The Forrester Wave™: Streaming Analytics, Q3 2019
Enterprises Must Take A Streaming-First Approach To Analytics Evaluation Summary Vendor Offerings Vendor Profiles Leaders Strong Performers Contenders Evaluation Overview Vendor Inclusion Criteria Supplemental Material
Enterprises Must Take A Streaming-First Approach To Analytics
Successful enterprises must learn from the past, act in the present, and prepare for the future.
Descriptive analytics analyzes the past. Streaming analytics analyzes the present. Predictive analytics
analyzes the future. Unfortunately, most enterprise business insights teams focus primarily on
analyzing the past through dashboards, stunning Tufte-like charts that they slice and dice ad infinitum
to rationalize often failed business strategies. The problem: Most business insights pros equate
“analytics” and “insights” with the skills they are vested in — traditional, historical, backward-looking
analysis. Fortunately, that’s changing! Many leading enterprises realize that real-time analytics — the
analytics of the present — is an incredible competitive advantage because they can act now to serve
fickle customers, fix operational problems, power internet-of-things (IoT) apps, and respond decisively
to competitors. That’s what streaming analytics delivers.
As a result of these trends, streaming analytics customers should look for providers that offer:
›› Analytics rigor. Streaming is a loaded word. It can mean watching “Money Heist” on Netflix. It can
also mean capturing database transactions and transporting a copy to a data warehouse using
Apache Kafka. Streaming analytics is not about moving bits. It is about analyzing bits as they
move. Streaming analytics is categorically about analytics: specifically, analytics on data in motion,
where time and trends are not defined in the past but instead defined right now. Enterprises should
look for streaming analytics vendors that have both a breadth of built-in real-time analytics and
extensibility capabilities to use externally created analytics such as machine learning models.
›› Spike-proof scalability and availability. Streaming analytics implementations are not meant to
produce nice-to-have reports that executives and managers review in update or status meetings.
Rather, streaming analytics is the real-time brain of business that must maintain consciousness.
The time frame of the insight-to-action cycle is right now! Streaming analytics is mission-critical
analytics that is the signal for enterprises’ actions, both human and automated. That means
enterprises should look for streaming analytics vendors that are fault-tolerant and can scale quickly
to handle spikes in data caused by customer, operational, and market activity.
›› Deployment freedom. Enterprises are spread thin, but not in a bad way. Applications and data
increasingly span on-premises (or managed) data centers, multiple public clouds, and the edge
(to support IoT applications).2 That will be the reality for enterprises for the foreseeable future and
for good reason. Compelling value propositions exist for SaaS, PaaS, and IaaS cloud applications
and services.3 Streaming analytics must span an enterprise’s portfolio of applications and data
wherever they may be deployed. That doesn’t necessarily mean that a streaming analytics platform
must be deployed where data and applications exist, because data can be streamed to the
platform for analysis. The key question for enterprises is: Where do I need to perform the analytics?
The answer depends on the latency tolerance and connectivity of the insight. Enterprises should
look for a streaming analytics vendor that can perform real-time analytics with enough time to act
on the real-time insights.
1 Streaming analytics is used for IoT advanced analytics. See the Forrester report “The Forrester Tech Tide™: Internet
Of Things, Q3 2019.”
2 The “edge” typically refers to computing devices that are in the field versus in a central data center or cloud. Edge
devices are also often referred to as IoT devices.
3 SaaS: software-as-a-service; PaaS: platform-as-a-service; IaaS: infrastructure-as-a-service.
4 SDK: software development kit.
5 ERP: enterprise resource planning; CRM: customer relationship management; HRM: human resource management.
6 Because these two approaches are architecturally different, we have to choose which approach to score for each
criterion. It would be inaccurate to score two distinct architectures as one solution. Streaming analytics vendors often
offer multiple programming and/or development paradigms for streaming analytics queries. However, it is rare that the
underlying execution architecture varies based on the programming paradigm.
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