The past decade has seen major strides in the IT industry with cloud computing, blockchain and big data emerging as successful trendsetters for more innovation. According to a study led by EMC and Cap Gemini, 65% of big companies have estimated that they run the risk of becoming obsolete if they do not adopt adequate Big Data analytics solutions to support their modern data platform. IDC further brings to the fore predictions that suggest Big Data annual spending will reach $48.6 billion in 2019. With big data initiatives taking center stage, organizations are keen at leveraging the agility of big data processes in combination with artificial intelligence (AI) capabilities to speed up the delivery of business value.
How big data fits in the whole scheme of seeking artificial intelligence
With organizations grappling with hordes of data while managing their legacy IT processes, customer relationships, business intelligence, sales forecasting, logistics and so on, big data analytics is presenting itself with opportunities to work with massive data sets that offer value beyond that of just sample sets. That being said, the data storage industry is looking at newer ways of leveraging the power of huge data sets that are now readily available. And one of the areas that have an almost insatiable appetite for data storage is Artificial Intelligence (AI).
For organizations to make the best use of this integration, it is important that they understand the significance of developing a sustainable Big Data ecosystem. Considering that the most interesting developments in AI are arriving from a data-intensive process called machine learning, it is no exaggeration to say that data is all set to rule the world of AI. To understand the reasons for expanding the scope and sustainability of a Big Data environment, let us first understand how existing data capabilities can be used to harness other areas of machine learning and AI.
How can existing data capabilities within an organization be leveraged to build a well-defined data environment ready for taking the plunge into the realm of AI?
With data being seen as a huge driver of business, the power of Big Data analytics can be sufficiently used to make sense of business. That essentially translates to business intelligence — using data to identify pain points and analyze information to make better decisions.
Another area in which data proves to be useful is product data science — the technique of building algorithms and systems for improving a particular product. It is within this umbrella that concepts such as recommendation systems, search algorithms, and data visualization come into play. As an offshoot of product development, this big data capability has immense scope to expand its services through AI.
The last data capability that is often overlooked is the R&D capability — using data to fire up new products, new businesses, and new revenue opportunities.
So how is AI part of the larger framework?
AI empowered by big data is driving a much-needed change in the direction of disruptive technology. The power to make in-the-moment decisions by rapidly processed information is now increasingly becoming the norm.
Big data is poised to stretch its influence well beyond simple data and analytics, and AI is making the best use of this factor to build a powerful foundation for heightened innovation and business disruption. While the first wave of big data was about speed and flexibility, it appears that the next wave will be all about leveraging the power of AI and machine learning to deliver business value at scale.
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