follow

Official Book Companion Website

How to Interpret Data

Welcome to the companion website for How to Interpret Data!


The book is available internationally and can be ordered on Amazon.


Get notified below on updates to the companion website.

Chapter 1 - Why Data Matters to Your Career

Chapter 1 explains why data skills matter, shows how every analyst starts small, and introduces our running analytics example.

  • CASE STUDY EXAMPLE

  • EXAMPLE SOLUTION

Step into your new role

You’ve just joined Achilles Footwear’s HR-Analytics team.


Achilles is a premium athletic-shoe brand with ~1,200 corporate employees supporting ten flagship stores and a fast-growing e-commerce division.

How to Interpret Data Case Study Example

Your mission for the book:

  • Use data to raise employee-engagement, reduce regrettable turnover, and uncover growth opportunities for people development.
  • Translate insights into clear stories that persuade leaders in Stores, E-com, and Design.

Over the coming chapters you will:

  • spot employees most at risk of leaving,
  • diagnose drivers behind low engagement scores,
  • and craft data-backed proposals (e.g., cross-team secondments or revamped supervisor training) that leadership can act on.

You’ll analyze metrics such as training hours, internal-communication frequency, supervisor performance ratings, and more—then craft insight-driven stories that influence decision-makers. All of your hands-on work will use realistic—but simulated—HR datasets from Achilles, so you can practice without confidentiality worries.

Imagine you must choose five metrics, no more, to investigate the drivers of employee engagement. Use the list below, pick the five you believe matter most, and jot down why you chose them.

Cell #

Metric

Description

1

Commute Distance

How far employees travel to work

2

Salary Growth Rate

% salary increase year-over-year

3

Supervisor Performance

Satisfaction with direct manager

4

Number of Sick Days

Total sick days last 12 months

5

Internal Communication

Participation in company-wide updates

6

Cross-Departmental Projects

# of multi-team projects

7

Training Hours

Hours of training completed

8

Engagement Survey Score

Latest survey result

9

Recognition Programs

Frequency of awards received

10

Peer Feedback

Qualitative feedback from colleagues

Hint: If employees who work on cross-departmental projects and receive regular peer feedback score higher on engagement, how could you turn that insight into a new policy?

Chapter 2 - From Data Acquisition to Influence

Chapter 2 walks through the five‑step “data interpretation journey” and shows how preparation and follow‑through turn raw numbers into action.

  • CASE STUDY EXAMPLE

  • EXAMPLE SOLUTION

Small‑Team Planning for Achilles Footwear

You’re still the lone data analyst in HR. Turnover is rising and leadership wants answers fast.


Your task in Chapter 2 is to draft a one‑page “journey map” that shows exactly how you’ll move from questions to influence over the next six weeks.

Sketch the Five Steps

Use the empty canvas below (print‑out or digital whiteboard) and jot down, in one sentence each, how you will handle:

Step

Your one-sentence plan

Why it matters (hint below)

1  Defining Needs


Keeps everyone pointed at the same goal.

2  Data Acquisition


Locks in owners & permissions early.

3  Preparation


“Garbage in, garbage out.”

4  Analysis


Only explore what answers the goal.

5  Communication


Insight ⟶ action is the real win.

Quick reminder from Ch 2:
Preparation + follow‑through ⟶ “the golfer’s swing” analogy.

Spend 5 minutes filling the table, then open the next tab to compare notes.

Chapter 3 - Asking the Right Questions

Chapter 3 shows how well‑crafted, business‑aligned questions turn scattered data into a focused mission, introducing the 5 Whys and SMART frameworks so you can translate vague problems into clear, measurable goals that drive action.

  • CASE STUDY EXAMPLE

  • EXAMPLE SOLUTION

Clarifying the Real Problem

You’re still the solo HR analyst. Leadership knows “turnover hurts” but can’t agree why.


Your brief for Chapter 3 is to turn a fuzzy gripe into a single, focused, measurable question that will steer every later step.

Task: Complete the canvas below in two passes:

  1. 5 Whys to uncover the root cause.
  2. Rewrite that cause into a SMART question.

The 5 Whys Canvas

(Print it or copy to your whiteboard tool.)

#

“Why?” asked

Immediate answer

Deeper insight

1

Why are developers quitting?


Cell

2

Why … ?


Cell

3

Cell


Cell

4

Cell


Cell

5

Cell


Root cause →

The SMART‑Question Builder

SMART element

Draft wording

Specific (who / what)


Measurable (metric / target)


Achievable (evidence it’s realistic)


Relevant (ties to a strategic goal)


Time‑bound (deadline)


Spend 10 minutes filling both tables, then switch to the Example Solution tab to compare.

Chapter 4 - Turning Questions into Requirements

Chapter 4 shows how a sharp, business‑aligned question becomes a targeted data “shopping list.”

Using the five‑step BRICE framework (Break down → Refine → Identify → Contextualize → Extract/Validate), you’ll learn to pinpoint exactly what data you need, where it lives, and how to check its quality—so later analysis cooks up insights instead of “garbage in, garbage out.”

  • CASE STUDY EXAMPLE

  • EXAMPLE SOLUTION

Getting Ready to Work

Leadership approved your Chapter 3 question:

“What career‑development initiatives can we implement for the software‑development team to reduce turnover by 10 % within six months?”

The task – Draft a one‑page Data Requirements List that follows BRICE. When you’re done you should know exactly which tables, surveys, or exports you must pull (and who owns them).

BRICE Step

Jot your notes (1–2 bullets max)

Why it matters

B – Break down

List the 3–4 drivers you’ll measure (e.g., training, promotions, workload).

Surfaced drivers = clear scope.

R – Refine

Convert each driver to a metric/KPI.
Flag proxies if the metric doesn’t already exist.

Keeps requests measurable & aligned to the Ch 3 SMART goal.

I – Identify sources

Name the system/owner of each metric (HRIS, LMS, Jira, survey tool, etc.).

Locks in access and avoids last‑minute scrambling.

C – Contextualize

Pick timeframe (last 12 mo).
Choose granularity (monthly).
Segment (dev‑team only).

Prevents over‑collection & mis‑alignment.

E – Extract & validate

Note pull method (API, CSV, SQL).
Surfaced drivers = clear scope.Decide 2 spot‑checks you’ll run.

Data you can trust = insights that stick.

Spend 5 - 10  minutes filling the canvas. When you’re ready, open the next tab to compare.