16 Trends Reshaping the Enterprise Data Landscape in 2016

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10-More Cloud in the Data and Analytics Space

Cloud has been the big story in the data analytics space in recent years and will continue to be so in the year ahead. “In 2016 we’re going to see the continued acceleration of adoption of cloud data platforms like Google BigQuery, Microsoft Azure, and Amazon Web Services,” said Ashley Stirrup, CMO of data integration software provider Talend. “Even though these businesses are quite large today, they are still just crossing the chasm. You are going to see competitors emerging, new cloud and database technology offerings, and new analytics offerings at every layer of the stack.” Roughly 40% of big data projects are being done in the cloud and that number is only going to accelerate, Stirrup predicted.

Ashish Thusoo, CEO and co-founder of big data as a service provider Qubole, sees cloud-based big data ecosystem solutions disrupting the market beyond the simple “early-adopter” margin. “We can now clearly see impressive and accelerating triple-digit growth rates from AWS and a plethora of supporting technologies emerging from obscurity,” Thusoo said. “Some of the large, leading-edge enterprises have already begun to split workloads in a bi-modal fashion and run some data workloads in the cloud.”

As enterprises get comfortable with data in the cloud, “archive as a service” may become a reality in the year ahead, as well. “The year 2015 was the year when cloud service providers—like Google and AWS —offered more and less-expensive options for storing long-term data in the cloud,” said Janae Stow Lee, senior vice president of strategy for Quantum, a provider of scale-out storage, archive, and data protection. Moving forward, enterprises want help with maintaining this data—with a special emphasis on compliance data, which Stow Lee calls “write-once-hope-to-read-never” data.

11-Corporate Cultures Evolve—Old Industrial Model Fades Away

“A greater number of business units spending dollars on data management and analytics will force their companies to evaluate and potentially overhaul their culture but also data methods, processes, and tools that help the company connect the dots and better understand their customer,” said Belliappa. This, in turn, will help “eliminate existing data silos, antiquated tools, and apply new techniques,” as well as bring data managers and professionals closer to customers, he added.

12-NoSQL Continues Its Rise

“It’s no secret the NoSQL data movement is real, growing, and the future of data management,” says Jeff Carr, CEO of SlamData, which provides a visual interface to explore and query NoSQL data. “While the diversity will be greater than we saw in the RDBMS age, the challenges will also be greater. A common problem has started to emerge—existing analytic tooling was built for the age of the RDBMS and does not work well in the world of NoSQL, including Hadoop. Relational algebra, the foundation of relational analytics, cannot deal with the complexity and lack of uniformity of NoSQL data, period.” Carr recommended the use of projects such as Quasar and Forward, which are “built to move analytics beyond the realm of purely flat data.”

13-More Data Coming From the Outside

The coming year will see greater reliance on data generated and derived outside the organization. Such data, often streamed and used in real time, will not entirely be under data managers’ control, so they need to prepare for it, said YY Lee, chief operating officer of business analytics platform provider FirstRain. “Building systems to smartly and responsively leverage dynamic external data is challenging,” he added. “Some of this external data is objective encyclopedic information, some is dynamic and social, and most of it is unstructured, heterogeneous, and unpredictable. This mix of data will be instrumental to truly understanding market behavior and for responding to critical customer developments.”

14-Data Lakes Proliferate

The data lake, an emerging approach, is likely to be seen in more enterprises during the coming year. “You don’t want to try to get your IoT data into a traditional data warehouse where data is structured for operational reporting and historical analysis,” said Darren Cunningham, vice president of marketing for SnapLogic, which provides an integration platform as a service. “With the right data management strategy in place, IT organizations and lines of business will either sink with a data warehouse or swim with a data lake in 2016.” This enables organizations “to track and manage data they’ve never had access to in the past—sensors, mobile devices, and the sheer exhaust of the web,” he continued.

15-Increased Use of Engineered Systems

There’s a lot going on with information technology, but the most disruptive trend may be less obvious. “For a trend to be truly disruptive you need supercomputing power,” said Sunder Singh, global head of Tata Consultancy Services’ Oracle Practice. Engineered systems or appliances meet the growing processing requirements demanded by social media and the IoT. “Processing that much data in as near real-time as possible requires incredible amounts of data to be acquired, stored, processed, and protected,” he said. “Engineered systems today extend their price-performance capabilities into high-volume data warehousing, transaction processing, and analytics.”

16-Supply Networks Become More Supple

The convergence of IoT and digital services means more insight, innovation, and alignment—if they are aligned with data sources in the right way. “Machine networks link sensors, components, equipment, and activities that enable companies to capture market inputs, reduce operational risk, achieve nimble supply chains, and deliver unsurpassed customer experience,” said David Parker, senior global vice president for SAP. “By automating data collection and operations, companies can manage remote processes, monitor trends, and gain new levels of competitive advantage.”

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