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. |
