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What is the data analytics stack?



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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.

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What is the data analytics stack?

  1. 1. What is the data analytics stack?
  2. 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. 3. The data products Venn diagram
  4. 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. 5. Databases • Data integrity at scale • Unparalleled data storage & delivery • Limited data analysis
  6. 6. Dashboards & BI platforms • Broad category: visualization software to data warehouses • Democratized data visualization • Improved reporting & analysis distribution • Can be expensive & inflexible
  7. 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. 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. 9. Remaining platform agnostic • Some “stacks” work better together… • Some tools are preferred to others… • … focus on mastering the wisdom of context first