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.