Game-Changing Technologies For Today’s Data Scene

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Widespread or emerging? “Ecosystem integration solutions are closer to widespread use than experimental,” said Patel. “Many organizations across a wide range of industries—including supply chain/logistics, manufacturing, retail, ecommerce, and software/information services, to name a few—are utilizing or embedding ecosystem integration platforms to deliver invaluable insights from their data.”

Potential challenges: “Organizations must break from the past, which involved black-box integration solutions or toolkits that required significant technical depth,” said Patel. “They  must stop integrating for the sake of integrating and think about the desired business outcome of their integration—think through the desired end state and build from there.” 

Future prospects: “Ecosystem integration solutions will likely be firmly mainstream within 5 years’ time because those organizations that don’t adopt an ecosystem approach to integration are at a high risk of becoming obsolete,” Patel said. “Due to the fact that ecosystem integration solutions truly do enable and activate entire ecosystems, we can expect these dynamic solutions to be a core part of not only any enterprise’s technology stack but also their overall business strategy.”


Cloud computing has been on the scene for a number of years now, but it is still as seen as a disruptive technology. “Cloud-based computing platforms and services are empowering enterprises to better optimize computing and storage power to manage data,” said Jai Ganesh, SVP and head of Mphasis NEXT Labs. “Cloud-based computing alleviates the stress of dealing with archaic processes and enables businesses to easily manage data, scale up or down when needed, and do away with costly data centers.”

Widespread or emerging?  “Cloud technology has been building momentum over the past decade but still has more room to disrupt various industries as we know it,” said Ganesh. “Most enterprise workloads will be in the cloud within the next year.”

Potential challenges: Of course, with any burgeoning technology, there will be areas where the technology needs to improve. “From an engineering perspective, the current models of engineering may not be appropriate or sufficient to exploit these new technologies and solve the new global problems,” Ganesh said.

Future prospects: “We will witness accelerated adoption of cloud computing across industries and government,” Ganesh predicted. “We are not far from the days where even quantum computing will be on the cloud on a pay-per-use basis.”


As can be expected, AI and machine learning topped experts’ lists of cutting-edge technologies that are reshaping the data center.

“Enterprises recognize the strategic value of deploying artificial intelligence for competitive advantage. However, studies have shown that the adoption rate of AI is only at 4%,” said Dinesh Nirmal, VP of data and AI development for IBM. “The disparity is largely the result of increasing data complexity and silos—for  instance, an organization that stores its data across multiple cloud environments.”

Emerging or widespread? AI technology is still in emerging or experimental stages, said Nirmal. “The ability to access AI across multiple clouds is still relatively new,” he added, noting that in February, IBM started offering Watson Anywhere, which cuts across cloud environments. 

Potential challenges: Data science and machine learning talent remain in short supply, said Helena Schwenk, global analyst relations manager at Exasol. “The skills gap is still a significant barrier that organizations struggle to overcome. This complicates the most time-intensive and complex task—the data preparation stage of a machine-learning project. The output of a machine-learning model is only as smart as the people who train it.” The other big barrier to machine learning’s success is operationalization, where models don’t make it into production environments to be used against live business data, Schwenk continued. “Industry research indicates that around 50% of data science projects don’t make it into production.” Schwenk advised, “setting up a strong governance framework, building the right data platform, developing a clear strategy to build machine learning skills.”

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