Book
Analytics & BI Tools
The four layers of the analytics stack, then Power BI, Tableau, Looker and Alteryx each in practice — what they are genuinely good at, where they hurt, how to choose with real scenarios, the licensing models that bite, and making analytics actually get used.
Written to be comparative rather than promotional. The recurring argument is that most tool disputes are really disputes about which layer owns the metric definition.
CHAPTER 1
The analytics stack, and what each tool is for
Four layers. Most tool arguments are really arguments about which layer owns the metric definition.
CHAPTER 2
Power BI, in practice
The volume leader, for reasons that are mostly commercial. What it is genuinely good at and where it hurts.
CHAPTER 3
Tableau, in practice
The strongest visual analysis tool, and the one whose cost model surprises people.
CHAPTER 4
Looker, and the semantic-layer approach
The tool that took the opposite bet — model first, visualise second. Why that matters more than it sounds.
CHAPTER 5
Alteryx and the preparation layer
Where analysts build pipelines without calling them pipelines — and what that means five years later.
CHAPTER 6
Choosing, with real scenarios
Five situations, and what actually fits each. The answer is rarely the best tool.
CHAPTER 7
Cost, and the three models that bite
BI licensing is priced in ways that punish specific shapes of organisation. Know which shape you are.
CHAPTER 8
Making analytics actually get used
The failure that no tool prevents: dashboards nobody opens, and numbers nobody trusts.
CHAPTER 9
How a BI engine actually works
Columnar compression, the three storage modes, and the one number that decides whether your model is fast or hopeless.
CHAPTER 10
Power BI internals: the model, and what DAX is really doing
The engine, the two evaluation contexts, and the three mistakes that account for most slow or wrong reports.
CHAPTER 11
Tableau internals: the engine, and the order of operations
Extracts, the calculation hierarchy, and the one diagram that explains why a filter sometimes has no effect.
CHAPTER 12
The semantic layer, and who owns the metric
The real question behind most BI tool arguments, four architectures for answering it, and why the good answer usually fails for non-technical reasons.
CHAPTER 13
Making a dashboard fast, and keeping it that way
A diagnostic order that works on any tool, the four places time actually goes, and the design decisions that beat any amount of tuning.
CHAPTER 14
The preparation layer: Alteryx and what competes with it
Why visual data preparation gets bought, what it is genuinely best at, and the lifecycle every workflow follows if nobody intervenes.
CHAPTER 15
Eight decisions, worked
Real situations, the answer, and — more usefully — why the obvious answer is wrong in each one.
CHAPTER 16
The wider landscape: who sells what, and what changed
Sixteen products beyond the big three, the six renames that make older material wrong, and what it tells you that half the market stopped publishing prices.
CHAPTER 17
The economics, worked
The break-even that decides a licensing model, the four costs nobody budgets, and three organisations costed end to end.
CHAPTER 18
What actually interoperates
Every vendor claims to integrate with everything. Here is what the connections really are, and the one gap that undermines the whole story.