In solving analytics problems with software, the poor craftsman blames his tools... but at the same time, if they only tool one has is a hammer, everything looks like a nail. I have identified four categories of applications that an analyst should have at their disposal: spreadsheets, databases, BI/dashboard platforms and programming languages. None of these is "better" than another: they are all slices of the same stack. The superior analyst has the wisdom to choose the right set of tools for the right task.

1. What is the data
analytics stack?

2. Analytics problem-solving: context is
king
“The poor craftsman blames his
tools”
but also
“If the only tool you have is a
hammer, everything looks like a
nail”

3. The data products Venn diagram
http://www.datacommunitydc.org/blog/2013/09/the-data-products-venn-diagram

4. Spreadsheets
• Flexible, easy-to-use
• Great for back-of-the-envelope calculations and
prototyping
• Easy data entry
• Lacks data quality, storage & delivery methods

5. Databases
• Data integrity at scale
• Unparalleled data storage & delivery
• Limited data analysis

6. Dashboards & BI platforms
• Broad category: visualization software to data
warehouses
• Democratized data visualization
• Improved reporting & analysis distribution
• Can be expensive & inflexible

7. Programming languages
• Basis for reproducible analysis
• Gateway to advanced statistics & analytics
• Often open-source and free
• Steep learning curve: what is the ROI?

8. No slice is “better” than another!
• Each slice has pros and cons
• “Python is better than Excel” becomes a
meaningless statement… better for what?
• Context matters – they’re all slices of the same
stack!

9. Remaining platform agnostic
• Some “stacks” work better together…
• Some tools are preferred to others…
• … focus on mastering the wisdom of context first

10. LET'S TALK
WEBSITE
stringfestanalytics.com
EMAIL ADDRESS
george@stringfestanalytics.com
LINKEDIN
linkedin.com/in/gjmount