In recent years, the divide between tech and commercial teams has narrowed significantly, and we're now closer than ever to achieving our goals. This shift was accelerated by data technology, which introduced new skills, problems, and collaborators for the CTO and their team to tackle. While technical teams aren't inherently commercial, they've expanded to include building and delivering tools for various teams across the organization.

However, what's often overlooked is the importance of commercial guidance in the development of these tools. Without it, we're essentially shooting in the dark, building products that may not meet the needs or goals of their intended users. This is particularly evident in data science projects, where many are never productionized simply because they're built by people who don't fully understand the commercial requirements.

What if we assume that commercial teams are inherently technology-savvy, and that's not the case? What might we miss by not considering their needs and processes? For instance, what if the commercial team is using a specific workflow or software that our data science project doesn't account for?

Let's put the commercial needs of our users at the forefront of our development process. Instead of building tools that are only effective for us as tech experts, let's focus on creating solutions that deliver measurable value and impact the bottom line. We can do this by adopting a lean-value approach to product development, where we prioritize research with the highest likelihood of delivering value or progressing organizational goals.

We need to challenge our assumptions and ask questions like: What if our technical solution is not the best fit for the commercial team's needs? What if there are alternative solutions that would be more effective? By considering these questions, we can create products that are truly valuable to our users, rather than just serving our own interests as tech experts.

Data Science Without Commercial Guidance is Like Shooting in the Dark - A Recipe for Disaster

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