The document discusses data analytics and how organizations can take a new approach. It recommends that data warehouses should attract and share data throughout organizations by default. It also suggests that data analysis should be straightforward for anyone and as deep as analysts' skills, not limited by IT rules. Examples are provided of custom dimensions in Google Analytics and definitions of key data analytics terms.

1. Uutta otetta data-
analytiikkaan
Jukka Huhtamäki @jnkka
IISLab, TTY
Data ja analytiikka liiketoiminnan kehittämisessä
torstaina 26. marraskuuta 2015
#Data4BizTraining @edutechtut

2. ETL DAD
Extract
Transform
Load
Data Engineering
Data Warehouse
Forever
Discover
Access
Distill
Data Science
Whatever means
Now

3. Smart data
We need a movement with a quickness
You are the witness of change
And to counteract
We gotta take the power back
De la Rocha et al. (1992)

4. MAD skillz (Cohen et al. 2009)
Magnetic: Data warehouses should draw data
like a magnet, not repel it. Sharing data within
organization should be the default
Agile: It should be straightforward for anyone
to start analyzing data (analytics democracy)
Deep: Analysis should be as deep as analysts’
skills allow, not as shallow as IT rules

6. https://en.wiktionary.org/w/index.php?title=juonikas&oldid=31154683

7. Case Google Analytics
• Custom dimensions:
https://developers.google.com/analytics/devg
uides/collection/analyticsjs/custom-dims-mets
http://bit.ly/gacustom2015

8. (Parker, 2014)