Roadmap report

CIO Think Tank: Data and analytics at scale

The world is awash in data. So in almost every enterprise, the question looms: How can we scale data analytics to deliver the greatest possible value?

A user reviews data and statistical models. [analytics / analysis / tracking / monitoring / logging]
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In July and August 2021, CIO held three virtual CIO Think Tank discussions that brought together 31 IT leaders to unpack one of the most important issues in enterprise technology today: maximizing the utility of data collected through multiple channels.

The goal of these discussions was to identify key challenges facing analytics initiatives and to offer a roadmap for IT leaders—as well as the technology industry—to overcome those obstacles. All participants drew on their experience and knowledge to describe strategic and tactical approaches to scaling analytics.

Data now flows through and among all aspects of an enterprise, as well as through customers and partners. This omnipresent reality means that data analytics transcends individual applications, with interwoven ecosystems of people, processes, and technologies. Our CIO Think Tank panelists shared their views about confronting this broad scope of work, including culture, hiring, training, organizational models, governance, and more.

Putting data analytics to work

For most CIOs, analytics projects top the to-do list. The ideal outcome is an array of insights that results in faster, more efficient operations.

For example, Mastercard receives thousands of requests from partner banks needing logo approval to issue new credit cards. Gurpreet Atwal, senior VP at Mastercard, said this approval process was previously slow and manual. Today, an algorithm crunches applicant data to score most requests, kicking exceptions to manual review.

"What used to take us weeks usually takes us minutes now," he said.

Inpro CIO Steve Baumgartner related similar achievements in assessing 10,000 to 15,000 potential bids each month as his company pursues construction projects.

In other cases, companies are looking at more strategic results, potentially affecting entire business models. Many organizations also have an eye out for direct monetization of data itself, now or in the future.

"My interest at this point is to come up with software products that could potentially not provide us just immediate revenue, but provide a way to collect that strategic data, which could be very valuable going into the future," said Subbu Ramanathan, divisional CIO at Brady, a manufacturing company. As examples, he cited Google and Facebook and their monetization of data gained through products that are often given away.

It's an alluring prospect, one that no longer seems within reach of hyperscale IT vendors alone.

"More and more now, people are thinking that data should not be treated as cost anymore, but instead as an investment in a new asset class, eventually with value on our balance sheet," said Dr. Eng Lim Goh, SVP and CTO, High-Performance Computing and AI at HPE.

IT leaders also know that getting there isn't easy, however. "Analytics at scale" is a large, complex hairball of people, process, and technology issues. Download the full report to learn more.

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Copyright © 2021 IDG Communications, Inc.

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