Enterprise disruption and streaming analytics: from data to digital transformation.

Disruption is key to success in any market. Cloud is a good example where disruption has been felt by non-cloud technologies, but also by the Enterprise, where the Cloud is now driving complete digital transformation programs. And successful disruption doesn’t always mean being the fastest or the best, at least, not to begin with. Consider the database market. RDBMS technology was far from the highest performer when it emerged, but its ease of querying with SQL was revolutionary and ensured long-term dominance. Hadoop has taken the reverse approach to disruption, with an initial focus on performance, but with a programming and query model that still lags behind in terms of ease of use and commercial viability. Hadoop has been disruptive in its niche so far, but is pushing for long-term dominance.

Ironically the disruptive business case for streaming analytics would have been much simpler to make had Hadoop not happened. The shortcomings of the RDBMS as a platform for real-time, operational analytics from unstructured event data would have been an easy target. Hadoop offered to reduce business latency from one day to a few hours or less, which as it turned out was sufficiently compelling for many, even if not real-time. Hadoop got its disruption in first.

It could be argued that Complex Event Processing (CEP) platforms, the first generation of streaming analytics platforms, were truly disruptive, successful in capital markets and financial services by disrupting an existing IT market. CEP offered flexible programming and query platforms with rapid application deployment in a market typified by bespoke, expensive systems. However, streaming analytics has only seen steady but not earth-shattering adoption in other markets throughout the noughties (2000 – 2009 or so, plus a few years). There may not have been sufficient pain to justify the pain of disruption.

This has changed. Bloor’s research suggests that 
the Internet of Things has the potential to topple financial services as the leading market for streaming analytics. The characteristics 
of streaming analytics are particularly suited to the processing of sensor data. However, as with any market, a step change in adoption of IoT services requires extensive business disruption. Beyond the need for real-time operational intelligence and analytics lies an IoT world inhabited real-time control systems, hardware and devices. This is an area ripe for disruption, but where disruption means the replacement of monitoring and control systems with cloud-based platforms, and importantly, the use of streaming analytics platforms to drive real-time process and automatic updates. 

Therefore successful, long-term disruption in IoT requires enterprise digital transformation. This is not for the faint-hearted, and only a few streaming analytics vendors offer the scale, product portfolios and expertise to support an Enterprise through their digital transformation journey. Those vendors best placed to disrupt the Internet of Things are those who offer a broad product portfolio combined with solutions services and consulting. Not that the IoT will not be an important market for smaller, independent streaming analytics vendors. But for independent vendors, it is important to be able to differentiate their strengths as part of a large value chain. 

That’s why Bloor has released its 2016 report on streaming analytics, to help organisations understand the capabilities of streaming analytics, and which platform and vendor is most appropriate for their specific requirements and use cases. The report offers a survey of the wider market – open source, cloud and on-premise, with an in-depth survey of the leading vendors in the streaming analytics market today. The focus is streaming analytics, but the Internet of Things features strongly as the key market over the next five years. The full report is available through Bloor at the following link http://www.bloorresearch.com/research/market-report/streaming-analytics-2016/.