The Data Scene in 2017: More Cloud, Greater Governance, Higher Performance

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One emerging tactic, copy data management, in which duplicate data is minimized to reduce storage requirements, has caught the attention of data managers, fueled by major vendor announcements embracing the approach. “Copy data management is part of the trend toward IT and infrastructure modernization,” said Peter Eicher, director of marketing for Catalogic, “Current practices are too manual, too slow, too error-prone, and too IT-centric,” he observed. “Users want speed, automation, greater reliability, user self-service, and usable APIs, and they want it all to be application- and database-aware. IT management wants the data center to operate more like Amazon: If I need resources, I just go get them and they are ready for me in minutes. I don’t have to go through 15 steps and seven different layers of approval to spin up a VM or get a copy of an Oracle database for testing.”  As a result, he explained, copy data management makes up for shortcomings in data storage platforms that cannot deliver this speed.

IT Automation on the Rise

Mainstream enterprises are learning what the major web players have done in their efforts to service millions of users simultaneously and understand that everything needs to be automated as much as possible. Automation “has injected itself meaningfully into enterprise systems over the past year,” Ken Cheney, vice president of business development for Chef Software, said. “In 2017, automation will extend into security practices in the enterprise. Companies will automate compliance and risk management practices so enterprises can better protect themselves against potential hacking and rely on flexible and secure software that can automatically adapt to manage vulnerabilities. Companies can prepare by beginning to build security into the software pipeline, upending the traditional security model with systems that are able to adapt in real time to changing threats.”

Predictive analytics is also playing a role in the rise of “self-healing system management capabilities across the entire infrastructure stack,” said Rod Bagg, vice president of analytics and customer support at Nimble Storage. “IT departments are increasingly automating routine tasks, such as workload balancing, provisioning, and application management. Vendors recognize that increasing the efficiency of infrastructure hardware has been delivering diminishing returns for some time, and have now begun focusing on future-proofed intelligent solutions that provide benefits beyond speed and performance. The advent of self-healing data center infrastructure will lead to significant reductions in the cost of maintaining enterprise IT infrastructure.”

Machine Learning Attaches More Intelligence to Data

Machine learning, in which computers program or re-program themselves based on data coming in, caught fire in 2016. It takes data analytics to enable this, according to Doug Rybacki, vice president of product management at Conga. “Having a CRM system will no longer be enough. Organizations will need to ensure that a system can make the business more efficient by helping users decide the next best course of action.” Machine learning is still in its early stages, he added, but progress is occurring at a rapid clip. “Those who can make it happen now are seeing amazing benefits,” he said. “Long term, it will be absolutely critical for almost any data-driven application to incorporate machine learning to accurately digest the increasing volumes of data our systems and processes create.”

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