Data product discovery
In product discovery, we seek to answer two critical questions: Will people use this product and will this investment be worth it? To answer these questions for data products, we evaluate more factors than we would in a typical product discovery project. This is because user adoption is not a given and we need to ensure we have data feasibility.
Users of data products typically have deep expertise in their field and are already on a challenging journey to becoming more data-driven in their work. So, our goal is to understand the levers that will determine whether users will replace their existing tools with something new. To get to the root of this, we’ll ask questions like: What are the users’ mental models for their work? What will build their belief and trust in the product? What kinds of explainability and control might be critical for users?
To determine whether we have the data feasibility required to ensure the product is viable and worth your investment, we’ll ask: Can we acquire the data? Can we derive the knowledge and insights needed from that data? What scale and performance of data and compute will be necessary?
Data product delivery
The successful construction and evolution of data products occurs at the intersection of human-centered design, rigorous engineering, and data science. We work in teams to tightly integrate these capabilities, and deliver business and user outcomes. Our teams deliver their work in small increments to accelerate your time to market and facilitate rapid learning.
We architect data products to support the instrumentation of performance measures, ongoing tuning of statistical / machine learning models, and the scaling of data and computation. Business and users depend on the reliability and the evolvability of these solutions.
Great data products change the way their users work. At TXI, successful delivery isn’t only about building technology, it’s about supporting users in the journey from their current way of working to something new. We focus on the end user throughout the design, delivery, and evolution of the data products we deliver.