The business landscape has changed, and those changes have never been more apparent than now as economies around the world continue to battle the repercussions of a global pandemic. Businesses and markets are in a constant state of flux in efforts to meet the ever-changing demands of customers. In these circumstances, organizations in different industries have turned to data to help them keep up with market trends and constantly changing customer demand and sentiment.
In recent years, in-memory data grids have been getting attention due to their promise of super fast data processing, high performance, and low latency. Still, some businesses remain hesitant to adopt the technology for fear of the involved high costs, significant time investment, and high learning curve. Fortunately, these fears have been more or less allayed, thanks to implementations that have proven in-memory data grids to be reliable and cost-effective.
How In-memory Data Grids Help Business
Although in-memory data grids are far from being a mainstream solution, 64% of respondents in a Deloitte survey indicated that they have plans of adopting in-memory solutions in the next three years, with an additional 15% planning to do the same in the next four years. This signifies a growing understanding of how these solutions help businesses in a practical sense. With customer sentiment changing almost on a regular basis, investing in the appropriate tools and platforms and actually getting value from them is vital. In-memory data grids enable businesses to manage large amounts of data that grows at an exponential rate. Aside from being 100 times faster than other solutions, in-memory data grids also help companies achieve the following:
- Data-driven decisions. Real-time analytics can help businesses predict patterns of behavior and make sound decisions based on this data. Moving forward, businesses can learn from historical data and be able to make business decisions faster.
- Safety. Organizations can significantly protect themselves from risk due to the predictive nature of analytics. Known as operational intelligence, in-memory data grids provide alert systems that notify organizations of detected anomalies in transaction data. This helps in the early detection of potential risk so that it can be addressed immediately.
- Productivity. In-memory data grids eliminate the guesswork so resources can be allocated to other critical aspects of the business. Having the appropriate customer and market data will help organizations map out next steps and contingencies so they save both time and money through improved business process efficiency.
- Enhanced customer experience. Data analytics provides a deeper understanding of customer sentiment. By going through this data at lightning speeds with the help of in- memory data grids, businesses are always a step ahead, providing customers what they want even before they ask for it.
Why Choose an In-memory Data Grid?
Today’s businesses—and the businesses of the future—rely on data to ensure that they keep up with current trends. As the amounts of data grow larger, so too, should an organization’s capabilities to handle that data. In-memory data grids provide speed, high availability, and easy horizontal scalability that enable them to provide real-time insights. By using RAM instead of disk, data movement is reduced to avoid bottlenecks within the network, further increasing data processing speed. Eliminating the need to constantly access disk-based storage systems, reduces latency and ensures maximum throughput.
Compared to conventional solutions, in-memory data grids provide an advantage in the following areas:
- Performance. Using RAM instead of disk reduces data movement, increases processing speed, and allows for the collocation of applications and their data in the same memory space. The persistent store feature also means that frequently accessed data resides in-memory for quick and easy access.
- Operations. Easy and straightforward scaling of in-memory data grids avoids disruption of business operations when deploying or upgrading systems. This also means cost-effective and hassle-free maintenance without the need to always acquire the latest and greatest hardware.
- Data structure. In-memory data grids use a key/value store for greater flexibility. In contrast to traditional solutions that use relational databases, in-memory data grids rely on a data model and application code that are inextricably linked.
Ultimately, the goal is to design a system that mitigates business risk and can adapt to changes in an instant. This system should remain operational even if a site or server fails or becomes inoperable. You will also need to make tough decisions between prioritizing performance and data consistency. In-memory data grids provide an ideal balance between these two aspects so you won’t have to make these choices too often.