🎯 Data-Driven Decisions
0.5.dev0+git.20260720.c8f33de - July 20, 2026 16:01 UTC

🎯 Data-Driven Decisions

Section 2 of the Data Analytics hub — 27 of 27 lessons.

Turning questions into decisions: stakeholders, metrics, and communicating results that drive action.

↑ Back to the Data Analytics hub

🧭 Framing the Problem

Problem types, asking the right questions, and how data drives decisions.

• 001 · Using Data Analysis to Choose the Right Advertising Strategy — a worked prediction problem: past campaign data steering the next ad spend

• 002 · Understanding Common Problem Types in Data Analytics — the six shapes of analyst problems, from predictions to patterns

• 003 · Applying Data Analytics Problem Types in Real Business Scenarios — recognising the six types in the wild — and why naming the type speeds the work

• 004 · Why Asking the Right Questions Matters in Data Analytics — SMART questions, and the leading/closed/assuming questions to avoid

• 005 · The Relationship Between Data and Decision-Making — data-driven vs data-inspired: how evidence and judgement actually combine

• 006 · Quantitative and Qualitative Data in Decision-Making — what/how many/how often meets why: two data kinds, one decision

• 007 · Data Creates Value Only When It Is Communicated — an insight nobody hears changes nothing: communication as the last mile

📐 Metrics & Dashboards

Data versus metrics, dashboards, and quantitative thinking.

• 008 · The Difference Between Data and Metrics, and the Role of Metrics — from raw facts to quantified goals: what makes a number a metric

• 009 · Dashboards — live metrics in one place: what dashboards are for, and when a report beats one

• 010 · Mathematical Thinking — step-by-step decomposition, orders of magnitude, and choosing data sized to the decision

📗 Spreadsheets for Analysis

Organising, calculating, and troubleshooting analysis in spreadsheets.

• 011 · Spreadsheets in Data Analysis — where the spreadsheet sits in the decision workflow, phase by phase

• 012 · Building and Organizing a Spreadsheet — one row per record, clean headers, raw kept raw: layouts that survive analysis

• 013 · How Data Analysts Use Spreadsheets — the everyday spreadsheet toolkit — sort, filter, pivot, chart — mapped to real tasks

• 014 · Spreadsheet Calculations with Formulas — formulas, cell references, and the absolute-vs-relative distinction that makes fill-down work

• 015 · Common Spreadsheet Errors and How to Fix Them — reading #DIV/0!, #VALUE!, #REF!, #NAME?, #N/A — and fixing causes, not symptoms

• 016 · Spreadsheet Functions — named operations — SUM, AVERAGE, IF, VLOOKUP — the analyst’s core vocabulary

🗣 Stakeholders, Communication & Execution

Scoping with stakeholders and communicating results that drive action.

• 017 · Defining the Problem Domain — scoping before analysing: problem, stakeholders, deliverables, timeline, success

• 018 · Context and Bias in Data Analysis — context makes data meaningful; unexamined context is where bias enters

• 019 · Stakeholder Expectations in Data Analysis — who has a stake, what they need, and how to align before the work begins

• 020 · Staying Focused on the Project Objective — guarding scope: keeping every step tied to the one question that matters

• 021 · Clear Communication with Stakeholders and Teams — the five Cs of clarity: making findings land with the people who act on them

• 022 · Adapting to Communication Expectations at Work — reading the room: matching channel, register, and detail to each audience

• 023 · Managing Stakeholder Expectations and Project Constraints — the iron triangle of scope, time, and resources — and honest trade-offs

• 024 · Balancing Speed and Accuracy in Data Analysis — fast-enough vs right-enough: matching rigour to the decision’s stakes

• 025 · Sharing Data to Drive Impact — the difference between reporting numbers and actually changing a decision

• 026 · Effective Meetings — purpose, preparation, and follow-up: making the time analysts spend together count

• 027 · Conflict Resolution in the Workplace — when the data disagrees with a person: disagreeing productively and separating issue from ego

🔤 Every lesson, A–Z index

🔠 Open the full alphabetical index

Adapting to Communication Expectations at Work Applying Data Analytics Problem Types in Real Business Scenarios Balancing Speed and Accuracy in Data Analysis Building and Organizing a Spreadsheet Clear Communication with Stakeholders and Teams Common Spreadsheet Errors and How to Fix Them Conflict Resolution in the Workplace Context and Bias in Data Analysis Dashboards Data Creates Value Only When It Is Communicated Defining the Problem Domain Effective Meetings How Data Analysts Use Spreadsheets Managing Stakeholder Expectations and Project Constraints

Mathematical Thinking Quantitative and Qualitative Data in Decision-Making Sharing Data to Drive Impact Spreadsheet Calculations with Formulas Spreadsheet Functions Spreadsheets in Data Analysis Stakeholder Expectations in Data Analysis Staying Focused on the Project Objective The Difference Between Data and Metrics, and the Role of Metrics The Relationship Between Data and Decision-Making Understanding Common Problem Types in Data Analytics Using Data Analysis to Choose the Right Advertising Strategy Why Asking the Right Questions Matters in Data Analytics

Tags: purpose: reference topic: data analytics