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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
Four Analytical Eras—Accelerating!
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1.0 2.0 3.0 4.0
Artisanal
analytics
1975-? 2001-? 2013-? 2017-?
Big data
analytics
Data economy
analytics
Cognitive
analytics
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
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”
4 | 2017 © Thomas H. Davenport All Rights Reserved
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
5 | 2017 © Thomas H. Davenport All Rights Reserved
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
7 | 2017 © Thomas H. Davenport All Rights Reserved
8 | 2013 © Thomas H. Davenport All Rights Reserved
► “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
Analytics 4.0│The Cognitive Era
1.0 Artisanal Analytics
The Data Economy
Big Data 2.0
3.0
9 | 2017 © Thomas H. Davenport All Rights Reserved
Cognitive 4.0
►Analytics used to make
automated decisions
►Mostly “autonomous
analytics”
►Replacement of human
tasks—digital/physical
►Augmentation is human
focus
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”
10 | 2017 © Thomas H. Davenport. All Rights Reserved
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
11 | 2013 © Thomas H. Davenport All Rights Reserved
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
12 | 2013 © Thomas H. Davenport All Rights Reserved
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
13 | 2013 © Thomas H. Davenport All Rights Reserved
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
14 | 2013 © Thomas H. Davenport All Rights Reserved
Why Move to Cognitive?
15
Tedious work
Expensive labor
Too
much
data Humans poor decision-makers
Powerful technologies
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
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
17 | 2017 © Thomas H. Davenport. All Rights Reserved
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.
18 | 2017 © Thomas H. Davenport. All Rights Reserved
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
19 | 2017 © Thomas H. Davenport. All Rights Reserved
Five Ways of Stepping
20 | 2017 © Thomas H. Davenport. All Rights Reserved
► 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
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
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