In today’s era, businesses thrive on data. It helps to replace speculation with verification and take the right decisions. A decision backed by the right data can put you on the right growth trajectory.
However, data is big and diverse. It is a cumbersome job to extract a diverse set of structured and unstructured data relating to market trends, patterns, or correlation without strong computing powers.
Modern organizations have found answer to this problem in big data analytics.
Big data analytics uses the most advanced data visualization algorithms to learn from data sets irrespective of their source, size, or structure. This makes big data analytics best suited to analyse and make sense out of the high volume and variety of big data.
What’s the Importance of Big Data?
In a world where data is the biggest currency, it’s hard to overlook the importance of big data for corporations. In the business world, statisticians and data scientists use Big data’s insights to uncover new information, improve service, and streamline operations.
It is a market that’s about to reach $103 billion by the end of 2023, and the following reasons show why:
- Reduces Cost- Big data analytics is a cheaper alternative to traditional methods when it comes to cost. It uses cloud-based analytics and the Hadoop framework that help organizations save significantly when storing a large amount of data. Besides, it’s much more efficient and reduces operating costs as well.
- Faster Decisions- The speed with which big data analytics works is the secret reason behind its success. It combines the speed of in-memory analytics and Hadoop. This feature is functional in reinstating the use of big data analytics to analyse new information and sources of vast sizes almost instantaneously. This is crucial for business executives to act fast and make better decisions.
- Better Products– Big data analytics gives companies the insights to better understand what the customer wants. The use of big data helps them gauge customer satisfaction and predict customer needs. That’s a crucial piece of information as they can devote resources to build new and improved products to meet these needs.
How Big Data Analytics Work?
Big data analytics has a four-pronged process for developing, growing, and producing better products. These are:
- Collecting
- Processing
- Cleansing
- Analyzing
Understanding these processes helps organizations to derive maximum benefit from big data analytics.
Data Collection
Big data analytics allows data scientists to gather structured, unstructured, and semi-structured data from a variety of different sources. These sources include websites, databases, applications, CRMs, and so on.
The collected raw data is then extracted, transformed, and loaded (ETL). ETL pipelines deliver this data to centralized repositories, where they are processed and stored. This approach allows data transformation to take place before the data reaches the target repository.
The other process is building an extract, load, and transform (ELT) pipeline. Here the data undergoes transformation only after being loaded in the target system.
Data Processing
Before processing, data is first stored in a cloud data warehouse or data lakes. From here, intelligence tools access the data whenever it needs to. Once they’re in place, the data gets converted into digestible forms for getting actionable results.
Depending on the number of machines used, processing environments can be classified into:
- Centralized Processing- The entire processing takes place in a single system. Thus, there’s only a single point of control.
- Distributed Processing- It’s a preferred way for processing large data. Distributed processing allows large datasets to be broken down into smaller pieces and process all the pieces simultaneously.
Depending on the requirements of the operations, companies can opt for batch or real-time processing.
- Batch Processing- The accumulated data is processed in batches. It is an ideal processing form if computational resources are easily available.
- Real-time Processing- This type ensures that the processing is done in a short span of time. The implementation is much more complex, but it’s a faster process.
Data Cleansing
The data always gets cleansed before being analyzed to ensure better quality and accurate results. It includes scrubbing away any errors, inconsistencies, or duplications. Usually, multiple data quality tools are used for detecting flaws.
Data Analysis
This is the final step where the big data gets converted into actionable results. The step can be broken into the following stages:
- Natural Language Processing- This involves making computers responsive to human input, whether text or verbal.
- Text Mining- Allows comprehending big data coming in textual formats.
- Sensor Data Analysis- Different sensors are used to examine data to understand the equipment’s condition, detect faults, and predict failures.
- Outlier Analysis- The analysis is helpful for detecting anomalies and fraudulent activities.
How To Make Use of Big Data Analysis
Big data analytics takes business intelligence to a whole new level. A variety of industries have stressed on the importance of big data to achieve business and operational excellence.
- Banking Industry- From retail traders to hedge funds, they can all rely on big data for high-frequency trading, predictive analytics, risk management, and fraud mitigation.
- Media and Entertainment- An on-demand industry like media and entertainment can utilize big data analytics for various applications. That involves collecting and analysing consumer behaviour, planning content, and understanding media usage.
- Healthcare- Collecting data from patients can help doctors to recommend evidence-based medications, fast-track identification of diseases, and track their spread. It is important to remember that big data was also implemented in government programs like Obamacare.
Reliable Big Data Analytics Tools
Big data analytics require combining various tools for collecting, processing, and analysing data. Some of the major players are:
- Apache Hadoop- It is an open-source software designed for quick storing, processing, and managing data. Hadoop’s ecosystem has three components designed to control the different functions.
- Mongo DB- It is a NoSQL database with a flexible schema and is ideal for storing large volumes of data.
- Talend- It is another data management platform that facilitates data preparations for users. It is an effective integration tool that’s focused on big data analytics.
- Apache Kafka- It is a scalable, fault-tolerant platform, useful for collecting data from different sources. Kafka’s fast and real-time processing empowers companies to respond to any developments immediately and makes managing big data simpler.
How We Assist You to Leverage Big Data Analytics?
The use of big data analytics in today’s world is only going to grow. In order to stay on top of the market, we can be of help to you.
Our team of experienced professionals has worked with the biggest names in the industry. We have helped them implement big data-driven solutions to improve operations and gain better results. Our flexible and transparent approach can help reinvigorate your business and gain a competitive edge.
Since 2008, we’ve worked on over 600 projects for clients globally, focused on hassle-free delivery of ITES projects. Our insights enable companies to quicken decision-making by more than 60% thus improving the overall productivity and reducing cost.
With a team of 350+ developers, we are happy to help your businesses’ operating environment by implementing our data-driven solutions. Discover what you have been missing out on by talking to us today.
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This article is authored by industry veterans at Flatworld Edge, a pioneering big data analytics and artificial intelligence solutions provider. Our scopes of services include data lake design and implementation, data strategy design, big data engineering, big data training, and many more.