What is Analytics? 🤔

Analytics is a broad term that encompasses the processes, technologies, frameworks, and algorithms to extract meaningful insights from data. Raw data in
itself does not have a meaning until it is contextualized and processed into useful information. Analytics is the process of extracting and creating information
from raw data by filtering, processing, categorizing, condensing, and contextualizing the data. This information obtained is then organized and structured to
infer knowledge about the system and/or its users, its environment, and its operations and progress towards its objectives, thus making the systems smarter
and more efficient. 🚀

The choice of technologies, algorithms, and frameworks for analytics is driven by the analytics goals of the application. For example, the goals of the analytics
task may be:

• to predict something (for example whether a transaction is a fraud or not, whether it will rain on a particular day, or whether a tumor is benign or malignant),
• to find patterns in the data (for example, finding the top 10 coldest days in the year, finding which pages are visited the most on a particular website, or finding the most searched celebrity in a particular year),
• to find relationships in the data (for example, finding similar news articles, finding similar patients in an electronic health record system, finding related products on an eCommerce website, finding similar images, or finding a correlation between news items and stock prices).

The National Research Council has done a characterization of computational tasks for massive data analysis (called the seven "giants"). These computational
tasks include

1. Basis Statistics
2. Generalized N-Body Problems
3. Linear Algebraic Computations
4. Graph-Theoretic Computations
5. Optimization
6. Integration
7. Alignment Problems

This characterization of computational tasks aims to provide a taxonomy of tasks that have proved to be useful
in data analysis and grouping them roughly according to mathematical structure and computational strategy.

In this article, we will explore four types of analytics that are commonly used in various domains and applications:

• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics

Descriptive Analytics 📊

Descriptive analytics comprises analyzing past data to present it in a summarized form that can be easily interpreted. Descriptive analytics aims to answer -
What has happened? A major portion of analytics done today is descriptive analytics through the use of statistics functions such as counts, maximum,
minimum, mean, top-N, and percentage instances. These statistics help in describing patterns in the data and present the data in a summarized form.

For example, computing the total number of likes for a particular post, computing the average monthly rainfall, or finding the average number of visitors per
month on a website. Descriptive analytics is useful to summarize the data.

Diagnostic Analytics 🔎

Diagnostic analytics comprises analysis of past data to diagnose the reasons why certain events happened. Diagnostic analytics aims to answer - Why did it
happen? Let us consider an example of a system that collects and analyzes sensor data from machines for monitoring their health and predicting failures.
While descriptive analytics can be useful for summarizing the data by computing various statistics (such as mean, minimum, maximum, variance, or top-N),
diagnostic analytics can provide more insights into why certain a fault has occurred based on the patterns in the sensor data for previous faults. Among the
seven computational tasks, the computational tasks such as Linear Algebraic Computations, General N-Body Problems, and Graph-theoretic Computations
can be used for diagnostic analytics.

Predictive Analytics 🔮

Predictive analytics comprises predicting the occurrence of an event or the likely outcome of an event or forecasting future values using prediction models.
Predictive analytics aims to answer - What is likely to happen? For example, predictive analytics can be used for predicting when a fault will occur in a
machine, predicting whether a tumor is benign or malignant, predicting the occurrence of natural emergencies (events such as forest fires or river floods), or
forecasting pollution levels.

Predictive Analytics is done using predictive models which are trained by existing data. These models learn patterns and trends
from the existing data and predict the occurrence of an event or the likely outcome of an event (classification models) or forecast numbers (regression
models). The accuracy of prediction models depends on the quality and volume of the existing data available for training the models, such that all the patterns
and trends in the existing data can be learned accurately.

Before a model is used for prediction, it must be validated with existing data. The typical approach adopted while developing prediction models is to divide
the existing data into training and test data sets (for example 75% of the data is used for training and 25% data is used for testing the prediction model).
Among the seven computational tasks, tasks such as Linear Algebraic Computations, General N-Body Problems, Graph-theoretic Computations,
Integration,
and Alignment Problems can be used for predictive analytics.

Prescriptive Analytics 💡

While predictive analytics uses prediction models to predict the likely outcome of an event,
prescriptive analytics uses multiple prediction models to predict various outcomes and suggest actions for each outcome.
Prescriptive analytics aims to answer - What can we do to make it happen? Prescriptive analytics can help in decision making by providing recommendations based on different scenarios.

For example,
prescriptive analytics can be used for optimizing routes for delivery vehicles based on traffic conditions,
suggesting products or services to customers based on their preferences,
or recommending treatments for patients based on their symptoms.

Prescriptive analytics requires more complex models than predictive analytics,
as it involves not only predicting outcomes but also evaluating trade-offs and constraints among different options.
Among the seven computational tasks,
tasks such as Optimization,
Graph-theoretic Computations,
and Integration can be used for prescriptive analytics.

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I hope this article gave you an overview of what analytics is and what are some of its types.
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