Empathic Data
— agnostic fuel for meaningful insights

Firstly, the designer should be data-agnostic, that is, handling quantitative and qualitative data.

Secondly, data points are taken in account only if they help to understand aspirations, needs and motivations of the people who are relevant to the business.

It's a simple mindset shift, but a highly effective one.

Empathic Data are combinations of data sources that validate one another, helping designers to  empathize with research subjects.

Empathic Data is any dataset that helps a designer to understand what it's like to be the subject they are designing for.

Empathic Data is composed of a multiplicity of data structures, bringing organically structured insight about the multitude.

Empathic Data brings certainty where certainty is possible, and inspiration to bridge the knowledge gap with brain work and connectivity between humans.

Understanding people's needs in multiple data sources is the present challenge to service designers, corporations and small businesses. 


Customer research (the research phase in design work) is often focused in anecdotal data. That is, out of a few interviews, a few big remarks are pulled out of the transcript, and presented as "proof".

Business leaders are often not convinced. And that is for a good reason: they know that, statistically, those are not proof and may lead the company to dedicate resources to an idea that won't fly.

Then, leaders may ask the analytics team to bring the eCommerce figures. They may ask consultants to bring in predictions of multiple business cases. Or even the estimated investment that companies will make in the sector.

In the end, leaders are left alone for decisions under a pile of disconnected reports:


A pile of stakeholder interviews, 6 user interviews, 10 screen recordings, website analytics from the past year, rising trends in the market segment, a HotJar heatmap of the landing page...

Yet, there's nothing wrong with any of these data sources. They are just disconnected. Often, they are also answering questions they shouldn't be answering. Data sources are often playing their own strengths.

What is the solution to get relevant and reliable insights from all these sources?

The purpose of seeing data as empathic data is to focus only on what is relevant to understand customers.


It is easy to drawn in numbers. So this process has a sharp edge: focus on what helps you to understand what is it like to be your customer.


Every single data point taken into consideration is taken in account only if it helps us to understand the aspirations, needs and motivations of the people who are relevant to the business.

The approach of empathic data serves quite well the established principles of desirability, viability and feasibility. 


Empathic data focuses on providing reliable information for desirability ("people want the solution") and part of the viability aspect ("the solution has a market").


Feasibility ("can we build this?") is not answered by empathic data — but if enables the question of feasibility to be answered, because the other questions are realiably answered.

Service Designers must use these data sources strategically. Here are three principles on how to use them:

  1. Empathy. Find the human aspect in each of them.

  2. Relevance. Find the business edge and opportunity,

  3. Validation. Use the multiple sources in combination, so that insights are rebutted or confirmed by one another. 

Together, and never alone, these sources may bring clear and ready-to-use insights for your business. 

The goal of empathic data is to 

strong business insights for in that truly reveal what people aspire for — opening the way for structured and focused creativity.

Combining multiple data sources into meaningful insights is the only way forward. You don't need to be a data scientist for that. You just need to be curious.

— A primer for Empathic Data

Service building blocks
Customer Journey Essentials
Media collection
Journals and reporting
Video interviews & coded assessment

Social profiling
Social Network analysis
Influence & Opinion
Decision-making processes

Hypothesis & tests 
Community & Iteration
Beta launch & A/B Testing
Problem-Solution/Product-Market Fit

The customer

The customer

Personal profiling
Digital ethnography
Digital footprint investigation
Values & Psychographics

Insight packages
Value proposition creation
KPI setting & Journey Ops

Multitudes and Individuals meet

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Data Mixology

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A seat at the strategy table: keeping business in sight

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Getting started: the building blocks of your customer journey map

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