Artificial Intelligence: Re-Imagining Big Data's Applicability
Data is everywhere; in the webpages you visit, in blog posts you read, on social media platforms you post on, the videos you watch – all of it and more. It would suffice to say that everything you do on the Internet creates data – as does everything you don’t. We generate nearly 2.5 quintillion bytes of information daily, while Google alone is estimated to store over 10 Exabytes of data on a daily basis.
Digital data from all the above sources and more contributes heavily to the massive influx of Big Data and is changing how businesses operate. The quantity and variety of data and the speed at which it is generated and processed each minute makes this phenomenon truly “big”. It is these variables that define Big Data and make it such a dominant factor in manufacturing, sales, marketing, operations, marketing research and business analysis to name a few of its applications. Big Data has led to significant developments in analytics of both text and video, detecting fraud, and predicting consumer trends. However, while solving several problems for both businesses and consumers, Big Data brings with it challenges of its own.
While methods of storing data and analyzing it have advanced considerably in the last 10 years, the fundamental systems based on Big Data have begun to create potential problems. As a result, businesses are left asking some pertinent questions that are relevant to the future of Big Data.
The volume of data out there is massive and what is needed is a system that can sift through it and make sense of it. While many Big Data systems are relatively new, they are becoming redundant or are overloaded, and are often incapable of responding to demands as they emerge and grow.
Currently, all processes relating to Big Data are executed and overseen by humans. However, imagine if complex processes like extricating relevant data and refining it could be delegated to an intelligent machine. Putting other Big Data processes on auto mode and delegating the machine to manage it could make business processes quicker and smarter. Sounds exciting? This day, however, might just arrive earlier than we think.
Big Data and AI almost seem like a match made in digital heaven. While Big Data can expertly sieve out useful information, AI can help businesses look for useful insights from this information. There are several ways AI can do this, such as through automation of business processes and self-learning and optimization of its performance.
However, its most important function is to act as a platform to facilitate interactions between humans and machines. This would be most evident in sales and marketing processes. In addition to a heavy emphasis on CRM and market research, AI and Big Data are set to revolutionize the role of the salesperson in the next few years. Their application of several horizontal technologies allows AI to easily integrate with any function, product, or service. Businesses that have adopted AI-enhanced tools have seen impressive results as to the effectiveness of sales and marketing activities.
Hence, the point to be noted is that Big Data and AI are technologies that are a synergistic match. They have proven their worth in several practical situations as well, generating high ROIs for their users. The stream of data is nowhere close to slowing down and the sheer volume of data to be utilized continues to grow. But eventually, it is the correlation between Big Data systems and AI that will drive critical decision making.
This next wave of the espousal of AI and Big Data and all the scope for innovation it will bring is an exciting proposition for data scientists and analysts. For businesses, it’s a highly promising development. But like any other new technology and process, it needs companies to be prepared, and grab the proverbial bull by the horns before it knocks you down. If current Big Data systems are finding it challenging to adapt to the rising volume and velocity of Big Data today, then it’s time to re-assess their relevance and root out outdated, inefficient systems.
[Disclaimer: The views expressed in this article are solely those of the authors and do not necessarily represent or reflect the views of Trivone Media Network's or that of CXOToday's.]
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