You’ve asked for it, and we’ve delivered it: qualitative indicators are now available in b.world to add more nuance to your impact measurement. As a multidisciplinary group of professionals, including UX researchers, let’s just say that WE ARE WITH YOU; the b.world team fully understands the need for and importance of collecting quantitative and qualitative data for your impact projects.
1) Qualitative Indicators
When we think about impact data, our minds often go straight to numbers and calculations—sums, averages, minima, maxima, etc. But believe it or not, qualitative—or narrative—data is just as important. Qualitative data can be collected from interviews, contextual observations, focus groups, diary studies, social media feeds, etc. b.world now enables selecting ‘Qualitative’ as the indicator’s data type and entering text Entries as the data.
Not only do many data standards—including two popular ones, GRI and CSRD, and lesser-known but important ones like the AWS International Water Stewardship Standard—include both quantitative and qualitative reporting requirements, but a mixed methods research approach can maximize data quality. As Dr. Michael Bamberger, a sociologist by training who spent a significant portion of his career working for the World Bank, notes in his piece ‘Introduction To Mixed Methods In Impact Evaluation’ that there are several primary advantages to using a mixed methods (i.e., quantitative and qualitative data) research approach:
Input: using the results of one method to help develop the approach for another. For example, it can be helpful to use a survey to cast a wide net at the beginning of your discovery research and then use the results to hone more targeted questions and recruit a more appropriate set of interview participants for the next round of research.
Corroboration: enhancing the validity or credibility of findings by showing similarities between information obtained from different data collection methods.
Investigation: providing opportunities for deeper analysis, especially where findings discovered through different methods may diverge.
Setting up qualitative indicators in b.world is similar to setting up quantitative indicators. It is done in the same place—in the Logframe—and the setup looks the same; the only difference is that some of the fields that are only relevant to quantitative indicators—like ‘Unit of Measurement’ and indicating if the data is ‘Incremental vs. Cumulative’—are not shown.
2) Qualitative Results
If you’re familiar with b.world, you’ll know that one of our key modules for impact measurement is called Results. This is one of the places in b.world where the magic happens—where indicator data from within and across different projects can easily be aggregated to glean a sense of organizational impact. You might ask, “How can I possibly aggregate and crunch data that aren’t numbers?” Well, utilizing the power of AI, b.world performs qualitative data aggregation, which is an exercise in narrative interpretation, synthesis, and summary. AI enables much more quickly synthesizing data, which, when performed manually, can take a painstaking number of hours, days, and weeks to categorize and code phrases and paragraphs of prose. We've made impact measurement more intuitive and faster, saving you time and headaches.
b.world now can auto-generate qualitative Results, which utilizes b.world’s existing AI Insights functionality to summarize narrative data. Users can auto-generate a summary of their data and ask a specific question about it. They also have the option to create a qualitative Result manually—their summative thoughts about a set of qualitative indicator data. (Note that users can also take advantage of this Insights functionality with individual qualitative indicators.) As with quantitative Results, users can scroll down the page to see the component indicators that comprise the qualitative Result, and each component indicator has a quick link to its respective logframe.
The
Now that we’ve discussed the functionality side of things, we’d be remiss if we didn’t bring up a valuable point (which may go without saying at this point): AI cannot take the place of human review and validation nor draw conclusions that can only come from reading between the lines. This underscores the importance of maintaining a healthy balance between taking advantage of AI’s capacity-building assets and employing human reasoning and analysis skills. As b.world’s UX research expert, Adina Fudym, shared:
“If we solely rely on AI to complete qualitative analysis, we could be missing opportunities for innovation by overlooking key nuances hidden in our users’ actions and words. Essentially, it’s not always wise to translate—verbatim—what a user requests into a system or product requirement, which could easily happen by depending on generative AI alone to interpret and summarize qualitative data. For example, if women in Africa are responsible for harvesting water—a time-consuming and potentially dangerous task—request a male bodyguard for safety, or perhaps a bike to ride to help them transport the water—should these be the solutions put into place? Perhaps. However, while these volunteer solutions could be viable, they are likely not the only ones and may not be the ones that will 1) reduce the women’s burden the most and 2) solve other pain points arising from their water-collecting duties. In this situation, human review and discourse are important because AI won’t be able to perform design thinking exercises and draw conclusions which could significantly change the direction of a product, service, or interface.”
So, what is the takeaway here? Utilize AI functionality in b.world and other trusted tools, 100%. (We didn’t build it just for fun, although it was fun to build 😂.) But also continue to use your human brain for depth of analysis, connecting the dots, and forming a system of checks and balances between you and technology for a nuanced and comprehensive impact measurement process; you'll thank yourself along the way.
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About threshold.world
threshold.world is a growth-stage software company dedicated to improving the efficiency and transparency of nonprofit impact by leveraging the world’s most innovative technology. Based in Puerto Rico with operations in the USA and a global customer base, threshold.world is recognized internationally for its commitment to challenging conventional ‘wisdom’ to create solutions of consequences that solve humanity’s most pressing issues and is a leading Microsoft partner in the design and development of the Common Data Model for Nonprofits launched in 2018. They are also a Microsoft Cloud for Nonprofit partner, Microsoft for Startups partner, Entrepreneurship for Social Impact Partner, SoftwareONE Partner, and NetHope Corporate Partner.
For more information, info@threshold.world
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