Code-only AI for data science doesn't work. Period. Context-aware tools are the only way that data scientists are able to get work done using AI.
I have worked with products that looked sharp in a demo but collapsed in real workflows. They generated code but ignored the sequence of steps, the data in play, and the questions being asked.
That is not data science. That is gambling with disconnected outputs.
I have seen how that leads to drift, wasted time, and broken trust.
No one wants that.
Why Context Matters in Data Science
Data science depends on context. Each step informs the next: queries, transformations, models, evaluations. If your tools only see the current cell, they are blind to the process that got you there. That is how small mistakes turn into larger failures.
Context-aware AI fixes this. It carries knowledge forward across the workflow. It knows the path you took, the datasets in use, and the assumptions you set. Suggestions stay relevant because the tool has the same memory you do. That lets you test faster, refine quicker, and avoid repeating yourself.
I have seen teams cut wasted cycles in half once their tools carried context forward instead of resetting at every step. That is the difference between AI that spits out code and AI that understands data science.
The Inspiration Behind Zerve
This is why we built Zerve. We were tired of tools for software development trying to be forced into the realm of data science. They look like they work great on public/kaggle datasets (toy problems!) and leave a huge gap when the data is real.
Zerve was designed as an environment built for data science itself. Interactive and iterative. It does not stop at generating a snippet. It keeps track of the workflow, the assumptions, and the iterations. It acts like a teammate who remembers what has been tried, what worked, and what failed. That memory compounds. It makes each step stronger than the last.
If you only take away one thought from my rant, let it be this. Tools that forget context waste your time. Tools that keep it move the work forward.
FAQs (Frequently Asked Questions)
Why is context important in data science?
Context is crucial in data science because it informs every step of the process, ensuring that data analysis is relevant and meaningful to the specific problem or scenario being addressed.
How does understanding context improve data science outcomes?
Understanding context helps data scientists select appropriate methods, interpret results accurately, and make informed decisions that align with real-world situations, leading to more effective insights.
What inspired the creation of Zerve in relation to data science tools?
Zerve was created out of frustration with existing tools that lacked contextual awareness, aiming to provide solutions that integrate context to enhance data analysis and decision-making.
What challenges do traditional data science tools face without context?
Traditional tools often produce generic or irrelevant results because they analyze data without considering the specific circumstances or background, limiting their usefulness and accuracy.
How does Zerve address the need for context in data science?
Zerve incorporates contextual information at every stage of the data science workflow, enabling more precise analyses and actionable insights tailored to the unique needs of each project.
Can incorporating context in data science lead to better business decisions?
Yes, by grounding data analysis in relevant context, businesses can derive insights that are more aligned with their goals and environment, leading to smarter strategies and improved outcomes.
Based on my article in The New Stack.
