Data data everywhere!

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Data data everywhere!

Written by Marty Bortz | 6th September 2022

The world is awash in data. There is data on our purchasing habits, the way we travel, our political preferences, the weather, and our home rituals. Of course the world of social impact is no different. Social impact practitioners must support their claims and advice with data that is robust, credible and compelling.

Typically, we divide data into two forms – quantitative and qualitative. The simplest way to understand the difference is numbers and words. Quantitative data is represented through numbers. Qualitative data is represented through words.

Often, I have observed people using the word ‘data’ to only mean numbers. This is a misconception. Data is words as well. In fact, we can gain a much richer understanding of impact if we learn how to use words and numbers to support our findings and analysis.

To do so, it is important to understand the benefits and drawbacks of each. It is also important to understand how we might combine them.


To understand these differences, we first need to understand some basic aspects of research. Research is about learning something we didn’t know before. To do this, we need to be able to answer questions that haven’t yet been answered. Indeed, all great research starts with powerful questions.

For instance, a program manager might want to know how effective their program is in reducing rates of homelessness in their local area. Or a philanthropic foundation may want to uncover the impact on long-term unemployed of a work integrated social enterprise that they have funded. Both the program manager and philanthropist are asking questions that can’t yet be known without significant investigation.

Great questions can come in a range of forms. They can be ‘how much’ questions (i.e. How much would consumers pay for a certain product?). They can be ‘how often’ questions (i.e. How often do 18-year-olds encounter the criminal justice system?). They can also be more exploratory or seek to understand the ways in which people experience a particular product, program or organisation (i.e., What is the experience of identifying as LGBTQ in the foster care system?).

The type of questions you are asking will help shape the nature of the data that you want to collect. Continuing with some of the previous examples, ‘how much’ or ‘how often’ questions (unsurprisingly) lend themselves to more quantitative forms of investigation. As part of this, you may choose to provide consumers with several figures and ask them what they are willing to pay. Or you may look at administrative or demographic data from a particular agency involved in the criminal justice system (i.e. the Department of Justice).

On the other hand, you may want to understand people’s experiences (i.e. LGBTQ people in the foster care system). This would lend itself to more qualitative forms of research. So you may consider running a series of in-depth interviews with that people. This would produce a rich dataset (comprised of interview transcripts) that you could interrogate for deeper insights about this topic.


It is also important to understand the benefits and drawbacks of both qualitative and quantitative data. Quantitative data is particularly useful when you want to make generalisations from a smaller sample. Say we want to know the political preferences of the Victorian population. We don’t have the resources to go out and ask every single voter. But we don’t need to do this. So with the right statistical techniques, we can survey a much smaller sample of that population. We can then use the results to make some general insights about preferences of the entire population.

While quantitative data can provide very general claims, those general claims don’t provide a lot of depth. This is fine if this is what your research questions demand. However, there are several areas of inquiry for which we may want to produce deeper insights, particularly when it relates to people’s experience of a particular policy or program. Through gathering qualitative data you can understand a particular topic in much richer ways.

The trick here is to consider the difference between breadth and depth. Quantitative data will generally give you breadth without much depth. Qualitative data will give you depth without much breadth. Both are useful for different purposes. It is the job of the social impact practitioner to match the right data, or combination of both to the purposes of the study.

Quantitative and qualitative data can be synthesised together in what is called ‘mixed methods’ research. There are various ways to do this. You can either run your quantitative and qualitative processes in parallel to each other, and then consider how the findings relate to each other at the very end. Or you can move between the two sequentially. For instance, you might collect and analyse your quantitative data first, work out what it has told you, and then use qualitative data to address any gaps or areas of ambiguity, or to understand causative factors.


For a great while, qualitative data was considered the ‘poor cousin’ of their quantitative counterparts. However, this is no longer the case, with more researchers and practitioners acknowledging that both approaches have benefits and drawbacks. Rather than privileging one form of data over the other, we should instead seek to understand how they might (or might not) be useful for whatever questions we are trying to answer. We should also consider how they might be ‘brought together’ as part of a mixed methods approach. Doing so will most certainly provide social impact practitioners with new and interesting ways to articulate how programs and policies affect change.

If you’re interested in how Think Impact can assist you with your approach to social impact data please contact chat Senior Consultant, Marty Bortz at