Analyzing data to identify trends and make judgments about the information they contain is known as Data Analytics. Here data may be in the form of Excel file, Html file, Sql Database etc.
Example: Customer Segmentation.
Suppose there is a bank, it wants to know which customers are likely to leave or unsubscribe from your service so that it can use marketing efforts with each customer to maximize their likelihood of staying.This is very important for banks and financial services.Customers have different behaviors and preferences, and reasons for canceling their subscriptions.
We will use Data analytics as a tool to detect the hidden patterns among the two types of customers: One who are currently in that bank and Second one who had leave it.After successful detection we will able to predict chance of a person to leave the bank and indirectly increase in profit.
Financial analytics is an essential tool for Investment and corporate world. It is synonym of data analytics for finance industry.It entails analyzing financial statements and other pertinent financial data to assess an organization’s financial performance and status. The goal of financial analysis is to provide stakeholders with the information they need to make knowledgeable decisions about the future of firm.
Financial statements are the main information source used in financial analytics. Following are the three basic financial statements.
The balance sheet shows the financial standing of a company at a specific point in time. This document contains a list of the equity, liabilities, and assets of the company. An organization's assets are the resources it currently has and plans to use to make money in the future. Liabilities are debts that the company owes to third parties. Equity represents the organization's remaining interest in its assets after debts have been paid.
The income statement, also referred to as the profit and loss statement, provides particular information about an organization's revenues and expenses for a given time frame. By deducting expenses from income, the firm's profitability is determined.
Cash flow statement is essential document to track how much money going in and out from a company.For a financial stability it is an important record.It records the Operating activities,financial activities,Investing activities etc.
Big data analytics is the process of analyzing large, complex data sources to uncover trends, patterns, customer behaviors, and market preferences to inform better business decisions. The complexity of analyzing big data requires various methods, including predictive analytics,machine learning, streaming analytics, and techniques like in-database and in-cluster analysis.We use tools like Tableau,Power BI,Power SQL etc. To handle large amount of data.
Descriptive analytics simply means what happened?. For example: How many people viewed the website? Which products had the most defects? This type of analytics employs simple mathematical and statistical tools, such as spreadsheets, instead of complex calculations to create visualizations, like bar charts or line graphics to describe a data set.
A subset of data analytics called "diagnostic analytics" is concerned with looking at historical data to determine what led to particular incidents. It involves analyzing data to understand why something happened or to find patterns and relationships that may help explain a particular outcome.
Predictive analytics is like having a psychic octopus as your personal data guru! It takes all your past information, shakes its magic tentacles, and whispers in your ear, "Fear not, my human friend!
So Predictive analytics identify the likelihood of possible outcomes, which can guide us on the best course of action.Example: Stock market Analytics in which we use Regression which is a mathematical tool and a part of predictive analytics to draw the future patterns of stock curve.
Prescriptive analytics is which gave us answer of “What to do now to get this?”.It provide us a recommendation system for customers by using tools like machine learning which indirectly increase the profit.
For instance, email marketing
By categorizing leads based on their objectives, attitudes, and motivations, marketers may provide email content to each group of leads individually. This process is known as email automation. Any interactions that leads have with emails can classify them into a separate group, which will cause a different set of messages to be sent to them.
Data analytics typically involves several phases in its process. These phases may vary slightly depending on the specific approach or framework used, but here are the common phases involved in data analytics:
Data Collection: The first step is to gather relevant data from various sources, such as databases, files, APIs, or external datasets. This process involves identifying the data needed to answer specific questions or solve problems.
Data Cleaning and Preparation: Once the data is collected, it needs to be cleaned and preprocessed to remove errors, inconsistencies, or missing values. Data is transformed into a usable format, and any outliers or irrelevant information may be handled or removed.
Data Exploration: In this phase, analysts explore the cleaned data to gain a better understanding of its characteristics. Data visualization and statistical techniques are often used to uncover patterns, trends, correlations, and other insights.
Predictive,Prescriptive,Diagnostic and Descriptive Analytics:Its totally based on the business problem to perform what type of analytics should be done.
Example: (1)If we have a business problem of analysing the over consumption of electricity in North India then we will perform Diagnostic Analytics.
(2)If we have have Stock Price Prediction problem we will perform Predictive analytics.
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