August 29, 2022

Data-driven design and its impact on supercharging innovations in products

It is very much interesting to say Data is the new Oil but we definitely have to consider the reasons at which this statement appears to be true because without the right usage of data it is absolutely useless.

Data-driven design and its impact on supercharging innovations in products

It is very much interesting to say Data is the new Oil but we definitely have to consider the reasons at which this statement appears to be true because without the right usage of data it is absolutely useless.

For More Context on the above statement:

The huge amounts and abundance of data are totally insubstantial if it can’t be visualized or interpreted which brings me to cull up some histories on data; The idea of hiring a dedicated resource to maneuver and extract meaning from data only started in 2001, when the term ‘data science’ was first used in a paper by Cleveland (2001; Pollack 2012). This ushered the way for colleges to begin promoting the field of data science by opening data science institutions and centers to formally teach data science as a profession, examples of some are the Institute for Data Science at Berkley, Centre for Doctoral Training in Data Science at Edinburgh University, and the Centre for Data Science at New York University, amongst so many others. Leaving the past and taking a hop into a decade later, data science has become essential in every industry with numerous authors referring to data as the “currency of the future” and even going as far as comparing it to gold (Pollack, 2012; Johnson, 2012) making data scientists become integral to the success of this new currency. Bakhshi & Mateos-Garcia (2014) define data scientists as “experts who use analytical techniques from statistics, computing and other disciplines to create value from new (‘big’) data.”

As a very data-centric human, I get very intrigued to see the applications and implementations of Data in innovations or even the everyday usage of products in general. This article would be solely focused on three major capitals that make innovations or everyday products appealing to use.
It is also important to note that I’m more of a Data Scientist (in this context) who has helped businesses build products based on data-driven designs and experiences and that’s why I feel I have atleast a little knowledge on how this works + as a researcher I’ve also made loads of research into understanding how this works using real world examples as use-cases :)

Data + Design & Designers

Data science can be defined as analyzing to extract meaning and insights from data with the overlapping areas of statistics, scientific methods, and machine learning models. Data science encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.

Design can be seen as a solid plan that enables us to reach our desired outcome, whether that outcome be a product, service, process, or even strategy. A design is considered a “Good design” when it’s goal-oriented and based on insight from data and not some random guesswork.

Designers basically make use of all accesible information when trying to structure an accurate understanding of the situation with the aim to identify the challenges that need to be solved so that the desired outcome can be reached. Designers specialize in using qualitative research methods to understand human needs and behavior.

Data-Driven Design

You can be data-driven if you’ve got done the work of knowing specifically what your downside is, what your goal is, and you have got a awfully precise and unambiguous question that you just wish to grasp. This additionally assumes that your methodology and measurements square measure sound which the kind of question you wish to answer is one that knowledge will confirm. It depends on a keen understanding of the kinds of pitfalls that knowledge will bring, and taking steps to correct those pitfalls

Designers planning to perceive the data-driven style method is a very important career ability as plunging into user analysis and testing process, and understanding what data analytics is and the way it works, provides designers further tools to urge support for their concepts. It additionally permits them to make the most effective doable product, with the data to backup that assertion.

Data-driven UI design is both art and science. Understanding the way to collect and analyze data and implement designs supported on it’s a very important ability for beginner and skilled designers alike.

Is data-driven design the answer?

The need for any field to become more “data-driven” is a need that is increasingly familiar to people. Organizations are relying more strongly on data to aid their decision-making, including decisions about design and user experience. Although the term “data-driven design” is something that has become popular in the literature, King et al. (2017) discuss three different ways to think about data and how it is used within the design process. They discuss the familiar terms data-driven and data-informed and have additionally coined the term data-aware.

The following outlines the definition of those terms culled from the book Designing with Data:

  • Data-driven design: Data determines the outcome of a product and businesses can optimize the impact on their main metrics. Data-driven design is most common when the goal of the design project is clear and there is an explicit and unambiguous design and research question that needs answering.
Laying out the relationship between “data-driven,” “data-informed,” and “data-aware” — data-driven answers well-targeted questions, where data alone can help drive decision-making.
  • Data-informed design: Data is used alongside other sources such as strategic application, user experience, intuition and competition. A data-informed approach means it is not as focussed and directed, but data is one element that can inform how a problem space is viewed and decisions are made.
Laying out the relationship between “data-driven,” “data-informed,” and “data-aware” — being data-informed allows you to understand how your data-driven decisions fit into a larger design space of what can be addressed.
  • Data-aware design: With this approach, the designer is aware that there are many types of data that can answer a multitude of different design and research questions and the designer is usually aware of the different types of data available to them throughout the design process.
Laying out the relationship between “data-driven,” “data-informed,” and “data-aware” — data-driven answers well-targeted questions, where data alone can help drive decision-making.

I also recently discovered one very interesting approach from an article on implementing a better user experience design called:

  • Data-Driven UX, which the author defined as the opportunity to explore the experience around data itself — how to make data easier to work with, how to get more value out of data, and how data enriches our work and lives. The primary goal is for many tech companies these days is to create user-centered products. Therefore, having design and data intertwined and part of the same development cycle is a good idea. Companies are challenged to create memorable experiences for their customers across channels and platforms.

What Does This Mean for You as a Designer?

I believe that applying data in the design process is definitively not about replacing the things that design processes and designers do well already. It’s about helping designers extend the value of those things by offering another way to look at the impact of the design work on users. A/B testing can’t answer all questions, but it can answer certain kinds of questions that other methodologies and practices cannot. In fact, we think you’ll find that working with A/B testing is actually quite similar to other evaluative design processes. When applied correctly, it is creative, iterative, and empowering.

Failing to consider data (or using data in an ineffective way) can have serious implications for the success of a project. If you rely solely on instinct or best practices to make decisions without performing any data-driven investigation, you risk wasting money on changes to design choices that are ineffective (or even harmful).

Using data effectively can lead directly to improved business outcomes. Research by MIT’s Center for Digital Business found that “companies in the top third of their industry in the use of data-driven decision making were, on average, 5 percent more productive and 6 percent more profitable than their competitors.”

There are many examples of cases where data-driven UX techniques have delivered a tangible improvement on ROI. For example, in 2014, airline Virgin America used A/B testing to redesign a new, responsive website. This led to:

  • A 14% increase in conversion rates
  • 20% fewer support calls
  • Customers booking nearly twice as fast, across devices

Another interesting example comes from the e-commerce website Music & Arts, which used usability testing and heuristic evaluation to inform a website redesign. Upon the conclusion of the project, their online sales increased about 30% year over year.

User-centered design and data applied to understanding behavior are both focused on establishing effective, rewarding, and replicable user experiences for the intended and current user base of a business or product. I believe that data capture, management, and analysis is the best way to bridge between design, user experience, and business relevance. Based on my previous descriptions, this is why I believe a data-aware approach to designing great user experiences is a better description of what we aspire to rather than the more commonly used data-driven terminology, as I believe data feeds into a creative design process, and is itself varied and creative, providing many possible ways to approach a problem.

But first, let’s review some of the assumptions we are making about which kind of designer you are:

  • You’re interested in crafting great user experiences and have some goals that involve changing or influencing the behavior of the users of your product or service.
  • You are curious about human behavior as it relates to your product, and you are already making observations of users and how they use your product, even if only informally.
  • You are thinking carefully about who your current users are, and who might become your users in the future.
  • You are trying to solve many different kinds of problems and determine what works best for your users.
  • While you may not have a background in statistics or be a data scientist, you are interested in becoming familiar with how you could get involved with the design of experiments to test out your ideas.

I believe that experimental methods and the data they yield can help hone your craft, improve your products, and concretely measure your impact on users and, ultimately, on the business. In sum, I believe that becoming familiar with the ways in which experiments carried out on the internet with large numbers of users where you can gather large amounts of disparate data types — that is, experiments at scale — can help you in your design practice. and lastly I believe that great design and smart data practices are key to strategic impact in any business.


After getting to understand what data-driven design is and it’s processes there’s actually a dark side to using data for your designs.

A lot of designers fall into the trap of always trying to optimize the data while forgetting a broader picture that this may lead to a worse user experience and damage to a brand image in the long term because maintaining a healthy balance between your instinct and empirical evidence. You’ve to subconsciously learn the subtle art of knowing when to rely on which. So in basic terms, when data does not give you a very clear answer, or you need to stitch many different pieces in one harmonious ensemble, you can always feel free to trust your expertise, gut feeling and intuition.

Be informed by numbers but not a slave to them.

After all, data can inform, but the designer needs to add that secret ingredient and bring the design to life. This human factor is what drives real innovation, while numbers can only inspire or give some useful insights.

If you eventually got this end, this is a disclaimer that this article was a pure experiment of combining human and AI (GPT-3) to creating articles like this, so if you read any passage that felt a little ‘robotic’ that’s definitely something I’m still working on to perfect but if not, you can applaud my model for a job well done :)

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