There are no “universal analytics” that work in all cases. Traditionally, one would work with analytical tools in a reactive way. These tools are capable of generating reports and visualizations about what has happened in the past, but they do not offer useful information about possible business opportunities or problems that may arise in the future. This led to a need for a movement towards Predictive Analytics as well as the Descriptive Analytics that already existed. The world saw a move from linear analytics in a controlled environment, towards analytics that can be applied in a real world (i.e. less structured) environment: Data Science Analytics.
What is Descriptive Analytics? This is the most basic area of analytics, and is currently used by around 90% of businesses. Descriptive Analytics answers the question: What has happened? It analyzes historical data and data collected in real time in order to generate insights about how past business strategies have worked (for example, a marketing campaign).
Aim: To identify the causes that led to success or failure in the past in order to understand how they might affect the future
Based on: Standard aggregate functions of the database. They require a basic level of mathematics
Examples: This type of analytics is often used for social analytics and is the result of basic arithmetic operations such as average response time, page views, follower trends, likes etc
Application: Using tools such as Google Analytics to analyze whether a promotional campaign has worked well or not, with the use of basic parameters such as the number of visits to the page.
The Intelligence of Things transforms business
We bring together the capabilities of IoT, Big Data and Artificial Intelligence to deliver technology that further connects things and people. A connection that brings us closer and inspires new and better solutions for all.