Big Data is a huge amount of data sets that cannot be reserved, processed, or examined using standard tools. Big Data analytics recognizes fraudulent actions and dissimilarities. The organization leverages it to narrow down a list of suspects or root reasons for concerns.

Today, there are millions of data sources that generate data at a very rapid rate. These data sources are present across the world. Big data analytics usually retain data from both internal systems and exterior sources like weather data or demographic data on customers assembled by third-party data services providers. In addition, streaming analytics applications are evolving familiar in big data surroundings as users peek to perform real-time analytics on data provided into Hadoop systems through stream processing engines like Spark, Flink, and Storm.

The entire amount of data built, grasped, and replicated globally is predicted to grow rapidly, acquiring 64.2 zettabytes in 2020. Over the next five years up to 2025, global data creation is launched to develop more than 180 zettabytes. In 2020, the quantity of data made and imitated gained a new high. The transition was higher than previous predictions, driven by the expanded market due to the COVID-19 pandemic, as more people operated and learned from home and utilized home amusement alternatives more often.


Storage Capacity Also Growing:

Less percentage of this recently made data is kept though, as just two percent of the data created and ingested in 2020 was saved and maintained into 2021. In line with the robust growth of the data volume, the established base of storage capacity is predicted to rise, growing at a compound annual growth rate of 19.2% over the projection period from 2020 to 2025. In 2020, the installed floor of storage capacity went 6.7 zettabytes. The primary benefits of big data analytics are speed and efficiency. Today, industries can gather data in real-time and interpret big data to make rapid, better-informed decisions.

Stored data is doubling every four years. With the growing data, the volume reaches an increasing need for data storage. The worldwide installed base of storage capacity, which IDC dubs the “Global Storage Sphere” is expected to grow nearly 17% this year i.e. 6.8 zettabytes. 


The Growing Adoption of Big Data Analytics

The global big data and business analytics market is expected to conduct an annual revenue of 274.3 billion USD by 2022. Conveying rising usage, businesses will persist to depend on big data analytics providers, resulting in immense market growth projections. In 2019, the biggest stake of revenue by39% comes from spending on services, and this trend is expected to continue.

35% of immense organizations will experience standard online data marketplaces by 2022. The increasing growth of data is a testament to the importance of organizations across industries. Generally, a formal online data marketplace is expected to shorten in the next few years, with a fraction of big organizations operating as sellers or buyers.


Market Use Cases of Big Data

  • Amazon, the online retail giant, uses its massive data bank to access customer names, addresses, payments, and search histories and uses them in advertising algorithms and to improve customer relations.
  • The American Express Company uses big data to analyze customer behavior.
  • Marketing leader Capital One utilizes big data analysis to ensure the success of their customer offers.
  • Netflix uses big data to gain insight into the viewing habits of international viewers.
  • Brands like Marriott Hotels, Uber Eats, McDonald's, and Starbucks are also consistently using big data as part of their core business.


Why is Big Data Analytics Important?

Big data analytics enables organizations to harness their data and utilize it to determine the latest possibilities. That, in turn, directs to wiser business moves, efficient processes, greater profits, and happier customers. Industries that use big data with evolved analytics boost value in multiple ways, such as:

1. Reducing Cost

Big data technologies like cloud-based analytics can remarkably lessen the expenses when it comes to reserving immense amounts of data (for example, a data lake). As big data analytics helps organizations find more efficient ways of doing business.

2. Making Faster, Better Decisions

The pace of in-memory analytics blended with the ability to explore current sources of data, such as streaming data from IoT that benefits the businesses' survey details instantly and makes fast, notified conclusions.

3. Developing and Marketing New Products and Services

Being competent to gauge client requirements and customer satisfaction via analytics authorizes businesses to provide clients with what they want within the delivery time. With big data analytics, more firms have a chance to create creative products to fulfill customers’ changing needs.


Big Data Analytics Technologies and Tools:

Hadoop is an open-source framework for holding and processing big data sets. NoSQL databases are non-relational data management systems that are useful when working with large sets of distributed data. Data lake & Data Warehouse is an extensive storage unit that carries native-format raw data until it is required and keeps a huge quantity of data compiled by various sources respectively.


Big Data Analytics Benefits

  • Rapid examining of huge amounts of data from various sources in different formats and kinds.
  • Cost savings can result from new business process efficiencies and optimizations.
  • Many companies use Big Data for customer acquisition & retention- For example, Amazon leverages Big Data to offer a personalized shopping experience
  • Enhanced, better-informed risk management plans that pull from the big size of sample data.
  • Structured and unstructured data can be explored by big data analytics.
  • Big data analytics affects exploring structured and unstructured data.
  • Big Data helps businesses in effective risk management by optimizing complex decisions for unexpected events and potential threats. 


Future Path of Big Data Analytics:

By 2024, streaming data and analytics infrastructure will grow fivefold. It stems from the reality that 75% of businesses will move from driving to operationalizing or deploying artificial intelligence (AI) by 2024. Advanced analytics solutions have been shown to deliver crucial insights and keys for organizations, and utilization will simply grow in the upcoming years.


Use of Big-Data Analytics by CSM:

CSM used data analytics for different GovTech systems like state dashboards, offering high-level analytics. The big data analytics was implemented for the Covid-19 State dashboard for various state governments needed for the statistical, diagnostic, and predictive study that sustained fast and better decision making during the pandemic.

The collaboration with SAS Analytics helped to tap Modelling & Predictive Analytics' effective forecasting of the population susceptible to the pandemic.

Data analytics is operated for Omnichannel grievance management system in Janasunani platform where people can send their grievances to the government for escalation and redressal. Mo-Sarkar Feedback management system used data analytics to manage the feedback about the public services availed which tracked the performances of the departments and behaviours of the officials.

Data Analytics used in the Social registry platform developed for Social Protection Delivery Platform for Odisha and JanAdhar for Rajasthan facilitated in creation of golden records of citizens database with all the details enabling easy mapping of schemes with the stakeholders.

The unified farmer registry portal (Krushak Odisha) is built on the data analytics tools which created a golden database of farmers facilitating the government in weeding out the ghost beneficiaries for the effective implementation of the farmer empowerment schemes.

CSM used data analytics for different GovTech systems like state dashboards, offering high-level analytics. The big data analytics was implemented for the Covid-19 State dashboard for various state governments needed for the statistical, diagnostic, and predictive study that sustained fast and better decision making during the pandemic.

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