Sometimes Numbers Get in the Way

As innovation advisors, we love when clients come to us with challenging technical questions and we find solutions or sometimes identify why there’s not a solution out there right now.

Yet sometimes people just want numbers.

And sometimes numbers get in the way.

Here’s why. We think of the insights we uncover for clients as the difference between information and knowledge. We provide knowledge. Sometimes this means steering our clients away from the desire to organize results into specific types of analysis tools. Think sig figs. Remember your first chemistry class when you were so proud because you didn’t just write 3.2. Rather, you wrote 3.215!?! Admittedly, sometimes this precision is important, if not essential. Our job is to help our clients realize that meaningful insights can be overlooked if the research results are force-fitted to a scale or assigned an arbitrary numerical value.

image of numbers

Categorizing Findings so Clients Understand Qualitative Data

When we conduct broad research across sources and aggregate the information, sometimes we categorize our findings in ways to help our client understand this qualitative data. Many disciplines approach research this way. Consider focus groups for consumer products. You look for trends and insights, but you don’t assign numerical values to responses and then average scores from 10 people – much less to two decimal places. Certainly, there’s a place for data analysis in some research. At the same time, often we find that the insights we uncover through broad research can be the most meaningful to the client.

As an example, recently our client wanted to learn more about software vendors that could provide certain functionality. We captured information on each identified software product across criteria meaningful to the client. We assessed each software as being high, medium, or low for each criterion.

Why didn’t we assign a number?

Why use high/medium/low and not 10/5/0? Or 3/2/1? Or 75/50/25? Rather than figuring out how we could assign a numerical value to the information we collected, we spent our time looking at what the information meant. The qualitative information revealed some terrific information that would have been missed in a purely quantitative presentation of the data:

  • Several software companies didn’t want to tell us much unless they knew the name of our client. Their response tells us there’s probably competition in the field (which could bode well for our client).
  • Instead of assigning scores to each criterion for each software package, we instead considered possible solutions in addition to software. Our insights were that the client should organize different market segments into several groups and then apply different solutions to similarly structured markets.
  • By applying the design thinking framework of desirability/feasibility/viability, we were able to explain that the challenge is not software feasibility. Rather the challenge is the viability issues of wide-ranging regulatory laws and the associated costs to understand the laws.

It was those insights that the client needed to consider when planning the path forward.

Could we have assigned scores to each software?

Of course. However, we were so glad we discussed with our client the knowledge and insights we uncovered from the research rather than looking solely at numbers.

When you have an innovation challenge and need insights, we help you find the solution. There are times our projects may benefit from sound data science and statistics, and times our insights come from a qualitative approach. Regardless of the approach, we’ll give you the knowledge you need to solve your challenge at hand, and that you can build on as you develop, test, validate, and refine your future innovations. Because our goal is to arm clients with the knowledge they need to make decisions that push their organizations forward.

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About the authors

Susan Mayer is our technical food industry leader, with great problem-solving, strategic, and communication skills. Our clients rely on her experience in product development, product lifecycle management, and public-private food industry partnerships to understand how technology, research, and the right suppliers can create innovation opportunities. How does her work with us benefit food companies? Susan believes that our human-centered design perspective makes all the difference. ‘Product developers always believe they are thinking about the consumer, but our human-centered design approach to considering technology brings an entirely different perspective.’ Susan applies her love of food science to her hobbies; she and her husband formulate and brew beer, much to the delight of their friends and neighbors. Susan has an M.S. in Food Science and a B.S. in Foods from the University of Maryland, College Park, and is a Certified Food Scientist.
Lawrence Blume, Ph.D. is passionate about collaborative partnerships that bring innovative solutions to challenging research and development roadblocks. A lead advisor for our food science and biotechnology innovation, Lawrence brings extensive experience leading technology-focused opportunity forecasts in support of competitive advantage, product differentiation, and commercialization strategies for C-level executives at companies ranging from early startups to Fortune 500s. Over the last decade, Dr. Blume has applied his background in cannabis physiology and pharmacology towards novel commercial applications in the medical, CPG, and food and beverage spaces. He received a Ph.D. in Physiology & Pharmacology from Wake Forest School of Medicine and a B.S. in Biology with a minor in Biochemistry from Duquesne University.

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