The adoption of in-memory computing (IMC) is rising. As the number of data sources increase, there’s also an increasing need for simplifying architecture. Businesses require quick and complete access to data and analytics to inform all functions such as operations, marketing, sales, services and finance.
Using in-memory computing offers faster computational speeds, reduces IT costs, boosts business insights and increases efficiency.
Advantages of in-memory computing
If enterprises today want to maintain a competitive edge and offer customers an optimal experience, they must be able to deal with large quantities of data and demands for faster, superior performance.
In-memory computing, in a nutshell, is moving data traditionally stored on hard discs into memory. It has developed because traditional solutions are inadequate when it comes to offering fast computing and scaling of data in real-time.
In-memory computing software is designed to store data in a distributed fashion. Many of today’s operational datasets easily measure in terabytes. In-computer memory allows operational datasets that were typically stored in a centralized database to be stored in RAM across multiple computers.
The entire dataset is divided into the memories of individual computers. This partitioning of data makes parallel distributed processing a technical necessity.
In contrast to a single, centralized server that manages and provides processing capabilities to all the connected systems, parallel distributed processing allows multiple computers across different locations to share processing capabilities.
By using RAM data storage and parallel distributed processing, in-memory computing achieves high speed and scalability. Data processing and querying happens blazingly fast.
In-memory computing offers real-time insights. Real-time decision-making and responses are possible due to an immediate understanding of the impact and consequences when something happens.
Many business operations are enhanced by being able to analyze data from millions of events per second. Some of these operations are regulatory compliance and customer service. It’s also possible to prevent various negative occurrences, such as equipment breakdowns or cyber attacks.
Working with in-memory data stores eliminates bottlenecks and allows for mixed workloads to be handled within the same architecture.
Where in-memory computing is most relevant
Best use cases are not necessarily defined by a specific industry but rather by the need for top performance and scalability. In-memory computing is applicable in any market or industry where real-time insights, analysis and predictions based on streaming and historical data offer business value.
In-memory computing allows businesses such as banks, retailers and utility companies to analyze huge volumes of data on the fly, quickly detect patterns and adjust operations accordingly.
In the financial services industry, this allows companies to meet real-time regulatory compliance requirements. Online travel reservation companies can use it to improve customer experience. Health insurance benefit management companies are using it to review claims and identify unnecessary charges and overbilling. Manufacturing companies are handling complex scheduling issues by having real-time insights.
In-memory computing not so much about being able to produce a report quicker than before, but to be able to become predictive in analysis. For example, if a company can predict when a vehicle needs maintenance, that information can ensure that the spare part is ready at the right moment in time.
Another area where in-memory computing is relevant is simulation capability. You can say, “What if I did this? And with all the data available, you can simulate what would happen. Previously this could take hours and now simulation is extremely fast.
Banking, financial services and insurance (BFSI)
The BFSI sector is expected to experience the highest growth in adoption of in-memory computing due to the rise in demand across internet banking and mobile segments.
This sector constantly needs to improve operational performance, especially when it comes to risk management and fraud reduction. Additionally, the volume of data and a rising need for output of complex transactions and complex analysis in real-time are factors that could result in increased adoption of in-memory computing in this sector.
Financial and insurance institutions need to be able to calculate risks, achieve consistency, automate, reduce manual intervention, and improve the overall performance of many applications, often in multiple locations. In-memory computing makes this possible.
A use case example: To minimize the spread of loan fraud scams, a bank has to be able to access real-time data feeds and continuously update its model of what indicates a possible attempt at fraud in order to stop a possible new loan fraud scam attempt before a transaction is completed.
Logistics and transportation
By using sensors on fleets of equipment, such as trucks, companies are able to track operating conditions. By leverage real-time and historical data, action can be taken immediately when problems occur.
For example, a truck or train can be diverted from one route to another if necessary. This can help to eliminate or reduce downtime, increase safety and reliability and reduce maintenance costs.
e-Commerce
In the world of e-commerce, customer experience is all-important, even at high peak periods when traffic increases exponentially. The challenge is to support scalability requirements and offer real-time analytics on transactional data to ensure high levels of customer experience at all times. The way to offer this is through in-memory computing.
For a real-time personalized shopping experience to the customers, error-free and high-speed transactions, an almost 100% up-time of the online store, powerful and accurate data collection for real-time analytics are the main benefits of in-memory computing for online sellers.
What does adoption require?
Companies need to make sure that they have data consistency and a level of standardization. It doesn’t help to be able to analyze things in a split second if the data analyzed is bad data.
Companies need to begin by consolidating the core applications that run their processes as a basis for moving onto the next level. From evaluation to implementation, organizations need to have a clear strategy.
Conclusion
The costly economics of traditional computing simply can’t keep up and in-memory computing is revolutionizing data analysis by speeding up computing and scaling. This is unleashing a wave of innovation as it makes its way into businesses that seek to utilize the competitive advantages it offers.
Adoption is not yet widespread but early innovators are reaping the benefits. Organizations that follow a clear and well-worked-out strategy for implementation, experience an increase in performance, process innovation, simplification and flexibility.