 |
VOOZH | about |
|
VOOZH | about |
We’re so glad you’re here. You can expect all the best TNS content to arrive Monday through Friday to keep you on top of the news and at the top of your game.
Check your inbox for a confirmation email where you can adjust your preferences and even join additional groups.
Follow TNS on your favorite social media networks.
Become a TNS follower on LinkedIn.
Check out the latest featured and trending stories while you wait for your first TNS newsletter.
As we expand our businesses, we encounter a massive influx of information that can overwhelm even the toughest of data management systems. How do we remedy this? How can we reconcile the conflict between successfully fueling our business growth and agility with necessary data, and preventing our systems and teams from being overwhelmed with managing too much information? What’s more, these issues are stacked on top of the increasingly pressing need to move more quickly and respond to customer needs as they happen. Maybe it’s not a trade-off.
The need for real-time data is evident, but real-time data needs to be properly managed, so our teams aren’t overwhelmed with too much information. Additionally, we know we need to move faster, but we need to do so without driving up costs or sacrificing data security. This is why the market for event-driven architecture (EDA) has been growing in the past years: Businesses need a way to tap into the power of real-time data to contextualize the information that fuels their applications and systems.
Businesses have been slowly realizing this need with the recent uptick in moving away from batch processing and toward real-time data streaming, as evident by the growth of Apache Kafka and Apache Flink in the event-driven enterprise landscape. They powerfully work together to provide a firehose of events (Kafka), and then add relevant context and pattern detection (Flink). Doing so allows businesses to respond to threats or opportunities immediately as they arise, but you need to ensure that you aren’t bloating your events with too much information.
Additionally, the increasing trend toward AI and machine learning requires that businesses properly prepare their applications to ingest real-time data. To successfully set up AI/ML data ingestion, your business needs EDA capabilities fueled by unfettered real-time data access so they can process information instantaneously, but the data cannot be bad, dirty or biased. This is where strong EDA capabilities become a necessity, allowing your business to strengthen control over its data while also fueling business intelligence.
The power of real-time data should be brought to all aspects of your business. Your business events are important, and you should be putting them into the hands of those who can leverage them to drive intelligent decision-making. Tools like IBM Event Automation, composed of event streams, event processing and event endpoint management, democratize the use of business events through proper governance and controls, and by lowering the barrier to work with events.
Notably, the event-processing capability democratizes the ability to derive valuable insights from business data; it no longer limits business insights to those with deep IT and analytics skills. Powered by the impressive open source Apache Flink technology, it boasts low-code processing with a simple drag-and-drop UI to help less technical teams power their decisions with real-time data.
Additionally, it allows users to safely test their flows by back-testing on historical events on the topic. This encourages teams to test and iterate, perhaps identifying new creative ways to solve common business problems that pester their roles.
With the increasing amount of information businesses need to ingest every second, the ability to call out to APIs becomes integral. With it, event processing becomes stronger with additional context, insights and intelligent sources like those AI/ML applications. Events generally carry reference information — such as customer identification, orders, building numbers, etc.— to prevent them from being bloated with too much information.
However, API enrichment lets users retrieve deeper information externally, such as from other databases or APIs, when necessary or appropriate. This allows users to augment their knowledge through extra details, focusing their efforts on driving the action for their specified processed events instead of sifting through events carrying unnecessary information.
Significantly, API enrichment drives enhanced data quality by adding important details to events so users don’t miss out on any crucial pieces of context. Now, you’re getting a full picture of your events through the ability to call out to APIs to make the data more comprehensive, insightful and actionable.
Built on open source technologies, specifically Apache Kafka and Apache Flink, IBM Event Automation helps businesses fully leverage the power of their business events. Additionally, its composability allows for easy integration into a business’s existing technology stack, particularly for this API enrichment features. Businesses can easily integrate their existing data sources into IBM Event Automation without having to do a costly technology overhaul.
Join the IBM Event Automation team for a webinar on June 18 to learn more about this API enrichment capability and see a real example of how this works with watsonx to conduct sentiment analysis with event processing.
