50 things I believe about data

A new trend is rising in the analytics and web marketing community! Lists of 50 things you believe about your industry. My inspiration was the following great lists of “50 things I believe…” from Tim Wilson and Cory Underwood:

(Bonus: It’s also worth checking out this article from Jan Exner, which breaks down Tim Wilson’s list into more detail.)

The list below was created based on my experience working in digital analytics projects since 2008 and from numerous conversations I had while organizing the Digital analytics meetup, Thessaloniki since 2017.

The list

In no particular order:

  1. Have detailed understanding of data before reporting on them
  2. Documentation changes less often than data implementations
  3. Use the KISS (Keep It Stupid Simple) principle when providing requirements to data developer teams
  4. Encouraging a close relationship between data analysts and data developers can create bridges between these 2 different worlds
  5. Bugs must be expected in data collection implementations
  6. Document and be aware of bugs impacting data
  7. Automated 24/7 testing is necessary
  8. Manual testing can create a blueprint for automated testing
  9. Using the Pareto principle (80/20) during testing can save embarrassment
  10. Data is not important to most business owners. Information is.
  11. Tracking everything doesn’t make sense
  12. Building a data tracker is much easier, than making sure it always works right
  13. Data security and privacy are not optional in 2020
  14. Getting the trust and love from your data subjects will get you more data
  15. Understand the power of data notifications and use it very wisely
  16. Work-life of data professionals is lonely, communicate with similar-minded people will unlock value
  17. Having clear business requirements ready before data collection implementation is efficient
  18. Only a handful of people 🦄 can understand data collection AND appreciate data accuracy AND translate data into insights
  19. Accuracy and consistency is necessary to get respect when working with data
  20. Don’t expect accurate & consistent data from manual data collection
  21. Be agile & flexible when working with data
  22. Data collection can have a negative effect on performance in most cases
  23. Be more like a thermostat (act on data), rather than a thermometer (observe data)
  24. Real-time data analysis makes sense if you can have real-time actions
  25. Real-time data analysis brings considerable overhead
  26. Machine learning is not the solution to most data challenges
  27. Good understanding of statistics and math is necessary when working with data
  28. Spreadsheets might have limitations but are still a lifesaver when working with data
  29. Fluency with VLOOKUP, INDIRECT, MATCH functions & pivot tables in Excel can work wonders
  30. Questionable insights & inconclusive data will do more harm than good
  31. A mild case of OCD is a very useful trait when working with data
  32. Just like with food, data reports must provide value but they should also look nice
  33. Just like with food, nice reports that don’t give any value will leave a bad taste
  34. When updating the data collection process, make sure you can monitor newly collected data closely
  35. Data systems can be complicated can be like chaos, even a small change can cause a butterfly effect, if you have poor understanding of them
  36. Basic programming skills can bring joy to tedious tasks for every data analyst
  37. Combining different data systems is usually painful but also rewarding in value
  38. When combining different data systems, value formats and time zones are common traps to look out for
  39. A fast system for storing and retrieving data makes a ton of difference
  40. Immutable data will do more good than harm
  41. If you experience a data loss you understand that data backup & data recovery plan should be top priority
  42. Repetitive tasks must be automated
  43. A reusable and expandable system should be available for automating tasks
  44. Automated tasks can break, always have a fallback plan
  45. Don’t use technical resources for the work of data analysts
  46. Data can help find answers, but right questions come from skilled people
  47. Understand the power of rounding numbers and use it respectfully
  48. Understand the power of grouping granular values and use it respectfully
  49. Good data professionals copy, bad data professionals steal
  50. Combining qualitative with quantitative data can be eyeopening

Written By

Panagiotis (pronounced Panayotis) is a passionate G(r)eek with experience in digital analytics projects and website implementation. Fan of clear and effective processes, automation of tasks and problem-solving technical hacks. Hands-on experience with projects ranging from small to enterprise-level companies, starting from the communication with the customers and ending with the transformation of business requirements to the final deliverable.