Big Data Analytics – Importance, Applications and Examples

Big data is currently one of the most crucial subjects of conversation among company executives and industry titans. We live in a digitally-driven era, therefore every business is pursuing big data to extract important insights from vast quantities of raw data. As a result, we will discover in this article what big data analytics is, why it is so crucial, and what its many aspects are.

What is Big Data Analytics?

Big data analytics is the process of spotting patterns, trends, and correlations in vast quantities of unprocessed data in order to support data-driven decision-making. These procedures employ well-known statistical analysis methods, such as clustering and regression, to larger datasets with the aid of more recent instruments. Since the early 2000s, when advancements in software and technology allowed businesses to manage substantial volumes of unstructured data, the term “big data” has been popular.

Here is The List Of The Top 10 Industries Using Big Data Applications:

1. Banking and Securities
2. Communications, Media and Entertainment
3. Healthcare Providers
4. Education
5. Manufacturing and Natural Resources
6. Government
7. Insurance
8. Retail and Wholesale trade
9. Transportation
10. Energy and Utilities

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How Big Data Analytics Works

Big data analytics refers to collecting, processing, cleaning, and analyzing large datasets to help organizations operationalize their big data.

1. Collect Data

Every business has a distinct approach to data collecting. Organizations may now collect structured and unstructured data from a number of sources, including cloud storage, mobile apps, in-store IoT sensors, and more, thanks to modern technology. Data warehouses will be used to store some of the data so that business intelligence tools and solutions may quickly access it. A data lake can be used to hold raw or unstructured data that is too complicated or diverse to be kept in a warehouse.

2. Process Data

For analytical queries to yield correct answers, data must be appropriately organized once it has been gathered and stored, especially if the data is big and unstructured. Data processing is becoming more difficult for corporations as the amount of data available increases dramatically. Batch processing, which examines big data chunks over time, is one processing choice.

When there is a longer gap between data collection and analysis, batch processing is advantageous. Small batches of data are examined all at once using stream processing, which reduces the time between data collection and analysis to enable speedier decision-making. Stream processing is more costly and complicated.

3. Clean Data

Data big or small requires scrubbing to improve data quality and get stronger results; all data must be formatted correctly, and any duplicative or irrelevant data must be eliminated or accounted for. Dirty data can obscure and mislead, creating flawed insights.

4. Analyze Data

Getting big data into a usable state takes time. Once it’s ready, advanced analytics processes can turn big data into big insights. Some of these big data analysis methods include:

• Data mining sorts through large datasets to identify patterns and relationships by identifying anomalies and creating data clusters.
• Predictive analytics uses an organization’s historical data to make predictions about the future, identifying upcoming risks and opportunities.
• Deep learning imitates human learning patterns by using artificial intelligence and machine learning to layer algorithms and find patterns in the most complex and abstract data.

Benefits of using Big Data Analytics

The following are some of the benefits of using big data analytics:

• Analysis of large volumes of data from disparate sources in a variety of forms and kinds in a timely manner
• Quickly making well-informed judgments for successful strategizing to enhance the supply chain, logistics, and other tactical decision-making sectors
• Savings due to the increased efficiency and optimization of business processes
• More informed risk management techniques based on large data sample sizes
• Greater knowledge of consumer behavior, demands, and sentiment can result in better product development data and strategic management processes

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Big Data Analytics Tools

In this section, we will go through the various aspects of the big data analytics domain:

• Apache Spark: Spark is a framework for real-time data analytics, which is a part of the Hadoop ecosystem.
• Python: Python is one of the most versatile programming languages that is rapidly being deployed for various applications including machine learning.
• SAS: SAS is an advanced analytical tool that is used for working with large volumes of data and deriving valuable insights from it.
• Hadoop: Hadoop is the most popular big data framework that is deployed by a wide range of organizations from around the world for making sense of big data.
• SQL: SQL is used for working with relational database management systems.
• Tableau: Tableau is the most popular business intelligence tool that is deployed for the purpose of data visualization and business analytics.
• Splunk: Splunk is the tool of choice for parsing machine-generated data and deriving valuable business insights out of it.
• R: R is the no. 1 programming language that is being used by data scientists for statistical computing and graphical applications alike.

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The Challenges of Big Data

Big data offers enormous advantages, but it also presents enormous difficulties, including new privacy and security worries, user accessibility for business users, and selecting the best solutions for your company’s requirements. Organizations must deal with the following issues in order to benefit from incoming data:

• Making big data accessible. Collecting and processing data becomes more difficult as the amount of data grows. Organizations must make data easy and convenient for data owners of all skill levels to use.
• Maintaining quality data. With so much data to maintain, organizations are spending more time than ever before scrubbing for duplicates, errors, absences, conflicts, and inconsistencies.
• Keeping data secure. As the amount of data grows, so do privacy and security concerns. Organizations will need to strive for compliance and put tight data processes in place before they take advantage of big data.
• Finding the right tools and platforms. New technologies for processing and analyzing big data are developed all the time. Organizations must find the right technology to work within their established ecosystems and address their particular needs. Often, the right solution is also a flexible solution that can accommodate future infrastructure changes.

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Applications of Big Data Analytics

• Customer Acquisition and Retention: Consumer data is quite useful for marketing trends that increase customer satisfaction through data-driven initiatives. Personalization technologies, like those employed by companies like Netflix, Amazon, and Spotify, for example, improve customer loyalty and the shopping experience for consumers.
• Targeted Ads: To construct targeted ad campaigns for customers on a bigger scale and at the individual level, personalized data regarding interaction patterns, order histories, and product page viewing history may be quite helpful.
• Product Development: It can produce insights on product viability, performance metrics, development choices, etc., and direct changes that benefit the customers.
• Price Optimization: With the use of various data sources, merchants may build pricing models and utilize them to increase profits.
• Supply Chain and Channel Analytics: Predictive analytical models help with B2B supplier networks, preemptive replenishment, route optimizations, inventory management, and notification of potential delays in deliveries.
• Risk Management: It helps in the identification of new risks with the help of data patterns for the purpose of developing effective risk management strategies.
• Improved Decision-making: The insights that are extracted from the data can help enterprises make sound and quick decisions.

Big Data Analytics Examples

There are several sorts of Big Data analytics examples. On the basis of their enormous current and past data sets, some organizations utilize these Big Data Analytics Examples to produce various reports and dashboards. Big Data Analytics Examples is used to generate various reports among those some examples are given below:

1. Live Tracking Report which is generally used by Transport Sectors such as Meru, Ola, Uber, and Mega to track the vehicles, customer’s requests, payment management, emergency alert and to find the daily needs and revenues and so on.
2. Sales Report and Future target and goal analysis which is mostly used by all sectors to analyze their sales, revenues, and needs of customers and also used to determine the future target and so on.
3. Many reports based on live data mostly used to manage live data in many entertainment sites, share market, real-time Sensex data etc.
4. Generate different types of alarms based on different activities like alarm generated by data Centre, various notifications Big Data Analytics Examples has been used here.
5. Google Analytics report where we can get how many user’s visit counts, from which location the user is from, from which device the site is accessing and so on.
6. Many Health care Organization nowadays rapidly introduced Big Data predictive analytics to improve our daily life. It has been used to update many protocols of Healthcare Sectors and also used to improve the outcomes against entire populations.