Practical Data Dictionary: Intro

This handbook gives you a sneak peek into online data analysts work. And you also get here practical knowledge about how to organize your data, when you are starting with it at your online business (eg. SaaS startup, E-commerce business, etc.)

What will we cover in Practical Data Dictionary?

Chapter_01: Activity-related events
Chapter_02: User-types from an activity perspective
Chapter_03: Payment-related events
Chapter_04: User-types from a payment perspective
Chapter_05: All your segments
Chapter_06: Analytics, metrics KPI-s — How to calculate retention or Life Time Value?
Chapter_07: Case studies — E-commerce
Chapter_07: Case studies —Startup

Why do you need a dictionary for data analysis?

Well, maybe it’s not self-explanatory. But here’s the story, based on my experience — consulting with many companies during the last years:

When a company begins to use data, they usually read a bunch of articles and books on the subject. In good cases, they hire 1–2–3 data analysts and set up a data infrastructure and/or a data strategy. Then slowly everyone starts to use the resulting data in the company and an awesome data-driven organization is born. Hooray!

But along the way there will be some disorder caused by the use of materials pulled from various sources, and people’s different know-how. Because Data Science is not a written in stone kind of science, it’s not uncommon for the same concept to be known under another name in different places. What’s even more crazy is that this is true the other way around as well: the same word can be used for many different concepts as well.

Working on different projects I realized, this issue became increasingly problematic. For this reason, I decided to create a dictionary which unifies such data expressions and places them within a clear framework. The main points were:

  • consistency
  • simplicity, so not having to memorize 800 different types of users (created 8 categories for activity, and 5 for payment)
  • expressions for particular things should resemble each other as little as possible (not to have 3 different but similar-sounding categories, like Active User, Activated User, Re-activated user, etc.)

This is how Practical Data Dictionary came about, which I will open-source as maybe others have also experienced these kinds of issues. I advice this booklet so everyone within the organization speaks the same language, and to communicate about data quickly without any misunderstanding.

Ready to CHAPTER 1? Continue: here!

Want to have the full 54 pages e-book right now? Download it here: http://data36.com/datadictionary/

Cheers,
Tomi Mester

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