The True Impact of Designing with Data

Esteban Díaz
5 min readSep 26, 2023
Photo by Scott Graham on Unsplash

Leer “El verdadero impacto de diseñar con datos” en Español.

Data allows us to understand the true extent of the problems to be solved, explain the rationale behind the proposals, and make well-informed decisions, and it requires a skill that is receiving the name of data literacy.

People are complex, and so is the world we live in. When we face the challenge of designing a solution, understanding the need is always the first step. User-centered design has traditionally used qualitative techniques such as interviews, ethnographies, and others, which have a proven value in understanding motivations behind behaviors and the quality criteria by which users will evaluate proposals. Other disciplines use market research, statistics, and other metrics that generate a wealth of data to provide a more complete and precise view of the situation, when handled correctly. This brings us to the importance of developing data literacy.

Data literacy is the ability to read, understand, generate, and communicate data in a way that facilitates learning and decision-making.

This skill, the ability to use data to better supporting our decisions, is known as data literacy. It encompasses everything, from understanding which data is relevant in a given situation to being able to assess when we have enough data of sufficient quality to trust our conclusions.

Within the European Union’s digital competencies framework, data literacy is one of the main pillars of digitalization. These skills have been extensively developed in other disciplines and are finding their place in design processes more than ever.

Photo by path digital on Unsplash

As designers, it’s crucial that we have tools to understand the true scope of the problems we are creating solutions for, and data is an indispensable element. Data has always been available for the cold, business-focused view, but the secret is to combine it with a human perspective, using it in conjunction with qualitative insights to add depth to our reflections. It will then allows us to explain the rationale behind our proposals, identify knowledge gaps, and focus efforts on understanding what we don’t know while working with what we do know.

Gathering information, processing it properly, and collaborating with specialized profiles multiply the design’s ability to add value.

Furthermore, when we are aware of the value of data, we can make it easier for users to exploit it for their own benefit. According to the Programme for the International Assessment of Adult Competencies (PIAAC) (also by the OECD), Spain is one of the EU countries with the lowest level of mathematical and data comprehension skills. While this makes many people feel uncomfortable with data, we can alleviate this when designing a product by using data narrative.

Data narrative provides coherence and context to the information we present, guiding us through a storyline. The choice of data presentation, offering an explanation of its origin and interpretation, and ensuring that it all presents a comprehensible argument related to user motivation, such as making the right decision and minimizing risks, are all data narrative strategies.

Data in design as a reality anchor

Data allows us to create models, which are a way to simplify and generalize data so that we can better understand the reality they represent and identify patterns that might otherwise be hard to detect. This view enables designers to create scalable solutions for a large number of users efficiently and in a structured manner.

Using data to generate simplified models of reality is very useful but comes with risks. Data can be biased and represent a distorted reality. Data quality also plays a significant role, including accuracy, integrity, consistency, currency, and validity. There’s a saying in the data world, «garbage in, garbage out,» which refers to the impossibility of getting good results from poor data. Ensuring data quality is also a design competency, especially when it comes to data that users need to input into an interface or related to research.

Photo by Kaleidico on Unsplash

Data-based models cannot rely solely on quantitative data; an essential part of interpreting more numeric metrics is complementing them with qualitative data that adds depth and nuances to the models. This combination of data (quantitative big data and qualitative thick data) is what unlocks the ability to make truly well-informed decisions.

After making decisions, execution and monitoring follow. Another key moment for data, where you can check if your proposals have had the desired impact. Therefore, it’s crucial that models also serve to define which metrics are worth observing to distinguish between what works well, what’s average, or what needs to be changed.

As you can see, from the idea to delivery, there are numerous moments where data can provide a differential value. It can make the difference between success and failure, and more importantly, it can help you understand the reasons so that you can learn and correct confidently and without delays.

How can you start using data in your design process?

Begin by asking yourself these questions:

  1. How can you quantify the challenge? Think in terms of frequency, volume, proportions, costs…
  2. What patterns do you detect? Sometimes they will be obvious, other times they will suggest a possible explanation, a hypothesis that you will need to test.
  3. What other data can you look for to confirm, refute, or refine your hypotheses?
  4. Which of these data can be useful to users? Also, consider the narrative that best helps users understand the information you want to communicate.
  5. What risks affect data quality? Review if, for example, a form is controlling errors and helping the user not make mistakes when entering data. You can also consider the refresh frequency or the accuracy of calculations.
  6. What can you measure to check if we are achieving our goals? Remember that for a complete view, you will need both qualitative and quantitative data.

This article was first published in Spanish on MrMarcel School’s blog for a course about Data and design.