Have you used ChatGPT, Midjourney, Dalle or any other AI tool in the last week? Is your company or are your clients looking to harness the power of automation or AI? Are you directly involved in your company or client’s automation or AI strategy? If so, you will already understand that the success of an AI tool is dependent on both the usability of the interface and the quality of the data inputs that fuel it. GPT3 (the core model for ChatGPT) was available for a couple of years before it became mainstream. The difference it brought to table was its interface, which made it user friendly and accessible for public use. But have you considered the inter-relationship of user-experience (UX) design and your data strategy, so you get the best possible outcomes from it?
In this article, we highlight the often-overlooked relationship between UX design and data strategy and the need for UX to be integral to data strategy development rather than an afterthought. By examining problematic and effective scenarios, we illustrate how incorporating UX from the outset can enhance data collection, reporting, and overall user engagement, leading to better business outcomes.
The ubiquity of data.
First, a reminder of how integral to life data has become. We rely on it for many of our day-to-day activities:
- Nurses rely on accurate and legible patient data
- Line leaders require employee and team data
- Emergency services require up-to-date disaster management data
- Stock traders require a continuous pipeline of financial data
Without careful consideration of data usability, our world grinds to a halt. In the era of AI, it’s more critical than ever that we carefully consider the user experience design of our data strategies.
What is data in the context of UX?
UX Design focuses on creating user-centred experiences that are intuitive, easy to use and meet user needs. Data strategy, on the other hand, involves defining the processes, tools and systems that an organisation will use to collect, analyse and use data to inform decision-making.
UX and data strategy are not mutually exclusive. So why are they treated like they are?
The problem: linear, data-driven UX
A quick search on UX and data strategy reveals countless results on how data strategy drives UX, rarely the other way around. As UX is a relatively recent business development, it is often an afterthought to a pre-defined strategy. However, this forces designers to retrofit their designs to meet business objectives rather than user needs. Worse still, UX designers are usually not made aware of the business or team’s data strategy at all, leading them to create designs that aren’t optimised for data capture or consumption, which causes problems for developers down the line (as they aren’t able to generate good quality reports based on the interface inputs).
This way of working leads to lower quality data, and less data capture. Lower quality data is highly problematic for companies who are targeting AI strategy as this leads to lower quality AI outputs, and impacts the success of the tool.
But what if we brought UX in to help define the data strategy?
The solution: UX-driven data strategy
In the past few years, there have been many conversations about how designers are finally taking a seat at the leadership table. To achieve this, we need to understand that design isn’t the final layer of an already-defined strategy; it continually defines the strategy itself.
As wider understanding of experience design is growing, we’re shifting to a model where user research is shaping the company approach and data strategy. By starting with an understanding of how different users contribute and interact with data, we can define a customer centric data strategy that promotes:
- Targeted planning that balances business objectives with user expectations (creating better user engagement and revenue uplift)
- Better quality data collection and reporting as the data lifecycle is designed to be optimised
- Data-based continuous improvement by design
- Greater success and adoption rates for AI tools due to output quality
We’ve reached a stage where the influence of UX decisions on the entire data lifecycle — acquiring, integrating, storing, securing, and managing data — can no longer be ignored. These decisions significantly impact how effectively data is handled and used, highlighting the need for UX to be integrated into data strategy from the beginning.
If you would like help to implement UX-led data practices in your organisation, contact Natasha Spencer, Bill Bovopoulos or Charles Lee.
