The Power of AI
AI, ML, Big Data— what does this mean for designers? As someone committed to the process of creating a product that holistically reflects user needs, these tools are exciting new opportunities
Big Data in UX
When considering the context of users within a problem space, I am constantly questioning, finding myself saying things like:
Who is our target population? How many segments of the population should we target? How might this demographic approach the interface differently?
And most importantly: Are there patterns within user behavior that we aren’t considering? With big data, I now have the tools to answer these questions.
Understanding this tool allows for more iterative and exploratory design thinking processes, creating opportunities for truly effective and adaptable products.
How do I Use it?
Manage: warehouses store immense amounts of user data, encode, and store data more efficiently.
2. Understand: interpret, visualize, and graph data
3. Actions: actionable insights, product features, etc
Communication & Teamwork
User Centered Designs
When designing a product, the user comes first. As a content designer and UX researcher with training in HCI and Communication, I value the intricacies of how humans interact with technologies to understand messages in different contexts, on different platforms, and across demographics. The more data points we have about user behavior patterns to analyze into actionable features, the more user centered the product will be.
Integrating Big Data into the Design Space
Before big data, with traditional data analysis, we start with a question, create a hypothesis, synthesize, and analyze our data into the answer, which we would use to inform our product. With big data, the process looks a little different.
Rather than testing if a preconceived notion about user behavior is right or wrong, my design process can now be more iterative, more closely informed by patterns present in large amounts of user data than by my initial hypotheses.
Tool: Clustering
The ability to reconfigure data points to maximize intra group similarity and minimize inter-group similiarity
Applications: Data-driven customer segmentation, finding specific and accurate user groups to target
Tool: Association Rule Mining
Finding co-occurences in data, i.e. which user behaviors tend to go in tandem
Applications: Shopping recommendations, medical diagnoses (e.g. butter & milk, fever, cough & pneumonia)
To be clear: My expertise is in communication and Human-Computer interaction, not Machine Learning or in operating any AI technologies. However, I am literate in the applications of these tools as well as the scope of their capabilities and limitations;
I know efficiently how to apply these tools within a product design team, having experience working closely with designers, writers, and engineers alike to incorporate their expertise into the kinds of questions we pose when analyzing user behavior.
The power of AI within the design space has given UX experts the ability to design for anyone- with access to millions of data points at our fingertips.
