Principles to be considered while creating new policies#

Kickstarting the iterative process of defining the core values for the product and the team to ensure that our decision-making is aligned to the values we commonly agree upon.

Balance long term orientation with bias towards action in the short term#

Whenever we see a problem, we always think “How can we solve this problem for the next 1000/10000 users?” not just for the user we are solving the problem currently. Thus, we balance long-term orientation with a strong bias towards solving the user’s immediate needs.

User Centricity#

We want to be mindful that the most users we serve are novice audiences who are not experts in programming/data science. It can be a challenging experience to learn the language, tools, and content together, and handling outages/product issues can be an added cognitive overload that can affect students’ morale. As a result, it is essential to focus on the user experience for the entire teaching team, and the students so that they are spending the time on what they actually want to do with Datahub. This means we will go out of our way to support the needs of the teaching team and the students to ensure that they have the best experience possible while having additional discomfort at our end in the short term. The product gained traction because of the exceptional user centricity which led to solving complex problems, reducing friction for users, and easing their user experience. Now that we have scaled to 10,000+ users, it is equally important to keep the ethos that took us from 0 to 10k users in mind and not operate with the mentality of a standardized service. We are an early-stage innovation with 10,000+ users and not a mature stage product that is poised for stability.


We want to offer an equitable experience to all students irrespective of their class size, compute requirements or affiliation to STEM-based courses etc. In addition, we want non STEM users to have the same or in some cases better user experience than their STEM counterparts. This means we may prioritize R-related use cases over Jupyter stuff as most of our audience in humanities and social science prefer using R for their data science workflow.


We want to be the best stewards of CDSS money. We value being thoughtful and frugal about spending the limited resources we have on the right things. However, when it comes to a trade-off between frugality and user-centricity, we always take the extra effort to think about how we can solve the user problems first.


We are not a big team, and we don’t have a lot of experts related to Marketing, Design, Data Science, etc. However, we want to be resourceful with the limited resources we have and engage with the open-source ecosystem, our partners across Berkeley, and the instructors/students to work towards our mission.