The document discusses leveraging the value of data through data-driven decision making. It contrasts expert decisions made by humans with data-driven decisions made through algorithms and machines. It outlines the steps in the data chain for gathering, storing, cleaning, merging, modeling, predicting, and deciding data to execute outcomes. The document is written by David Bessis of tinyclues, a predictive analytics company that provides data mining as a service across several industries.

1. LEVERAGING THE VALUE OF DATA
David Bessis, tinyclues
April 3rd, 2012

2. datamining as a service

3. expert decisions vs data-driven decisions
decisions by experts data-driven decisions
 a few decisions  millions of decisions
 produced by humans  produced by robots
 designed for humans  designed for ROI
 format: pptx, xlsx, …  format: sql, csv, json, …
 old-style accountability  new accountability rules

4. the data chain
clean
gather store merge model predict decide execute
filter
 each step is critical
 each step is hard
 how many steps can you efficiently manage?

5. Thanks!
David Bessis
db@tinyclues.com
About tinyclues:
 tinyclues is a full-service predictive analytics provider
 founded in 2010
 7 people (mostly R&D engineers & mathematicians)
 100% cloud based, 100% control of its data software stack
 serves several industries:
 eCommerce
 digital marketing
 digital medias
 market research