Data is the raw material with which you can measure, track, model, and ultimately attempt to predict individual and social behavior. Data science was born from the promise that a business manager leveraging consumer data could make more effective and efficient operational decisions. This premise gains greater realism as society increasingly interprets a digitally enhanced and technologically connected existence, in which almost everything said, done, shared, purchased or searched for is captured and stored. This trend of datafication is illustrated by the fact that 90% of existing data was created in the last two years (Gobble, 2013). Organizations are collecting ever more data about their customers and pushing predictive models beyond ever wider boundaries. Today, companies don't just optimize decision-making; they are creating “data products” that are offerings based entirely on the acquisition of personal information. Every aspect of a modern individual's life is potentially mixed into a data sausage that is constantly being ground, blended and packaged into links of intelligence. But this so-called intelligence may “increase much faster than our understanding of what to do with it, or our ability to differentiate useful information from falsehoods” (Silver, 2012). Although the emerging field of data science is not yet to embrace a set of ethical standards, it has already begun to raise fundamental questions regarding individual privacy and professional responsibility. While society will eventually strike a balance between protecting an individual's privacy and the frantic rush to profit from it, data scientists may shift that balance, intentionally or unintentionally, through the methods and applications of... paper ......iple whenever they practice in the corporate world. When a new application for this technology presents itself, there is inherently a choice as to whether it will turn out well or not. As more markets and situations of all kinds become data-driven, data scientists will face greater opportunities to make value-based decisions. Creating and following a set of professional values ​​can keep the industry as a whole moving in the right direction right from the start, and data science professionals have the power and responsibility to establish these ethical standards. Before data sausage is ground, packaged and sold on an industrial scale, values ​​need to be seriously discussed. To this end, a proposed value set is included in Appendix A. In closing, Nate Silver (2013) suggests a final thought: “before we ask more of our data, perhaps we should ask more of ourselves”..”