Field Guide to Data Science

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From the collaborative (and very well designed) workbook called “Field Guide to Data Science” published by Booz Allen Hamilton. The short version eloquently describes the new and emerging space of data science. With a lot of chatter out there on what is and what isn’t data science, we would subscribe to this thoughtful outlook on the space. Image & Text Source: Booz Allen Hamilton

The Short Version

  • Data Science is the art of turning data into actions. It’s all about the tradecraft. Tradecraft is the process, tools and technologies for humans and computers to work together to transform data into insights.
  • Data Science tradecraft creates data products. Data products provide actionable information without exposing decision makers to the underlying data or analytics (e.g., buy/sell strategies for financial instruments, a set of actions to improve product yield, or steps to improve product marketing).
  • Data Science supports and encourages shifting between deductive (hypothesis-based) and inductive (pattern-based) reasoning. This is a fundamental change from traditional analysis approaches. Inductive reasoning and exploratory data analysis provide a means to form or refine hypotheses and discover new analytic paths. Models of reality no longer need to be static. They are constantly tested, updated and improved until better models are found.
  • Data Science is necessary for companies to stay with the pack and compete in the future. Organizations are constantly making decisions based on gut instinct, loudest voice and best argument – sometimes they are even informed by real information. The winners and the losers in the emerging data economy are going to be determined by their Data Science teams.
  • Data Science capabilities can be built over time. Organizations mature through a series of stages – Collect, Describe, Discover, Predict, Advise – as they move from data deluge to full Data Science maturity. At each stage, they can tackle increasingly complex analytic goals with a wider breadth of analytic capabilities. However, organizations need not reach maximum Data Science maturity to achieve success. Significant gains can be found in every stage.
  • Data Science is a different kind of team sport. Data Science teams need a broad view of the organization. Leaders must be key advocates who meet with stakeholders to ferret out the hardest challenges, locate the data, connect disparate parts of the business, and gain widespread buy-in.
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