Game-Changing Technologies For Today’s Data Scene

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Future prospects: AI has a bright future, Nirmal said. “We expect the trend toward running AI on the cloud to continue, due to the efficiencies, scalability, and lower costs associated with cloud. Running AI on the cloud speeds business digital transformation, helps unlock hidden insights from company-wide data, and  helps them begin to automate business processes that improve efficiencies and increase performance.”


“Digital threads” will help keep companies—especially manufacturers with software embedded into products—more agile. “The digital thread is the framework which connects data and produces a holistic view of an asset’s data across its product lifecycle,” said Mark Reisig, director of product marketing at Aras. “With access to hosts of new data from a product’s performance in the field, manufacturers can determine what adjustments need to be made to future iterations of that product, as well as future new products, to stay relevant in a rapidly changing, competitive marketplace.”

Emerging or widespread? At this time, “organizations are still in the midst of fully adopting a true digital thread solution,” Reisig said.

Potential challenges: “Legacy technology hampers digital transformation efforts,” Reisig said.

“Legacy systems consist of a mix of individual products accumulated over the years, sometimes through acquisitions. At best, they may have been loosely stitched together but more often, the result is silos within the product lifecycle management system architecture that impede collaboration,” he noted.

Future prospects: In today’s business climate, innovation is critical. Companies that are still focused on doing things the way they did yesterday “are in danger of being boxed out of the market by competitors and new entrants that are simply moving faster than they are,” said Reisig. “If properly deployed, the digital thread can support your organization’s needs next month—and next year—to stay flexible and leverage the data you have at your fingertips today to ensure you can compete tomorrow.”


Virtual databases have been on the scene for a number of years, but lately have risen to a new level around test data management using virtualization. “There is amazing tech that looks at the block-storage level of the database to provide virtual databases very quickly and via self-service,” said Robert Reeves, chief technology officer of Datical. “The virtual databases provided can have masked data to protect personally identifiable information. This is a game changer for large enterprises that need to develop faster yet have legacy databases. With virtual databases, developers and test teams can create ephemeral environments that mimic production systems. Thus, they can shift their system integration efforts to the left and improve delivery time and quality.”

Emerging or widespread? “Both virtual databases and DevOps [are] very widespread in use by many enterprise-level customers,” Reeves said.

Potential challenges: “DBAs have challenges with this technology as it automates a previously manual task,” said Reeves. “Thus, some organizations have problems adopting due to internal foot dragging caused by DBAs scared about their job security. What happens is that it allows DBAs to perform higher-value tasks and become more valuable to the enterprise.”

Future prospects: “Currently, this technology can be applied to NoSQL databases but is not widely adopted for that use case,” Reeves said. “However, with the expansion of those data stores in the enterprise, you will see virtual databases being used for both RDBMS and NoSQL.”


AIOps represents the next level of maturity for ITOps, and will include capabilities such as advanced analytics, machine learning, and AI, said Douglas McDowell, chief strategy officer at SentryOne.

Emerging or widespread? “AIOps is rapidly emerging,” McDowell said. “In fact, ITOps software developers—and their customers—that have not adopted AIOps at some level will be greatly limited or obsolete in the next 5 years. With the growth in the number of connected technologies, and their complexities, especially in distributed, microservice-based cloud architectures, it will be increasingly difficult for humans to efficiently and effectively monitor, diagnose, and optimize technical platforms, databases, and apps. AI-based automation and recommendations bring hope to DevOps engineers and admins, to allow for controlled development, testing, and operational ownership.”

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