Assessing a Team through Practice Maturity
The Great Upgrade Part Three C
This article series is intended for data practitioners of all levels, especially those interested in leadership positions and in advancing their careers. You may be more interested to lead as a technical individual contributor than as a people manager, or vice versa. The lion’s share of this article has been excerpted from Chapter 10 with some mentions of Chapter 8 of How to Lead in Data Science.
As discussed in The Great Upgrade introduction article, now is a prime time to take stock of your career and consider potential avenues of advancement to capitalize on The Great Upgrade. In previous articles, we examined industry considerations, assessing a company through its maturity and through its standing within the industry, as well as assessing a team through the hiring manager’s maturity and infrastructure maturity in evaluating opportunities. In this article, we continue to discuss considerations when assessing a team, in the context of practice maturity. Keep in mind that new opportunities may often be within the industry, the company, or even the team you are currently in.
As mentioned, assessing the team involves examining the hiring manager’s maturity, infrastructure maturity, and practice maturity. Understanding these team properties can better prepare you to set appropriate expectations for the opportunity.
Practice maturity
Practice maturity describes the rigor with which the Data Science team and their partners practice Data Science. Maturity in practices is vital for team efficiency and can be challenging to develop. When evaluating an opportunity, you can assess three perspectives: data engineering practices, data analysis practices, and data modeling practices. Data engineering focuses on strengthening trust in data through investments in infrastructure; analytics focuses on democratizing the use of data in an organization; modeling focuses on infusing intelligence into business functions and user experiences. Successful data scientists usually have a broad knowledge of all three aspects with strengths in a subset, and a team’s practice maturity is reflected in these aspects.
There are five levels of maturity for data engineering, from collection to ETL+storage, to governance, to streaming, to cultural.
Similarly, there are five levels of maturity for data modeling: from building ad hoc models to creating a culture of incorporating intelligence into business functions and user experiences.
As for the analytics aspect, it advises business partners with data-driven best practices and recommendations. This aspect also has five maturity levels: from building ad hoc reporting to creating a culture of self-served data insights (Figure 2).
In Chapter 8 of How to Lead in Data Science, we share details of each maturity level for each of these three aspects to help you recognize the stage your organization or the team you are assessing is in.
The table below lists some industry best practices to look for. You can use the rightmost column as a checkbox.
Most companies are not at these levels of maturity yet. You can assess the team’s willingness and executive support to head in these directions. It is your responsibility as a leader in data science to establish a path and to lead the team in building an efficient data-driven organization.
Now with an understanding of practice maturity, along with manager maturity, infrastructure maturity, and your strengths, you can decide if an opportunity on a team works for you.
As an example, the Building The Data Team section (about a quarter of the way down) in the long blog The GoPro’s Data Journey: 2016–2022 Reflections by Chester Chen, former Director, Data Science Engineering at GoPro, illustrates aspects of all three maturity areas when assessing a team in examining an opportunity.
In the next article, we will focus on assessing a role. That, along with assessing the industry, the company, and the team, are essential when examining your next-play opportunity.
As always, if you have views, questions, or examples in evaluating opportunities for the next stage of your career or in general, feel free to comment below or contact us directly.
About the authors:
Dr. Jike Chong and Yue Cathy Chang build, lead, and grow high-performing data teams across industries in public and private companies, such as Acorns, LinkedIn, large asset-management firms, and Fortune 50 companies.
The writing and opinions expressed are solely our own and are not shared, supported, or endorsed in any manner by our respective employers.
