Datafication

Data is the raw material with which one can measure, track, model, and ultimately attempt to predict individual and social behavior. Data science sprang from the promise that a business manager who leverages consumer data could make more effective and efficient operational decisions. This premise gains in realism as society increasingly plays out a digitally-augmented and technologically-connected existence, in which nearly everything that is said, done, shared, bought, or sought is captured and stored. This trend of datafication is illustrated by the fact that 90% of extant data was created in the last two years (Gobble, 2013). Organizations are gathering increasingly extensive data on their customers and pushing predictive models past ever-widening boundaries. Today, firms do not stop at optimizing decision-making; they are creating “data products” that are offerings based entirely on intake of personal information. Every aspect of a modern individual’s life is potentially mixed into a sausage of data that is constantly ground, churned, and packaged into links of intelligence. But this so-called intelligence may be “increasing much more rapidly than our understanding of what to do with it, or our ability to differentiate the useful information from the mistruths” (Silver, 2012).

Although the emergent field of data science has yet to embrace a set of ethical norms, it has already started to raise fundamental questions surrounding individual privacy and professional responsibility. While society will eventually strike a balance between the protection of an individual’s private life and the frantic race to profit from it, data scientists may shift that balance, intentionally or otherwise, through the methods and applications of the...

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...iple each time they practice in the corporate world. When a new application for this technology arises, inherently there exists a choice whether or not it will result in good. As more markets and situations of all sorts become data-driven, data scientists will be faced with more chances to make values-based decisions. Creating and following a set of professional values may result in the field as a whole heading in the right direction from the start, and data science professionals hold the power and the responsibility to set these ethical norms. Before data sausage is ground, packaged, and sold on an industrial scale, a discussion of values needs to be had in earnest. To that end, a proposed set of values is included as Appendix A. In closing, Nate Silver (2013) suggests a final thought: “before we ask more of our data, perhaps we should be asking more of ourselves.”