Keywords: Capacity building; data governance; data science training; research infrastructure; machine learning; data; Africa

How do we empower the teams behind Africa’s health data? Recent findings from an assessment of Health and Demographic Surveillance System (HDSS) sites across Africa paint a vivid picture: the potential of these sites is held back by significant human capacity gaps. In this blog, we explore the key training and capacity building needs identified, and why addressing them is essential for the future of population health research.

A Thirst for Advanced Skills

One striking takeaway is the unanimous call for more advanced data skills. Across the board, HDSS data teams are saying: “We need to level up.” In the survey, when asked about training needs in over two dozen topics – from data governance to machine learning – most sites reported needing either advanced or expert-level training in almost every area. For example, data management staff want deeper skills in data cleaning, integration, and analysis. Even experienced data managers indicated they would benefit from formal training on modern data tools (imagine an HDSS data officer proficient in R or Python instead of just Excel).

Especially notable is the appetite for emerging tech knowledge. Several HDSS teams specifically requested capacity building in topics like GIS mapping, data visualization, and AI/machine learning. One Ugandan site wrote that they are eager to “acquire skills in machine learning and its applications”, while a West African site asked for help to “use AI to automate data processing”. These are sophisticated techniques rarely associated with field sites traditionally focused on demographic monitoring – a promising sign that HDSS staff aspire to use cutting-edge methods if they can get the training. Likewise, data warehousing and database management skills were highlighted as needs, suggesting many sites struggle with organizing large longitudinal datasets efficiently and recognize the value in training on these topics.

Interestingly, the capacity gaps aren’t only in high-tech areas. Basic skills and cross-cutting competencies are also needed. For instance, respondents from multiple Francophone and Lusophone sites noted that language proficiency (English) is a barrier – they want their staff to improve English so they can participate in international workshops and access more learning materials. It’s a reminder that capacity building is holistic: it can be as much about communication skills and grant writing as it is about statistical models.

Irregular Training Opportunities

If there’s one word to describe current training at many HDSS sites, it would be “sporadic.” The survey found that structured, frequent training programs are virtually non-existent in these settings. None of the sites have monthly or bi-monthly training sessions for their data staff. Only one site said they do something quarterly. The majority reported that they conduct training “on an ad hoc basis” – essentially, when a need or project arises. And a few sites admitted bluntly that they never have organized data training events for their team.

What does this look like on the ground? Often, it means that an HDSS data manager might attend an external workshop once a year, or new field staff get a short orientation, but there’s no regular curriculum to progressively build everyone’s skills. People are learning by doing, which is valuable, but they are also keenly aware of what they don’t know. One participant wrote “no initiatives for data science skills” when asked about how they currently develop staff – a clear call for help to start something from scratch.

The lack of structured mentorship is another issue. For example, only a third of sites mentioned using mentorship programs to foster data literacy among new team members. Far more common were informal knowledge-sharing sessions or simply relying on each individual to pick things up as they go. Without intentional coaching and mentoring, it’s hard for junior data analysts at an HDSS to grow into senior experts. People may feel siloed – the survey indicated limited engagement with a broader community of practice (only a few sites regularly interact with external researchers or communities of data users beyond sending annual reports).

Who Will Pay for it?

Financing capacity building is a perennial challenge. The assessment asked who funds the training that does happen. About half the sites said they rely on external grants and partners to support training activities. For instance, an international research partner might sponsor a short course on data analysis, or a donor project might include a budget to train field staff on using tablets for data collection. This support is hugely valuable, but it can be piecemeal and project-tied – when the project ends, so do the training opportunities.

Several HDSS sites noted they have had to use their own institutional funds to train staff, which often means pulling from very limited core budgets (that also have to pay salaries, fuel for fieldwork, etc.). Government support for training was mentioned by only one site, suggesting that national research capacity programs are either not reaching HDSS or not prioritized. An encouraging model is academic partnerships: a number of sites collaborate with nearby universities, either by enrolling staff in academic programs or inviting students/interns, which creates a knowledge exchange. But again, this is only happening in some places and is not systematic.

The funding gap also affects training frequency and quality. One site might have the good fortune of a funded workshop one year and nothing the next. It’s feast or famine. As a result, staff often have uneven skill sets – maybe one person got advanced GIS training but no one else did, or everyone learned a new survey software but never got a refresher or advanced module later.

Field Example – When Training is Lacking

Consider the example of an HDSS in a remote area that had to transition from paper-based surveys to a digital tablet system. Ideally, this would come with thorough training on the new software, data syncing, troubleshooting, and data security on devices. In reality, the assessment revealed that at one site, data collection was interrupted for months to upgrade the system – partly because staff were not fully trained initially and ran into issues. Eventually they sorted it out, but valuable time was lost and data rounds were missed. This story repeats itself: without upfront capacity building, technology deployments or new methods can fail or stall, costing more in the long run.

Another anecdote shared by a data manager: “We spend so much time cleaning data, it’s hard to learn new tools.” This highlights the classic cycle – lack of training in advanced data management means tasks take longer with inefficient tools, which in turn leaves no time to get training that would improve the situation. Breaking this cycle requires dedicated time and resources for staff to step back from routine duties and skill up.

The Way Forward – Building a Learning Culture

What would help HDSS sites the most in terms of capacity building? The survey responses and subsequent discussions suggest a few pathways:

• Regular in-service training: rather than one-off workshops, establish a continuous learning program. This could be quarterly webinars, a rotating schedule of short courses, or even an online training curriculum that HDSS staff can follow at their own pace. The key is consistency – making training part of the job, not an optional add-on.
• Exchange and mentorship: facilitating exchanges between sites where, for instance, a data analyst from a more established HDSS mentors the team at a newer site. Peer learning came up as an underutilized resource – there are experienced individuals in the network who could coach others. Some sites suggested forming a community of practice for HDSS data professionals in Africa (through Slack or another platform) to ask questions and share tips continuously.
• Targeted skill workshops: Funders and networks like INSPIRE could identify the top 3-4 high-priority skills needed across sites (the survey data suggests perhaps: data management with statistical software, data visualization, database administration, and intermediate statistics or machine learning). Then, host regional workshops focusing on these. For example, a “Data Visualization and Communication Bootcamp” could train teams on using tools like Tableau or Power BI and on effectively communicating data to policymakers – a skill many sites need as they engage with stakeholders.
• Leveraging local universities: Strengthen ties with academic institutions so that HDSS staff can enroll in relevant courses (biostatistics, data science, IT) at discounted rates or through specialized programs. Some countries might create a certificate program in HDSS data management in partnership with universities, ensuring the curriculum is tailored to the realities of field site data.
• Language and soft skills: Don’t forget, capacity building isn’t just technical. Workshops on proposal writing, scientific English, research ethics, and leadership can empower HDSS teams to more confidently engage on the global stage. One Mozambican HDSS noted how crucial English proficiency was; similarly, others might benefit from training in writing policy briefs or managing research projects – skills that help secure funding and create impact from data.

The overarching theme is that capacity building needs to shift from sporadic to sustained. HDSS staff are highly motivated – they’ve made clear what they want to learn. By investing in those people, African research institutions and funders will be investing in the long-term success of Africa’s health data systems. When the data team is strong, the data itself gets stronger, and that benefits everyone from local communities to global health initiatives.