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Table 1 Lightweight theory-driven guidance for the BDA process

From: Theory-driven or process-driven prediction? Epistemological challenges of big data analytics

BDA step

Critical questions

Epistemological challenge

Possible lightweight theory-driven guidance

Acquisition

What data do I need?

What kinds of data [sets] are available/to be selected?

Data ‘sampling’

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

Analytics

Which method[s] to use?

What rules govern conclusions from these data [sets]?

Knowledge discovery

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