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How is Big Data and Advanced Data Processing helping us in everyday life

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By Sony Kunwar, Co-Founder and Sr. Vice President, Windsor Digital

In an age where our daily lives revolve on devices such as smartphones, smart cameras, smart-tabs, smartwatches, and smart speakers, a mountain of data is accumulated from these various digital sources. This vast amount of complex data is analysed and examined by leveraging the power of Big Data. Big data technologies and tools have evolved to aid in the combat of these difficulties, allowing the world to appreciate the technology’s wide range of uses, with corporations reaping the benefits for expansion purposes. 

In a nutshell, Big Data is the modern era’s new money and our reliance on data is growing as technology continues to have a significant impact on our lives. It has already begun to influence every aspect of our lives, from our music and television show tastes to smart assistants who can perform a variety of jobs for us, making our lives much more simple and comfortable. The worldwide data market is expected to grow to US$ 42 billion, according to Statista. 

Understanding Big Data

Big data is a collection of organized, semistructured, and unstructured data that may be mined for information and used in machine learning, predictive modelling, and other advanced analytics initiatives. Big data processing and storage systems, as well as technologies that facilitate big data analytics, have become a standard component of data management architectures in enterprises. 

The three V’s are widely used to describe big data: the vast ‘volume’ of data generated, gathered, and processed in many contexts, the extensive ‘variety’ of data types frequently stored in big data systems, and the’velocity’ at which much of the data is generated, collected, and processed. 

Companies use big data in their systems to enhance operations, provide better customer service, generate targeted marketing campaigns, and take other activities that can raise revenue and profitability in the long run. Businesses who properly use it have a potential competitive advantage over those that don’t since they can make more informed and faster business decisions.

Storage and Processing of Big Data

A data lake is frequently used to Big Data. While data warehouses normally employ relational databases and exclusively store structured data, data lakes use Hadoop clusters, cloud object storage services, NoSQL databases, or other big data platforms to store a variety of data types. Many big data settings use a distributed design to connect different systems; for example, a central data lake might be linked to additional platforms like relational databases or a data warehouse. 

Big data systems’ data can be left in its raw state and then filtered and arranged as needed for certain analytics applications. In other circumstances, it’s preprocessed with data mining and data preparation software so that it’s ready for routine applications. The underlying computational infrastructure is placed to a lot of strain by big data processing. Clustered systems, which divide processing workloads across hundreds or thousands of commodity servers utilizing technologies of processing engines, are frequently used to offer the requisite computing power. 

Application of Big Data in Various Domains

Now that we’ve established that Big Data is ingrained in our daily lives, let’s take a look at the various industry verticals that are adopting the technology and how they’re using it, as well as some practical examples. 

Education: When it comes to the education business, the amount of data collected from students, instructors, courses, and results is enormous, and its analysis can yield useful insights for improving educational institute operations and functioning. Big Data plays an important role in this sector, from improving effective learning to improving international recruiting for universities, assisting students in setting career goals, reducing university dropouts, allowing for precise student evaluation, improving the decision-making process, and enhancing student results.

Banking: Big data has made banks more effective for each industry, whether it’s in cash collecting or financial administration. Customers’ struggles have been decreased as a result of the technology’s implementation, creating more revenue for the bank and making their ultimate conclusions more obvious and intelligible. Big Data has a wide range of uses, including detecting fraud, simplifying and streamlining transaction processing, improving customer understanding, optimizing trade execution, and facilitating an upgraded consumer experience.

Agriculture: Big data analytics in agriculture propels smart farming and precision agriculture operations, which saves money and opens up new business prospects. Meeting food demand by providing farmers with updates on changes in rainfall, weather, and other factors affecting crop yield, assisting farmers in making accurate pesticide decisions, managing farm equipment, ensuring supply chain efficiency, planning when, where, and how to plant seeds and apply chemicals, and also in e-commerce.

Media: As newer techniques to consume content online via gadgets become the new trend, the buzz for conventional approaches to absorbing media is progressively fading. Big data has successfully ploughed its way into this industry as a result of the massive volume of data created. Whether it’s predicting what the audience wants in terms of genre, music, and content based on their age group, providing customer churn insights, optimizing customers’ media streaming schedules, making product updates more efficient in terms of time and cost, or assisting in effective advertisement targeting. 

Summing Up

The size of Big Data is growing every day, and it will expand even quicker in the future as more machines and IoT enable gadgets to engage in the big data game. Big data can be utilized to study the usage patterns of different machines thanks to the Internet of Everything (IoE) and Machine to Machine Communication (M2M). It may be useful in the future to provide improved functions for the machines that are built. However, when there is a massive amount of data available to companies, data security should always be the most important consideration. There’s no denying that a vast amount of data can be used to provide the greatest services, but data leaking is a concern. 

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