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Software is a tool that helps us to accelerate, improve or even enable our research. However, as researchers trying to answer questions that are relevant in our fields, technology must not drive our research but rather be shaped by it. For this, it is important that we never use software for software's sake and rather are aware how subtle yet strongly the digital transformation changes the way we work and think.Software aware or software driven?“Never use software beyond the point of usefulness” this advice I repeat in all my classes and workshops. In a rather digitally enthusiastic field it might sound out of place, yet lately I have stumbled upon several papers (especially the essay by Jarke 2018) that frequently warned against the (ab)use of software. As a last trigger, a comment by Susanne Friese (disagreeing with my article on QDA tools) pushed me to elaborate on my opinions in  detail. She wrote:[caption id="attachment_1679" align="aligncenter" width="500"] A comment by Susanne Friese. Read the full comment here.[/caption]Friese is right, of course: The researcher must be in charge, not the software. A point that is made by her time and again (compare Friese 2019 and Friese 2016) and also shared by Marres (2018) who calls in his book "Digital Sociology" for "device aware" instead of "device driven" research.Indeed, research must be software aware, not software driven! However, the argument that I was trying to make in my blog post was not about the ideal (software aware) but about my frequent observation (software driven). Especially as a software trainer I more often than not see many researchers being driven by the technology they use.Using the “Digital” LabelThe worst kind of being “driven by software” I met, are researchers that feel forced to adopt a specific tool just for the sake of framing themselves as “digital (enough)” for prestige or funding purposes. They hate to use software, but are forced to adopt it by their institutions. The result being, of course, digital topping on an “analogue” cake that does not match very well. In these cases, I prefer to discuss whether the software in question can indeed benefit their research.[caption id="attachment_1677" align="aligncenter" width="313"] Example of a simple decision tree I use to advice for or against using QDA software[/caption]In most cases, software (for QDA or not) can indeed be of great help. However, even then it is important to evaluate how much investment in time and resources is necessary to acquire the needed skills and technology. For example, when a dissertation is almost completed, adopting or changing research software might no longer be a viable strategy. Or: If a software with some benefits exists, yet is extremely tedious to adopt and apply, it might not be worth the expected return. In any case, I see myself as an adviser who must be at any time open and willing to advice against the services he offers and the software he knows well.Digitised methods and the "neutrality" of dataWhen it comes to considering the appropriateness of research method Rogers (2013) makes an interesting distinction: He differentiates digital native methods and digitised methods. While digital native methods were created in an already digital environment,  digitised methods were "translated" into a digital software, yet are based on analogue forerunners. Although this distinction is not undisputed (see Jarke 2018), I think especially the transformation process is worth thinking about.Whether we are aware of it or not: The digital transformation is changing our research and, I argue, even the way we think. What software tools we know and what data we have available will influence the way we design our research methods and even our research questions.When it comes to the data side of the analysis, not even here we can hope for a method neutral or "objective" data base. As Kitchin (2014) points out: Data is never neutral. It is always framed by the technological, economic, ethnic, temporal, local and philosophical context in which it was created. A good example for this is a public database were people can report damages on the road. Due to the varying degree of response by the population in poor and rich areas, the rather wealthy districts are overrepresented. Relying on this data for road improvement would even strengthen the social inequality that created it.Similar bias can influence our research: Neither the tools nor the data are ever objective, unbiased or methodologically neutral. The (pseudo) objectivity and strongly suggestive power of digital data and tools must not lure us into shutting down the most essential skill of any humanist or social scientist: contextualising our data and reflecting on our tools and methods. After all, the best tool and the best method are always those that allows us to answer our research question in the most appropriate and valid way.