Steve Bennett, Ph.D. - Sas Institute · Title: Steve Bennett Analyltics Economy Author: Selda...

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Copyright © 2015, SAS Institute Inc. All rights reserved. Steve Bennett, Ph.D. Director, Global Government Practice

Transcript of Steve Bennett, Ph.D. - Sas Institute · Title: Steve Bennett Analyltics Economy Author: Selda...

Page 1: Steve Bennett, Ph.D. - Sas Institute · Title: Steve Bennett Analyltics Economy Author: Selda Uzunmehmetoglu Created Date: 4/26/2018 10:21:22 AM

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Steve Bennett, Ph.D.Director, Global Government Practice

Page 2: Steve Bennett, Ph.D. - Sas Institute · Title: Steve Bennett Analyltics Economy Author: Selda Uzunmehmetoglu Created Date: 4/26/2018 10:21:22 AM

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3 attempts to digitize the process; $106 million total spent.

Research estimates that only 5% of large Federal IT projects in the last decade have succeeded.

Of the rest, 41% were considered failures and canceled before they were turned on.

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Digital Transformation (DX)

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Digital Transformation (DX)

Key Driver: Legacy systems constraining innovation

Objectives:• Improve performance• Improve outcomes• Citizen-centricity

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Digital Transformation (DX)

Key DX competencies:

• Technology and Process: Back-office integration• Information and Data: Analytics• Emerging Technology: RPA and Artificial Intellgence

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Back-office Integration

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Analytics

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Analytics is the scientific process of transforming data into insights for decision making.

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The analytics economy

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What business and agency leaders are saying about their analytics programs?

86%Of executives say their organizations have been at best only somewhat effective at

meeting the goals of their data & analytics programs, including more than ¼ who say they’ve been ineffective.

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PEOPLE PROCESS

TECHNOLOGY

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No established analytics culture

Talent gap

Skillset silos

PEOPLE PROCESS

TECHNOLOGY

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Business silos

Governance and regulation

Accidental analytic strategy

PEOPLE PROCESS

TECHNOLOGY

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Infrastructure limitations

Fragmented environments

Barriers to asset promotion

PEOPLE PROCESS

TECHNOLOGY

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Right-timeAnalytics

deployment

Scalingup

Packageversioning

Assetgovernance

Model inventory& management

SLAs

Content silos

Collaboration

IP retentionConsistentquality

Automation

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So, where do we go from here?

Transitioning to the next phase of analytics…

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Market Observation

Productivity Platform

1980’s onwards

Operational Platform

E R P S u p p l y

C h a i n

F i n a n c e

M R P

C R M H R

1990’s onwards

Big Data...

2000’s onwards

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Market Observation

Productivity Platform

1980’s onwards

Operational Platform

E R P S u p p l y

C h a i n

F i n a n c e

M R P

C R M H R

1990’s onwards

Analytics Platform

2017 onwards

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Analytics Platform

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Analytics Platform for DX – DataProducing data that cultivates analytics assets

ACCESS

Connect to data

Build views

INTEGRATE CLEANSE GOVERN

ProfileIdentifyStandardizeMatch

Explore lineage

Monitor

Enforce policies

Join, merge, transform

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REPORT EXPLORE FORECAST OPTIMIZEPREDICT

BUSINESS INTELLIGENCE

DATA DISCOVERY

TIME SERIES FORECASTING

MACHINE LEARNING & AI

OPERATIONS RESEARCH

DATA & TEXT MINING

CLASSICAL STATISTICS

Analytics Platform for DX – DiscoveryBuilding analytics that solve any question

AUTOMATE

GOVERN & EMBED

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CENTRALLY MANAGE

CREATE EFFICIENCIES

CONTROL & ADJUST EXPEDITE ANSWERS

GOVERN EMBED MONITOR AUTOMATE

Analytics Platform for DX – DeploymentRealize the value from analytics assets in action

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Emerging Technology: Artificial Intelligence

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1956 – Artificial Intelligence is born

“Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.“ – John McCarthy

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1959 – Machine Learning defined

“Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed” – Arthur Samuel

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GoMost complex game in existence

10172 board combinations(1082 atoms in the universe)

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Machine Learning

Deep Learning

Artificial Intelligence

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Machine Learning

Categorize or catalog like people or things

Predict likely outcomes based on identified patterns

Identify previously unknown patterns and relationships

Maximize a reward or goal be evaluating the result

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Deep Learning

Build systems that mimic the human brain – neural networks

Complete opposite approach of the previous AI approaches

Computer learns by itself rather than being programmed for expertise

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Facial Recognition

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Optimal Deployment of Operational Resources

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Pattern Recognition: Terrorism or Organized Crime Networks

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Anomaly Detection

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Process Automation

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Digital Transformation (DX)

Examples

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United Nations – Disaster Response

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U.S. State of North Carolina: CJLEADS

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Rijkswaterstaat, Netherlands

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Health Canada

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U.S. Air Force

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STAR, Denmark

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Child Safety

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Ministry of Social Development, New Zealand

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Getty Creative Number: 614083310

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VuMC, Amsterdam

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Digital Transformation (DX)

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@AnalyticSteve

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