As the years go by, the data processed by companies grows larger and more complex, so much so, that only 0.5% of all gathered data is ever processed by companies. Businesses may have access to more data today, but making that data actionable for analytics purposes remains a challenge despite available technologies. Operationalizing large amounts of data takes hours, leaving much of it siloed in proprietary software.

One way businesses save time in data processing is by relying on aggregate reports instead of much detailed reports, the generation of which, typically taking an entire night. This inefficient approach leads to wastage; in spite of available data, companies fail to get detailed information that can lead to bad business decisions. The in-memory data grid, however, significantly decreases the time required to process data in a cost-effective way.

Disk-based databases can add complexity and slow down data processing due to its high latency. By using in-memory data grids, the need to access hard-disk-drive-based or solid-state-drive-based data storage is significantly reduced because data is processed using RAM or flash memory. Collocating the application and its data in the same memory space also minimizes data movement over the network, resulting in a high-throughput data fabric. In-memory computing increases data access speeds by up to 1,000,000 times compared to disk-based processing, making it an ideal solution for real-time analytics.

The Business Value of In-memory Computing

Today, digital transformation is dependent on how fast and efficient a business can scale its operations. In-memory computing allows businesses to accelerate applications and scale easily without replacing existing data layers and currently used applications. Scaling systems can now be as easy as the addition of a new node to a cluster of server nodes in which in-memory data grids are deployed.

In comparison, traditional data analytics tools fall short when faced with the ever-increasing volumes of data that businesses have to process each day. Data has to be processed and analyzed at speed sufficient to help a business make sound decisions, but 37% of executives say it takes at least a full day to access the sources for analytics, sometimes even a week or longer. In contrast, companies that use in-memory computing for analytics can process three times more data 100 times faster than their competition.

Intelligent Business Intelligence

In-memory computing pushes business intelligence (BI) forward by providing a platform that gives companies and organizations the power to process big data faster—without the need for data analysts and IT staff. The platform allows business users to initiate queries, gather and analyze data, and filter and sort information on their own. The future of BI is self-serve, and in-memory computing is a big part of that. It will help deliver real-time insights that will help improve customer experience. In-memory computing also offers other benefits, including the following:

. It Reduces Data Fragmentation And Improves Accuracy.

This helps save time in data reconciliation so a business can switch to predictive analytics.

. It Simplifies Data Analytics Through A Reduction In Layers.

By doing this, it allows users to create simpler analytical models and plug in new data sources on demand.

. It Allows Users To Hold Currently Running Database Code And Active Data Structures.

Since the persistent database remains in memory, data does not need to travel back to a motherboard.

. Current Iterations On In-Memory Data Grids Can Handle Much Larger Data Sets.

More data is squeezed into memory through compression, minimizing infrastructure constraints when performing analytical tasks.

The ultimate goal of in-memory computing technologies is to tackle big data head on—making it small enough for business users to transform into actionable insights. By skipping the data analysts and IT staff, it makes data processing even quicker, and lowers training and support costs. It provides businesses a new source of customer information available on demand, providing them an edge over the competition. Combined with the right BI tools, the complexity of big data becomes manageable and becomes a crucial source of information that is flexible and easily scalable.

Implementing an in-memory computing platform within an organization, however, should be strategically managed so that the accuracy of data is maintained. Create a thorough roadmap that includes an inventory of analytical applications with defined owners and use cases to help in the rollout of in-memory platforms across the board. As with any new technology, buy-in is vital before implementation. The business needs to make everyone aware of its virtues so they know that migrating to an in-memory platform is not only done for the business but also for them.