Every day, customers generate huge amount of data. Every time they open their email, access social media websites, use mobile applications, makes an online purchase, watch a movie via streaming services, or ask a virtual assistance about your company, these diverse technologies collect and process data for your organization. That data is only for the customers. Apart from that, employees in your company, whether in supply chain, marketing, finance, information technology, and more generate tons of data. Big data is an extremely large volume of data that comes in diverse forms and from multiple sources. Organizations have finally realized the advantages of collecting and storing data as much as possible. But it is not enough to just collect the data, as you must make full use of it. Thanks to the advancements in the Big Data technologies, organizations can use big data analytics to transforms terabytes of data into actionable data.

What are big data Analytics?

Big data analytics is the process of uncovering trends, patterns, and correlations between variables within raw data to make fact-based business decisions. These processes use familiar statistical analysis techniques like clustering and regression, apply them in these large datasets with the ever-evolving tools. Big data has been a buzz word since the early 2000s, when technological capabilities made it possible for organizations to manage substantial amounts of unstructured data. With the explosion of data, companies like Amazon, Tableau, and others have developed big data platforms like Hadoop, Spark, and NoSQL to store and process this large data. Additionally, data engineers are now trying to integrate the vast amounts of complex information generated from the networks, transactions, smart devices, web usage, and others. Even now, big data analytics methods are being used with emerging technologies to discover and scale more complex patterns.

How this process works?

The process of Big Data includes collecting, processing, cleaning, and analyzing large datasets to help organizations resolve their business problems and strategies.

  1. Collection of Data

The process of collection of data differs from organization to organization. With today’s technology, organizations can gather not only structured, but also unstructured data from various sources. Previously, they had only databases to collect data from; however, they now have cloud storage mobile applications and beyond.  Raw or unstructured data that is too complex for a warehouse may be assigned metadata and stored in a data lake within an organization.

  • Processing Data

Once data is collected, it must be organized properly to get accurate analysis of the data, especially when it’s large and unstructured. Batch Processing is a processing option, which looks at large block of data in one time. This mode of processing is useful when there is a long turnaround time in between collection and analyzing of data. Stream processing is a processing mode of opposite nature. It looks at small batches of datasets at once, shortening the delay time between collection and analysis for quicker decision-making. Stream processing is often more complex and is also more expensive than batch processing.

  • Cleaning Data

Whether small or large, simple of diverse, every dataset you come across might need some cleaning and standardization to do before the analysis phase. To improve data quality and getting stronger results, all data must be formatted correctly, the relationships between datasets should be properly laid out, and any duplicate or incorrect data should either be removed or accounted for. Dirty data can obscure and mislead, creating flawed insights.

  • Analyzing of Data

After the tedious processes mentioned earlier, it is time to analyze the data. Advanced analytics processes can now turn big data into big and interesting insights. Some of these big data analysis methods include:

  • Predictive Analysis refers to the process of analysis of an organization’s historical data to make predictions about the future, identifying upcoming risks and opportunities.
  • Data Mining refers to the process of going through diverse datasets of an organization in order to identify trends and patterns within the heaps of data presented by identifying anomalies and creating data clusters.
  • Deep Learning is all aboutimitates human learning ways by using algorithms of artificial intelligence and ML (Machine Learning) to find patterns in the most complex and abstract data.

Big data tools and technologies

The process of big data analytics cannot be accomplished by the usage of a single tool. Instead, several tools are needed to collect, process, clean, and analyze data. Some of the major contributors in the big data ecosystem are:

  • Hadoop is a famous open-source framework that stores and processes big datasets on clusters of commodity hardware. This framework is free and easily available on the internet.
  • NoSQL is a non-relational data management system that do not require a fixed scheme, making them a great option for big, raw, unstructured data.
  • YARN is also known as “Yet Another Resource Negotiator.” It is another component of second-generation Hadoop technology. It is a cluster management technology that helps with job scheduling and resource management in the cluster.
  • MapReduce is an essential component to the Hadoop framework serving two functions for the system. The first is called as mapping, which filters data to various nodes within the data cluster. The second one is reducing, which organizes and reduces the results from each node to answer a query.
  • Spark is an open-source cluster computing framework that big data analysts normally use nowadays. It uses implicit data parallelism and fault tolerance for providing an interface for programming entire clusters. Spark can manage both batch and stream processing for fast computation.
  • Tableau is one of the widely used end-to-end data analytics platform that allows you to prep, analyse, collaborate, and share your big data insights. Tableau excels in visual analysis, allowing the users to ask new questions of governed big data and easily share those insights across the organization.

Benefits of big data analytics

Although there are many advantages of using big data analytics, but I will list some which are most important to general users:

  • Cost Savings: Assisting organizations in identifying ways to do business more efficiently.
  • Product Development: Analyzing and providing a better understanding of customer needs.
  • Market Insights: Tracking the process, purchase behavior and the trends in the market.

Huge Challenges of big data

Big data brings big benefits; however, it also brings big hurdles such as new privacy and security concerns, accessibility for business users, and choosing the right solutions for your business needs. For capitalizing on incoming data, organizations should do the following:

  • Making big data accessible: Organizations must make data easy and convenient for data owners of all skill levels to use as collecting and processing of data becomes extremely difficult as data grows.
  • Maintaining quality data: In order to achieve a level of quality, organizations are spending time than ever before in scrubbing for duplicates, errors, absences, conflicts, and inconsistencies.
  • Data security: Security data from prying data is as important as analyzing the same data. So, organizations are devising more and more SOPs and other rules to keep their data secure.
  • Finding the right tools and technologies: As we already know that there is a dearth of big data tools and platforms to have access to, but the selection of right tool is extremely important because if unsuitable tools will be used, then it will lead to wrong analysis of data.


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