Data and analytics leaders are increasingly targeting stream processing and streaming analytics to get faster time to insight on new or existing data sources. Year to date, streaming analytics inquiries from end users have increased 35% over 2016. I expect that trend to continue.
In getting to real-time, these leaders are presented with a range of proprietary commercial products, open source projects and open core products that wrap some existing open source framework. However, in many cases, streaming analytics capabilities are little more than commercially supported open source bundled with some other product. Creating a streaming analytics application is left as an exercise for the buyer.
The challenge is that getting real value from streams of data requires more than just a point solution. Stream analytics is a cross-functional discipline integrating technology, business processes, information governance and business alignment. It’s the difficulty integrating these areas that keeps many organizations from realizing the value of their data in real-time. I’ve been working with my colleague Roy Schulte on a streaming analytics maturity model to help organizations understand what’s required at each maturity level:
In “The Five Levels of Stream Analytics — How Mature Are You?”, we present structured maturity levels for data and analytics leaders to evaluate the current state of their stream analytics capabilities and how to advance their respective organization’s maturity to become smarter, event-driven enterprises. The report is focused on the use of event streams for analytics purposes, with the goal of improving decision making. Gartner clients can download it here.
Read Complimentary Relevant Research
Security Analytics: Six Principles for Success
Our research teaches six principles to help you successfully build and run a security analytics program as well as common ways to use...
View Relevant Webinars
Hadoop and Spark: Understanding Open Source Opportunities and Risks
As companies build foundational data and analytics infrastructure with Spark and Hadoop, the market continues to shift and evolve in...
Comments or opinions expressed on this blog are those of the individual contributors only, and do not necessarily represent the views of Gartner, Inc. or its management. Readers may copy and redistribute blog postings on other blogs, or otherwise for private, non-commercial or journalistic purposes, with attribution to Gartner. This content may not be used for any other purposes in any other formats or media. The content on this blog is provided on an "as-is" basis. Gartner shall not be liable for any damages whatsoever arising out of the content or use of this blog.