RESEARCH & RESOURCES

TDWI Checklist Report | Seven Steps for Executing a Successful Data Science Strategy

January 21, 2015

Data science is a hot topic among business and IT leaders. Excitement about the potential benefits of data science is tempered, however, by anxiety about how hard it is to find, hire, and train data science personnel, not to mention the difficulty of defining the term within the context of an organization’s goals and objectives.

There is no single definition of data science, nor one solution or technology. It is a term that joins together contributions from several fields, including statistics, mathematics, operations research, computer science, data mining, machine learning (algorithms that can learn from data), software programming, and data visualization. It can cover the entire process of acquiring and cleaning data, methods for exploring the data and extracting value from it, and techniques for making insights actionable for humans and automated processes.1 Most often, the focus of data science is to optimize decisions and realize higher value from data through advanced analysis.

One factor that makes data science distinct, however, is the word science. Data science is about applying scientific methods to explore and test hypotheses about the data. Indeed, many data scientists come from hard science fields such as chemistry and physics or professions such as neurobiology and nuclear physics. Data science pioneers have contributed mightily to the growth of social media and e-commerce; now, firms in other industries are keen to apply data science to their decision-making processes.

Continuous experimentation through examination of data to test hypotheses is at the heart of most data science projects. At the same time, the availability of technologies that can work with enormous data volumes and variety enables professionals to complement scientific methods with hypothesis-free approaches that employ machine learning to examine data and discover unforeseen patterns before articulating a hypothesis. This enables organizations to use data science to find previously hidden risks and opportunities and apply analytics to improve outcomes.

To solve business problems, develop new products and services, and optimize processes, organizations increasingly need analytics insights produced by data science teams with a diverse set of technical skills and business knowledge who are also good communicators. This TDWI Checklist Report describes seven steps to achieve a successful data science strategy.

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