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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!
1.0
2.0
3.0
4.0
Artisanal
analytics
Big data
analytics
Data economy
analytics
Cognitive
analytics
1975-?
2001-?
2013-?
2017-?
2
Analytics 1.0│The Artisanal Era
Artisanal
1.0 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
Artisanal
1.0 Analytics
2.0 Big Data Analytics
►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
Artisanal
1.0 Analytics
2.0 Big Data
The Data
3.0 Economy
►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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Social Security 3.0—Fraud Prevention Focus
► 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
7 | 2017 © Thomas H. Davenport All Rights Reserved
NYPD 3.0—Situational Awareness Focus
► “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
8 | 2013 © Thomas H. Davenport All Rights Reserved
Analytics 4.0│The Cognitive Era
Artisanal
1.0 Analytics
The Data
3.0 Economy
2.0 Big Data
4.0 Cognitive
►Analytics used to make
automated decisions
►Mostly “autonomous
analytics”
►Replacement of human
tasks—digital/physical
►Augmentation is human
focus
9 | 2017 © Thomas H. Davenport All Rights Reserved
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?
Expensive labor
Tedious work
Too
much
data
Humans poor decision-makers
Powerful technologies
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Are Knowledge Workers Next to Be Automated?
Cognitive/
Analytical
Computers
Transactional
Computers
Mechanical
Systems
Knowledge
Work Jobs
Admin/
Service Jobs
Manual
Labor Jobs
18th-19th C.
20th C.
21st C.
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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
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 computerrecommended 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
► 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
20 | 2017 © Thomas H. Davenport. All Rights Reserved
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
► 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
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