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What In-Memory Real-Time Analytics Means for SMBs

What in-memory does for big data and large enterprise can also be done for small and medium-sized businesses (SMBs) that want to harness the power of real-time analytics. In-memory computing is a computing method where data is processed and handled in the memory of computer servers, making processed data available at a moment’s notice, and detecting and analyzing information changes as they occur. This is accomplished by storing all the data to be processed in the random-access memory of a server. This simple change in how data is stored in computer memory enables more data to be stored in the same amount of space, alleviating the need for complicated algorithms to index files and other methods of information preprocessing. In-memory computing also removes processing latency by dispensing with the disk drive. Even though disk drives are fast, a physical actuating arm with a magnetic head moves back and forth across a rotating disk, which still takes time and adds to performance issues when large amounts of data need to be called upon and processed. In-memory processing occurs at the point of data storage in the memory, with no physical movement needed to process data.


Giving Enterprise Advantage to the SMB 

It is no secret that large enterprises often have a leg up over small businesses simply because they can afford to invest in cutting-edge technology. Small business owners also may not realize how a technology like in-memory computing can benefit them. When describing in-memory computing, people often use examples of extremely complicated data sets such as stock information, and they describe how in-memory computing can provide real-time analytics to the large investment banks that use that information to choose investment strategies. Yet, small business owners can use in-memory computing the same way large enterprises do, and now, with the cost reductions in computing capacity and cloud computing, small retailers can afford to put the technology to work.


Leveling the Playing Field 

One of the advantages large retailers have over small ones is the ability to collect and analyze data on consumer purchases to predict other products that might be of interest to a consumer. Large retailers like Amazon use real-time analytics to offer customers relevant products at the point of sale, or even at the point of browsing, to increase sales. A small business owner who runs a single clothing store can also use customer information to offer products. Combine a smartphone app with simple radio frequency identification to monitor customers’ movements in the store, send that information to a server, and customers can be digitally tracked as they move about the store, with the app sending them information about products that might be of interest in real time. Add social media to the equation, and the small retailer can leverage even more information about customers’ buying and shopping habits.


In-Memory and Enterprise Resource Planning

Store owners who operate brick-and-mortar outlets can use in-memory computing to provide guidance about how a store is laid out. If a store is properly designed with customer-tracking technology, a business owner can see in real time what products are moving and what products are not and adjust floor stock to meet that need. This requires an enterprise resource planning system to work in conjunction with the in-memory system to track inventory. While small business owners might still be concerned about the costs of upgrading their systems to function in a real-time in-memory computing environment, the investment is well worth the cost because they can rest assured that they are playing with the same cards as the large retailers.


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Keyword Tags:  ERP   enterprise resource planning   In-Memory Computing   IMC   real-time analytics

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