Practice Business Intelligence & Data Science
Business performance — from 89 books.
Mike West, Editor · Leading With Data · peopleanalyst.com/guides/business-intelligence
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Practice Business Intelligence & Data Science
Business performance — from 89 books.
Mike West, Editor · Leading With Data · peopleanalyst.com/guides/business-intelligence
The problem
Business Performance & Value Creation erodes when it is left to instinct instead of method.
You were taught the moves piecemeal, never the whole model.
Practice Business Intelligence & Data Science · Mike West, Editor · Leading With Data
The model
- 01Executive Sponsorship & Analytical Leadership
- 02Analytical / Fact-Based Culture
- 03Data Quality & Information Integrity
- 04Data Infrastructure, Warehouse & Architecture
- 05Dimensional / Data Modeling Discipline
- 06Data & Decision Governance
- 07Analytical Talent & Skills
- 08Strategic Targeting & Problem Framing
How it grows
- New to the role0–1 yr
- Running the function1–3 yr
- Shaping the whole3+ yr
Practice Business Intelligence & Data Science · Mike West, Editor · Leading With Data
Step 1 of 5 · Executive Sponsorship & Analytical Leadership
The strongest sponsorship signal is a leader publicly reversing a call because of the evidence.
Misconception
Executive sponsorship means securing funding and a supportive quote for the kickoff.
Reality
The scarcest form of sponsorship is a leader visibly changing their own decision because of data; funding is necessary but symbolic modeling is what actually shifts subordinate behavior.
Practice Business Intelligence & Data Science · Mike West, Editor · Leading With Data
Step 2 of 5 · Analytical / Fact-Based Culture
Psychological safety to be proven wrong is the precondition for evidence-seeking behavior.
Misconception
Culture change follows automatically once you deploy good tools and dashboards.
Reality
Tools amplify existing incentives; if the organization punishes people for being proven wrong or for surfacing inconvenient findings, better data just produces more sophisticated defensiveness.
Practice Business Intelligence & Data Science · Mike West, Editor · Leading With Data
Step 3 of 5 · Data Quality & Information Integrity
Set per-table, per-dimension quality thresholds and enforce them in the pipeline, not in downstream review.
Misconception
Practitioners believe data quality is a preprocessing step you finish once, before the 'real' analysis begins.
Reality
Quality degrades continuously as source systems change, definitions drift, and new records arrive; it requires standing monitors and reconciliation, not a single scrub.
Practice Business Intelligence & Data Science · Mike West, Editor · Leading With Data
Step 4 of 5 · Data Infrastructure, Warehouse & Architecture
Schema design should follow analytic grain and query patterns, not mirror operational sources.
Misconception
More powerful storage and compute will compensate for a poorly designed schema or fragmented integration.
Reality
Throwing hardware at a bad data model just makes bad joins expensive; integration logic and schema design determine analytic velocity far more than raw capacity.
Practice Business Intelligence & Data Science · Mike West, Editor · Leading With Data
Step 5 of 5 · Dimensional / Data Modeling Discipline
Declare and defend the atomic grain first; every later design decision depends on it.
Misconception
Modelers assume that with columnar storage and cheap compute, dimensional modeling is a legacy concern you can skip by querying raw tables directly.
Reality
Dimensional structure is about shared business semantics and correct aggregation, not just performance; without conformed dimensions, two teams computing 'revenue' will legitimately disagree forever.
Practice Business Intelligence & Data Science · Mike West, Editor · Leading With Data
A framework to use
Trade-Off Analysis Framework
This framework is part of the full guide. See the guide →
Practice Business Intelligence & Data Science · Mike West, Editor · Leading With Data
The cheat sheet
- Executive Sponsorship & Analytical LeadershipThe strongest sponsorship signal is a leader publicly reversing a call because of the evidence.
- Analytical / Fact-Based CulturePsychological safety to be proven wrong is the precondition for evidence-seeking behavior.
- Data Quality & Information IntegritySet per-table, per-dimension quality thresholds and enforce them in the pipeline, not in downstream review.
- Data Infrastructure, Warehouse & ArchitectureSchema design should follow analytic grain and query patterns, not mirror operational sources.
- Analytical Talent & SkillsAnalytical capability requires developing consumers of analysis, not just producers.
- Strategic Targeting & Problem FramingState the decision and downstream action before modeling; if you cannot, the analysis has no destination.
- Data Preparation, Cleaning & WranglingPreparation is where most performance and most leakage originate — treat it as high-stakes engineering, not chores.
- Method / Model Technique SelectionConstraints select the method; accuracy only breaks ties among methods that can actually be deployed.
Practice Business Intelligence & Data Science · Mike West, Editor · Leading With Data
Sources
- A Practical Guide To Conjoint Analysis
- Analytics at Work: Smarter Decisions, Better Results — Thomas H. Davenport, Jeanne G. Harris, Robert Morison
- Big Data: A Revolution That Will Transform How We Live, Work, and Think — Viktor Mayer-Schönberger, Kenneth Cukier
- Big Data_ A Very Short Introduction (Very Short Introductions)
- Business Adventures — John Brooks
- Business Applications of Operations Research — Bodhibrata Nag
- Business Intelligence and Big Data — Celina Olszak
- Business Intelligence Guidebook: From Data Integration to Analytics — Rick Sherman
- Competing on Analytics: The New Science of Winning — Thomas H. Davenport, Jeanne G. Harris
- Cultural Analytics — Lev Manovich
+ 83 more sources in the full guide
Practice Business Intelligence & Data Science · Mike West, Editor · Leading With Data