What is Event Processing?

Event processing is the process that takes events or streams of events, analyzes them and takes automatic action. An event is anything that happens at a clearly defined time and that can be specifically recorded. Analysis can be based on pre-defined decision tables or more sophisticated machine learning algorithms, and there are a wide range of possible actions from generating a new event to changing a customer’s experience to scaling cloud resources up or down.

Enterprises typically have three different kinds of events: business transactions like customer orders, bank deposits, and invoices; information reports like social media updates, market data, and weather reports; and IoT data like GPS-based location information, signals from SCADA systems, and temperature from sensors.

With event processing you connect to all data sources and normalize, enrich and filter the data. You can then correlate events and add contextual data to ensure proper interpretation of events. Then you apply real-time business logic and rules or machine learning to trigger action. Event processing lets you act while the value of your data is still high. It allows you to turn your data into action—and empower business users to define the rules, that take the action, that gives you a competitive advantage.

Why event processing?

Event processing actively tracks and processes streams of events entering an enterprise so that opportunities and risks can be proactively identified and business outcomes optimized. The traditional data processing approach of store-analyze-act introduces the fundamental challenge of decision latency. Information is often the most relevant as soon as it is captured, and event processing helps organizations process this information in a more timely and contextual manner. It helps solve numerous problems: identifying fraud as it happens, delivering a contextual offer while the customer is still in the store, or predicting disruptions to minimize delays. With the need to handle data in real time, event processing is becoming increasingly important.

Fierce competition and an increasingly complex business environment are forcing many organizations to spot and respond to opportunities and threats in real time. For this information to be truly meaningful, you need to identify opportunities and threats hidden in these events by processing them in real time to derive insight and take appropriate action.

How Businesses Benefit from Event Processing

The ability to process events in real time and take automatic actions can provide competitive advantage to organizations. Many are successfully using event processing capabilities to outsmart their competitors with:

Improved Customer Service

Organizations in various industries are using better event processing to provide significantly improved customer service.

• Retail organizations are creating instant offers enabling cross-selling and upselling based on customer status, location, inventory, and other factors.
• Logistics and transportation organizations are providing real-time visibility into order/consignment/package status.
• Airlines are proactively notifying customers of problems, changes, and delays.
• Service organizations are monitoring service level agreements (SLAs) and promptly taking corrective actions to avoid unmet agreements.
• Banking and credit card companies are preventing and detecting fraud.

Reduction of Costs and More Efficient Use of Resources

Event processing is also allowing organizations to reduce operational costs and improve operational efficiencies. For example:

• In retail, real-time inventory tracking and management with the ability to define and change product promotions dynamically based on trends and surplus
• In government, cyber intrusion detection and prevention
• In airlines, optimization of crew scheduling and efficient baggage tracking
• In logistics and transportation, optimization of shipping movements in-transit and in-port
• In manufacturing, proactive maintenance of key shop-floor equipment
• In the energy sector, predictive outage and fault management of the grid
• In hospitals, optimization of scheduling of expensive procedures such as MRIs in response to disruptions and no-shows

Optimized Operations

Real-time event processing capabilities also significantly enhance the organization’s visibility into its operations, enabling faster and better decisions. For example:

• In telecommunications, identification of under-performing business systems to help ensure SLAs can be met
• In hospitals, visibility into patient numbers and bed availability to ensure optimal decision-making
• In financial services, visualization of market data, order executions, trades, deals, settlements, and pre-post trade exceptions
• In retail and services industries, real-time visibility into order status
• In insurance, real-time visibility into status of processing new customer applications
• In factories, visibility into the status of machines and other shop-floor assets
• In the logistics and transportation industries, visibility into the current location of trucks and packages

Most organizations are surrounded by data. They have data coming from a variety of systems and sensors, from partners and customers, and data coming from social media, etc. Business value comes from connecting to those different data sources and understanding the value that might be within that data while there is still time to influence what’s going to happen. Being predictive and being able to respond quickly is how you will gain value from the data.

Traditional data processing systems are causing businesses to run into problems. They are just not built to respond to the volume and velocity of data. Typically what happens is the data is collected and stored within a repository, whether it’s a relational database or Hadoop cluster or some other type. Then, the data has analytics run through it to identify where there might be opportunities or threats. And then, you take action. This is too late for today’s real-time expectations. The ability to influence what is going to happen next may have already been lost. The value of data diminishes over time and in fact, it could come down to a matter of seconds where you are no longer able to predict or be preventative with the data. With traditional data processing, you are acting on historical information.

For example, if one of your priorities is to predict when a failure might occur in your factory and you are not able to prevent the failure, then you are not able to prevent the consequences of machine downtime. Likewise, if you have a customer passing by one of your retail outlets and you want to make a specialized offer to that individual, if your organization is not able to do that before the customer drives away, it is very unlikely that they will turn back around and come back to your organization. You need to be able to act on business events in real time and that’s a critical capability around being successful.