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Four Eras of Analytics Thomas H. Davenport, Babson College/MIT/Deloitte/International Institute for Analytics UNC Charlotte Analytics Frontiers Conference March 30, 2016 1 | 2016 © Thomas H. Davenport. All Rights Reserved
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Changing the Way Analytics Are Done
Small, structured, static data Back-office analysts Slow, painstaking Internal decisions Spreadsheets/OLAP/EDW Descriptive analytics Big, unstructured, fast-moving data Rise of data scientists Data products in online firms Rise of Hadoop and open source Visual analytics “Agile is too slow” Mix of all data Internal/external products/ decisions Analytics a core capability Move at speed and scale Predictive and prescriptive analytics Analytics embedded, invisible, automated Cognitive technologies “Robotic process automation” for digital tasks Augmentation, not automation 1975-? 2001-? 2013-? 2016-?
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Analytics 1.0│Traditional Analytics
Primarily descriptive analytics and reporting Internally sourced, relatively small, structured data “Back office” teams of analysts Internal decision support focus Spreadsheets, BI, EDW, etc. Tom 3 | 2016 © Thomas H. Davenport All Rights Reserved
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Analytics 2.0│The Big Data Era
Traditional Analytics 1.0 Primarily descriptive analytics and reporting Internally sourced, relatively small, structured data “Back room” teams of analysts Internal decision support Spreadsheets, BI 2.0 Big Data Complex, large, unstructured data New computational capabilities required “Data Scientists” emerge Online firms create “data products” 4 | 2016 © Thomas H. Davenport All Rights Reserved
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Analytics 3.0│Fast Business Impact for the Data Economy
Traditional Analytics 1.0 Fast Business Impact for the Data Economy Primarily descriptive analytics and reporting Internally sourced, relatively small, structured data “Back room” teams of analysts Internal decision support Spreadsheets, BI 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 2.0 Big Data Complex, large, unstructured data sources New analytical and computational capabilities “Data Scientists” emerge Online firms create data- based products and services 5 | 2016 © Thomas H. Davenport All Rights Reserved
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Analytics 4.0│The Cognitive Era
Traditional Analytics 1.0 Fast Business Impact for the Data Economy 4.0 Cognitive 3.0 Analytics used to make automated decisions Emergence of “cognitive technologies” Replacement of human tasks—digital/physical Augmentation is human focus Primarily descriptive analytics and reporting Internally sourced, relatively small, structured data “Back room” teams of analysts Internal decision support Spreadsheets, BI A seamless blend of traditional analytics and big data Analytics integral to running the business Data and analytics-based products in every business Industrialized decision-making at scale 2.0 Big Data Complex, large, unstructured data sources New analytical and computational capabilities “Data Scientists” emerge Online firms create data- based products and services 6 | 2016 © Thomas H. Davenport All Rights Reserved
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(Cumulative) Skills Across the Eras
4.0 1.0 Traditional Analytics Fast Business Impact for the Data Economy Cognitive 3.0 Natural language processing Neural networks/deep learning Event stream processing Work design Data integration and curation Storytelling with data Business acumen Statistics Machine learning Agile methods Change management 2.0 Big Data Experimentation Data restructuring Open source coding Product development Visual analytics 7 | 2016 © Thomas H. Davenport All Rights Reserved
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Why Move to Cognitive? Too much data Expensive labor
Humans not good decision-makers Tedious work Powerful technologies
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A Smooth Transition from Analytics to Cognitive
Traditional Analytics Correlation/ Regression Natural Language Processing Neural Networks Logistic Regression Deep Learning Text Analytics Machine Learning Cognitive Technologies
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Smart People Concerned About the Future of Smart People
“I am in the camp that is concerned about super intelligence…I don’t understand why some people are not concerned.” (Bill Gates) “The development of full artificial intelligence could spell the end of the human race.” (Stephen Hawking) “Advancing machine intelligence is the most important problem facing the world today.” (Nobel economist Robert Schiller) “We will soon be looking at hordes of citizens of zero economic value. Figuring out how to deal with the impacts of this development will be the greatest challenge facing free market economies in this century.” (Michael Malone, Bill Davidow) 10 | 2016 © Thomas H. Davenport. All Rights Reserved
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But Automation Is a Pain
At a minimum, substantial displacement of human workers In many cases, humans prefer to be served by humans Automation is often a race to the bottom Difficult to combine automation with creativity and frequent innovation 11 | 2016 © Thomas H. Davenport. All Rights Reserved
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Are Knowledge Workers Next to Be Automated?
18th-19th C. 20th C. 21st C. Mechanical Systems Transactional Computers Cognitive/ Analytical 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 13 | 2016 © Thomas H. Davenport. All Rights Reserved
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Ten Knowledge Work Jobs with Automatable Tasks
Teacher/Professor—online content, adaptive learning Lawyer—e-discovery, predictive coding, etc. Accountant—automated audits and tax Radiologist—automated cancer detection Reporter—automated story-writing Marketer—programmatic buying, focus groups, personalized s, etc. Financial advisor—”robo-advisors” Financial asset manager—index funds, trading Programmer—automated code generation Quantitative analysts—machine learning, etc. 14 | 2016 © Thomas H. Davenport. All Rights Reserved
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Technologies Driving Knowledge Work Automation
Analytics and big data Machine learning Neural networks/deep learning Rule engines Event stream/complex event processing “Cognitive computing,” e.g., Watson Robotic process automation Custom integrations and combinations of these in a “cognitive cloud” 15 | 2016 © Thomas H. Davenport. All Rights Reserved
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Just How Smart Are Smart Machines?
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The Impact on People: Automation or Augmentation?
Augmentation—smart humans helping smart machines, and vice-versa People do this by aiding automated systems that are better than humans at their particular tasks, or by focusing those tasks at which humans are still better The classic augmentation example: freestyle chess 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 We’ve seen this before: textile machinery, spreadsheets 17 | 2016 © Thomas H. Davenport. All Rights Reserved
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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 18 | 2016 © Thomas H. Davenport. All Rights Reserved
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The Five Augmentation Steps for Financial Advisors
Step in—advisors become experts in online advice, and assist clients to use it to their best advantage Step up—advisors identify the domains most in need of automation, or those already automated needing improvement Step aside—advisors primarily communicate with clients, but don’t make decisions for them—or work outside investments Step narrow—advisors identify a narrow client segment or investment type Build the steps—advisors use their expertise to build robo-advisor systems 19 | 2015 © Thomas H. Davenport All Rights Reserved
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Where Augmentation Is Likely to Prevail
Knowledge bottlenecks Veterinary diagnosis and treatment Need for decision quality and consistency Insurance policy underwriting Too much data or content for humans to digest/analyze Digital marketing, oncology Cognition currently too expensive for broad application Investment advice, college education 20 | 2016 © Thomas H. Davenport. All Rights Reserved
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Oncology, For Example Over 400 types of cancer
Hundreds of oncogenes and tumor suppressor genes—probably biome genes too Oncology information needs to be integrated with EMR data Over 75 different drugs for breast cancer alone Treatment options changing very quickly Diagnosis and treatment too hard for humans, but machines don’t find it easy either—yet Need for content integration, holistic medicine 21 | 2016 © Thomas H. Davenport. All Rights Reserved
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Implications for Organizations
Take an augmentation perspective from the beginning Start a pilot and develop a platform Pick the right cognitive technology for your problem 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 thinking about this
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Prerequisites for 3.0 and 4.0 Organizations
No silos of people, data, or technology Leaders who get it and are willing to engage and invest Relatively few battles between the business and IT A strategy that focuses on services and processes, not just the best products A willingness to pursue this for the long haul
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Some Concerns About 4.0 We don’t fully understand rapid, automated, interconnected decisions “Flash crash” Power outages Snowstorms and air transportation Need to model dynamic interactions of human and machine behaviors
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What Are We Waiting For? The technology is good, and getting better rapidly Knowledge workers are ready to have their work augmented and to augment smart machines Cognitive processes need improvement and innovation We’ll work out the kinks Start your engines! 25 | 2016 © Thomas H. Davenport. All Rights Reserved
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