|BDA step||Critical questions||Epistemological challenge||Possible lightweight theory-driven guidance|
What data do I need?|
What kinds of data [sets] are available/to be selected?
Apply data summarization, graphical representation, dimension reduction (e.g. PCA) and outlier detection|
Ensure multi-expert and multi-disciplinarily participation in data reduction and selection
Trace and examine all stages of extract, transform, load, and merge for completeness, correctness, and consistency
|Pre-processing||How can data [sets] be represented and processed without falsification or insight loss?||Data validity and reliability|
Which method[s] to use?|
What rules govern conclusions from these data [sets]?
Map the constructs of analytics to known theoretical concepts|
Ensure multi-expert and multi-disciplinarily participation in parameter selection and mapping analytical constructs with theoretical concepts
Develop/apply theoretical framework for choice of techniques (mining, machine learning, statistics) or models
|Interpretation||How to interpret such conclusion?||Non-/interpretability; reliability of prediction||Develop/apply theoretical framework for result interpretation|