A common misconception in the research phase of a consulting project is that “data” and support for a project’s hypotheses needs to come in the form of a specific numerical representation.

The phrase: “We need to support that with data,” or, “What do the numbers show,” typically translates into a scramble to find the X% of this, or the $XX of that. Finite numbers which show, prove, or otherwise represent a quantifiable outcome to a hypothesis are sought after. It can be a tricky process, especially if an industry outlook is rapidly evolving and even subject matter experts are hesitant to weigh in on the subject in specific terms.

This misconception that numbers are more “real” and “accurate” forms of data significantly discredits qualitative sources of data. In fact, these qualitative sources can provide robust yet precise details about many fluid characteristics of industry change.

Take the Stakeholder Analysis my team is conducting, for example.

The team has strengthened our industry and internal analyses by conducting University-wide interviews with key stakeholders

As part of the Fox School of Business’s full time MBA curriculum, students undergo a semester-long foray into consulting, called Enterprise Management Consulting (EMC), and work on a specific client project. My team’s client is attempting to navigate the quickly changing environment of higher education by determining how to best position itself in the online learning space. This question offers numerous opportunities to start digging into enrollment statistics, costs of comparable peer institution programs, and other quantifiable sources of data.

But what this quantifiable data is showing us is that the industry is headed for a significant and powerful change. We want to know how the client is prepared for this rapid change and what internal resources, frameworks, and mindsets might prevent widespread adoption of an online learning strategy. But the process to answer these questions is not as simple as looking at the client’s balance sheet and income statements; we won’t be able to gather insights from the enrollment trends by itself.

The team has strengthened our industry and internal analyses by conducting University-wide interviews with key stakeholders – from Deans of the internal Schools, to top-level Executives and Trustees, and (in our next phase) even students, alumni, and faculty of both traditional and pure online programs. However, the result of these interviews is an unstructured text-based output that is not easily scanned for trends, points of parity or difference.

How have we been able to find meaning in this qualitative data?

Overlay a quantifiable format over this unstructured data!

It’s not cheating, I promise. It simply will allow a meaningful and structured approach to combing through the data you’ve gathered – or, more appropriately, listened to – while on your stakeholder interviews.

And what we’ve found is very interesting.  Sample static quotes include:

There will always be a market for a traditional campus. College is more than just going to class. People are willing to pay for this. Although a commuter population probably will gravitate towards online…

I do not know what we should look like, but it is not what we look like now.

My issues are not the same as the university. One size does not fit all.

Our faculty interest in online learning is at the beginning state. Right now they don’t know what is available. While we would like to have several people who experiment with online modality, we currently don’t have the staff to handle that component.

These sound bites give us insight into the current mindset of the client, and understanding that there are numerous stakeholders who wield significant power in any University-wide decision.

This internal voice could not have been heard through the story the financial numbers were telling us. We needed to go directly to the source, and listen – first-hand – to what obstacles and resources exist within the client’s control.

So, when you barrel into the research phase of a large project, remember:

1. Data is Data: qualitative data is just as important as quantitative data
2. Start with your Nearest and Dearest: the people around and associated with the client have strong opinions and first-hand experience about your research topic. They’re living in the client’s reality. Make sure to measure their stories in your research.

Happy Researching!