Dr. Mario's 2nd 🧠

The 3 Levels of Data Analysis- A Framework for Assessing Data Organization Maturity

2 min read

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Highlights

If I had a nickel for every time I saw that Data Science Hierarchy of Needs visual in a presentation at a conference, I’d be a gazillionaire (technical term). (View Highlight)

the reality for most organizations is that their data teams are incredibly immature and spend the bulk of their time working on analyses (View Highlight)

A mature data organization, first and foremost, is a mature analytics organization. (View Highlight)

Most reporting questions are possible to answer in their recording system of truth: • You can build a Salesforce dashboard to show you your pipeline for the next quarter. • You can build a Heap dashboard to show you user retention. • Even bitmapist— an open-source Mixpanel alternative— comes with off-the-shelf user cohorting. (View Highlight)

Data analysts spending their time building analyses that are available in the system of record aren’t adding value, they’re paying tolls: they’re verifying data and getting buy-in from business stakeholders. (View Highlight)

insights are about understanding relationships between facts (View Highlight)

Analysts can only move on to providing insights if they’re not spending all their time building reporting, but accurate reporting is a prerequisite to insights. (View Highlight)

At GitLab, we firmly believe in DataOps and that analytics is a subfield of software engineering (View Highlight)

Accept that the margin of error is larger on reporting when it’s not produced by a member of the data team (View Highlight)

The difficulty is being able to assert you are directionally accurate

It is more important for the data to be directionally correct and accessible than perfect and bottlenecked (View Highlight)

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