Big data is slowly becoming the norm when it comes to gaining real-time analytics, customer insight, and enhanced business intelligence. As big data and the tools that help wield it prove themselves to be entities of the future, conventional data systems are gradually being pushed aside as legacy applications to make room for a complete digital transformation.

By definition, big data is data that exceeds the processing capacity of conventional database systems. These large sets of data, however, can provide insight on trends, patterns, and associations related to human behavior through computational analysis. In these modern times, especially due to the pandemic that has affected businesses all over the world, there needs to be a rethinking of how business is conducted across industries. Customer behavior and sentiment should be the main focus if companies are to thrive despite the crisis. In-memory data grids (IMDGs) provide the necessary speed and processing power for real-time data analytics that lead to data-driven actionable insights.

As organizations look to scale their business, optimizing their data analysis systems and processes should be on top of their list. New perspectives on data and how it’s managed will present opportunities for any organization and help them modify and adjust strategies as they see fit to meet current trends and address challenges as they arise. For now and in the foreseeable future, these concerns and challenges will, more often than not, involve big data and how it can be harnessed to an organization’s advantage.

The Main Challenges of Big Data

As with any migration, the move to big data and newer data processing tools comes with its own set of challenges. These challenges are what organizations need to address first if they wish to get the most value from big data and its analysis.

• Big data is too big. The challenge with big data isn’t just that it’s constantly in motion but also that it moves too fast. Its nuances also make the strictures of database architectures unsuitable and incapable of holding such data. To harness the power of big data, organizations must be equipped with the tools that can store, process, and analyze large datasets in seconds.

• Conventional data processing systems aren’t enough. Finding alternative methods or systems to process big data is vital if organizations are to obtain value from it. For businesses, big data falls under two categories: analytical use and enablement of new products or services. Analytics can uncover valuable insights from big data, including peer influence among customers, that were near impossible to gather using older tools. It also promotes an investigative approach to big data since big data is always in flux. This is a vast improvement from using predetermined reports, which is more static in nature.

In-memory Data Grids to the Rescue

The challenges above can be addressed by a platform that’s  fast and powerful enough to address large amounts of data in near real-time. Speed is particularly valuable here because a few seconds can mean the difference between gaining a competitive edge and falling behind and losing loyal customers.

Fortunately, in-memory data grids have provided a way to handle big data at lightning speeds without costing a large part of an organization’s IT budget. An in-memory data grid within a distributed cluster is a common implementation because the cluster provides scalability features for maximum capacity and data is stored in-memory for super fast processing speeds. In recent years, businesses have focused more on data analytics, cloud computing, and Internet of Things (IoT), and this has led them to demand for a capable platform that can handle large amounts of data without issues and as fast as possible. In-memory data grids are up to the challenge, and also provide a number of other benefits, including the following:

• Distribution and storage of data across multiple servers, with each server operating in active mode
• An object-oriented and non-relational data model
• Does away with tables and other traditional database features
• Easy horizontal scalability via adding and removing nodes as needed

Power Without Complexity

In-memory data grids, though powerful enough to process large amounts of complex data, can be simple enough for the average user. This thrust toward self-service helps companies save time and money, and allows them to allocate IT resources to other vital areas of the business. As the business landscape becomes more data-driven, the challenges of big data become more apparent: How do you store large amounts of data that continue to grow by the minute? What do you do with that data and how do you get value from it?

Big data has made the limits of traditional databases obvious, and this is only the beginning. Rethinking your approach to data systems and management is a must because big data is here to stay. To thrive in a data-driven, always-connected world that demands “instant everything,” the answer is using in-memory data grids.