With the world’s data increasing exponentially every year, the ability to extract actionable insights from data is becoming increasingly more important. As traditional data analytics grow outdated, the data science field has skyrocketed in popularity in recent years due to the more powerful insights data scientists can provide by cleaning and preparing data and identifying more granular patterns or trends through machine learning. However, data scientists remain in limited supply today, and many companies struggle to hire the high price point of a typical data scientist’s salary.
With the focus on making machine learning more accessible and intuitive for non technical users, my team and I created a research plan to document our goals and assumptions. We investigated the domain from multiple angles: primary research interviews, competitive analysis on similar products, and reviewing pain points from customers on the initial Ex Machina experience.
I conducted a series of 30 minute remote interviews with both internal and external users that roughly fit the job description of a non-technical Analyst persona. These interviews focused on understanding their current workflows and tools, challenges, and general knowledge of the value of machine learning.
Along with the general interviews, I set up multiple user feedback sessions with early beta Ex Machina customers to better understand their existing experience with the product. The focus during these sessions were centered on understanding their specific use cases in Ex Machina and general user experience.
We also surveyed a variety of competitor products to understand which areas were successful versus lacking within Ex Machina’s product experience. This process allowed us to investigate different frameworks and paradigms for simple and complex machine learning training configurations.