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Applications of Big Data Analytics That Yield the Most Value

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January 14, 2021
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January 14, 2021
Applications of Big Data Analytics That Yield the Most Value

The IDC estimates that worldwide revenues for big data and business analytics will grow from $130.1 billion in 2016 to more than $203 billion in 2020, at a CAGR of 11.7%. Dan Vesset, Group Vice President, Analytics and Information Management, IDC, said, “The availability of data, a new generation of technology, and a cultural shift toward data-driven decision making continue to drive demand for big data and analytics technology and services.”

An article by Forbes based on IDC data states that by the end of 2017, revenue growth from information-based products will double the rest of the product/service portfolio for one-third of Fortune 500 companies. Raw data and various value-added content will be bought and sold either via marketplaces or in bilateral transactions, and enterprises will begin to develop methods for valuing their data

The IDC states that data monetization is expected to become a major revenue source. The world will create 180 zettabytes of data (or 180 trillion gigabytes) in 2025, up from less than 10 zettabytes in 2015.

As data-rich organizations look for ways to leverage the endless opportunities insights into customers provide, big data analytics finds a variety of other applications, among which are cost reduction, search for innovation avenues, launch new products/services, add revenue, transform business for the future, and more. 

Research suggests that the applications that yield the most value have decreased their expenses—almost 50% of executives from Fortune 1000 companies report to have seen value as they started using big data to cut down on expenses; 44% started using big data and have seen value in finding innovation avenues. Interestingly, one of the most unsuccessful ways big data is applied is to establish a data-driven culture—over 40% of executives have started using big data but have not seen any value. Similarly, 33% of executives have not seen value in using big data for increasing the speed of current efforts; around 27% have not seen value in using big data to launch new products/services.

Source: HBR

Among the executives who have shared the significant impact of using big data is Vince Campisi, Former COO at GE Digital: "I’ll give you one internal perspective and one external perspective. One is we are doing a lot in what we call enabling a digital thread—how you can connect innovation through engineering, manufacturing, and all the way to servicing a product. And, within that, we’ve got a focus around a brilliant factory. So, take driving supply-chain optimization as an example. We’ve been able to take over 60 different silos of information related to direct-material purchasing, leverage analytics to look at new relationships and use machine learning to identify tremendous amounts of efficiency procuring direct materials that go into our product.

“An external example is how we leverage analytics to really make assets perform better. We call it asset performance management. And we’re starting to enable digital industries, like a digital wind farm, where you can leverage analytics to help the machines optimize themselves. So you can help a power-generating provider who uses the same wind that’s come through and, by having the turbines pitch themselves properly and understand how they can optimize that level of wind, we’ve demonstrated the ability to produce up to 10 percent more production of energy off the same amount of wind. It’s an example of using analytics to help a customer generate more yield and more productivity out of their existing capital investment.”

Explaining the impact of big data analytics, Victor Nilson, SVP, Big Data, AT&T, shared, "We always start with the customer experience. That’s what matters most. In our customer care centers now, we have a large number of very complex products. Even simple products sometimes have very complex potential problems or solutions, so the workflow is very complex. So how do we simplify the process for both the customer-care agent and the customer at the same time, whenever there’s an interaction?

“We’ve used big data techniques to analyze all the different permutations to augment that experience to more quickly resolve or enhance a particular situation. We take the complexity out and turn it into something simple and actionable. Simultaneously, we can then analyze that data and then go back and say, ‘Are we optimizing the network proactively in this particular case?’ So, we take the optimization not only for the customer care but also for the network, and then tie that together as well.”

ZhongAn, for example, an online property insurance company, specializes in the use of big data to automate underwriting and claims processes, design and tailor products, and create precision marketing campaigns and risk management strategies

IDC states that large and very large companies (500+ employees) will be the primary driver of the big data and business analytics opportunity, generating more than $154 billion in revenues in 2020. However, SMBs will remain a significant contributor, with nearly a quarter of the worldwide revenues coming from companies with fewer than 500 employees.

The financial services industry faces a significant transformative effect on the convergence of advanced technology and big data. As Irene Aldridge, Visiting Professor of Financial Mathematics at Cornell University & big data scientist, notes, Big Data Finance 3.0 is presently upon us. Big Data Finance 3.0 is about managing the scale of data and extracting the information within. Today’s big data is about faster, better analytics, the ability to extract that “needle from the haystack” using the latest data science inferences, and storing, managing, and integrating ultra-large sets of streaming and historical data of all kinds: market data, social media data, news, regulations, announcements, and so on.

Data is the lifeblood of any financial institution; and will be a critical foundation for the successful adoption of any advanced analytics and artificial intelligence. The vast majority of the top financial technology companies use data, analytics, and artificial intelligence to fuel their business models.

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