Demystifying Big Data Analytics with Growth Estimates and Use Cases

by July 9, 2020
Big Data Analytics

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Why is Big Data Analytics so important?

It wouldn’t be an exaggeration to say that we are surrounded by data all round! Our digital footprint says it all, a comprehensive term to define the massive data we generate browsing webpages, watching our favourite shows on OTT platforms or by merely shopping! For enterprises, this data is even more complex it translates from IoT frameworks, and data science algorithms. A collective name is given to this massive data pile- Big Data, which describes both structured and unstructured data inundating enterprises on a day-to-day basis.


The amount of data is not important, what insights are taken from Big Data is what keeps the Business Moving!

These voluminous sets of data are so huge, that they are defined by their distinctive characteristics called as the “4Vs” or “5Vs” or “7Vs” of Big Data-



• Defines the size of data (which is enormous).

• Unimaginable sizes and unfamiliar numerical terms are used to describe data volume.

• For instance- We would generate 40 zettabytes of data, which is 43 trillion gigabytes in 2020



• Defines the high speed of data accumulation.

• Characterises by a massive and continuous flow of data, potentially how fast the data generates and is processed to meet the demands.

• For instance- Google now processes over 40,000 search queries every second on average or 3.5 billion searches per day and 1.2 trillion searches per year worldwide.



• Defines the nature of data- structured, semi-structured and unstructured data.

• Structured data is an organized data- names, dates, addresses, credit card numbers, stock information, geolocation and more

• Semi-structured data is a semi-organised data, not conforming to the formal structure of data- CSV, XML and JSON documents; NoSQL databases; HTML; Electronic data interchange (EDI); RDF

• Unstructured data refers to unorganized data that does not fit neatly into the traditional row and column structure of the relational database- audio, video, images, e-mail message, email content, or word-processor document.



• Defines the inconsistencies and uncertainty in data.

• Veracity originates from diverse sources presented in a variety of formats with varying signal-to-noise ratios.

• For instance- Data originating in bulk could create a commotion whereas less amount of data could convey incomplete information.



• Defines the multitude of data dimensions resulting from multiple disparate data types and sources.

• Characterised by different interpretations of raw data depending on its context, a condition true with natural language processing.

• For instance- A single word can have multiple meanings as peruse. New meanings are created and old meanings are discarded over time.



• Defined as the enormous source of returns big data carries with it.

• Offers vital clues to new strategy formulation, selling and targeting previously undetected market demands.

• Value derived can be monetary or improved work processes.



• Defines the transformation of data from mere numbers to intelligent and comprehensible insights through charts, graphs, and maps.

• Relies on data pipelines to ingest raw data and process it to generate graphical dashboards.

• Utilizes data visualization tools like Qlik, Tableau, Power BI, Google Charts.


The Growing Prominence of Big Data Analytics

Big Data has grown by leaps and bounds, Analytics Insights forecasts the Big Data growth at a compound annual growth rate (CAGR) of 10.9% for the forecasted period 2019-2023.

• The greatest growth will be seen in the Telecommunication and IT industry growing from 63.9 billion US$ in 2020 to, 105.2 billion US$ in 2023.

• BFSI will grow from 29.8 billion US$ in 2020 to, 51.7 billion US$ in 2023.

• Government and Defence will witness a spike from 13.0 billion US$ to 20.0 billion US$ from 2020 to 2023 respectively.


Analytics Insights Big Data Market Estimates – According to Industry (in US$ Billion)

By Industry 2020 2023 Estimates
Telecommunication and IT 63.90 105.20
BFSI 29.80 51.70
Government and Defence 13.00 20.00
Manufacturing 17.70 26.80
Healthcare and Life Sciences 15.50


Use Cases of Big Data Analytics


• Big Data Analytics in Banking

Banks use big data to safe keep massive financial information’s. This big data is analysed for spending patterns from savings, to credit card purchases to detect frauds and prevent them before happening at the first go.


• Big Data Analytics in Energy Consumption

Big Data in tandem with smart IoT devices make way for smart meters to self-regulate energy consumption allowing efficient energy use.


• Big Data Analytics in Online Shopping

Big Data in retail has led to a drastic change in the entire industry. Retailers leverage big data from the moment a customer begins their shopping. Targeted advertisements to the delivery of your parcel, big data is everywhere.


• Big Data Analytics in Education

The education industry is a storehouse of big data related to students, faculty, courses, results and so on. A proper study and analysis of this data are effective to provide valuable insights which can be used to improve the working of educational institutes and its operational effectiveness.


• Big Data Analytics in Healthcare

Healthcare is another industry which generates a huge amount of data every day. It helps in predicting outbreaks of epidemics and also in deciding what preventive measures could be taken to avoid preventable diseases by detecting them in the early stages. Patients can be provided with evidence-based medicine which is identified and prescribed after researching the past outcomes of the same drug.