Now available - The Forrester Wave™: Big Data Streaming Analytics, Q1 2016
It’s a changed world since the inaugural publication of The Forrester Wave™: Big Data Streaming Analytics, Q3 2014. Back then, streaming analytics were just emerging as a business priority with a few top vendors and open source projects gaining market momentum.
Over the past two years, several new entrants to the market and existing players have been rapidly building offerings, and the use cases for streaming analytics are pervasive. The Forrester Wave™: Big Data Streaming Analytics, Q1 2016, reports “Streaming analytics are critical to building contextual insights for Internet of Things, Mobile, Web, and Enterprise Applications.”
Forrester Research defines big data streaming analytics as
Software that can filter, aggregate, enrich, and analyze a high throughput of data from multiple, disparate live data sources and in any data format to identify simple and complex patterns to provide applications with context to detect opportune situations, automate immediate actions, and dynamically adapt.
At IBM, we have been observing market trends and working with various clients in telecommunications, healthcare and government and seen that some emerging use cases for streaming analytics are prevalent.
- Applying business rules to data streams such as those rules build in ILOG or IBM Operational Decision Manager.
- Keeping content of data over time to find patterns in unreliable data streams leading to unique insights. For example, the Internet of Things and temporal analysis can identify patterns of vehicles slowing down to help prevent accidents.
- Sequencing of events across a few seconds or years. This is important since a transaction could occur in a few seconds, but content is needed from a transaction two weeks earlier to deliver the right offer.
In order to be considered for The Forrester Wave™: Big Data Streaming Analytics, Q1 2016, vendors had to meet very specific criteria. To be considered for The Forrester Wave™: Big Data Streaming Analytics, Q1 2016. First, the offering must include comprehensive analytics features on streaming data, not just high speed ingests of data. At IBM, our clients and prospects demand comprehensive analytics functionality such as natural language processing, spatial temporal analysis and machine learning for streaming analytics solutions are required beyond ingestion.
Vendors must also offer solutions that are general-purpose streaming analytics products, not just something that is built for domain-specific applications. For example, some streaming analytics offerings grew up in event processing for financial markets for algorithmic trading, making it a vertical solution that wouldn’t be evaluated. Next the offering must be available on on-premises and cloud deployment models including cloud, on premises and hybrid. Finally, at least two client references must be available for extensive interview by Forrester Research.
Fifteen vendors made the final cut for evaluation. Reading the report, talking with clients and prospects, and interviewing leading minds from IBM Research reveal some common themes about streaming analytics solutions. From my perspective, there are four core qualities that make up a streaming analytics solutions: events, speed, context and analytics.
IBM Streams received a Leader designation, with a score of 4.88/5.00 for current offering and 4.60/5.00 for strategy.
According to The Forrester Wave™: Big Data Streaming Analytics, Q1 2016, “IBM Streams enables cognitive solutions. Cognitive computing encompasses all of intelligence—natural interfaces, situation awareness, smart decisions, and learning to become more effective. Streams can ingest and understand the always-on stream of data from applications and IoT devices needed to make the decisions that underlie cognitive solutions.”
IBM Streams has a strong ecosystem. A new open source project, Quarks, is a technology that embeds streaming analytics onto Internet of Things (IoT) devices. Analyzing data at the edge continuously can help companies generate insights more quickly and reduce network communication costs. Now developers and data scientists can use the open source code in Quarks to build new apps that can handle massive amounts of IoT data streaming from sensors, smart meters, mobile communications and other connected devices, and send this analysis to IBM Streams applications for deeper analytics.
Businesses across industries—from automotive and healthcare to telecommunications and manufacturing—can reduce communication costs and decrease time to insight with IBM Streams and Quark to deliver real-time analytics, boost application intelligence, and improve cognitive systems.