Easy everything in Product Management, page 16
Chart Selection: Match Purpose, Not Looks
Start with the purpose, not the chart. The question you're asking—'how do these compare?' or 'what's the trend?'—determines the best visualization. A line chart shows trends; a bar chart compares categories.
Anscombe's Quartet: When Numbers Lie
Anscombe's Quartet shows how four datasets can share identical summary stats (mean, variance) but look completely different when plotted. It's a classic reminder to always visualize your data before trusting numerical summaries.
Business Intelligence (BI) Tools: From Raw Data to Dashboards
BI tools turn raw company data into visual dashboards and reports. They let non-technical teams explore sales trends or user behavior from a data warehouse, but remember: a slick dashboard built on messy data is just a pretty lie.
Descriptive Statistics: What Your Data Looks Like
Descriptive statistics summarize the data you have, painting a picture of your sample without making guesses about the wider world. It's used for calculating things like average age or max response time.
Report Generation: Turning Raw Data into Human-Readable Documents
Report generators translate raw data into formatted documents for human eyes. They power business dashboards and sales summaries. The main footgun is forgetting the report is a stale snapshot, not the live data source itself.
Data Validation: Garbage In, Garbage Out
Data validation is the bouncer for your app, checking data at the door to ensure it's correct and useful. It's used on user forms, API requests, and file imports. The footgun is skipping it, which risks corrupted data, security holes, and future crashes.
Data Cleansing: Fixing Your Data Before It Fails You
Data cleansing is quality control for your dataset, finding and fixing errors before they skew your analysis. It's a crucial first step in any data pipeline, from training an ML model to generating business reports. The footgun is assuming data is clean.
Data Dictionary: The 'About' Page for Your Data
A data dictionary is the instruction manual for your database, defining what each piece of data means and how it's formatted. It's used by engineers to understand a schema or by analytics tools to interpret columns. The biggest footgun is letting it go stale.
Online Analytical Processing (OLAP)
OLAP databases are built to quickly answer complex, multi-dimensional questions, unlike transactional (OLTP) databases that handle individual records. They power business intelligence tools for sales and marketing analysis.
Data Warehouse: The Single Source of Truth for Analytics
A data warehouse is a central database optimized for analytics, not transactions. It integrates historical data from disparate sources like sales and marketing to create a single source of truth for business intelligence.
ELT: Load Raw Data First, Transform It Later
ELT pipelines load raw data directly into a data lake *before* any transformation. This speeds up ingestion and lets you figure out the data's structure later.
Analytics Consent Management: Respecting User Choices
Consent management is the system that asks users for tracking permission and technically enforces their choice. It's legally required for sites using Google Analytics or Ads.
UTM Parameters: Know Exactly Where Your Traffic Comes From
UTM parameters are like labels on your website visitors, telling you which marketing campaign brought them. They're added to URLs in ads or emails to measure channel effectiveness. The footgun is inconsistent naming, which splits data and ruins analysis.
Event Tracking: Measuring What Users Do
Event tracking turns user actions like clicks and purchases into analyzable data. Analytics platforms use this data to report on engagement and conversions. The biggest footgun is inconsistent naming, which pollutes your data and breaks reports.
Leading vs. Lagging Indicators: Looking Forward vs. Backward
Leading indicators predict the future; lagging indicators confirm the past. This distinction is key for analyzing business cycles or system health. The main footgun is relying only on lagging data, forcing you to react to problems that have already occurred.
Funnel Analysis: Pinpointing Where Users Drop Off
Funnel analysis treats a user journey like a real-world funnel, showing exactly where people 'leak' out before reaching a goal. It's key for optimizing e-commerce checkouts or app sign-ups. The footgun is only looking at the final conversion rate.
Analytics Measurement Plan: From Why to What
An analytics measurement plan forces you to define success before you look at data. It connects high-level business objectives to specific user actions and sets clear targets.
Key Performance Indicators (KPIs)
A KPI isn't just any metric; it's a measurable value showing how effectively you're achieving a key business objective. It's used to track things like website uptime or customer acquisition cost.
The ADKAR Model for Change Management
The ADKAR Model is a Prosci methodology for managing organizational change. It's applied during large-scale projects like ERP implementations, digital transformations, and mergers across industries like healthcare and finance.
The Satir Change Model
The Satir Change Model maps a team's emotional journey through disruption. When a new process is introduced, performance doesn't just improve; it first dips into resistance and chaos.
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