3 pillars of data analytics drive real-time business strategy

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Every business element today is subject to new demands, competitors, networks, risks, and opportunities. Businesses that evolve digitally will be able to more directly communicate with consumers, speed up the rate of innovation, and thereby demand a greater share of profit in their sectors. Every company has the ability to become a digital enterprise. Digitally transformed organizations today have an advantage, and only digital enterprises will thrive tomorrow.

Companies were forced to rapidly scale up their digital strategies to thrive in our modern global business world amidst the pandemic. Those organizations that could not pivot and change their market plan quickly struggled to survive. 

Traditionally the aim has been to be a data-driven company. That was all very well then, but things have changed. Today, only to survive in the unpredictable post-COVID-19 world, must businesses be guided by data. The potential to adapt and adjust the business plan rapidly as it relates to individuals, goods, and processes based on activities, whether they happen on the global market or internally, has never been more evident than is today. This means that advanced data analytics to operate real-time business procedures is the need of the hour.

The ability to leverage the benefits of advanced data analysis which certainly helps drive versatile business strategies needs significant pillars of data analytics. Such pillars include speed, agility, and scalability.

Speed

As per the 2019 CIO Agenda survey conducted by Gartner, companies that deployed AI soared from 4%-14% between 2018-2019. With AI continuing to expand, companies need to look beyond the conventional usage of CPU power and support their AI and Machine Learning applications with Graphics Processing Units. This will encourage them to build, train, and retrain, and run their analytical models quicker, which in turn will contribute to higher consumer products and services.

GPUs allow organizations to high scale operations to support the training and/or inferencing of analytical models, providing the scale and efficiency needed to effectively achieve several epochs in a shorter period and to fine-tune the model. 

Agility

A data model must pool all important data and related tables from various data sources so analysts can query them in their entirety and in relation to each other. Evaluation of the environment in which the company works should be at the center of the data strategy. During a pandemic in the midst of so much uncertainty, we see the role that the environment plays right now. One way of building an agile application model is to use application vault technology, as well as a data warehousing approach.

Performance 

AI algorithms can help interpret value from vast quantities of unstructured data, powered by data scientists and data analysts with profound experience in designing the best models and methods for working with that data.

To stay competitive, companies need a method of putting together a large variety of data in a 360° context that offers broader, more reliable, and more precise insights into analytics. 

The 3-pillars will definitely take the business to a higher level.

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