Data Analytics Basics: A Comprehensive Tutorial

As we know, a huge amount of data is produced and stored in large databases due to various data sources such as social media, the web, sensors, etc. This has increased further due to the evolution of smartphones and phone applications. Hence, it has become difficult for companies to manage this big data.

In order to optimize storage efficiency and utilize data for businesses, it has become essential to adopt tools and methods for data analytics.

Data Quality Issues

Data in the real world is often “dirty” due to the following reasons:

• Incomplete data: Originates due to wrong collection of information, human/software/hardware issues, differences in criteria, etc.
• Noisy data: Originates from faulty equipment, human/computer errors, data transmission, etc.
• Inconsistent or duplicate data: Originates from different data sources, non-uniform naming codes/conventions, etc.
• There are about 179 dimensions to be considered to evaluate the level of data quality. They are accuracy, accessibility, security, timeliness, amount of data, consistency, completeness, interpretability, objectivity, understandability, etc.

Why is Data Analytics Needed?

Companies use data analytics for running businesses better for the following reasons:

• Increase in revenue
• Decrease in costs
• Increase in productivity

Data analytics tools and software solutions perform the following tasks to obtain high-quality data:

• Removes inconsistency from the dirty data by removing duplicates.
• Corrects incomplete data from errors.
• Converts poorly structured data into structured data.
• Generates summarized reports in the form of text documents and graphs.

Functions of Data Extraction, Data Profiling, Data Cleaning

Data analytics is the data science that applies algorithms to datasets to derive useful information with the help of software/hardware.

As shown, there are three main parts in data analytics:

1. Data Sources: Include various sources that generate data in various forms. This includes data from social media (Facebook, Twitter, Google, LinkedIn, etc.), the web (emails, queries, etc.), business transactions, sensor networks, patient records from hospitals, purchase transactions from online e-commerce websites, subscriber information from telecom service providers, etc.
2. Data Analytics: Includes various tasks performed on the data in order to convert dirty data into high-quality data. It covers data extraction, data profiling, data cleansing, and data deduping.
3. Data Targets or Results: Include cleaned and high-quality data along with results to be used for the benefits of running the business.

Let’s understand the functions/definitions of core methods used in data analytics.

• Data Extraction: The process of extracting and storing data from the data sources mentioned above is known as data extraction.
• Data Profiling: The process of examining and collecting informative summaries in the form of smaller databases from larger ones is known as data profiling.
• Data Cleaning: The process of converting sourced data with errors, duplicates, and inconsistencies into cleaned target data is known as data cleansing or data cleaning.
• Data Deduping: The process of replacing multiple copies of data into a single instance storage in order to save storage space/bandwidth is known as data deduping or data deduplication.

Data Analytics Use Cases or Applications

Following are a few of the applications or use cases of data analytics in different fields:

• BFSI (Banking, Finance, and Insurance): Data analytics helps the banking, financial, and insurance industries to better understand customers, competitors, and markets. This helps them provide better services and rights to customers to win their confidence, which helps people invest more due to mutual trust, increasing revenue.
• Telecom: Network capacity and traffic density are the two major drivers for the growth of telecom companies. Data analytics helps telecom companies plan capacity according to traffic statistics and historical data of subscribers. Hence, telecom companies can save on maintenance and equipment installation costs. Moreover, they can provide better services to subscribers based on the analysis of data logs collected by their advanced software.
• Hospitals: Data analytics in hospitals utilizes medical records of patients, medical equipment, test facilities, etc. This helps hospitals save administrative costs, make better decisions, reduce fraud/abuse, provide better care, and improve the wellness of patients, etc.
• Aerospace: Data analytics collects information from aircrafts and airport ground stations to predict the status of the Aerospace system and its surroundings with the help of mounted sensors. This makes it possible to provide better safety features to passengers.
• E-commerce: Data analytics software helps e-commerce companies derive useful information from their online customers based on their purchase behaviors and previous historical data. This will help them push desired advertisements based on machine learning algorithms, which benefits both customers and e-commerce website owners or online stores.

Conclusion

From this Data Analytics tutorial, it can be concluded that data analytics is very useful for everyone, including various businesses and individuals.