What are the main challenges and opportunities for design research in the era of big data and AI?

The design industry has overstepped the use of the word research because: 1. they don't know what research is and isn't, and/or 2. they know but don't share they type of academic research content falls, and/or 3. It sounds good. Without taking the time to educate those who are trusting the use research in design it is easy to say but not actually apply correctly in practice or worse yet apply in practice thinking it is actually research but in reality is a bunch of static / propaganda.

One major challenge is balancing quantitative data with the qualitative, human-centric insights crucial to design. While big data offers patterns and trends, it often misses the deeper context of user behavior and motivations. Relying solely on data can lead to “analysis paralysis,” where teams are overwhelmed by information without actionable insights. However, AI and big data can enhance design research by identifying hidden patterns, predicting user needs, and personalizing experiences. Automated tools can handle large-scale data analysis, freeing up researchers to focus on more nuanced, empathetic user research. The key is integration, combining the precision of AI with the empathy of design research. Once again Systemic approach wins

Most founders over-index on data quality and under-index on whether the insights actually drive better decisions. At what point does "better research" stop improving your product and start just slowing you down?

Explanation of why, how, what, versus what, how, why is key. In many cases the approach and process is more important than the results. Sharing with users and stakeholders about WHY the scientific research was needed and its purpose and HOW it was conducted builds a deeper ability to comprehend the WHAT - outcomes (data sets, visualizations, etc.).

Most SMBs don’t need better data visualization—they need clearer decision-making. Are you using data to drive action, or just to feel more certain?

"Garbage in garbage out". If actual data is used, understood, and ultimately trusted by users and stakeholders then likelihood of outcomes will be as well. People who don't trust solutions don't by into how it was produced and why it was done in the first place. Ethics begins and ends with users understanding the WHY (purpose/problem) and the HOW (application of scientific research principles, classification of research, data type used, etc.). Application in practice via AI or other means should become a mute point.

In today’s world of big data and AI, design research grapples with issues like data privacy, biases in AI, and blending data-driven insights with traditional design methods. But there are also exciting opportunities: AI can help us personalize experiences, predict trends, and speed up prototyping. By harnessing AI effectively, we can make design processes more efficient, create better user experiences, and spark innovation. Of course, we must stay mindful of ethical concerns and build trust with users.

AI won’t replace good design, but it will expose bad design faster. Are you using AI to accelerate creativity or just to automate mediocrity?

Agreed but that is exactly why our education models need to help our future generations develop the acumen to know what is fake and what is real! It’s a journey of growth and learning we all need to embrace and keep a growth mindset. Learn and relearn !

There is some much information that it is difficult to separate data from noise. Odds are the noise is treated as data and applied in practice. If there was a "library board" that could isolate the data, and capture purpose (why), means (how), and outcome (what) more would trust and use correctly in practice. Need to overcome the "well I did my research by reading a book, article in magazine, or white paper and it says _ so we should do _" without knowing if we can trust "the book", "the magazine", "the white paper" - etc. Most users and stakeholders are well enough informed in the actual process of data creation so they can't question the source and therefore trust the messenger - qualified or not.

In my experience there is a real need for data and AI literacy in the Education sector. Tools available to Leveraged AI to support Educators to enhance student experience and free up precious time educators need to create authentic experiences for students is a need today more than ever before. In this transient world the one thing that is changing at a glacial pace is education. Our children are the most important commodity we have for our future.

Most SMBs don’t need more data literacy—they need a bias toward action. Is your team learning data to make better decisions, or just to feel smarter?

Generative AI is built on massive collections of original and copyrighted content. It is now bringing unprecedented benefits to users, but failing to compensate (living) authors for their contributions. Worse still, most if not all such contributions were scraped from the Internet without the authors' consent or knowledge, and the use of generative AI may end up undermining or even destroying creative professionals' livelihoods. An "illustration in the style of [X]" could mean one less job for [X]. Transparency is a major challenge for design research in the era of generative AI, like personal data ownership is a major challenge generally in the era of online social media. Fair compensation is an opportunity we must yet address.

The best insights usually come from doing, not analyzing. What’s one assumption you could test this week with real customers?