Why Advanced Analytics Goes Hand-in-Hand with Big Data

Born with Big Data, Advanced Analytics can be overused today and still unclear for late adopters. However, it is Advanced Analytics that will add value to the data and make it available to business users.

Advanced Analytics brings more added value and intelligence to data.


What is Advanced Analytics?

Advanced Analytics is just the tip of the Big Data iceberg. In fact, Advanced Analytics conceals disciplines and tools that help to exploit and make Big Data visible and intelligible.

The term “Analytics” was already used in Business Intelligence (decision-making) to designate the analysis activities carried out on data. With the addition of the word “Advanced”, we go to the next level,  the 3Vs:

  • Volume: Processing large volumes of information,
  • Velocity: Analysis at high speed,
  • Variety: On a wide variety of data types.

It is thanks to the Advanced Analytics that we are in the era of data uses and that the data reveals all of its value to the company.

Advanced Analytics brings together 3 disciplines:

  • Data Discovery
  • Data Science
  • Dataviz


The aim of Data Discovery is to provide business users with Advanced Analytics.


1. Data Discovery

Data Discovery helps bring gains in productivity and flexibility. It initially responded to a shortfall in business solutions. Indeed,  it’s nothing new that anyone who manages figures has been using automated solutions and dashboards. But data scientists and analyst users require greater flexibility.

In Data Discovery tools, many features are provided for ease of use:

  • graphical navigation,
  • strong interactivity,
  • performance (thanks to the in-memory database),
  • preparation and ensurance of data quality,
  • ability to cross data stored in enterprise databases with locally stored data (in Excel for example), etc.


Data Visualisation = Dataviz


2. Dataviz

Dataviz seeks to bring value added to the graphic representation of data. In the age of Big Data, where data are numerous, varied and complex, you must find new ways to represent it.

Data Visualisation goes beyond age-old tables, pie charts or other histograms to view complex data at a glance. Currently three types of Dataviz solutions are on the market:

  • Dataviz for designers. The first Dataviz solutions are those of designers (Photoshop, Illustrator, InDesign, etc.). This seems so obvious that we don’t acknowledge it. Yet, many companies rely on designers to better present their figures, sometimes even for internal needs.
  • Dataviz for business analysts with Data Discovery. These capabilities combined with their flexibility of use make these tools excellent solutions to achieve a single, updatable Dataviz automatically and maintainable by business users.
  • Dataviz for computer scientists with JavaScript. This solution is certainly the most powerful. Graphics capabilities are endless, with Dataviz available on the Web and automatically refreshed when data is updated.


Data Science is the crossroads between statistics and Machine Learning. It brings together the best of both complementary worlds.


3. Data Science

Data Science is the discipline that brings more intelligence to data. It helps to understand and design complex behaviors and predict the future with algorithms.

This discipline is not recent. These are the mathematicians who developed it first using statistics in the 1950s. At the same time, scientists worked on artificial intelligence which gave birth to Machine Learning. This practice allows the computer to learn by itself and improve its behavior following the expected result independently.