Engage the Right People to Support Data Goals
How can you best resource your data strategy with deep expertise and broad buy-in and engagement?
As demands for and uses of data continue to evolve, some organizations will hire more data experts whose jobs are focused on data and who can go deep on certain competencies (e.g., data science, M&E, machine learning). Yet, simultaneously, data must be integrated broadly throughout the organization so that all staff are comfortable understanding and using data as the work scales. Ultimately, the exact make-up of a data team is determined by the organization’s unique strategy, model, and infrastructure. The advice below outlines some key considerations in creating that team.
IN ACTION: The BOMA Project’s people behind the data
Data management is housed within BOMA’s M&E department, which is overseen by a dedicated M&E manager and supported by two data analysts. Closely aligned with this department is the core IT team which ensures that the data collection tools and equipment, including the data management system, function optimally. This team also now includes staff with Salesforce expertise to support BOMA’s Salesforce-based data platform. The ultimate responsibility for all programmatic data collection lies with the head of program — currently the Kenya Program Director. Beyond these dedicated data resources, BOMA provides regular data-use training to all program staff and many of BOMA’s people touch the data collection process, as illustrated below:
Dedicated data-related position on your executive team? Not quite yet…
Less than half of survey respondents have a data-related position on their executive team. Of the 21 respondents, three have an executive level M&E or data position, and seven have an executive level technology position.
Top Tips for engaging people to support data goals
- Use a “dosing strategy” to go broad and deep as needed.
As Health Leads has continued to evolve its data infrastructure over time, it has adopted what VP for Strategy, Learning, and Impact, Sara Standish, calls a “dosing strategy” with respect to its internal staff’s data capacity. For example, all staff are exposed to the basics of the continuous improvement frameworks of Model for Improvement and Results Based Accountability, with a handful of staff members being deeper experts. As projects require specific expertise or as complexity increases, Health Leads can deploy those experts to supplement the effort. In our survey, we also asked respondents whether and how they plan to deepen their data expertise in the future, see below for results. - Build a team with experience in diverse methods and local context.
Given the many and often changing demands on the team leading an organization’s data strategy, our interviewees spoke to some of the
experiences that they have found essential. For Living Goods, CTO Asif Akram shared that while there is a temptation to recruit data and technology experts from the West, Living Goods deemed it essential to recruit and invest in local staff (in Uganda and Kenya) who have a better understanding of the local context, live near the work, and can build the required technical competencies. With respect to experience in different methods, Damon Francis, Chief Medical Officer for Health Leads shared, “One thing that has been really helpful for us is having a person with community-based participatory research and qualitative methods (she’s an anthropologist) on staff.” There is also a translation role to play for Francis. “I think my role as a physician with research training has also been helpful. Both of us can be strong allies to front-line staff in technical conversations that drive decisions because we have the language and credibility to advocate in ways that are more influential to people in power who value research-based evidence and often dismiss other types of expertise. Both of us will often illuminate the limitations of research- based and quantitative evidence, and the reasonableness of alternative interpretations based on other types of expertise.” - Institutionalize regular trainings.
As data availability expands and expected use increases, our interviewees spoke about the importance of regular training throughout the organization to maximize ability to use the data. BOMA provides continued needs-based refresher and new function training to its M&E and program staff; “systems champions” within BOMA departments and regions provide decentralized support that can be delivered quickly to end-users. This support structure, alongside a clear problem escalation matrix, helps to ensure that the central IT team can focus its efforts on system optimizations and developments. At the field level, MiracleFeet established regional technical support for users of its CAST app, bringing support much closer to its heaviest users. - Maintain & evolve your data platform.
Most organizations engage external expertise to support the development of a data platform. But once it is up and running, organizations must determine how to resource ongoing maintenance and development. Our survey respondents reiterated the importance of this step; when we asked those who said their data platforms didn’t sufficiently meet their needs what they would have done differently to make it more useful, the top answer (64 percent) was that they would have ensured they had the right skills and bandwidth on their team to use and manage the system. A number of interviewees reported creating internal positions to administer and continue development of their data systems; this move helps to decrease dependence on expensive external consultants and allows for more flexible, timely, and proactive efforts to evolve these systems which are now integral to program operations and ability to scale. Living Goods partnered early with an external partner (Medic Mobile) to develop its digital tools, but as the tools became a critical part of operations, Living Goods found it had development and customization needs beyond what an external partner could meet on a short timeline. At that point, Living Goods invested in the internal expertise to be able to manage and modify the tools and decreased the external partner’s role to focus on platform hosting and maintenance. - Bridge the divide between data and non-data talent.
In The Data Lab’s guide to data teams, it stresses that “it is not enough to simply enable the [data] team and leave them to it; the rest of the
organization must also adapt to the capability and absorb the new insights into their processes.” To ensure organization-wide buy-in of the data vision and strategy, and demystify the capabilities of data, The Data Lab suggests the organization clearly articulate (and share) the key components of the strategy. See below for an additional consideration in integrating data experts within a social sector organization.
Integrating Data Experts within Social Enterprises
In Code Switching Across the Social/Digital Divide, Asha Curran and Julia Rhodes Davis explore generalized differences between data experts and the social sector that could lead to challenges in working together if not intentionally bridged. They write, “Too often, the information (the data, not useful in and of itself) exists on one side, and the people who want to use evidence to make change and drive impact are on the other. The key is identifying the problems that need solving and which questions are answerable.” They share the following table that describes common characteristics of each group, so that social sector organizations ensure they engage “code switchers” and translators who can bridge these divides.
Read next: As You Grow: Activate Data Use at all Levels, for more strategies related to talent see all articles in People Power, or return to see all articles in Data for Scale.
This article was written by Erin Worsham, Kimberly Langsam, and Ellen Martin, and released in June 2020.
