Data science is a collaborative art, however , in the real world, those two cannot sit farther apart.
Choosing low-code software for data science means that we investigate a common ground between the data scientists and the business unit (end-users). It’s investing in more efficient working groups and in knowledge sharing and upskilling. Most importantly, it’s investing in getting data to quickly power many more decisions in your organization.
The more time it takes for anyone in the organization to understand the value of data, the harder it is for the data science team to work. They waste time explaining, documenting, and advocating for their work, while projects get delayed, blocked, or canceled.
On the other side, business users are not exposed to enough problems to know what questions to ask or whether this function has value at all.
When adopted across the enterprise, a low-code tool has two positive effects. First, more people in the organization understand what can be done with data and, therefore, know better what questions can be asked of it. Second, more people in the organization are empowered to perform basic data science tasks themselves.
With a low-code tool, we’re no longer only talking about a tool that’s efficient for data scientists to do their jobs. We’re now talking about a tool that advances basic data understanding in the enterprise and makes the use of the most complex technologies transparent.
To fully demonstrate the effects of understanding data science, it’s helpful to think about it spread along two axes:
Horizontally: When teams outside of the data science group “get” the work that the data science teams do and how they prioritize projects. It includes sales and marketing groups, finance groups, operations teams, etc. Teams are often the ones actually closest to the data that the company gathers and thus well-positioned to ask questions of it. The more they work with efficient data science teams, the more bespoke their questions get.
Vertically: Similarly, data will start to be understood by people at various levels. Not just the data team, but the team lead, the manager, the VP, the CxO, all the way up to the CEO and the board of directors. Since these people sit far away from the data entry points, they need to find a way to stay connected with what’s happening in the data trenches.
Involving more people in the organization to “do” data science also has an effect on the organization. By upskilling makers, an organization all of a sudden 100x’d its data science bandwidth.
A Ph.D. or folks with 10+ years of experience in data mining are not the only ones who can derive insights from data. Some might call this data literacy — or creating “citizen data scientists.”
A pervasive understanding of how the data science platform works won’t really lead to a clean cut of who does what data science work. The use cases for data science far exceed the bandwidth of any enterprise data science team, and even the simplest automation and ETL projects take months to come true. Here, the real blocker to data science success is a lack of data understanding.
Low-code is not just well-suited for data science programming, it’s well-suited for bringing the business and the data science team closer together.