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.

SEE: Research: Automation and the future of IT jobs (Tech Pro Research)

1: Movement to the cloud

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.

2: Aggregation of digital unstructured and machine IoT data

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.

3: The use of more dark data

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. The data can also provide supporting evidence for trademark infringement and/or intellectual property violation claims.

SEE: Why you should devote as much time to dark data as big data (TechRepublic)

4: Stronger administration of data security permissions

As more big data moves to data warehouses and repositories, the goal is a “single version of the truth,” where users all use the same data but don’t necessarily have the ability to access all of it. Companies will tighten up data access permissions to ensure that each data user has the correct access permissions in place. This will likely involve creating or revising data access permissions policies and implementing technology that monitors and detects potential data exfiltration by users; data exfiltration is a process in which users–without authorization–copy, transfer, or retrieve data that exceeds their access clearances.

5: Immediately gratifying analytics

Like it or not, executives and line managers want to see analytics that give them immediately actionable data. They don’t want to wait for batch analytics reports, which the majority of big data analytics still is. The pressure will be on IT to deliver actionable analytics results faster, and to focus more big data and analytics activities on real-time or near real-time data.

6: “Where’s the beef?” business evaluations of big data projects

In 1984, hamburger chain Wendy’s rolled out a “Where’s the beef?” commercial that focused on the amount of meat customers were actually getting on their sandwiches. Many CIOs and big data project leaders will be encountering their own “Where’s the beef?” moments as they get called in by CEOs, board members, and other chief stakeholders to demonstrate how big data and analytics projects that the company faithfully invested in are now delivering tangible value to the end business. If tech leaders are only able to show projects that were successful in terms of IT execution, without significant returns on investment for the end business, they could find themselves in the line of fire.

SEE: Why the most boring IoT data may actually make the most money (TechRepublic)

In conclusion

IT leaders have learned that big data and analytics endeavors can be extremely exciting, but they must still pay off for companies. More analytics systems will be moved into the mission-critical category in 2017, but they will also be expected to perform well operationally, meet governance standards, and fulfill promises of business value to the company.