Gordian Knot Analytics Group Overview

GORDIAN KNOTAnalytics Group LLC

GORDIAN KNOT Analytics Group

Vergil™ Overview

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Introducing Vergil™

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By the Gordian Knot Analytics Group

Vergil™ is the first data analysis tool that provides “High-Definition Analytics.” This enables the client to gain far better resolution of their accumulated data and the prevailing ambient environmental & economic conditions. Vergil™ then delivers the collective intelligence and actionable results that increases revenue and reduces cost.


The 3 Stages of Big Data

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Gather and Create the Data

Stage 1

Get it All in One Place

Stage 2

Use it to Make Intelligent Decisions

Stage 3

Vergil™ Lives here


The 3 Tiers of Big Data Analysis

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There are 3 tiers within using big data to make intelligent decisions

Good Better Best(Vergil™)

• Consolidate past data to one place

• A dashboard capability to view past data

• Real-time data collection

• Dashboard views of real-time data

• Charts/graphs with trending and cuts by geography, demographics, etc.

• Predict future states

• Explain why they will occur

• Determine how they will change if/when conditions change

• Identify precisely which actions lead to desired outcomes

PastView

Present View

Future View

Reactive Proactive


Key Questions in Big Data Analysis

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How many different factors can I consider? Do I have to guess ahead of time what I think matters before performing any analysis?

Can I consider all factors at my disposal or do I have to leave some out because the data types in the model have to fit a specific format? In other words, do I have to force-fit binary, text, and continuous data into scales?

Can the modeling capability tell the difference between random relationships and real relationships? And how does the model explain its conclusions?

What do I do with the model once I have it? Can I implement it to update and constantly keep me in the loop on changing conditions and future predictions?

When looking at solutions that identify drivers of key business objectives and drive smart future decisions (i.e., the “best” category), there are some key questions:

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2

3

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The VergilTM Difference

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Other analytic methods are very limited in how

many things you can even consider when trying to

figure out what drives your business and what

will happen. Vergil™ has no such limits.

Other analytic methods are very limited in the types of data they can use. Vergil™ can use all major data types simultaneously including, binary, scales, text, and continuous number ranges.

Other analytic methods not only often make erroneous

predictions based on the major limitations of

existing models, but also are unable to adequately

explain predictions. Vergil™ overcomes these

limitations *and* explains why predictions are true.

Consider morefactors than ever before

Consider factors you couldn't before

Explain what will happen and why

Predictions adapt to changing conditions

Imagine…

It’s like having a higher resolution picture with more pixels and/or a larger screen

Imagine…

It’s like fixing dead pixels to form a complete picture

Imagine…

It’s like having a clearer picture with greater detail

Imagine…

It’s like having a picture that adjusts dynamically to light

in the room

Other analytic methods are most often not actionable on an ongoing basis. Vergil™ can create algorithms that constantly update and adapt predictions to a wide of array of changing, fluid conditions.


A Typical Engagement with

Gordian Knot Provides...

▪ Analysis to identify – across all data factors – the impact of each on your business

▪ Identification of combinations of factors that together have an impact on your business

▪ Current predictions based on known conditions

▪ Explanation of why these predictions will happen

▪ An algorithm to dynamically predict in real-time based on changing conditions

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Contact Information

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Lawrence J. [email protected]

Brad [email protected]

609-439-8206

For more information, please contact:


Appendix

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Case Study: Golf Course Data Company (1/2)

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Our client’s primary business is to provide data visualization to golf courses to give direction on water usage, pesticide treatments, etc for their golf course clients…

Our Client’s Model Without Vergil™

An online dashboard shows the consolidated real-time data in a variety of individual measures,

trending charts, and indicator dials

The users view the data and make decisions based on their interpretations of the measurements and indicators (water

levels, humidity, pesticide levels, etc)

The client gathers data from courses from various places like

irrigation, weather sensors, pesticide treatments, labor hours,

etc and consolidates it in one place

This is “Stage 2” of Big Data

These represent the “Better” Version of Big Data AnalyticsThese represent the “Better” Version of Big Data Analytics


Case Study: Golf Course Data Company (2/2)

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Vergil™ transforms our client from a visualization company to an “action" company

Our Client’s Model With Vergil™

Users still have access to the traditional dashboard view

Users still have the option to view the granular data if they choose

The client still gathers data from disparate sources and puts it together

This is the “Best” Version of Big Data Analytics

VERGIL™Based on hundreds of variables, the courses are

given instructions for exactly what they need to do to create the optimal experience on the green while reducing costs (water usage, pesticides, labor, etc.)


Case Study: Large Media Content Provider (1/3)▪ An international provider of news and information experienced year-over-year declines in the

utilization of its main subscription service

▪ Preliminary analysis revealed that subscribers were turning to other media sources throughout the

day; there was no obvious pattern to this leakage

▪ The client's objective was to determine what combination of product features and advertising

channels could not only stem to decline but ultimately increase utilization

▪ The client had 54 possible investment decisions for developing content and deploying marketing to

communicate enhancements to the subscription-service product

▪ Conventional wisdom at the client favored significant investment in TV content (programming and

talent), building out website functionality, and using print to communicate its enhancements

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Market moving news

In-depth analysis / commentary

Summary of events

Content

type

Before market hours

During market hours

After market hoursDaypart

Channel


VergilTM

in action... the method (2/3)▪ We analyzed 3 different data sources to discover actionable recommendations

for product development, messaging, and marketing deployment

Self-reported

user information

Captured / observed

user behaviors

Media spending

Database

of 186

variables

128,000

granular

factors

148

composite

variables

Actionable

recommendations

Data Sources

Proprietary “Genetic” Analysis Proprietary VergilTM

Model

Isolate Key

Drivers

of Utilization

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Variables broken down into their “DNA” that are then modeled using non-linear mathematics.

If we used standard methods,

only 41 variables would be usable;

modeling would be based only on

those 41 variables

VergilTM

analyzes exponentially

more variables than conventional

analytics


VergilTM

delivers... the solution (3/3)▪ Rather than costly investments in TV, we discovered that the client could increase

utilization of its subscription-products by modifying two of its existing media properties and

marketing those developments using the optimal channels and messaging

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Recommendations

Media

Property

Content

Type Daypart(s)

Product

Development Messaging

Advertising

Channels

• Radio • Market

moving

news

• Before

market

hours

• During

market

hours

• Include

streaming in

sub-

scription

service

• Speed

• Objectivity

• Print

• Social

• TV

• Mobile • Summary

of events

• After

market

hours

• Link to news

item

selected by

subscription

service

• Speed

• breadth

• Radio

• Print

54 Investment Options

▪ The client’s subscription product utilization shifted from a year-over-year decline of (7%)

to an increase of 15% while spending less than one-third of what they had originally

budgeted based on “conventional wisdom” and qualitative intuition


Enhancing directional investing example

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Industry

trends

Corporate

financials

Economic

indica-

tors

Health

metrics

Brand

A&U

Mobile

usage

Commodities

pricing

Opinion

polls Social

media

Search

Market-

ing

spread

Weather

▪ Ideally, directional investing requires as much data as possible in order to discover sources of portfolio alpha

▪ Current approaches only provide a small part of the puzzle, but Vergil™ enables a holistic understanding of why stock

prices change and identifies the specific drivers of alpha that need to be monitored.

Industry trends x Social x Commodities pricing

Industry trends x Weather x Mobile usage

Corporate financials x Brand A&U x Social media

Marketing spend x Health metrics x Commodities pricing

Current

VergilTM

Drivers of investment alpha

Date post:	16-Apr-2017
Category:	Data & Analytics
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Gordian Knot Analytics Group Overview

Data & Analytics