Not that long ago, solution providers could offer the ability to derive intelligence from data as a nice-to-have feature for customers. Today, as machines are increasingly more connected to each other and to customers' internal computer systems, it is becoming critical that all the information collected is analyzed and used. Customers' want to see a return for all that storage they bought, and all the software in which they've invested. That's where operational intelligence comes in.
OI can handle fast-changing data better than traditional methods. The Internet of Things has created a vast, quickly flowing stream of information that threatens to bury companies that are actually trying to make use of the data they are now able to — or are planning to — collect. In-memory computing helps solution providers meet those challenges by implementing solutions that have less latency and are scalable.
[See also: In-Memory Computing Changes the Way Businesses Compute and Compete: http://buff.ly/1SvcGMu]
IoT solutions offer customers in transportation, retail, construction, transportation, etc., the ability to track inventory, projects and other deliverables quickly and efficiently. IoT can save costs and make the workday far more efficient. But, if the hardware isn’t up to par, the data crunching is a slow and laborious process. And if solution providers’ customers can’t get their data in a timely manner, their IoT investment is worthless. So, solution providers face the challenge of not only extolling the virtues of IoT technologies, but also must prove it is a faster, more efficient way of doing business more intelligently.
In-memory computing addresses the challenge of speed. The founder of ScaleOut Software, William L. Bain said in-memory computing can significantly speed up processing, resulting in potential new revenue streams, for solution providers as well as their customers:
[In-memory computing] can analyze a terabyte of continuously changing data in a few seconds and can ingest and analyze events from millions of sources within milliseconds.
Uses for OI and in-memory computing include the ability to quickly:
- Provide factories real-time data on the health of their factory equipment. Predicting problems and troubleshooting in advance of a potential shutdown saves the costs incurred when manufacturing lines are stalled.
- Give retailers the tools to be more successful against e-commerce competitors. The data collected by retailers can be analyzed to give brick and mortars the same depth and breadth of customer knowledge attained by their online competitors.
- Offer fleet management companies a means to not only track deliveries but also lower costs. By monitoring drivers’ habits, fuel costs can be better contained. Possible solutions may include driver training or reevaluation of routes.
Following is an infographic from Intel that shows how implementing OI via in-memory computing can maximize the potential of IoT technologies.