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

The Forrester Wave™: Streaming Analytics, Q3 2019

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.

 

Endnotes

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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