Data Science Operational Considerations
Introduction
Data science is so hot right now.
making charts, visualizaintg, storaging data , the SQL, the big data, the machine leanring.
There lots of smart people who are out thre doing the work, answering questions, asking new questions, making reports and infographics.
But how is it all organized?
How do you live with it over the months and years an ogranization might live, how does the knowledge and data survive over time ?
I want to go over some of the things to think abut when managing data for an organizaiotn.
1. ontologies
2. data dictionaries
3. data catalogs
4. hypothesis catalogs
5. stories
The modern organization is data driver.
- They collect data
- They secure data
- They clean data
- They use data: to try and understand systems, to find value in the data.
So they have all this data, and their staff use the data.
Some staff have access to some data, some have access to other data.
Some staff know about some data , some don't.
data is s
References and Reading
https://en.wikipedia.org/wiki/Ontology_(information_science)
