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Where is Big Data headed?

Organizations will opt for database technologies that provide analytics at the same speed as their main business (i.e. processing large transactions at extremely low latencies and allowing in-memory analysis/ decision making.)

The ability to achieve cloud-like flexibility in the enterprise data center with commodity x86 servers, DevOps practices, and software based application services will enable businesses to maximize Big Data performance and scale.

You should see more tools available for small businesses to self-prepare their data for analysis, since Big Data is now trickling down from larger corporations as a way to make better business decisions.

The primary change is prescriptive analytics expansion — in streaming, security, impact on lifestyle, better governance to cure the Big Data excess-collection bellyache, and cleaning up faulty open source algorithms.

We’re already seeing cross-platform ads – search the Amazon mobile app, and then see the product again in your browser-accessed Facebook. Big Data will further enable more sophisticated re-targeting and pre-targeting.

As demand for high-fidelity enterprise Big Data brokers continues to grow rapidly, traditionally paid services will become free to capture more structured data about end users.

2016 will be the year for small business in the world of Big Data. Advanced
technologies that at one point were only available to the enterprise are now analyzing Big Data for long tail SMB’s. The subsequent transparency will level the playing field for small business in a big way this year.

The ability to explore and discover insights will move closer to the source production data stores and in many instances the production store will also act as the analytical platform.

The biggest change will be how Big Data is leveraged to advance business goals. Big Data analytics in the APM (Application Performance Management) arena will now gather not just data but actionable insights.

The future of Big Data is in industry-specific strategies. Can you accurately predict when a meeting room is available? Or who will come? Or the best way to get there?

As businesses stop worrying about whether Big Data provides business value, they will rightly focus on ensuring that the data that powers these applications is always available and always protected.

Data as a Service (DaaS) built into data-driven applications will dramatically change the game, not just for acquiring external data, but for sharing data internally and providing the opportunity to monetize data through outbound licensing.

Companies will outsource Big Data to the cloud, as cloud providers will leverage APIs and cloud-to-cloud integrations to deliver insights from systems of record.

In the next two quarters there will be a huge upswing of cloud service providers pushing Big Data as a service for their customers. There will surely be a significant price cut to make Big Data tools available for the average coding shop.

Big Data prediction can help us find out what thought patterns are not serving us or limiting us and how to find the best activity to help the individual change his/ her patterns.

“Big Data” will become “Huge Data” with data streaming in from the “Internet of Things”. Advances in hardware devices & software tools will spew a new generation of productivity for humankind.

How the television industry handles data will change. Forget ratings. New data provides more accurate ways to determine financial success — such as which items were bought inside a show.

Big Data tools will continue to become easier to use for those that have little coding experience with drag & drop capabilities, making Big Data more accessible.

2016 will be the year where a paradigm for virtualizing Big Data infrastructure will become established as part of the journey towards adoption by mainstream enterprises.

Cloud providers will increasingly benefit from Big Data. The unlimited availability of storage and processing power that cloud providers offer is too attractive for organizations to ignore.

Big Data is a Reinforcer, Not a Replacer. One student might take four years to complete a college degree and another six. Data doesn’t consider the why factor.