Businesses across industries are under increasing pressure to be more competitive, reduce customer churn, and improve customer engagement and experiences. To achieve this, enterprises have turned to big data and analytics which in turn has produced oceans of data continuously generated in real-time.
According to a study conducted by 451 Research, 65% of enterprises have incorporated the Internet of Things (IoT) into their business model. Recent advancements in IoT technology have enabled 69% of these organizations to collect data from endpoints, with 94% of these firms leveraging the data for business purposes.
IoT data is generated from the following sources:
- Automobiles/Fleet Equipment (11%)
- Buildings and Other Structures (21%)
- Cameras and Surveillance Equipment (34%)
- Datacenter IT Equipment (51%)
- Environmental Sensors (15%)
- Factory Equipment (14%)
- Medical Devices (7%)
- Retail Operations (8%)
- Smartphones and End Users (30%)
This enormous amount of data that is continuously generated has the potential to make a significant impact on the bottom line. However, this can only be achieved if real business value can be derived from it quickly.
This is where in-memory computing comes in.
What is in-memory computing?
In-memory computing is an option that provides speed as it’s enabled to eliminate the storage and processing of data in complicated relational databases operating on comparatively slow disk drives to achieving it in the main random access memory (RAM).
In the financial sector, in-memory computing and IoT have created an opportunity to process payment transactions in real-time while conducting sophisticated analytics with enormous datasets. This helps firms ensure they’re in compliance with regulations while offering fraud prevention (along with deriving valuable insights for revenue generation).
To achieve this, blockchain infrastructure can help by streamlining all sorts of transactions that can save substantial amounts of money and time. But to ensure security, this technology requires a significant upgrade because it will also have to concurrently maintain validation information, security identifiers, and real-time hybrid transactional/analytical processing (HTAP) capabilities.
In-memory computing, on the other hand, is already enabled to offer parallel, distributed processing across a pool of RAM deployed on a computing cluster. As a result, it can enable the rapid processing of transactions and data analytics along with the ability to seamlessly scale by adding new nodes to the cluster. This same approach can be translated to manufacturing enterprise resource planning (ERP) systems or electronic health records (EHR) systems in healthcare.
In-memory computing and IoT can potentially boost customer experiences
In-memory computing and IoT make the perfect technology combination because it helps enterprises cope with ever-growing streams of data. When organizations embrace in-memory computing, they can avoid the pitfalls of relation databases like new hardware costs and increased latency.
This technology also comes with the additional benefits of high availability, scalability, and cost-savings. As businesses become more customer-centric and focus on delivering enhanced customer experiences, in-memory computing will play a key role in bridging the gap with IoT to help businesses achieve it in real-time.
