Big Data - Why Businesses Invest?

Businesses and institutions invest considerable budgets in the installation of Big Data analytical tools in their setups because these help them for handling huge volume of data through Artificial Intelligence (AI) enabled algorithms which get complete support from Machine Learning (ML) and Deep Learning (DL) which combining process massive amount of data in thousands of gigabytes in just few seconds.

AI enabled big data processing helps companies and conglomerates in achieving maximum efficiency and productivity, increasing sales, curtailing operational costs, maximizing revenue streams, maintaining optimum inventory level and raw materials, recording vast database of clients, building corporate image, and analyzing tons of business data in few seconds which happen to make informed decisions.

Valuable Business Asset

There are numerous sources which combining generate tons of structured, unstructured, and semi-structured data on daily basis all over the world. According to the study of Seagate UK, there will be 175 Zettabytes of data in the global datasphere by 2025.

Major sources of data generation come from companies, big corporations, banks, financial institutions, stock exchanges, private business enterprises, airlines, news channels, laboratories, industrial complexes, manufacturing concerns, government institutions, hospitals, airports, shipping enterprises, transportation, Ecommerce, logistics, search engines, traffics, system of railways, social media, film industry, and this list goes on.

The big chunks of database are generated on daily basis in thousands of megabytes which include texts, search engines' queries, huge electronic computations, social media, activities on websites and Ecommerce, graphics, audios, videos, images, pdf files, but all of them carry exclusive meanings and particular behaviors which can be decoded using artificial intelligence and machine learning statistical models.

Millions and billions of data points are considered valuable asset for the businesses and institutions because huge structured and unstructured data clusters provide them useful insights for making informed business decisions which help in increasing sales, revenues, and brand image on local and international scale.

Reduction in Operating Costs

Those businesses and institutions which take advantage of big data computing through efficient use of AI enabled machines, computers, and applications produce excellent results in their organizational setups which include reduction in the operational costs of doing business, optimum management of finished inventory, advance ordering of raw materials well before replenishment, enhanced efficiency among staff, improvement in the quick computations of financial, administrative, human resources, purchasing, sales, marketing, and management data.

To reduce substantial operating business costs, companies heavily invest in the installation of powerful software programs which support big data computation, otherwise it would be costly exercise to compute big data through traditional methods.

Predictive Business Models

Artificial Intelligence enabled algorithms with the help of Machine Learning and Deep Learning are capable of processing millions of gigabytes in just few seconds which traditional computing machines can take months to process that huge amount of big data.

Big Data predictive modeling that is powered by AI can make accurate future predictions about optimum sales volume, minimum operating costs, purchasing behavior of consumers, maximization in revenue streams, management in optimum inventory levels, optimal fuel and power consumption, generation of positive business cash flows, appropriate maintenance of machines and equipment, and avoidance of leakages in the systems, and this list goes on.

Future predictions are based on past business operational activities and huge amount of available data points on which predictive modeling is built by AI for making informed business decisions automatically.

Machine Learning is a subset of AI that makes prediction based on inputs and outputs of past data with neural networks and statistical analysis. ML discovers hidden insights in the data without direct human intervention and manual programming, so, ML automatically builds analytical models for future business applications.

Deep Learning is a subset of ML that involves the power of machines, applications, or computers to build automatic learning capabilities from huge number of datasets using vast neural networks with multiple layers of processing units which process complex tasks including speech and images.

Improved Products and Services

Having acquired greater computing power through AI enabled big data technology, companies extract and track useful data about their clients, so, based on collected information, corrective measures and right business strategies are formulated which help in increasing sales, curtailing costs, providing better discounts, and maximizing revenues.

With the help of big data analytical tools, companies gather precious data about customers’ online activities and additional information that is stored in their systems of corporate database.

The collected database of customers include their online browsing behavior, level of interests in different products and services, age brackets, social background, education, personal interests, so, based on gathered information companies are in a better position for delivering quality products, services, special offerings, and discounts to their target customers.

Corporate Brand Image

Businesses and intuitions handle big data of their customers, so, they obtain useful insights about exclusive preferences, inclinations, popularity of brands, and disliking of their clients, thus, extracted information helps them in offering quality products and services, so, as a result, companies build trust and strong corporate brand image among their target audience on local and international scale.

Efficient Inventory Management

AI enabled predictive abilities help companies and institutions in maintaining optimum inventory level to avoid wastages and manage optimum level of raw materials’ replenishment cycle for avoidance of production stoppage.

It is extremely difficult for big businesses to manage optimum inventory level and raw material position through the conventional computing software, so, AI enabled big data analytical tools with the support Cloud Computing and future technology of quantum computing combining will be in better position for solving huge computational data in just few seconds.

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