AI and ML The rise of everything ‘smart’ changing ‘how we ......Three statisticians went out hunting, and came across a large deer. 1. The first statistician fired, but missed,

AI and ML

The rise of everything ‘smart’changing ‘how we work’

Three statisticians went out hunting, and came across a large deer.

1. The first statistician fired, but missed, by a meter to the left.

2. The second statistician fired, but also missed, by a meter to the right.

The third statistician didn’t fire, but shouted in triumph…

“On the average we got it!”

Before we get started…

The most important thing….

AI is at the top of EVERY discussion

AI Overlord

You MUST!

DON’T forget!

You DIDN’T!

HOWEVER, it’s still OUR innovation

AI Overlord

You MUST!

DON’T forget!

You DIDN’T!

We will NOT be controlled by Robots….

Innovation will change how we live, work, and learn.

Innovation will alter our understanding of business, how it operates, and define new meanings of success.

What is Intelligence: The Skinny on “A I”

Augmented IntelligenceArtificial Intelligence

• Self-sentient, designed to think on its own.

• Teaches itself.

• System-generated output.

• Systematic.

• Requires outside input.

• Evolves via learning loops and human interaction.

• Intuitive conversation.

• Environmental.

Despite all that, human ingenuity IS our

competitive advantage…

• It isn’t a feature• It isn’t a pattern• It isn’t math• It isn’t software• It isn’t ARTIFICIAL

• It IS free

• Its Under leveraged

Innovative tech is converging fast

We innovated more in 2018 with technology solutions than we did in the prior 2 decades.

• Knowledge curation

• Information dissemination

• Automation of decisions

• Everything ”As a Service”AI

How we leverage it all together is not well defined.

What’s Beyond IoT?

Synthesis and Interwoven Tech

Unlimited Potential/Immature Plans

Evolution of Analytics - The Foundation

A community of devices that contain a certain level of intelligence and learns from the ecosystem and its environment benefiting the quality of life, how a business

functions, and even how we learn.

Synthesizing and Interwoven

Obstacle - Connected and Distracted

Knowledge as a Service doesn’t exist.

It’s still difficult to get and spreads slowly, or doesn’t spread at all.

Information needs to reach everyone in context at a much higher velocity.

Innovator's Dilemma

Data Sciences has a much bigger and more important role than we think

90%of Organizations have a BI

programs.

Less than 5% put this knowledge in

all employees hands at the rate that

benefits their processes now,

functions, or changes how WE

Less than 1% use their employees

devices as knowledge collection-

points or conduits to feed insights bi-

directionally.Analytics IoT AI

The Issue

We are facing with cognitive overload

Inefficient collation of knowledge points.

Interweaving of emerging technology derails focus.

Recalibrate how we plan, solve, use, and evolve

VS1. Most AI and ML programs innovation are “aim and

fire”.2. Fire fights often drives change in focus and impact

depth of DS longer term innovation.3. Results in too many DS point solutions and limited

self-evolving methods.

With today’s technology, our minds, and emerging platforms our research, assessments, planning, and simulation that result in impactful business strategy change should be faster and with less friction.

Having a method and a plan doesn’t mean success.

Typical method for AI/ML Solutions

Current State Assessment

Exploratory Data Analysis

Productionalization of Findings

Understand business need

Document current state architecture

Document Data Science Architecture

Gain access to IT systems

Define scope of analysis

Expose key characteristics of data

Validate findings with SMEs

Design data stream plan

Build AI/DS science services

Provide maintenance, monitoring, and

support

Create documentation

One time or infrequent

taskRepeated

Repeated task (pending validation from EDA

phase)

Typical method for AI/ML Roadmaps

Current State Assessment

Exploratory Data Analysis

Productionalization of Findings

Understand business need

Document current state architecture

Document Data Science Architecture

Gain access to IT systems

Define scope of analysis

Expose key characteristics of data

Validate findings with SMEs

Design data stream plan

Build data science service

Provide maintenance, monitoring, and

support

Create documentation

One time or infrequent task

Repeated Task

Repeated task (pending validation from EDA phase)

Narrow focus use

Impractical mass consumption format

Disjointed roles and layers

Layers of team abstraction

Imperfect research stack

YES!!!Buy me!

Slow to move, lift in organizational intelligence is momentary, mostly point in time

Tech Clutter – WHY?

Can’t find it Too Much of it Its in too many places

DS Must become ’Smart’ to overcome being shifted ‘Innovative to Stasis’

Cutting edge technology, wide open innovation, no rules, boundless solutions and limitless problem solving, planting seeds.

• Step Changes

• Significant Growth

• Marketplace Disruption

Tooling, role, research boundaries vs focuses on outcome.

Innovation IrrelevanceStasis

Maintenance and cautious growth, gradual governance, slowed innovation and limited advancement.

• Incremental Change

• Predictability

• Limited Innovation

Stagnation or gradual decline, heavy process limiting progress, paralysis by analysis, sluggish to respond, belabored and slow delivery.

• Red Tape

• Bureaucracy

• Group Think

We innovate with DS, but we need to address the root of the problem. Frequency, availability, with

context, and for everyone.

Point and time Forging Sustainable ‘Smarts’

Solves a particular topic. Typically a fire

or emerging issue

Every AI solution is a feature to

solve the problem and is part of a bigger accessible and repeatable

ecosystem.

Tomorrow

Programs must shift the way we solution and deliver

Did You Know?

• The very most important input into Artificial Intelligence or Augmented Intelligence is the data sciences?

• It can play the same role to the brain of an AI identity as memories, experience, or knowledge. It’s just digitized!

Building Blocks of a ROI Rich AI and ML program that

alters work process design

• Establish the Data Science & Innovation teams as a supporting, value-added partner to help business leaders achieve their goals, not just produce models, research, or new data.

• Leverage the foundations for Advanced Analytics that leads to expansive AI, including fully leveraging technology platforms and immersion capabilities.

• Pursue Analytics across a broad continuum, activated through building the foundations for analytics on strategy focused on outcome use cases to generate EBITDA.

• Develop proprietary predictive AND optimization solutions/features to improve how WE WORK.

• Collaborate with & educate various business functions around advanced analytics applications.

• Enable our organization to thrive in a new, interconnected marketplace, building capabilities and solutions to improve things like pricing, product recommendation and customer performance.

You MUST have a bigger and purposeful mandate beyond silo’s of tech.

Your mandate needs to advance the 6 pillars of ‘analytics maturity’.

NOT just solve outcome focused problems.

PULLAI/Data Science works on use cases identified by the business.

PUSHAI/Data Science experiments with data and generates use cases.

PRODUCTIONIZEAI/Data Science productionizes use cases that are used by external parties for frictionless relationships

You MUST have a hyper adaptive engagement model that builds the roadmap out of PUSH AND PULL engagement methods.

Data Science team engagement model

You MUST have VERY clear focus areas derived from your engagement model in business terms. Use a unit of measure we all understand to describe the benefit (e.g. EBITDA).

Example Data Science Team Roadmap Item Summary and Value Alignment for planning with the business.

You MUST must break down the barriers between Data Sciences, Technologist, and the business with approaches and communications everyone understands by mastering each domain.

What do you mean data scientist are business strategists,

technologist, wield emerging technology, and are AGILE?

Perfect isn’t best. Almost done isn’t useful. Use research and clear/frequent communication to educate, align, and find value routes faster. Not everything is an SDLC.

Simple, but broadly available Measurement system or Models

Relate BI self service assets to variations in measurement system and models

Enable closed Loop collaboration between business to capture causality and emerging knowledge

Smarter more human like data affects models

Systems can suggest

Broader Accessibility

and AI Based

Distribution of

Knowledge.

Competitive Watermark

Increase Measurement Frequency. RT Enabled.

Big Data Powered.

nsCommon Measures/Models

Feedback loops feed learning

‘Assistant Based’ AnalyticsAI like

Realize every Data Science solution has 50 more uses and opens the door for emerging data and smarter data systems

• Business Measurements and performance results

• Action Plans around Measurement Variation.

• Observed Changes in Analytical Patterns or Business Performance and correlated human reaction.

• Dashboards• Reports• Measurements• Patterns

ML/Predictive/Prescriptive outputs & Models as a Service

The meaning of good and what we did

Results or expected results

Digital Knowledge Digital Experience

Realize that every input, output, or result is usable in the greater quest of Knowledge as a Service and it will be Data Scientists that breathe life into broader AI to the enterprise

Analytical Brain

Learning Loop

Observed Intuition

Elective Evolution

Solution each item on the roadmap with the end in mind. Individual solutions, when combined, create hyper smart an efficient exploratory data tools.

If you do, you WILL change ‘how we work’

I can help you use at scale

and tune this!

Solve Invent Create

Subject matter experts, experienced, meaningful context, rapid accurate results.

Advanced Algorithms The Human Factor

Community accelerated insights. Crowd sourced intelligence refinement is fastest

Advanced analytical models provide direction at scale.

Example of Composite ‘Smart’ Solutions

Advanced Analytics: ‘Smart’ Enabled Apps

Constant

AwarenessCoaching

& Options

Advanced Analytics: ‘Smart’ Enabled Apps

Reporting and Analytics Decision Models and Outcomes

Augmented Reality - A New Dimension Analytics

IoT Meets Progressive Analytics

Diagnose

system failures

Non-Acute/

Trend Detection

Event Detection

IoT Meets Progressive Analytics – Use Case

Diagnose

system failures

Non-Acute/

Trend Detection

Event Detection

By comparing acute and non-

acute event detection to the failure

modes matrix, we can diagnose

failures and provide calls-to-action

that are easier for contractors to

understand.

Rolling windows are compared to

determined whether more recent

data is showing significant

uptrending or downtrending. This is

used to identify changes that might

not get detected by analyzing only

short-term (acute) events.

An analytics application generates

thresholds off of recent data for each

metric, for each system, and at

various ambient temperatures. The

inbound data stream is compared to

these pre-calculated thresholds to

identify anomalies.

1. Leadership analytics on listing

activity (digital and in person)

with data from IoT

1. Listings enriched with

augmented reality to help

improve or inspire with a new

dimension of creative assets

and its relation to hire closes

Analytics, IoT, and Augmented Reality

Combining Technologies Seamlessly

Use Case Deep Dive

Using Analytics, AI, and DS to Change how we work

To reimagine customer experience and reinvent with new possibilities, it’s imperative that we embed software enabled intelligence into integrated

products and services. It’s expected

How does a world’s leading hospitality company unleash the value trapped inside the ecosystem and realize these opportunities of consumer connectedness and their direct correlation to revenue streams?

Use More Data, Inspire More People, Be Better…

The future is about helping people be equipped and amazing not about people being replaced by robots.

Thank YOU!!!!

Joshua.sutton@panderasystems.com

407-529-5090

AI and ML The rise of everything ‘smart’ changing ‘how we ......Three statisticians went out hunting, and came across a large deer. 1. The first statistician fired, but missed,

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