Major strides were made in big data and analytics in 2016, and companies will expect even more from those types of projects in 2017. So, what big data and analytics trends are on tap for 2017 that CIOs and big data project managers should be aware of? Here are six of them.
Small and midsize companies and even large enterprises are mapping strategies that take more of their applications to the cloud and out of the data center, and this holds true for big data and analytics as much as it does for traditional transaction processing systems. Companies want to see reduced spend in their data centers and greater flexibility in terms of plugging into and out of solutions. The ability to do this comes with subscriptions to services and not having to lock in for multiple years to on-premises equipment.
An additional factor for big data and analytics is the difficulty that even large organizations have in finding the requisite talent to run in-house Hadoop clusters and processing. This is forcing many organizations to go to the cloud and to cloud services providers that offer the big data processing platform as well as the expertise.
For all that's been written about it, using and incorporating Internet of Things (IoT) data is a future endeavor for most organizations. What we do know is that everyone is thinking about it.
Organizations' big data aggregation goals will expand to visions where standard digital data originally entered by humans and data issued from machines will be aggregated into composite visualizations that will transform the way work is done. A good example is drone-hosted data that will combine an assortment of sensory and standard IT inputs into a single pane of glass view for an operator of how a drone is functioning. Big data and analytics vendors and consultants will be called upon to assist companies in defining and achieving these new data aggregation goals.
Companies will begin to troll the wealth of information that is contained in paper-based documents, photos, videos, and other corporate assets that are lying dormant in vaults and storage closets but that could be put to use in big data aggregation. These assets can give organizations a more comprehensive view of historical performance trends and product cycles that can be useful for planning.