Table Of Contents
Why Enterprises Need Contextual Decisioning
The world today is faster-paced than ever before, and the explosive growth of both 5G and the Internet of Things (IoT) is generating a greater variety of data at extremely high velocity. As a result, enterprises are now faced with an extremely short window of opportunity to respond to events and produce optimal results, like keeping a customer’s attention, completing a transaction, and preventing fraud.
Even a half-second delay can be too long: mere milliseconds lost can lead to missed revenue, data leaks, and other negative outcomes. In fact, the new standard for processing data and responding to an event is now under 250 milliseconds. That means that the decisioning part must happen in single-digit milliseconds (<10), without compromising data accuracy. However, many enterprises still rely on legacy technologies that cannot meet these modern demands. Because decisions must now be made in-event, not after the event, there simply isn’t enough time for data to travel to and from a data lake.
The solution: optimize decision management by immediately incorporating machine learning outcomes into your business rules. This is what we call contextual decisioning. The following are the four things that you must have in order to implement contextual decisioning at scale in your enterprise.
The Things You Need for Contextual Decisioning at Scale
1. Real-Time Stream Processing
Real-time stream processing is essential for easily moving data to the appropriate processing layer quickly. Stream processing technology allows you to process, store, analyze, and most importantly, act on data streams in real time. Real-time stream processing enables data to flow continuously, allowing you to handle data in real time without having to wait for it to be collected in a batch form before it is processed, stored, or analyzed, as is the case with most legacy systems.
2. In-Memory Data Stores
In-memory, real-time data stores enable fast access to contextual data correlating to the incoming event information. An in-memory database stores data in the system’s main memory (typically RAM) rather than disk/device storage used by traditional databases.
Because you only access the RAM when you process data with in-memory databases, you can avoid the latency (lag) of accessing secondary memory and navigating the entire stack each time you want to read or write records. As a result, in-memory databases provide predictable low-latency and a 1,000,000x increase in performance compared to in-disk databases. This enables you to scale seamlessly and make intelligent, data-driven decisions in mere milliseconds–critical timing that can prevent lost revenue and unlock potential revenue.
In-memory databases also deliver high availability and redundancy that prevents revenue loss by eliminating downtime. Furthermore, these real-time data stores enable instantaneous ACID adherence, allowing applications to make accurate decisions at massive scale in 5G environments.
3. Business Rules Optimized with Machine Learning
Contextual decisioning can no longer be based solely on static rules. Thanks to the sheer volume of data being generated, insights become outdated almost immediately. Static rules, by nature, do not adapt dynamically, hindering the ability to make contextual decisions based on the most recent and best insights. To achieve the desired outcomes and ultimately maximize revenue, both analytics and rules are critically important to the equation.
Therefore, as noted in a recent Gartner report, business rules must be optimized with machine learning to ensure the best possible results. To accomplish this, Gartner recommends identifying the most important decision criteria and calculating decision weights and coefficients using machine learning.
4. Streaming Aggregation
Finally, critical to any 5G application is the ability to make complex decisions using both historical and recent data to understand the event and take appropriate action. However, most legacy data platforms focus on either historical data or recent data, lacking the ability to aggregate these two sources of data for a holistic view of the event.
Modern data platforms like Volt Active Data use materialized views to aggregate high-speed event streams by seconds and further aggregate by minutes or hours to enable fast aggregation at several different levels of granularity. Streaming aggregation keeps a running track of your key performance indicators (KPIs) so you can continuously monitor and meet your targets.
Conclusion
Volt Active Data is the only data platform that provides all of these contextual decisioning capabilities at scale in a single, unified platform, without compromising on latency or data accuracy. With Volt Active Data, you can have a simplified stack that avoids the pitfalls of many tech layers.
