> For the complete documentation index, see [llms.txt](https://playbooks.equalexperts.com/mlops-playbook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://playbooks.equalexperts.com/mlops-playbook/pitfalls-avoid/dont-make-it-difficult-for-a-data-scientists-to-access-data-or-use-the-tools-they-need.md). # Don’t make it difficult for a data scientists to access data or use the tools they need The data scientists who create an algorithm must have access to the data they need, in an environment that makes it easy for them to work on the models. We have seen situations where they can only work on data in an approved environment which does not have access to the data they need and they have no means of adding data they want to create their algorithms. Obviously they will not be able to work with these tools and will likely seek opportunities elsewhere to apply their skills. Similarly, data science is a fast moving domain and great algorithms are open-sourced all the time - often in the form of Git repositories that can be put to use immediately to meet business needs. In a poorly designed analysis environment it is not possible to use these libraries, or they must go through an approval process which takes a long time. In many cases these problems are a result of over-stringent security controls - whilst everyone needs to ensure that data is adequately protected, it is important that data architects do not become overzealous, and are able to pragmatically and rapidly find solutions that allow the data scientists to do their work efficiently. In some situations, IT functions have taken a simplistic view that analytical model development is identical to code development, and therefore should be managed through the same processes as IT releases using mocked/obfuscated or small volume data in no-production environments. This shows a lack of understanding of how the shape and nuance of real data can impact on the quality of the model. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://playbooks.equalexperts.com/mlops-playbook/pitfalls-avoid/dont-make-it-difficult-for-a-data-scientists-to-access-data-or-use-the-tools-they-need.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.