Difference between AI, Data Science and BI

 

Introduction

Today, a conversation with a friend inspired me to write this whitepaper. I initially believed that people had a good understanding of AI, Data Science, and Business Intelligence (BI). However, through our discussion, I realized that many people find it challenging to differentiate between these overlapping subjects.

In today's world, the buzz around Artificial Intelligence (AI) and Data Science is ubiquitous. Executive management is increasingly integrating AI and Data Science into various business domains. Developers are keen to implement these technologies across all possible areas. This whitepaper aims to distinguish between Automation and AI/Data Science.

Difference between Automation and Artificial Intelligence

Automation refers to the process of streamlining existing workflows to enhance efficiency. On the other hand, AI and Data Science involve predicting and forecasting based on historical data.

Data Science and Artificial Intelligence

 

Data science integrates mathematics, statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with domain-specific expertise to uncover actionable insights hidden within an organization’s data. These insights can guide decision-making and strategic planning based on historical data. The rapid increase in data sources and the exponential growth of data have made data science one of the fastest-growing fields across all domains.

Artificial Intelligence, on the other hand, can learn, reason, solve problems, and make decisions autonomously, often by mimicking human intelligence. AI and Data Science complement each other very well. Machine Learning (ML) and Deep Learning (DL) leverage the exploratory analysis of organizational data using data science and autonomously solve problems by mimicking human intelligence through ML and DL models.

 

Business Intelligence (BI) and Data Science

Business Intelligence (BI) is typically a platform that enables data preparation, data mining, data management, and data visualization. BI tools and processes allow end users to identify actionable information from raw data, transforming data-driven decision-making within organizations across various domains.

Data science tools overlap with BI, but they focus more on historical data, providing descriptive insights to understand past events and inform future actions. BI is geared toward static, structured data. While data science uses descriptive data, it typically utilizes it to determine predictive variables, which are then used for categorization and forecasting.

Data science and BI are not mutually exclusive; organizations use both to fully understand and extract value from their data.

Types of Learning

Criteria	Supervised Learning	Unsupervised Learning	Reinforcement Learning
Definition	The Machine learns  by using Labeled Data	The machine learns using unlabeled data without any guidance	An agent interacts with its environment by performing actions and Learning from rewards and errors.
Type of Problems	Regression and Classification	Association and Clustering	Reward-based
Type of data	Labeled Data	Unlabeled data	No Predefined data
Approach	Maps Labeled Inputs to the known outputs	Understand Pattern and discovers the output	Follows the trial and error method.