I picked up sketchnotes recently to help me understand better the thoughts I have in my head. And one of the fuzzy things that I am trying to figure out is, in the new big data, analytics hype. Who is selling what and who needs what?
The rough data stack concept I have now is
Data analysis services (data analysts/scientists/statisticians performing the actual analysis to extract insights)
—————————–
BI / BA / Data visualization Tools or Apps
—————————–
ETL processes, data warehouses
—————————–
Data sources
—————————–
It seems that typically, the top and bottom layers are so ill-defined as to what data sources exists, and what kind of information or insights will be useful to you. Sure, in exploratory data analysis the questions are not well-formulated up front, thus those are effortful tasks.
The middle two layers are so jam packed with products that the user or middle person like me, has to spend much time understanding and evaluating them. Less time to do actual data analysis.
Maybe it’s a wrong role for now, I really really want the right role asap though. Wish me luck please.
Exploring and venting about quantitative issues
Historical questions raised by a quantitative approach to language
To keep useful stuff I googled
Blog to document and reflect on Columbia Data Science Class
A Quick-R Companion
[R] + applied economics.
Scientific computing, data viz and general geekery, with examples in R and MATLAB.
