Big Data analytics are everywhere. According to a recent Forbes article, companies have now “purchased tools, hired solid analytic teams, and often tried to create a data-driven culture.” But despite the evolution of Big Data technologies and the willingness of enterprises to invest, the “ability to act on analytic insights is painfully slow, certainly much slower than the pace today’s business requires.
This is decision latency—a direct consequence of the store-analyze-act data processing approach, and which often puts companies in the untenable position of having all the data and all the knowledge necessary to succeed in business, but lacking the speed to make informed, real-time decisions on mission critical problems. Fortunately, there’s a better way: event processing.
Growing Market Share
According to recent research by Marketsandmarkets, the complex event processing (CEP) market will be worth more than $4 billion with CAGR at 37% by 2019. It’s no wonder, since enterprises are now looking for ways to actively track and process streams of events in real time because they know the value of immediacy. In a world of instant communication, viral advertising, and dwindling consumer attention spans, data often has the highest worth immediately after being captured. Each second, minute or hour that goes by makes this information less relevant until acting on it produces the opposite result intended.
Traditional analytics tools simply aren’t up to the challenges of handling this data in real time. Most are built to store and process large volumes of data even at high velocity, but aren’t equipped to analyze this data moment to-= moment and present actionable outcomes. The “decision latency” created puts companies in the unfortunate position described by Forbes, with big money invested in analytics platforms that simply aren’t paying dividends. Bottom line? Having all the right information only matters if you have it at the right time.
Triple Threat
For event processing systems to deliver ROI, three capabilities are critical. First is high availability. Business not only need inference rules which can relate point-in-time events to historical data, but the ability to scale this process on demand while ensuring it remains available to all users. User empowerment is also key; the emergence of self-service capabilities and granular control lets business analysts and data scientists focus on their jobs: capturing business logic and implementing rules. Finally, event processing platforms must empower developers to build better distributed systems with custom UIs which easily integrate across existing systems. The system should foster collaboration between the business users and IT developers for better productivity.
It’s no longer enough for enterprises to collect, store and analyze data streams—opportunities and challenges must be answered instantly to retain a competitive edge and derive value from analytics platforms.
