20 practical guides
Data Analytics and Business Intelligence Guides
Learn how to turn operational data into trustworthy measures, useful analysis, maintainable business intelligence, and decisions that preserve uncertainty and context.
Built for analysts, managers, and professionals who need reproducible methods for answering business questions with data.
Choose outcome KPIs
Select a small set of decision-ready KPIs by connecting customer or financial outcomes to controllable operating drivers and guardrails.
Build a KPI tree
Translate a company goal into mathematically connected outcomes, operating drivers, owners, and testable assumptions without double-counting effects.
Design a funnel analysis
Build a defensible funnel by defining ordered events, eligible entrants, conversion windows, repeated behavior, and segment comparisons.
Analyze customer cohorts
Compare customer groups fairly by fixing cohort membership, observation age, outcome definitions, and segment context.
Analyze subscription retention
Measure logo, user, and revenue retention with explicit renewal eligibility, churn timing, pauses, expansions, and cohort maturity.
Calculate customer lifetime value
Estimate customer lifetime value from contribution margin, retention, timing, and uncertainty without confusing revenue with profit.
Segment customers with RFM
Use recency, frequency, and monetary value to create reproducible customer segments while controlling windows, returns, and unequal distributions.
Design an executive dashboard
Create a concise executive view that connects outcomes, exceptions, trends, ownership, and supporting detail to a real review cadence.
Avoid misleading charts
Prevent distorted business reporting by checking scales, denominators, aggregation, missing values, filters, uncertainty, and visual emphasis.
Model Power BI stars
Build Power BI models with fact and dimension tables, explicit grain, reliable relationships, and reusable measures.
Write useful DAX measures
Create reusable DAX measures by understanding filter context, safe division, iterators, time comparisons, and total behavior.
Build Tableau calculations
Create reliable Tableau calculations by controlling row-level logic, aggregation, level of detail, nulls, and filter order.
Create a data dictionary
Document business meaning, technical lineage, ownership, quality rules, and approved use so teams interpret shared data consistently.
Validate data before reporting
Use source, transformation, semantic, and presentation checks to catch stale, incomplete, duplicated, or implausible data before publication.
Reconcile dashboard totals
Trace dashboard differences through scope, timing, grain, joins, transformations, and semantic calculations using a controlled reconciliation bridge.
Analyze an A/B test
Evaluate randomized experiments using assignment integrity, predefined outcomes, effect sizes, uncertainty, guardrails, and practical decision thresholds.
Forecast with seasonality
Build an interpretable business forecast by defining time grain, diagnosing trend and seasonality, backtesting, and reporting prediction ranges.
Investigate root causes
Move from a changed KPI to testable explanations through validation, decomposition, segmentation, timelines, and corroborating evidence.
Communicate uncertainty
Show measurement limits, sampling error, forecast ranges, scenario assumptions, and decision implications without burying the recommendation.
Write a decision memo
Convert analytical work into a concise recommendation with evidence, alternatives, uncertainty, implementation, and measurable follow-up.