Four Eras of Analytics in Government and Elsewhere: From ......2.0 Big Data 3.0 Seamless blend of...

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Four Eras of Analytics in Government and Elsewhere: From Artisanal Analytics to Augmented Automation Thomas H. Davenport Babson College/MIT/Deloitte University of Maryland April 21, 2017 1 | 2017 © Thomas H. Davenport. All Rights Reserved

Transcript of Four Eras of Analytics in Government and Elsewhere: From ......2.0 Big Data 3.0 Seamless blend of...

Page 1: Four Eras of Analytics in Government and Elsewhere: From ......2.0 Big Data 3.0 Seamless blend of traditional analytics and big data ... analytics to the next level Massive data sources,

Four Eras of Analytics in

Government and Elsewhere:

From Artisanal Analytics to Augmented Automation

Thomas H. Davenport

Babson College/MIT/Deloitte

University of Maryland

April 21, 2017

1 | 2017 © Thomas H. Davenport. All Rights Reserved

Page 2: Four Eras of Analytics in Government and Elsewhere: From ......2.0 Big Data 3.0 Seamless blend of traditional analytics and big data ... analytics to the next level Massive data sources,

Four Analytical Eras—Accelerating!

2

1.0 2.0 3.0 4.0

Artisanal

analytics

1975-? 2001-? 2013-? 2017-?

Big data

analytics

Data economy

analytics

Cognitive

analytics

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Analytics 1.0│The Artisanal Era

1.0 Artisanal Analytics

►Primarily descriptive analytics and

reporting

►Internal, small, structured data

►“Back office” teams of analysts

►Internal decision support focus

►Predictive models based on

human hypotheses 3 | 2017 © Thomas H. Davenport All Rights Reserved

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Analytics 2.0│The Big Data Era

1.0 Artisanal Analytics

Big Data Analytics 2.0

►Complex, large, unstructured data

►New computational capabilities

required

►“Data Scientists” emerge

►Online firms create “data products”

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Analytics 3.0│The Data Economy Era

1.0 Artisanal Analytics

The Data Economy

Big Data 2.0

3.0

►Seamless blend of traditional

analytics and big data

►Analytics core to the business

►Data and analytics-based products in

every business

►Industrialized decision-making at

scale

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Analytics 3.0│Private Sector Goals

Developing products and services based on data and analytics—now available to every industry

► “Precision agriculture” offerings for farmers

► Conditional and predictive services for industrial equipment

► In telecom, analytical recommendations and insights from mobile devices

Data and analytics-based decisions at scale and supporting the front line of organizations

► Real-time routing

► Granular, targeted marketing programs

► In telecom, treating every customer differently

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► Primary focus reducing fraud in disability and identity theft contexts

► Text mining and analytics allows “express lane”--rapid approval of 20% of disability claims among very ill and elderly

► Predictive analytics used to identify disability fraud

► Analytics used to identify holders of duplicate SS numbers for identity fraud

Social Security 3.0—Fraud Prevention Focus

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► “Domain Awareness System” takes crime analytics to the next level

► Massive data sources, including:

► 9000 closed circuit TV cameras

► 500 license plate readers, 2 billion reads

► Audio gunshot detectors over 24 sq. miles

► 54 million 911 calls, converted to text

► 100 million summones, other crime records

► “Predictive policing” to 10,000 cops’ smartphones

NYPD 3.0—Situational Awareness Focus

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Analytics 4.0│The Cognitive Era

1.0 Artisanal Analytics

The Data Economy

Big Data 2.0

3.0

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Cognitive 4.0

►Analytics used to make

automated decisions

►Mostly “autonomous

analytics”

►Replacement of human

tasks—digital/physical

►Augmentation is human

focus

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A Constellation of Cognitive Technologies

► Machine learning

► Neural networks/deep learning

► Natural language processing/generation

► Rule engines

► Event stream/complex event processing

► Robotic process automation

► Custom integrations and combinations of these in a “cognitive cloud”

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Codelco 4.0—Cognitive for Safety

Chilean national copper mining company has emphasized automation for worker safety

Started with remotely-operated rock hammers in 1990s

Wide use of autonomous trucks, mine trains

Truck loading and smelting increasingly automated

Integrated operations center monitors and controls automated devices

Goal to eliminate underground human miners by end of this year

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Vanguard 4.0—Cognitive for Investor Advice

“Personal Advisor Services” combines automated and human investment advice

Proof-of-concept for

Substantially lower cost (30 basis points) and lower wealth thresholds than most human investment advice sources

Assets of $50B under management and growing rapidly

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Defense Health Agency 4.0—

Cognitive for Federated Data

Used machine learning to read analyst reports and identify machine learning as an important technology for the DHA

Used SEMOSS, open source tool developed for the Military Health System, to gather and match patient data across five different electronic medical record systems

Used same tool to identify redundant systems that could be shut down with relatively low impact—saved $58M

Working on projects to predict patient wait times and disease onset

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NASA—Cognitive for Back-Office Financials

Large-scale implementation of robotic process automation at National Shared Services Center

Proofs-of-concept for four financial processes—funds control, funds distribution, technology spending, shared services financials

More TK

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Why Move to Cognitive?

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Tedious work

Expensive labor

Too

much

data Humans poor decision-makers

Powerful technologies

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Are Knowledge Workers Next to Be Automated?

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18th-19th C. 20th C. 21st C.

Mechanical

Systems

Transactional

Computers

Cognitive/

Analytical

Computers

Manual

Labor Jobs

Admin/

Service Jobs

Knowledge

Work Jobs

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My Answer Is…Yes…and No

► Many knowledge work job tasks will be automated

► Some knowledge workers will lose their jobs, depressing hiring

► 8 lawyers where there were 10

► There will be a lot of jobs (no one knows how many) working alongside smart machines

► Immense productivity gains could fund retraining and redeployment of people

► But workers can’t afford to be complacent

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Ten Knowledge Work Jobs with Automatable Tasks

1. Insurance underwriter—the oldest automated profession

2. Lawyer—e-discovery, predictive coding, etc.

3. Accountant—automated audits and tax

4. Radiologist—automated cancer detection

5. Reporter—automated story-writing

6. Marketer—programmatic buying, focus groups, personalized e-mails, etc.

7. Financial advisor—”robo-advisors”

8. Financial asset manager—index funds, trading

9. Programmer—automated code generation

10.Quantitative analyst—machine learning, etc.

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The Impact on People: Automation or Augmentation?

► Like freestyle chess, but applied to business

► Better than humans or automated chess systems acting alone

► Humans can choose among multiple computer-recommended moves

► Humans know strengths and weaknesses of different programs

► Automation is a race to the bottom

► Most current cognitive projects involve augmentation

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Five Ways of Stepping

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► Step in—humans master the details of the system, know its strengths and weaknesses, and when it needs to be modified

► Step up—humans take a big-picture view of computer-driven tasks and decide whether to automate new domains

► Step aside—humans focus on areas they do better than computers, at least for now

► Step narrowly—humans focus on knowledge domains that are too narrow to be worth automating

► Step forward—humans build the automated systems

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What’s Your Entry Point into Cognitive?

Mostly Buy

• Existing vendor’s software with cognitive capabilities

• Pick a small project and a low-hanging fruit vendor

• Start with IT automation

Some Build, Some Buy

• “Autonomous analytics” with statistical machine learning

• Go big with “transformative cognitive computing”

• Give chatbots a shot

Mostly Build

• Make an existing application smarter or more autonomous

• DIY with open source

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Becoming a Cognitive Organization

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► Pick an entry point, and start some pilots

► Pick the right cognitive technology for your problem

► Take an augmentation perspective from the beginning

► Get good at work design for smart humans and smart machines

► Give your people the options and the time to transition to them

► Put someone in charge of this