to a dashboard, or any product that supports achieving a more informed decision.

TEXT MINING & SENTIMENT ANALYSIS

Not all data starts off as numbers. Text mining and analytics become important when the data is in textual formats such as emails, blogs, websites, and social media. Sentiment analysis is one of its areas and consists of translating qualitative text into quantitative variables.

MACHINE LEARNING

Machine learning is the process by which a computer can work more accurately as it collects and learns from the data it is given. We use machine learning for regression and automatic classification. The uses are wide ranging, such as finding oil fields, gold mines, or archaeological sites based on existing sites (classification and regression), finding place names or persons in text (classification) or recognizing birds based on their whistle (classification).

BIG DATA

Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, and purchase transaction to name a few. We put data strategy to work by creating big data tools such as Data Acquisition Pipelines, Data Lakes and Visualisation tools.

NETWORK ANALYSIS

This approach uses theoretical methods from mathematics (graph theory), social sciences, and sociology to create a collection of interconnected objects. Some of its applications are pseudo-social networks for direct marketing, linking service providers to uncover hidden costs or employee networks for guiding HR activities.