Introduction

Big Data analytics in using complex processing on large volumes of data to procure gainful insights and information from the data, which typically is from heterogeneous sources and in overwhelming volumes. For Ex: BI-Business intelligence helps in informed decision making, strategic marketing decisions, customer satisfaction improvement etc. It uses statistical algorithms, predictive models, what-if analysis powered by Machine Learning and Artificial Intelligence analytical systems.

  1. Why is big data analytics important?
  2. How does big data analytics work?
  3. Technologies and tools
  4. Uses and examples
  5. Benefits
  6. Challenges
  7. History and Growth of Big Data Analytics

1. Why is big data analytics important?

Organizations are using big data analytics software, technologies and systems to make real-time data-driven decisions in various fields for improved outcomes that are business-related. Insurance, banks, health care providers, the government’s social security networks, nuclear physics, traffic management and literally any field all use Big Data analytics in the process of data analytics types used for staying ahead of competitors, modelling, designing newer processes and products, effective marketing, customer personalization, generation of new revenue options, improved operational efficiency and more.

2. How does big data analytics work?

Analysts, predictive modellers, data scientists, statisticians etc. collect raw data from various sources, process and clean such data using data analytics techniques into formats understood by the computing systems and then analyze this huge volume of data that is transactional structured data to analyze it for gainful insights using algorithms that have ML and AI included in them. Typically there are 4 stages in the process of Big Data analytics.

  1. Data collection: The raw or uncleaned data is a Big Data collection from heterogeneous sources and may be in structured, semi-structured or unstructured formats. Some sources are internet data, web-servers, cloud and mobile apps, social media like Facebook, health devices, machine process data etc.  
  2. Data processing: Data from data warehouses or lakes is cleaned, meaning configured, formatted, organized, partitioned etc., in computing language and made ready for analytical queries.
  3. Data cleaning: Here, data is scrubbed using enterprise software and /or scripting software, and inconsistencies, errors, duplications, formatting errors are addressed.
  4. Data Analysis: The data is then analyzed with tools like relational tools of data mining, deep learning tools of ML, predictive tools used in modelling, statistical analysis software for text mining, BI, AI, visualization tools etc., in what is Big Data analytics.

3. Technologies and tools

There are many tools, technologies and frameworks used to support Big Data analytics. The significant ones enhancing analytics techniques are

  • Hadoop is a processing and storage open-source framework widely used to handle Big Data with or without structures and an application of Big Data.
  • Spark a cluster-computing open-source framework used for stream data and batch data processing.
  • In-memory data fabric for data distribution.
  • Software and hardware for predictive analytics, which uses ML, AI, statistical algorithms etc., on complex data to provide predictions, forecasts etc., used in marketing, risk management, fraud detection and more.
  • NoSQL databases, data lakes, data warehouses used to store raw data.
  • Tools for stream analytics used to aggregate, analyze, filter, and later store big data on platforms and in different formats.
  • Data mining and knowledge discovery tools.
  • Data for distributed storage, which is typically from the non-relational database and replicated to prevent data corruption, node failures etc.
  • Big Data analytics tools for data virtualization, integration, quality, preprocessing, software etc.

4. Uses and examples

Of the many uses of Big Data analytics, the outstanding Big Data examples are 

  • Improved decision-making in the data analytics process.  
  • Customer retention, service and retention. 
  • Product and process development. 
  • Targeted markets, strategies and ads. 
  • Cost and price optimization. 
  • Risk mitigation and security management. 
  • Supply chain, store and channel analytics. 

5. Benefits

Some notable benefits of Big Data analytics are as follows :

  • Social data from various sites and search engines like Twitter, Facebook etc., help in Big Data advantages like customer retention, product decisions and marketing or business strategy decisions from data insights.
  • Business Intelligence is readily available from the insights, predictions and patterns of forecasting.
  • Customer service issues can quickly be resolved with NLP features (natural language processors) to evaluate customer satisfaction, resolve issues, provide simple product information etc.
  • Improved operational efficiency results are produced when the huge volumes of data are well-analyzed and used to tweak products, services, risk mitigation, security issues etc.
  • Early error identification reduces the risk to services or products offered.
  • Big Data analytics create large data-warehouses for integrating multiple sources, technologies and processes using data differently.

6. Challenges

Some of the biggest challenges of Big Data analytics are discussed below :

  • Data Accessibility: The huge volumes of Big Data generated daily needs to be properly processed, formatted and stored for analytics. This traffic is increasingly becoming a challenge where less experienced analysts and scientists do not access quality data in real-time.
  • Maintenance of data: With such large volumes coming from heterogeneous sources, maintenance and cleaning of data for storage need too much time, effort and costs.
  • Security of Data: The security concerns in Big Data are a complex issue, and the Big Data ecosystem a very complicated field.
  • Data tools and technologies: A wide array of platforms and tools for Big Data analysis also means selecting the right tools and personnel to match the client needs.

7. History and Growth of Big Data Analytics

In the 1990s, ‘big data’ was defined as large volumes of data. Roger Mougalas, in 2005, referred to huge data volumes and huge datasets. In 2001, Meta Group Inc.’s Doug Laney defined data as the variety, volume and velocity of data being generated, stored and used by organizations. 

Hadoop software framework called Nutch became an open-sourced resource Apache in 2006. This was merged with MapReduce from Google to provide types of Big Data analysis flexibly and in large volumes for Big Data analytics. Storage means also grew exponentially, and Magnetic storage slowly evolved to floppy disks, hard drives, large volumes of data storage computers, 1989’s SaaS- Salesforce offered Software-as-a-service and finally came cloud storage Big Data analytics examples which is the present-day rage for its infinite scalability, easy access and secure services.

Conclusion

Big Data analytics is increasing exponentially, and the importance of Big Data present in all walks of every person’s life. It drives improvements not only in the field’s it is used in but also causes developments and improvements critical to the future of Big Data analytics, Big Data analytics applications, storage devices, data handling device technologies, AI, ML and more.

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