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Data & Analytics Decision Trees
Data decision trees for storage tiers, warehouse design, analytics tooling, ML vs rules, visualisation type, and data quality response. Free to run.
6 published trees in this category. Every one runs as a guided wizard in the browser — no signup — and can be copied as the starting point for your own version.
Trees in this category
Data Warehouse Design
As a data warehouse product manager responsible for integrating new data sources into a four-tier medallion architecture, use this decision tree to identify the most effective integration strategy. The tool assumes three guiding principles: all data consumption occurs from the final data products layer; the further right the integration point (towards Platinum), the more efficient the processing in terms of storage and compute; and we prefer materialised views over additional storage layers wherever the source system supports it.
How should I respond to a data quality issue?
Determine the appropriate response when a data quality issue is discovered in a pipeline, dataset, or report. This tree helps data engineers and analysts triage severity, decide whether to halt or continue processing, and escalate correctly based on impact and regulatory exposure.
Should I use machine learning or rule-based logic for this problem?
Decide whether your prediction or classification problem calls for hand-crafted rules, a classical ML model, deep learning, or a pre-built AI API. Answering questions about your data volume, explainability needs, and problem complexity will surface the approach with the best effort-to-value ratio.
Which BI and analytics tool should I choose?
Select the right business intelligence or analytics tool for your team's technical profile, cloud environment, and operational constraints. This tree weighs skill level, hosting preferences, and the importance of a governed semantic layer to surface the best-fit platform.
Which data storage tier should I use for this dataset?
Determine the appropriate storage tier for a dataset based on access patterns, latency requirements, age, regulatory obligations, and cost sensitivity. The right tier balances data availability against infrastructure spend and compliance risk.
Which data visualisation type should I use?
Quickly identify the most effective chart or visualisation type for your dataset. Answer questions about your data's shape, what story you want to tell, and whether geography plays a role to narrow down to the best option.
All Data & Analytics trees
- Data Warehouse Design — As a data warehouse product manager responsible for integrating new data sources into a four-tier medallion architecture, use this decision tree to identify the most effective integration strategy. The tool assumes three guiding principles: all data consumption occurs from the final data products layer; the further right the integration point (towards Platinum), the more efficient the processing in terms of storage and compute; and we prefer materialised views over additional storage layers wherever the source system supports it.
- How should I respond to a data quality issue? — Determine the appropriate response when a data quality issue is discovered in a pipeline, dataset, or report. This tree helps data engineers and analysts triage severity, decide whether to halt or continue processing, and escalate correctly based on impact and regulatory exposure.
- Should I use machine learning or rule-based logic for this problem? — Decide whether your prediction or classification problem calls for hand-crafted rules, a classical ML model, deep learning, or a pre-built AI API. Answering questions about your data volume, explainability needs, and problem complexity will surface the approach with the best effort-to-value ratio.
- Which BI and analytics tool should I choose? — Select the right business intelligence or analytics tool for your team's technical profile, cloud environment, and operational constraints. This tree weighs skill level, hosting preferences, and the importance of a governed semantic layer to surface the best-fit platform.
- Which data storage tier should I use for this dataset? — Determine the appropriate storage tier for a dataset based on access patterns, latency requirements, age, regulatory obligations, and cost sensitivity. The right tier balances data availability against infrastructure spend and compliance risk.
- Which data visualisation type should I use? — Quickly identify the most effective chart or visualisation type for your dataset. Answer questions about your data's shape, what story you want to tell, and whether geography plays a role to narrow down to the best option.
How to use these trees
- Run one to see how the questions are ordered and where each path ends.
- Copy the source. Every tree is plain text, so you can lift it and edit the wording for your own organisation. Syntax reference →
- Embed it in your wiki, help centre, or intranet with one snippet that stays current whenever you edit the tree. Embedding guide →