How Big Data Is Empowering AI and Machine Learning at Scale

Big Data is powerful on its own. So is artificial intelligence. What happens when the two are merged?

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Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
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Big data is moving to a new stage of maturity — one that promises even greater business impact and industry disruption over the course of the coming decade. As big data initiatives mature, organizations are now combining the agility of big data processes with the scale of artificial intelligence (AI) capabilities to accelerate the delivery of business value.

The Convergence of Big Data and AI

The convergence of big data with AI has emerged as the single most important development that is shaping the future of how firms drive business value from their data and analytics capabilities. The availability of greater volumes and sources of data is, for the first time, enabling capabilities in AI and machine learning that remained dormant for decades due to lack of data availability, limited sample sizes, and an inability to analyze massive amounts of data in milliseconds. Digital capabilities have moved data from batch to real-time, on-line, always-available access.

Although many AI technologies have been in existence for several decades, only now are they able to take advantage of datasets of sufficient size to provide meaningful learning and results. The ability to access large volumes of data with agility and ready access is leading to a rapid evolution in the application of AI and machine-learning applications. Whereas statisticians and early data scientists were often limited to working with “sample” sets of data, big data has enabled data scientists to access and work with massive sets of data without restriction. Rather than relying on representative data samples, data scientists can now rely on the data itself, in all of its granularity, nuance, and detail. This is why many organizations have moved from a hypothesis-based approach to a “data first” approach. Organizations can now load all of the data and let the data itself point the direction and tell the story. Unnecessary or redundant data can be culled, and more indicative and predictive data can be analyzed using “analytical sandboxes” or big data “centers of excellence,” which take advantage of the flexibility and agility of data management approaches. Apostles of big data have often referred to their approach as “load and go.” Big data enables an environment that encourages data discovery through iteration. As a result, businesses can move faster, experiment more, and learn quickly. To put it differently, big data enables organizations to fail fast and learn faster.

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Topics

Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
See All Articles in This Section

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Comments (3)
Andrea Maria
Great article. Big data is getting a new type of maturity, being a boost power to artificial intelligence. In fact, AI is data-driven. Without a right piece of data set, no algorithm can show intelligence that the business world is looking for.
Tinko Stoyanov
If possible I just would like to correct the title. The "big information" (not the big data) will really empower the AI. The data is a subset of information (and ML is just a part of AI). AI needs "big" information to start functioning (properly).
Samir Asaf
Excellent article by Randy, highlighting the power of AI and Big Data. This convergence is necessary but not a sufficient condition for delivering business value. CXOs need relevant real-time insights focused on their Critical Success Factors (CSFs), so that analytic outputs directly support strategic and operational decision-making.