Beyond the Blindspot: Data for Change and the Integration of AI Technology and Financial Empowerment for SMEs

This post was created following a discussion with Hannah Redders of Data for Change in Geneva, Switzerland. To learn more about Data for Change, head to the bottom of the article!

*The information shared is only for context and does not constitute an official endorsement of Internatnotes Blog.

Introduction

When observing today’s highly volatile environment, a thought that best comes to mind is American theorist  Geoffrey Moore's declaration that “without big data analytics, you are blind and deaf in the middle of a freeway.” In the 21st century, the globe hosts countless initiatives, each aiming to drive exponential revenue and expand into various markets. While many currently flourish, many businesses fail to penetrate new markets, and despite their cultural and regional differences, a singular factor remains vacant: data. 

In fact, according to World Bank estimates, the current financing gap for formal Micro, Small, and Medium Enterprises (MSMEs) in global markets is over $5.7 trillion U.S. dollars, a deficiency directly fueled by the lack of accessible, verifiable data. This not only highlights an issue in the representation and transparency of regional business and institutions worldwide, but also hinders their growth as successful parties. 

For these groups, their weaknesses amidst the race towards progression and development stem from a visibility crisis, and thus, entrepreneurs and policymakers are unable to lead effective policies due to a deficiency of critical data that could otherwise provide direction. Additionally, for some regions, the absence of data collection has amplified inequality. Still, many appear to strive past these limitations. 

The SDG-Driven Directive

One of those instances occurred on November 19th, 1999. On that very day, the Joint UN/OECD/World Bank/IMF Senior Expert Meeting on Statistical Capacity Building was held in Paris, France. The assembly was a direct response to the administrative failures, in addition to the reality of a global data deficit—particularly in developing nations—actively undermining the entirety of poverty-reduction and development programs. Soon after its closing, the PARIS21 initiative was founded. 

PARIS21 is not a foundation on its own; rather, it’s an initiative that consists of the joint meeting’s attendees—such as the United Nations, the International Monetary Fund, and the World Bank—and projects, including the PARIS21 Academy, Climate Change Data Ecosystems (CCDE), and the Gender Data Network. To ensure proper management of the initiative's projects, PARIS21 has an executive board, holds annual meetings each year with its partners, and manages regional forums where stakeholders can advocate for new ideas in data collection.

Aiming to collaborate with national governments and global organizations, the network’s main objective is to ensure the growth of data collection in low to middle-income nations. Since its initial development, these focuses have led to a rise of $875 million U.S. dollars in donations for data and statistics, according to the PARIS21 Partner Report on Support to Statistics (PRESS), which has fostered the recent development of sustainable, consistent data strategies in developing countries.  

So far, the network has made great progress in fostering economic development for developing nations, but the efforts of its sub-organizations should be recognized for their crucial contributions. In light of this realization, I recently discussed with Hannah Redders the significance of combining ethical technology, verifiable data, and cultural sensitivity to achieve sustainable development goals.

The 2030 Agenda for Sustainable Development was first established in 2015, roughly 16 years after PARIS21’s creation, and led to the creation of the 17 Sustainable Development Goals (SDGs). These references helped strengthen the objective to monitor progress globally through data patterns and ensure that no one is left behind on the development pathway. As of present time, some of the biggest data gaps are within the climate and gender sectors, which was reflected in the conversation with my interviewee. 

For example, according to Data2X—an additional data-driven initiative hosted by the United Nations—less than half of countries had data available to monitor SDG 5 (gender equality) indicators in 2021, which illustrates a clear sign of systemic failure in statistical reporting. Interestingly, the absence of this data stems from various individual reasons.

First, countless nations in need of SME data have financially unstable foundations Sub-Saharan African SMEs, despite contributing nearly 50% to the region’s GDP, face a funding gap estimated at over $140 billion, with the lack of reliable financial data being the primary constraint., and thus, many are unwilling to invest in initiatives that could further affect their struggling capital. Furthermore, some national governments may feel an unwillingness to give attention to those initiatives, especially SDG fiv,e, fearing that exposing data on inequality will create political accountability or pressure to change long-standing social structures.

Given these trends, organizations such as Redder’s Data for Change, an affiliate organization of PARIS21, aim to address these concerns, specifically in regions such as Sub-Saharan Africa (e.g., Zambia) and through models linked to Rwanda's established Gender Data Lab and resources such as Zambia’s BongoHive.

One of the many elements of Data for Change’s solution includes the creation of AI conversational centers, which Redders states can “capture whole statements and descriptions in a cultural context,” as it expands on instituting more accessible, accurate data collection within low to middle-income nations. Specifically, Data for Change is training these AI models using full conversational transcripts and contextual inputs, moving beyond simple word-for-word translation to capture the intricate cultural nuance and idioms of low-resource languages, ensuring the data is both accurate and reflective of local realities.

Furthermore, Redders emphasizes that the growth data collection in these regions isn’t just fostered by technological innovation, but by mentorship as well. Through programs such as Mentopreneur Zambia—which Redders previously founded—experts work with local entrepreneurs to assist in creating bookkeeping practices to encourage the management of their business data and financial history. As a result, many of these processes aid businesses in the preparation of their products for export as they become able to comply with policies requiring certain fields of data, involving them in new supply chains. 

This commitment to capacity-building is demonstrably effective: the formalization achieved through these mentorship programs enables local SMEs to move from relying on informal credit to securing formal bank loans for expansion, directly addressing the critical $140 billion SME funding gap.

However, aside from the fields of digital infrastructure and mentorship, our discussion led to an additional, crucial component in the development of data collection: trust. Having trust is a critical prerequisite to combating the profound causes of inequality. For instance, a World Bank report illustrates that losses in human capital wealth due to gender inequality alone can lead to a staggering $160.2 trillion U.S. dollars globally, exposing the substantial economic opportunity that is blocked by two current factors: the lack of both data and the trust required to collect it. As Redders explains, this principle must be first used fundamentally, “If you want to just design a project based on needs, you need people to tell you their true needs [and make] themselves vulnerable.” But to even enter this sphere fundamentally, one must transform their relationship into a safe, collaborative space. 

Methods for this could include complying with relationship-driven communication, such as opening up business conversations with more personal, less professional topics to build trust. Additionally, a method could include utilizing more informal spaces to channel progress. Even in Redders’ experience, she admits that she would learn more about the progress of a project when she would “have dinner instead of sending emails and writing requests.”

At last, in a world where profit is reliant on accessible data, the usage of these innovative technologies and partnership strategies can direct rising SMEs towards a pathway of success—moving away from Moore’s supposed “highway”—through enhanced data collection. Still, these new teaching methods and technologies would not be available without the presence of the countless initiatives and organizations that support them, and these institutions don’t aim to stop in current times. 

In November 2024, the Medellín Framework was established to accelerate current objectives while introducing new directives, such as utilizing machine learning for collection and activity, normalizing the integration of SDG data into policy and action. Ultimately, though, the continuation of data collection is not solely the result of isolated actions, policies, or initiatives, but instead is driven by the momentum of an accelerating global movement to serve the millions aiming to enter their respective markets. 

Bibliography

"Financing." Data2X, data2x.org/state-of-gender-data/financing/.

"MSME Finance Gap." International Finance Corporation, Mar. 2025, www.smefinanceforum.org/sites/default/files/Data%20Sites%20downloads/IFC%20Report_MAIN%20Final%203%2025.pdf.

"The PARIS21 Partner Report on Support to Statistics 2024: Ensuring Resilient Data Systems in a Changing Funding Environment." PARIS21, 2024, www.paris21.org/sites/default/files/media/document/2024-12/paris21-press-2024.pdf.

"PARIS21 2026-2030 Strategy: Partnering for a Sustainable Future through Quality Data and Systems." PARIS21, 31 Mar. 2025, www.paris21.org/sites/default/files/media/document/2025-03/paris21-2026-30-strategy.pdf.

Wodon, Quentin T., and Bénédicte De la Brière. "Unrealized Potential: The High Cost of Gender Inequality in Earnings." World Bank Group, 1 May 2018, https://doi.org/10.1596/29865.

What is Data for Change?

The Data for Change Foundation works with grassroots organizations and SMEs (small to medium-sized enterprises) in Africa to harness data for real change. Their mission involves collecting hyperlocal data that is often missed by national reports, building data literacy among activists, and co-designing campaigns that lead to behavioral and institutional reforms.