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Data Science Maturity Model - Roles Dimension (Part 3)

Mark Hornick
Director, Advanced Analytics and Machine Learning

In this next installment of the Data Science Maturity Model (DSMM) dimension discussion, I focus on 'roles':

What roles are defined and developed in the enterprise
to support data science activities?

A role can be defined as "a set of connected behaviors, rights, obligations, beliefs, and norms as conceptualized by people in a social situation." As with most any new field, data science within an enterprise can benefit from the introduction of new roles. Following the 'strategy' dimension, the 5 maturity levels of the "roles" dimension are:

Level 1: Traditional data analysts explore and summarize data using deductive techniques. 

Enterprises at Level 1 may have persons dedicated to data analysis - data analysts - and draw on skills of database administrators (DBAs) or business analysts to deliver business intelligence. They likely use a variety of tools that support, for example, spreadsheet analytics, visualization, dashboards, database query languages, among others. Persons in these roles typically use deductive reasoning in the sense that they formulate queries to answer specific questions.

Level 2: Introduction of 'data scientist' role and corresponding skill sets to begin leveraging advanced, inductive techniques.

The Level 2 enterprise recognizes the need for more sophisticated analytics and the value that those trained in data science - the now much admired role of the data scientist - can bring to the enterprise. Once considered unicorns, data scientists are now more numerous as universities offer degrees at both the masters and doctorate level. Even so, data scientists may have different strengths, ranging from their ability to prepare/wrangle data, write code, use machine learning algorithms, use visualization effectively, and communicate results to both technical and non-technical audiences. As such, a given data science project may require a team of data scientists with complementary skills. Level 2 enterprises can now more confidently explore, develop, and deploy solutions based on machine learning, artificial intelligence, data mining, predictive analytics, and advanced analytics - depending on which term or terms most resonate with your enterprise. At Level 2, data scientists are typically added as needed to individual departments or organizations.

Level 3: Chief Data Officer (CDO) role introduced to help manage data as a corporate asset. 

Although not necessarily a pure data science role, the Chief Data Officer role is highly beneficial, if not critical, for the data science-focused enterprise. The CDO is responsible for enterprise-wide governance and use of data assets. Along with a CDO, the role of data librarian may also be introduced to support data curation within the enterprise. With the introduction of these roles at Level 3, not only is data science being taken more seriously, but the key input to data science projects - the data - is as well.  

Level 4: Data scientist career path codified and standardized across the enterprise. 

Level 4 enterprises strive for greater uniformity across the enterprise for the data scientist role with respect to job description, skills, and training. In some enterprises, data science activities and/or data scientists may be organized under a common or matrix management structure.

Level 5: Chief Data Science Officer (CDSO) role introduced.

Just as the Chief Data Officer role is beneficial for enterprises taking data more seriously, the Level 5 enterprise also recognizes the need for a Chief Data Science Officer. In this role, the CDSO oversees, coordinates, evaluates, and recommends data science projects and the tools and infrastructure needed to help achieve enterprise business objectives.

In my next post, we'll cover the 'collaboration' dimension of the Data Science Maturity Model.

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