Shared Struggles, Shared Solutions: Education and Cross-Sector Data Use Insights

“Education is the most powerful weapon which you can use to change the world.” ~ Nelson Mandela

Education has long been recognized as a crucial driver in creating a more sustainable and equitable world. However, to understand how education is being delivered, who it reaches, where, and how effectively, we must first have access to quality, reliable educational data.

Across many sectors, countries, and contexts, Development Gateway: An IREX Venture (DG) has seen first-hand what makes data, technology, and evidence effective. Through this process, we have learned best practices for advising governments, agencies, and organizations on how to take practical, action-oriented steps to collect, monitor, evaluate, and use data to achieve impact.

This blog draws on DG’s experience in climate, health, aid management, and agriculture to explore connections between the challenges of data collection, data hosting, and data governance across different sectors and what the solutions to overcoming them can teach us about strengthening education data systems.

Overview of the EDI Project

In our latest education project, the Elimu Data Initiative (EDI), we aim to understand the existing open- and closed-source educational information management systems that track education data.

In the first iteration of this project, Hewlett 1.0 (2022 – 2024), DG conducted desk reviews and a series of stakeholder assessments to understand the education data ecosystems in Kenya and Senegal, culminating in a comparative white paper. As we enter into the project’s second phase with EDI, our aim is to grow on the successes of Hewlett 1.0. The project also aims to:

1. Assess and understand the existing landscape of digital systems being used for education data management through a paper comparing education management information systems;
2. Contribute to thought leadership on digital tools for education through discussion of and engagement with our learnings from the paper; and
3. Conduct stakeholder interviews with Nairobi County officials, who indicated a strong need for digital approaches in education. 

As we’ve begun these project activities, we’ve encountered recurring challenges in data collection, data hosting, and data governance. Reflecting on these challenges across multiple sectors, ranging from health to agriculture, can lead to innovative solutions that improve outcomes for the education sector.

Data Challenges: Perspectives from Multiple Sectors

Challenge #1: Data Collection

A challenge we have repeatedly encountered in various sectors is the collection and collation of data. In our white paper on Education Data-Driven Decision Mapping, for instance, stakeholders cited challenges with collecting data on student performance and other data entries. Teachers are often asked to spend a significant amount of time entering data into multiple online systems. Due to the lack of data sharing between these systems, teachers often struggle to balance other teaching priorities while simultaneously completing numerous lengthy forms. This pattern is a recurring trend in the education sector we have witnessed in a number of countries, from Jordan to Kenya.

However, data collection challenges aren’t unique to the education sector and have been an issue in DG’s creation of digital solutions in other sectors, too. In our climate program, the Great Green Wall, for example, it was difficult to secure financial commitment data from countries. We addressed this challenge by manually aggregating individual survey responses in order to provide a first-level baseline. We also ensured that this data was shared publicly through the Great Green Wall Accelerator, preventing the challenge of multiple concurrent closed datasets.

In the health sector and our Tobacco Control Data Initiative (TCDI), the demand for up-to-date tobacco control research required us to implement new national-level data collection efforts, collecting and collating data ranging from vaping in South Africa to the behavioral reasons why Nigerians choose to start smoking shisha (also known as “hookah”).

Lessons Learned: Having a clear understanding of what data already exists, how readily available it is, where gaps remain, and the supply of and demand for new data enables us to design systems that are usable within present data constraints and scalable as additional data is collected.

Challenge #2: Data Hosting

Another common challenge relates to how and where the data collected are housed. In any program, a key decision must be made as to whether data solutions will be housed locally on servers within national borders or hosted on the cloud. There is no one-size-fits-all solution to this challenge, and what works for one context may not work for another.

In the EDI education project, we began understanding this nuance while seeking to understand education data flows in different country contexts. In Togo, data sovereignty – i.e., hosting the data locally – is considered a priority when it comes to their national data, including education statistics such as the number of students enrolled, the number of teachers per school, and so on. However, they were not able to host this education data locally within their existing infrastructure. As such, they decided to pursue a compromise.

Currently, Togo uses their own data collection tool, StatEduc, to collect school data at the national level. In order to use the analytics and reporting capabilities of the University of Oslo’s platform DHIS2 for Education, they then manually export the data outside of Togo to the DHIS2 team. The DHIS2 and Togolese officials then work together to review and validate the data before inputting the validated education data into the DHIS2 platform. Although this process is cumbersome, it has been functioning reasonably well. However, now that Togo has built a new data center, they are pursuing migrating the data to be hosted either physically or virtually through Togolese servers. Doing so will streamline the integration process of education data and ensure that education data remains compliant with Togo’s national data regulations, while also cutting down on current manual data validation procedures.

In another example from our work in the agriculture sector, DG’s ‘A Livestock Information Vision for Ethiopia’ (aLIVE) team worked closely with ministry officials in Ethiopia to determine whether hosting nationally or in the cloud would be a better fit for their needs. As noted in chapter 4 of our white paper ‘Demystifying Interoperability: Insights from our work in Ethiopia’s agriculture sector,’ the advantages of cloud computing, such as improved scalability, can be outweighed by the cons, such as increased cost over time. Similarly, national hosting solutions also raise certain challenges. Although the advantages include improved bandwidth and higher degrees of control and access to the data, these may be outweighed by the challenges of maintaining a local team to manage hardware and software infrastructure. In the end, after considering a wide range of factors, it was decided to host the livestock platform on local servers.

Lessons Learned: Recognizing that there is no one-size-fits-all approach to data hosting means each solution must be carefully considered and evaluated in accordance with existing data protection laws, technical staff availability, and budget constraints.

Challenge #3: Data Governance

Besides data collection and data hosting, a third major challenge we’ve seen during our work in the education sector is related to data governance. When talking with education experts from Kenya, Senegal, and Europe, we find that a common trend in the conversation is the underutilization of human resources dedicated to data processing and analysis, which inhibits data quality assurance and the effective use of data for decision-making.

Through our work in Hewlett 1.0, for example, we discovered that in one Kenyan county, there was only one data official working on data analysis for the entire county. These limitations in human resources exacerbate existing data quality and access challenges and can result in these challenges not being prioritized at a management level. 

The lack of resources and the scarcity of officials dedicated to data governance is a challenge we have encountered in a wide variety of sectors, particularly those that deal directly with governance or public infrastructure. In the agricultural sector, for example, the aLIVE team faced data governance challenges related to understanding the existing data management practices prior to the program’s initiation. The team found that these data practices were often not documented and rather relied on the memory of staff members to inform others about them. Due to staff turnover and the volume of systems, each with its own governance standards, much of this information was either lost or convoluted in such a way that understanding the overarching data governance landscape was a significant challenge.

While it may be challenging to gain a full image of the data governance structures in place, it is still vital to understand as much as possible what already exists in terms of data management, practices, and processes. It’s extremely rare for there to be “zero” data governance and, as such, the best practice is to document all you can of this foundational knowledge and build from there. This documentation not only helps to alleviate duplication but also builds buy-in for new practices among local system owners.

Moreover, we have encountered this challenge in our work over the years, providing the United Nations Children’s Fund (UNICEF) with data strategy services, supporting the development of their global data governance strategy. As part of this long-term agreement, we have focused on what responsibilities data stewards should have and the functions they need to fill across UNICEF and the overall UN system. This process has highlighted the challenge of data governance in education and how building up the “data savvy” capacity in staffing functions across all levels of an organization can help to solve it.

Lessons Learned: The people, rules, and procedures that govern the use of data across systems must be reviewed as thoroughly as the technical requirements. By clearly documenting the current state of affairs, we can define the “unwritten” rules of data governance and streamline the integration of data into everyday decisions.

Charting a Path Forward in Digital Solutions

As we continue our work in health, agriculture, and education, we’ll continue to aggregate our lessons learned on data collection, data hosting, and data governance. Although there are common challenges across sectors, we see how a clear understanding of the data landscape, including what data governance practices and processes are in place, can inform the successful integration of new systems for many years to come.