"Maturity models" aid enterprises in understanding their current and target states. Enterprises that already embrace Machine Learning in Mining as a core competency, as well as those just getting started, often seek a road map for improving that competency. A data science maturity model is one way of assessing an enterprise and guiding the quest for data science nirvana.
Check out this updated technical brief that revisits data science maturity along several dimensions with the goal to provide both an assessment tool and potential road map:
Strategy—What is the enterprise business strategy for data science?
Roles—What roles are defined and developed within the enterprise to support data science activities?
Collaboration—How do data scientists collaborate with others in the enterprise to evolve and hand off data science work products?
Methodology—What is the enterprise approach or methodology to data science projects?
Data Awareness—How easily can data scientists learn about enterprise data resources?
Scalability—How well do the tools used for data science scale and perform for data exploration, preparation, modeling, scoring, deployment, and collaboration?
Asset Management—How are data science assets managed and controlled?
Tools—What tools, including open source, are used within the enterprise for data science objectives?
Deployment—How easily can data science work products be placed into production to meet timely business objectives?
In this technical brief, I discuss each of these dimensions and levels by which business leaders and data science teams can assess where their enterprise is, identify where they would like to be, and consider how important each dimension is for the business and overall corporate strategy. Such introspection is a step toward identifying architectures, tools, and practices that can help achieve an organization's data science goals.