We consider data science as the combination of mathematical and statistical skills, technology and software development skills, and business and functional understanding.
The application of data science is generally the investigation and testing of a hypothesis to uncover an insight of value to the organisation. The reality with any such testing is that the identification of value is not guaranteed, with the risk that if left open-ended can result in resource consuming, costly, and never ending analysis.
We see actionable analytics as the application of data science skillsets, time-boxed within pre-determined time frames, in order to quickly understand the potential success or failure of a proof of value. With clearly defined timeframes and success criteria, we can prove potential value before moving forward into implementation of repeatable, and actionable insights.