Card Library
Browse our collection of data & analytics pitfalls and principles. Learn from real-world examples and avoid common mistakes.
Showing 15 cards
Dashboard Overload
Creating dashboards with too many metrics, charts, and widgets that overwhelm users instead of guiding them to insights.
The Vanity Metric Trap
Focusing on metrics that look impressive but don't drive business decisions or outcomes (total users, page views, downloads).
Analysis Paralysis
Spending so much time analyzing data and seeking perfect certainty that decisions are delayed or never made.
Data Definition Chaos
Different teams defining the same metric differently, leading to conflicting reports and loss of trust in data.
Building Without Stakeholder Input
Analytics teams building solutions in isolation without involving stakeholders, resulting in technically correct but useless outputs.
Ignoring Bias in Models
Deploying models without examining them for bias, leading to discriminatory outcomes and legal/reputational risk.
Chasing Every Request
Saying yes to every stakeholder request without prioritization, resulting in a team spread too thin and delivering nothing well.
Data Hoarding
Collecting and storing every possible data point "just in case," leading to massive costs, complexity, and security risks.
Not Tracking Technical Debt
Accumulating quick fixes and workarounds without documenting or planning to address them, eventually making the system unmaintainable.
Ignoring Data Quality
Building sophisticated analytics on top of dirty, inconsistent, or incomplete data, leading to wrong insights.
Tool Obsession
Believing that buying the best analytics tools will solve data problems, when the real issues are organizational or process-related.
Underestimating Project Timelines
Consistently underestimating how long data projects will take, leading to missed deadlines and stakeholder disappointment.
The "We Need AI" Syndrome
Jumping to AI/ML solutions before understanding if simpler approaches (rules, heuristics, basic statistics) would work better.
Correlation = Causation
Assuming that because two metrics move together, one causes the other, leading to misguided strategies.
Poor Data Security
Treating data security as an afterthought, leading to breaches, compliance violations, and loss of customer trust.