Developing and continuously improving data literacy is an individual, organizational, and cultural challenge. I’ve written quite a lot about data literacy recently, with much of it consolidated in my e-book Building a Data Literacy Program: What, Why, and How. In that e-book, I define data literacy as the ability to understand, find meaning, interpret, and communicate using data. I also illustrate the many activities of a data literacy program, as shown in figure 1.. 

Figure 1. Data Literacy Program Activities

The activities of assessment, planning, learning, etc. are essential. But ultimately, developing data literacy begins and ends with attention to the human dimension—individuals, groups, and interactions. I have highlighted in the lower part of the diagram references to data coach and data coaching. 

Figure 2. Connecting People to Develop Data Literacy

Coaching has a real impact on developing data skills, but it is only the beginning of a people-focused approach to data literacy. Connecting people in all the right ways to share data knowledge and to acquire data knowledge makes a big difference. As shown in figure 2, coaching, mentoring, and community are the ingredients of a robust approach.

Data Coaching

A coach is a person who directs, advises, and guides others through the performance of some activities to produce a desired result. Coaching is the act of providing direction, instruction, advice, and guidance. Data coaching is project oriented, bringing knowledge and skills to data analysis, data integration, data quality, and other data-focused projects. Data coaching provides direction, advice, and guidance for project activities when working with data. Ideally, data coaches have a high level of skill and knowledge that they pass on to others as an integral part of undertaking data projects. Obviously, there are many activities in working with data. For purposes of illustration in this article, I’ll narrow the scope to data analysis activities as shown in figure 3. 

Figure 3. Data Analysis Activities

Many data analysis activities lead to many opportunities for data analyst coaching, with each activity requiring a distinct set of skills. Every individual who participates in data analysis is likely to have greater skill for some activities than for others. Highly skilled people are well positioned to provide data coaching, whereas less skilled can  benefit from coaching, especially when gaining new knowledge and skills as an integral part of doing project work. One person, for example, may help others find, access, and understand financial data, yet request help from others to do data analysis and data visualization. It is practical and pragmatic for one person to both coach and be coached.

It is practical and pragmatic for one person to both coach and be coached.
The purpose is to create a peer-to-peer network of data coaches.

To build a practice of data coaching, you’ll need to identify who has skills in which areas and publish that information. The purpose is to create a peer-to-peer network of data coaches that allows people to find the help that they need to execute a project. Data coaching is a project-level activity with the goal to improve project performance—in a data analysis project, for example, better analysis done faster.

Data Coaching + Data Mentoring

Data mentoring is another method that is used to share knowledge, transfer skills, and develop data literacy. Coaching and mentoring are distinct but compatible approaches. A coach provides guidance to a person doing project work to help them achieve project goals. A mentor shares their knowledge, skills, and experience with another person to help them learn and grow. Coaching and mentoring are both consequential practices to cultivate data literacy. Mentoring builds on a foundation of coaching, and the two techniques used together are especially powerful. To combine them effectively, it is important to understand the unique characteristics of each. Figure 4 summarizes those characteristics.

Figure 4. Coaching vs. Mentoring

Coaching, as already discussed, is oriented to achieving project work. Mentoring is oriented to development of individual capabilities. It is likely that many people—potentially anyone with a role in project work—are candidates for coaching. The pool of mentees—individuals to be mentored—will be much smaller. Direct mentoring resources to individuals with untapped potential, those where talent retention is most critical, and those who are seen as future leaders. 

Coaching is a short-term effort shaped by the needs and the duration of a project. Mentoring is a long-term professional (and sometimes personal) relationship between individuals. A coaching effort ends when the activity in need is completed. A mentoring relationship ends when mutually agreed by mentor and mentee.

The purpose of coaching is to elevate project performance. The purpose of mentoring is to drive individual learning and growth. In general, this means that coaching is directive and advisory—the coach expressing what needs to be done to perform an activity and produce a result. Mentoring style is more interrogative, and is driven by the interests and questions of the mentee. Differences of purpose and style lead to differences in relationships. Coaching relationships are many-to-many with several individuals coaching several others. Coaching roles and relationships change, shift, begin, and end based on dynamics of projects. Mentoring relationships are one-to-one—a direct connection of mentor and mentee. Occasionally a mentor may work with more than one mentee, or a mentee may have multiple mentors. But this isn’t a scalable approach, and each mentor-mentee relationship remains a one-to-one connection. 

Coaching relationships are based on project needs, typically with project participants seeking coaches when they recognize needs for direction, advice, and guidance. A directory of data coaches describing who is available to coach, and for what topics and skills is a valuable resource to establish these relationships. Mentoring is based on a more formal agreement between mentor and mentee recognizing it as a voluntary relationship and expressing mutually agreed goals, purpose, and working arrangements.

Coaching is typically a somewhat structured process where the coach assumes a role on the project team. Time commitment, availability, and scheduling are agreed upon, and compensation may be part of the agreement. Quality and speed of project results are the foundation for coaching metrics. Mentoring is a more fluid and dynamic process with meetings and interactions occurring on an as-needed basis but constrained by agreed goals, purpose, and working arrangements. Metrics to measure mentoring success are based on development of mentee skills and competencies. 

Data Literacy Communities of Practice 

“Communities of practice are groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly.” (Introduction to communities of practice, Etienne and Beverly Wenger-Trayner.) Data literacy communities of practice share concern and passion for data and working with data to acquire business knowledge and elevate business performance.  The authors say, “A community of practice is not merely a community of interest … Members of a community of practice are practitioners. They develop a shared repertoire of resources: experiences, stories, tools, ways of addressing recurring problems—in short a shared practice.” 

Recall my definition of data literacy as the ability to understand, find meaning, interpret, and communicate using data.  Consider the power of practitioners learning together about data understanding, meaning, interpretation, and communication. 

Think plural--communities, not community of practice. 

Consider the power of communities—practitioners sharing and learning together—as a means to continuously advance and expand data literacy skills. Shared experiences, stories, tools, and problem-solving techniques can be powerful in so many data disciplines. Think plural—communities, not community of practice. What impact could you achieve with any of these communities?

• Data Analyst Community of Practice

• Data Science Community of Practice

• Data Governance Community of Practice

• Data Quality Community of Practice

• Data Stewardship Community of Practice

… and many more. Perhaps you’ll want something more specific—Healthcare Data Science Community of Practice or Actuarial Data Analyst Community of Practice, for example. The possibilities are abundant, and the potential impact when your data coaches, data mentors, and data practitioners engage in communities is huge.

The hard question is how to get started. First, you’ll need to decide which communities to pursue. As is true with many change efforts, it makes sense to start small and learn. If you’re a large enterprise you may want to foster internal communities that begin by engaging your data coaches, mentors, and leaders. Broader communities with members from many organizations work well for smaller businesses, and also have advantage of bringing diverse perspectives and experiences. You may find communities that already exist, or you may need to make an effort to establish new communities. Working through existing membership organizations—DAMA, CIPS, etc.—to form new communities can work well. You may also find local organizations that can help (one of my favorite examples is Technology Association of Oregon). Whatever your needs and goals, community development takes effort and dedication. If you’re serious about data literacy, communities of practice are well worth the effort. 

Final Thoughts

Developing data literacy is first and foremost a human thing—it depends on people and culture. Individual literacy is only the beginning. Organizational literacy is energized by the relationships and interactions among people. Coaching, mentoring, and communities of practice are effective ways to supercharge your organization’s collective data literacy.