Beyond Data-Informed: Using Qual & Quant Data to Solve Problems

August 14, 2012 by Bryann Alexandros. Average Reading Time: about 5 minutes.

Say you want to visualize the impact you’re making for the community. But suddenly, you see that the numbers and milestones are pretty sad. Certain initiatives are failing. There’s a fear that programs may begin to implode. Worse, the results must be reported — soon.

Circulating the nonprofit blogosphere and social media channels are tips on becoming “data-informed “ and “data-driven.” There’s sage advice on developing metrics, measuring the right things, collecting data from various sources, and then vividly communicating that data to our funders with charts, graphs, dashboards and infographics.

I’d say that there’s a more serious challenge: making sense of data and coming up with new ideas. This is especially vital when a program or initiative is deadlocked. It’ll take more than quantitative analysis, statistical corellations, and visual alchemy (read: infographics) to surgically extract insight.

First, what is data?

Data doesn’t have to be all numbers. Data is a lonely unfiltered concoction of content. A quantitative and qualitative chaos of facts, figures, and interview snippets without organization and context.

Here’s something to note from NTEN:

In gathering data, we’ve only created the potential to inform. The next step is to make those data meaningful, relevant and actionable information by communicating them in some way via a channel to an audience. Information equals data plus communication.

But what’s the process of actually getting there? What challenges an organization is filtering relevant data, understanding what it’s really telling them, and deciding what they should do next.

So here’s something I learned a long time ago which just might help you dissolve data deadlocks and push your mission forward.

First some context:

A Crash Introduction to DIKW w/o the Hurt

The journey of understanding problems can be framed in the context of the DIKW hierarchy, or Data-Information-Knowledge-Wisdom.

First we obtain raw data which is meaningless by itself. Find a way to organize, filter, and interpret the data, then you can find yourself with information. As you construct a story, theory, or hypothesis about the information obtained, you now have knowledge. Finally, when you attempt to understand that knowledge and apply it towards a new and better solution, you may very well find yourself on the way to wisdom.

This slide is the simplest visual to demonstrate:

However, that journey is never easy. Jon Kolko, Founder of the Austin Center for Design, likens the leaps to jumping huge chasms.

The 1st chasm entails organizing and visually framing data to make it meaningful to us. But this is where organizations find themselves plummeting fast.

So, why the hold-up?

Off the top of my head, just a few reasons why nonprofits may find it hard to cross the 1st chasm:

hyper-reliance on quantitative data
hyper-emphasis on digital visualization (charts, bars, graphs — infographics)
improperly collected data

Let’s say you discover certain programs aren’t delivering. Or certain milestones have plateaued. You want to know why. Maybe you have a theory, but you want to extract the major themes surrounding it. Ideally, once you’ve derived insight and understanding, you just might have a hypothesis on how to improve.

What does it take to jump chasm 1 before actually jumping.

Anyone struggling here can definitely cross the 1st chasm from Data to Information. But it’s going to be painful the first time, because its prerequisites aren’t easy:

1) It requires a different kind of collaborative culture.

Not a typical staff meeting, or a general cooperation between two organizations, but internal collaboration. It’s how everyone currently works together in-house and together with their communities. It also requires some new habits and an appreciation of visual collaboration.

2) It requires creativity in conducting and collecting research.

There can be a holy matrimony between qualitative and quantitative data.

But make a pass at this HBR article. It’s a succinct post on the nuances of qual vs quant research, and why the former will yield your most powerful insight.

Qualitative, and especially observational or ethnographic, research enables us to delve much more deeply into the relationship between our firm and its product/service and the customer. Because we aren’t obsessed about adding all the responses together for ‘rigorous quantitative analysis’, we can let the customer use his own voice/words/vocabulary.

Statistics may show what large groups of people might logically do. But observational and ethnographic research will bring you closer to the human experience of your constituents and communities. Conducted right, it can reveal one’s deeply held needs, feelings, aspirations, as well as break down the barriers of our own assumptions and questions: Are their needs really being met? What isn’t being said? What may the real problem be?

Some research methods include contextual interviews and mobile ethnography.

Now say that we’ve gotten both types of research in our hands. It’s massive. Hopefully it was conducted and collected properly. How do we make sense of it in the shortest time possible?

Tool: Affinity Diagrams

The affinity diagram is a creative process used for gathering and organizing large amounts of datas, ideas and insights by evidencing their natural correlations. —servicedesigntools.org

Affinity Maps are great external visualization techniques for traversing chasm 1 and onto information. As a rapid collaboration technique, they’re summoned to tame, prune, and organize the massive qual/quant data chaos. They’re good for discovering themes, relationships, and developing a narrative. After an affinity map is conducted, the discoveries are further enhanced by coming up with personas, or even customer journey maps.

A tutorial on creating an affinity map in groups.

Meaningful, visual collaboration using qualitative and quantitative data has its place.

And for many reasons:

To properly structure by visually framing your data
To rapidly collaborate
To make sense of your data in the shortest time possible
To dissolve mysteries and create a different narrative about your outcomes (i.e. “why did ‘x’ happen?”)
To clarify what the next actions might be

Possessing data is only half the battle. Making sense of it, and transmuting all of your observations into actionable insight can mend the heartache of staring at too many charts, graphs and spreadsheets for far too long.