Whether you use qualitative or quantitative methods, there are plenty of ways to be more inclusive in your data analysis. 

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Practice reflexivity and acknowledge that the results you draw from your data are influenced by your own identity. You are actively interpreting the data outputs in some way, and thus the knowledge produced from your research is very intertwined with your identity. 

See also: Why themes don’t ’emerge’ from the data (article by K. Lumdsen)

What is positionality and should it be expressed in quantitative studies? (article by A Jafar)

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Be aware of bias: both in your dataset and in yourself. Be upfront with biases inherent to your dataset, and if possible use techniques like blinding to limit personal biases.

See also: 8 types of bias in data analysis and how to avoid them (TechTarget)

Data Ethics Club (many resources & discussions about bias and ethics in data science)

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Try to use open source software where possible- for example, switching from MATLAB to Python. Open source software benefits research by removing paywalls and thereby making science more accessible, as well as making science more collaborative. 

See also: Turing Way Guide to Open Source Software (Turing Way)

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