humans in the loopproviding analytical value now

dr. clement fredembach, teradata8th march 2017

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fringe science

fringe ©FOX network 2008

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the future?

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the present

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meanwhile

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the analytic cycle and the place of humans in it

question

test

validate

operationalise

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in reality

same (or very similar)question

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better

operationalise

AaaS reporting

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all science is data science(the reverse is not true)

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• research: develop new ideas, solutions and questions

• development: operationalise and transition to analytics as a service (AaaS)

operationalising data science = R&D

observation

question

research

hypothesisexperiment

data

conclusion

question

test

validate

operationalise

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• researcher• idea-driven• math knowledge• ask and answer

(new) questions• theme-centric• platform

independent

• developer• result-driven• software engineering• productionise findings• project-centric

• platform dependent

• machines• deliver predictably• score models• generate reports• query-centric

• are the platform

R&D skillset and position

long term medium term short term

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big data is not (yet?) a substitute for research

one day we’ll have sensors for everything now there’s no substitute for research experience the scientific method starts with observation by the scientists

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conclusion

• no analytics silver bullet now– unlikely in the foreseeable future (despite the hype)

• humans are awesome at data science and can provide value now– investigation of reality is a necessary step– not everything is captured by data

• delivering (long-term) value from data science requires an R&D model– research then development and agile/devops– different roles/skills, don’t get your data scientists to productionise

insights!• all science is data science => follow the scientific method

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