Business analytics uses tools like data science, artificial intelligence, and information technology to analyze data and add value to companies. Historically, businesses made decisions based on opinions rather than data analysis. Business analytics aims to increase decision making efficiency through data mining. It begins with understanding a business's context and goals. Technology is used to capture, record, and automate actions from analytical models. Data science determines the best mathematical models and machine learning algorithms to solve problems. Descriptive analytics describes what happened, predictive analytics predicts what could happen, and prescriptive analytics recommends actions.

1. Business
Analytics

2. Introduction
Business analytics is a set of mathematical and operations analysis
tools, as well as artificial intelligence, information technology, and
management methods, that are used to frame a business challenge,
gather data, and analyse it in order to add value to companies.
Most businesses made strategic choices based on "opinions" rather
than data interpretation in the early twentieth century. Opinion-
based decision-making is dangerous and often contributes to poor
outcomes. One of the main goals of market analytics is to use data
mining to increase the efficiency of decision making.

3. Components of
Business Analytics
Data
Science
Technology's
Business
Context

4. Business Context
Market analytics initiatives begin with an understanding of the business environment and
the organization's desire to pose the right questions.
Technology
We need evidence to figure out whether a client is pregnant or whether they failed to buy
anything. Under all scenarios, the customer's previous sales must be documented at the
point of sale.
Data capture, recording, planning, review, and sharing was all done with the help of
information technology (IT). Automation of actionable items resulting from computational
models is a key output of analytics; automation of actionable items is typically
accomplished by technology.

5. Descriptive analytics
Prescriptive analytics
Predictive analytics
1
3
2
Data Science
The most critical part of analytics is data science, which includes computational and
operations analysis approaches, as well as artificial learning and deep learning algorithms.
The aim of the data science part of analytics is to determine the most suitable
mathematical model/machine learning algorithm that can be used to solve a problem.
Business analytics can be grouped into three types:

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