Big data helps organizations sharpen their batch reporting with the addition of unstructured data, which combines with traditional structured data to produce a more complete picture of what goes on in a business over a specific period of time. These useful reports enable decision makers to more accurately respond to situational trends.

But businesses also want data and analytics that are real-time or near real-time so they can act quickly on. The good news is we are beginning to acquire enough empirical results on real-time and near real-time big data and analytics to see where this data has been immediately “actionable” to businesses, and where it is making a competitive difference.

Why this matters

Many companies are still grappling with their big data initiatives, and yet they are reaching critical “pay off” times in which the executive decision makers are expecting to see results. Decision makers now want to “win” in the area of real-time, actionable analytics.

This is what we know about “winning” real-time use cases where big data analytics have delivered results that have favorably impacted corporate revenues, expenditures, and customer satisfaction.

Buying preferences

Web-based analytics on etail users have generated more sales by assessing customer buying preferences and then tendering offers based upon those preferences before the customers consider an alternate retailer. These preemptive analytics enable companies to “strike first” with highly attractive offers to buyers. Amazon was instrumental in igniting this buyer preemptive preferences strategy, and thousands of other etailers have followed suit.

Network intrusion detection and forensics

Network diagnostics toolsets have taken off. These tools analyze network and machine-generated data that seek entry into corporate networks from the internet and then produce actual and predictive reports on potentially dangerous malware-laden websites. The tools also report on internal network nodes and workstations that either have or are most likely to access these potentially toxic websites.

The ability to combine standard network traffic data and machine-generated unstructured data gives network administrators a real-time view of network traffic that is approaching 360-degree visibility. These tools save companies money and the embarrassment of network security breaches.

Logistics orchestration

The transportation industry now equips trucks with sensors that track deliveries and routes, optimizing these routes for the fastest and safest delivery. Sensors even auto report back to headquarters on driver “safe driving” and fuel economy habits. These real-time analytics are saving logistics providers millions of dollars annually in fuel costs; companies are also seeing on -time deliveries and customer satisfaction rates improving.

Predictive maintenance

Sensors placed on railways, tram tracks, and equipment can “report in” on early equipment and railway signs of failure. This enables maintenance crews to proactively repair or replace equipment and railway tracks before they fail, which improves system performance uptime and contributes to greater customer satisfaction.

What these systems have in common

All of these systems are using web-based unstructured data and then combining it with traditional system of record data for a composite analytics picture of what is going on in a particular area of the business. Equally important, none of these systems is trying to do “too much” with the data they are collecting.

In the case of constructing buyer preferences, the analytics simply collects data that is incoming from a buyer’s website traffic patterns (e.g., items the buyer has looked at recently) with the historical records of items the buyer has already purchased. In the case of a delivery truck, mapping analytics lay out the best delivery routes and then combines that with real-time tracking sensors on the trucks to track the deliveries in real time.

For those charged with delivering customer satisfaction from business analytics to their end business users, understanding these real-time big data use case success stories is pivotal, because 2015 is when real-time actionable analytics becomes a defining measure of whether a company’s big data efforts are moving forward as expected.