Game-Changing Technologies in the Data Environment of 2020

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If there were ever a time for digital transformation, it is the year 2020. Customers are turning to online transactions in response to decreased physical mobility due to the COVID-19 crisis, employees are working from home, and an uncertain economy is demanding smarter ways to compete. At the root of all these capabilities is data and the ability to analyze and act on data-driven insights. What technologies are coming to the forefront to enhance enterprises’ ability to compete on data? We asked a number of leading industry experts and solution providers to describe what they see as the most impactful technologies shaping today’s data environments.


AI and machine learning were cited by several industry leaders as the most important technologies shaping today’s data environments. “We’re starting to see more success in specific use cases of machine learning, such as anomaly detection with system events, natural language processing, entity extraction, and classification technologies,” said Ranga Rajagopalan, vice president of product management for Commvault.

AI is critical to competing in the emerging economy, as it “makes it possible to go beyond what the human eye can detect and focus on a range of bad behaviors,” said David Ngo, vice president of product and engineering at Metallic. “It helps predict, identify, address, and solve our data needs.”

AI and automation are making IT and data professionals’ roles easier as well—enabling automatic processing of billions of dependencies in real time, continuous monitoring of the full stack for system degradation and performance anomalies, and delivering precise answers prioritized by business impact, said Jakub Mierzewski, product manager at Dynatrace. “With the right AI and automation technologies and practices in place, teams can shift from reactive to proactive, from guessing to knowing, from sifting through logs or becoming tied up in war rooms to having deep insights and data that drive innovation, acceleration, and business value. It’s like having an entire new team working for you 24x7, allowing your people to focus on what really matters.”

One of the main challenges moving forward with AI is potential bias. Ngo advises preprocessing and profiling data before using it. “This will ensure the data is clean and will allow the system to accurately detect patterns and make recommendations in an unbiased way,” he said. “Companies will need to be careful in how they implement this technology, setting up parameters around how and what data is collected and how it is used.”

Another challenge is organizations’ expectations and understanding of AI. “We’ve seen customers shocked at the infrastructure that can be required to crunch the numbers at scale,” said Rajagopalan. “This is a different compute profile than many are familiar with.” In addition, there can be issues with data quality and volume, which can “impact the result significantly, especially if there isn’t supervision or reinforcement to guide the learning model,” Rajagopalan said. It is imperative to not underestimate the importance of data quality, he added.

Still, Rajagopalan has high expectations for AI. He predicts that “within the next 5 years, we’ll see greater accessibility or democratization of machine learning by reducing the economic and skill barriers.”

AI adoption will grow due to the limited resources and skills available to manage increasingly sophisticated technology infrastructures. “There’s no time to manually configure, script, and source data in a modern IT environment,” Mierzewski noted. “Automation must be continuous. Entity maps need to be mapped and updated continuously. Everything needs to be automatically watched, analyzed, and adjusted 24x7. Automating these activities will become the norm—not the realm of science fiction—within the next 5 years.”


As networks and technologies converge and interact, an emerging approach is the use of data meshes, which enable greater scalability across networks. With the rise of interconnected technologies from cloud to IoT to data lakes—all integrated into a service mesh—APIs will be a defining technology approach, predicted Nick Borth, vice president of product marketing for Software AG. The use of APIs will evolve as a key component of enterprise meshes, Borth said. “More companies will expand participation in their mesh networks to include all things connected.”

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