"Maturity models" aid enterprises in understanding their current and target states. Enterprises that already embrace data science 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.
As an assessment tool, this Data Science Maturity Model provides a set of dimensions relevant to data science and 5 maturity levels in each - 1 being the least mature, 5 being the most. Here is my take on important maturity model 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 in the enterprise to support data science activities?
Collaboration - How do data scientists collaborate with others in the enterprise, e.g., business analysts, application and dashboard developers, to evolve and hand-off data science work products?
Methodology - What is the enterprise approach or methodology to data science?
Data Awareness - How easily can data scientists learn about and access enterprise data resources?
Data Access - How do data analysts and scientists request and access data?
Scalability - Do the tools scale and perform for data exploration, preparation, visualization, modeling, and scoring? Are data science tools fully leveraging available and potential compute power?
Asset Management - How are data science work products (assets) managed and controlled?
Tools - What tools are used within the enterprise for data science objectives? Can data scientists take advantage of open source tools in combination with high performance and scalable production quality infrastructure?
Deployment - How easily can data science work products be placed into production to meet timely business objectives?
In this blog series, I'll discuss each of these dimensions and levels by which business leaders and data science practitioners 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 facilitates identifying architectures, tools, and practices that can help achieve identified data science goals.