Most business leaders know how critical it is to have a big data strategy, but they don't always understand how quickly the landscape is advancing and evolving.
In addition, these tools are typically available on a pay-per-use basis, making them both cost-effective and powerful. "They change the velocity to value equation," Belliappa says.
Another advantage of the cloud, Cognizant's Schlesinger says, is the shift toward focused solutions that address specific customer needs, while also functioning seamlessly within a broader data framework. As an example, he points to Cloudera Navigator because it offers a framework for addressing data governance, and it can help with data compliance required by initiatives such as the General Data Protection Regulation (GDPR), which is set to take effect in May 2018.
"We are seeing a continued growth and maturation of the big data space," he explains. "Many solutions now address problems beyond basic data management and analytics."
Understanding Both Data and Connection Points
An agile, flexible cloud framework is only part of the story. Another key to constructing an effective big data strategy is to understand both data and connection points in deeper and broader ways. This requires data scientists and data analysts, but it also demands new and different thinking from business and IT leaders.
"The CIO and C-suite must work together in a far more collaborative way to identify value points and put data to full use," Schlesinger says. "The right combination of data leads to a competitive advantage." This means understanding use cases, reverse engineering processes and where the value of the data resides, as well as how it can be transformed into new, better products and services.
The mindset of this approach is entirely different from how organizations approached big data and analytics in the past. "Rather than starting with IT and saying, 'All right, let's store this data and then see what value we can derive from it,' we're seeing the business side saying, 'Here's the business problem we're trying to solve,'" he says.
Schlesinger also believes that it's critical to keep an eye on developments and advances in artificial intelligence (AI)—particularly machine learning—which offer powerful ways to crunch data and extract value. Finally, he thinks it's crucial to keep an eye on what competitors and companies in different industries are doing. "The goal is to spot opportunities to cut costs, boost operational efficiency, and create new products and services," he adds.
Capgemini's Belliappa says that, in the end, it's important to understand that the cost of collecting and managing data has reached a historic low. "All of this is a game-changer," he observes.
Nevertheless, transforming data into actionable information and real-world knowledge and results remains a challenge. "You really have to look at how you can assemble the right set of parameters to create value," he advises. "You have to create an analytics framework and an analytics engine that focuses on key factors."
All of this involves more than technology and more than sheer data. "It's about understanding the business so that data scientists, data analysts and line-of-business executives can coordinate to produce real-world results," Belliappa concludes.
Samuel Greengard writes about business and technology for CIO Insight, Baseline and other publications. His most recent book is The Internet of Things (MIT Press, 2015).
This article was originally published on 06-23-2017