Booz Allen’s Take on Data Science
Just finished reading Booz Allen’s Field Guide to Data Science as an introduction to the topic. I found it easily accessible and full of wonderful graphics. Some quick points for those who want the high-level overview:
What Companies are doing with Data Science today
“Companies with strong Data Science teams often focus on a single class of problems — graph algorithms for social network analysis, and recommender models for online shopping are two notable examples”
- Movie Recommendations
- Weather Forecasts
- Stock Market Predictions
- Production Process Improvements
- Health Diagnosis
- Flu Trend Predictions
- Targeted Advertising
Where Data Science is going
“ From influencing retail markets, to setting public health and safety policies, or to addressing social unrest, organizations of all types are generating value through Data Science. Data is our new currency and Data Science is the mechanism by which we tap into it.”
Booz Allen has a Data Science team (to help their customers). They also have a Github account:
GitHub is where people build software. More than 14 million people use GitHub to discover, fork, and contribute to over…github.com
What is Data Science? 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.
Selling Points for Business Case
- 17–49% increase in productivity when organizations increase data usability by 10%
- 11–42% return on assets (ROA) when organizations increase data access by 10%
- 241% increase in ROI when organizations use big data to improve competitiveness
- 1000% increase in ROI when deploying analytics across most of the organization, aligning daily operations with senior management’s goals, and incorporating big data
- 5–6% performance improvement for organizations making data-driven decisions.
Read the full Field Guide here
- I’d like to apply some of these principles to some generic projects to learn and document the results here on Medium.