4 Critical Prescriptions for Your Live & Predictive

Analytics Platform

©2014 TransVoyant LLC. All rights reserved.

In order to achieve success, analytics platforms that provide you with real time and predictive

intelligence should be closely tailored to the following 4 criteria:

1) You Cannot Afford Data Latency

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Predictive analytics requires that you see your data assets in real time, in motion, as they

unfold.

5  5  

Near-zero latency requires leveraging cloud computing.

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Amazon Web Services (“AWS”), which TransVoyant uses with its clients, is the market’s strongest

choice with 99.9999% latency.

AWS provides robust security, including in private clouds that allow data to be stored behind a customer’s own firewalls.

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z  The extraordinary processing power made possible by the

cloud is critical to allow statistical inferences to turn data into

actionable intelligence—quickly.

2) Your Platform Must Be

Multi-Dimensional

Many analytics challenges historically have either only required or could only be solved by the use of traditional (X,Y) coordinates.

That has changed with the fundamental shift towards four dimensions: (X,Y), temporal, and geospatial.

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The latter two are critical to answer more complex questions

such as:

“Has a Chinese frigate entered U.S. territorial waters for more than one

minute?” (Temporal)

b  

“Are there five planes flying 1,000 feet above Andrews Air Force

Base?” (Geospatial)

b  

Many complex questions require analysis of several dimensions at once.

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For example, “How long has that hurricane been approaching our moving oil supertanker and are

four Coast Guard helicopters still hovering below 300 feet and within one mile of the tanker?” (Temporal,

X, Y, and Geospatial)

Such data results in actionable intelligence—the tanker can be rerouted; trucks may be reallocated to avoid inventory carry; and buyers can be notified of any delay.

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The more data a business line analyst has at his or her disposal, the more informed decisions he can make in real-time, perhaps

saving lives, averting an international conflict, or realizing

significant benefits to supply chains.

3) You Must Be Able To Visualize Your Data In Real Time

and Easily

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With so much data flowing so quickly from so many streams,

even big data that is highly specific to a particular challenge can be difficult to conceptualize,

let alone visualize.

Proper data visualization is thus critical to predictive analytics solutions.

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Why?

b  

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According to Gartner, “for a data intensive industry, the ability to transform data into business

insight, to rapidly evaluate and model the business impact of

decisions, and to effectively take the best action…

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will be a key differentiator in competitive performance.”

[Source: Jeff Cribbs & Kurt Schlegel, Showcase for Best Practices Dashboard Design (Gartner, Inc. Nov. 27, 2013)]

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For data to be transformed into value, visualizing data must be

simple, easy, and even fun for the National Academies of Sciences so-

called “naïve users”, especially business line managers without IT

expertise.

[Source: Frontiers in Massive Data Analysis (National Academy of Sciences 2013)]

4) Live & Predictive Analytics Must Accommodate

Different Verticals

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Real time and predictive analytics must be flexible enough to accommodate different verticals in your

organization.

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Transportation & Logistics, for example, requires answering

questions far different than those raised by Finance.

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Both are critical verticals that requiring knowing how data

should be turned into actionable intelligence based on questions

particular to those respective fields.

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This sort of flexibility requires a robust horizontal platform solution coupled with deep

vertical insights.

Predictive analytics is hardly

a new field...

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But getting it right still requires deliberate choices to enable your

organization to realize the actionable intelligence it deserves

from its data.

Want to learn more?

Visit transvoyant.com