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

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The past year was a blockbuster one for those working in the data space. Businesses have wrapped their fates around data analytics in an even tighter embrace as competition intensifies and the drive for greater innovation becomes a top priority.

The year ahead promises to get even more interesting, especially for data managers and professionals. Leading experts in the field have witnessed a number of data trends  emerge in 2016, and now see new developments coming into view for 2017.

The Era of Big Data Is Here

Big data is here and it is omnipresent. It is now simply the data organizations work with from day to day.  “Today, large datasets are standard and the focus is on what we do with that data,” said Sean Naismith, head of analytics services for Enova Decisions. New developments seen in recent months include activation of advanced analytics models for real-time decision making to improve the customer experience and operations, he added. “This includes tracking the data and metadata behind those decisions to meet increasing regulatory requirements. In industries such as consumer lending and insurance, businesses will need to keep track of not only the macro-decisions but every micro-decision along the way in order to meet regulatory requirements.”

Cloud Rules and Changes the Rules

Cloud computing dominated IT and data managers’ mindshare this past year and will continue to do so through 2017, Patricia Dues, president of the Oracle Applications Users Group (OAUG), predicted. “The Oracle community has been talking for years about cloud, but up until recently, it’s been more theoretical talk than action,” she said. Things are changing fast, though. “Part of that initial trepidation came from the fear of the unknown,” she added. “But as the industry has become more and more educated on its offerings and benefits, we are starting to see the industry migrating more toward the cloud, particularly in a hybrid model.”

The move to cloud has been occurring in a slow, piecemeal process, Dues added. That’s because “on-premises hardware and software infrastructure is so costly, and companies are hesitant to fully abandon these capital expenditures to go through another learning curve when migrating to the cloud.” The hybrid model provides a gradual implementation path to the cloud, she explained. In addition, education is required for managers and professionals to understand cloud’s benefits and challenges, and this has been a commitment of the OAUG.

Another technology emerging over the past year as a result of cloud is webhooks, an online event notification service that posts a message to a URL when a change or update is made to data. This accompanies the rise of cloud-based applications, explained Mark Geene, CEO and co-founder of Cloud Elements. “We’ve seen a huge proliferation in the amount of data available from the increasing number of SaaS, PaaS, and IaaS platforms, which are causing siloed islands of data. Data synchronization has become a massive challenge for SaaS apps and enterprises.” Webhooks are significant to the market “because of this real-time data synchronization and access,” he noted.

Demand for Even Faster Performance

Over the past year, data managers and vendors have been looking at more ways to boost performance to meet the needs of data-hungry enterprises. With so much data—in all its forms, new and legacy—enterprise systems are quickly becoming overwhelmed, and, as a result, performance is a major issue. In 2017, enterprises will search for solutions to address the challenges faced by data teams that find themselves spending much of their day digging through machine logs in order to identify the root cause of problems on a big data stack, said Shivnath Babu, CTO of Unravel Data and associate professor of computer science at Duke University. “Ideal solutions will be ones that resolve problems automatically, detecting and pinpointing performance and reliability issues with big data applications running on clusters; solutions that open up the doors to data equality across the enterprise, and that with just the click of a button, drastically accelerate the time-to-value of big data investments.”

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