What is Data Analytics? Definition, Use Cases, and Applications

This guide provides a basic overview of data analytics, including its definition, use cases, and applications. We’ll also touch on the merits and demerits of data analytics.

Introduction

The world is awash in data. The proliferation of internet-enabled devices and applications has led to the generation and storage of massive datasets. Machines and sensors generate data at rates far exceeding human capabilities. Analyzing these complex and large datasets is challenging with normal database management tools and traditional data processing applications and software.

Data Analytics tools have emerged to tackle these high-volume, high-velocity, and high-variety information assets.

Data Analytics Definition

Data analytics is the process of analyzing data sets after performing extraction, profiling, cleansing, and deduplication operations. The goal is to derive useful information and conclusions with the help of hardware and software.

Figure 1 illustrates the data analytics block diagram:

Figure 1: Data Analytics Block Diagram

Companies that effectively use data analytics tools often outperform their competitors. Data analytics can increase revenue, decrease costs, and improve overall productivity. As shown in the diagram, data analytics encompasses data sourcing, data analysis, and data representation.

Data Sourcing

• Data is gathered from various sources, including both open and closed datasets.
• These sources encompass personal, official, and public data from a wide variety of origins.
• Scientific data from fields like astronomy, genomics, and biological research is utilized.
• Web data, such as log files, web indexing, text, and documents, is incorporated.
• Official data from business transactions is employed.
• Data from sensors (temperature, humidity, proximity, etc.) is used for weather forecasting, surveillance, and more.
• Personal data from social networks (Facebook, Twitter, Google Plus), call records from telecom providers, and medical records from hospitals are analyzed.
• Photos, images, and videos are included.
• Purchase transaction records from online websites are considered.
• GPS signals from cell phone users are utilized.

Data Analysis

Data analysis includes the following functional modules:

• Data Extraction: This is the process of extracting and storing data from the diverse sources mentioned above.
• Data Profiling: Data profiling involves examining and collecting informative summaries in the form of smaller databases derived from larger ones.
• Data Cleansing: This process converts sourced data containing errors, duplicates, and inconsistencies into clean, target data. It’s also known as data cleaning.
• Data Deduplication: Data deduplication replaces multiple copies of data with a single instance to save storage space and bandwidth.

The analyzed data is then represented in various textual and graphical forms for evaluation and use by companies to examine different parameters.

Data Analytics Use Cases | Data Analytics Applications

Here are several use cases and applications of data analytics:

• Banking, Finance, and Insurance: Firms use it to evaluate customer behavior.
• Online Gaming: Businesses leverage it to increase revenue by advertising based on online historical data using machine learning.
• Security Agencies and Military: Utilized for surveillance purposes.
• Retail Businesses: Employed to understand purchase behavior and tailor marketing strategies accordingly.
• Telecom Companies: Used to analyze call records and customer databases.
• Scientific Data Analysis: Many companies use it based on numerous sensors placed at different locations as per requirements.
• Social Media Companies: Platforms like Facebook, Google, and Twitter use it to display advertisements based on user behavior and historical data.