to make the most of Big Data, there are “Machine Learning” solutions that promise remarkable results.

Similarities and main differences between the two concepts

Business Intelligence and Big Data have some points in common. They both enable the collection and processing of data, and serve as a basis for business operations and corporate decision-making.

Big Data and BI are also similar in the sense that they create value.

However, there are still differences between these two concepts, whether it is in the way data is processed, data analytics skills, the type of information to be processed or the final objectives.

  • Business intelligence uses descriptive statistics to detect trends and measure various phenomena. It deals with rather superficial questions such as “what” or “where”. Data science intelligence deals with data that is already structured and centralized.
  • In contrast, Big Data technology uses inferential statistics. It helps, in a more explicit way, to ask questions, provide answers and open up perspectives that are sometimes unsuspected. It also seeks to know the why of the how. Data Business analyzes all types of data, whether they are structured or not. This data can also come from different sources.

Complementarity of BI and Big Data

Data Science for complex data and BI for retrospective knowledge

Business Intelligence Data and BI offer convincing arguments in corporate decision making.

If Big Data Business Intelligence is on the rise thanks to its predictive capacity, it is much more relevant when coupled with Business Intelligence. The complementarity of these two concepts can bring real added value to a company’s strategy:

  • Improvement of decisional and operational processes;
  • Implementation of preventive actions;
  • Expansion of available information sources.

BI tools allow you to create dashboards and to extract classic indicators. It also allows to identify issues that require predictive analysis. Data science is then used to explore the data in depth.

The use of Artificial Intelligence

Artificial Intelligence (AI) allows us to understand the needs of the market and to respond more effectively. In business, it allows to:

  • Improve operational performance;
  • Strengthen the ability to anticipate through data-driven management and predictive analysis;
  • Stand out from the competition by creating value for stakeholders.

Installation of Big Data functionalities on BI platforms

This allows the company to offer real-time reporting. This is a great asset in the face of security failures and the influx of data on websites.

About the author

ITTA is the leader in IT training and project management solutions and services in French-speaking Switzerland.

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