Drawing a Line Through Alignment
Runge’s Phenomenon — in a basic form — is the idea that interpretation of a greater amount of data will lead to a clearer, more accurate result.
On the surface, this makes sense. I am generally an advocate for larger data sets, to build fact patterns and correlate data into trends. However, in some instances, this strategy as it relates to the discovery process itself can fail in the following ways:
The focus groups and survey data could be collected in an oversimplified way.
- You are utilizing a large set of groups and individuals/groupings engaged in the focus group process that do not have enough baseline knowledge to provide meaningful or relevant data.
- There is bias or an “agenda” predetermined by the way data is elicited through questions, process or structure.
The second instance above is a case of “you asked some of the right individuals, and some of the wrong ones”, and is the one that can succumb to Runge. If you’re working from outside the organization, it will be difficult for you to separate or distinguish this data from those two groups within the larger group.
As it relates to strategy, the lesson to be learned from this phenomenon is that fact patterns should be established from relevant and informed sources. The reason this is important is that interpolation is necessary for finding themes or developing strategic tenets particularly from large groups of data, but if you deviate beyond the bounds of an established margin of error, the result will be atonal or seem disconnected, deviating significantly from a strategy that seems well aligned or conversant with the goals of the organization.
Where True Innovation Happens
An additional consideration is that this potential issue is often remedied by a method of analysis called a cubic spline, which is less prone to error and based on correlation of a smoother, less turbulent data set. It is a method of how data can be mapped in a continuous and less interpolated or over-interpreted way. If you are developing analysis of a data set that responds to a determined fact pattern — one that relates in a way that is conversant to the capabilities and conforms to the desired direction of the organization — you have more freedom to “riff” or build upon the data you have gathered in a way that is less predictable, but more accurate. This is where true innovation happens. The foundation is solid, tested and resilient, so you have more freedom to build a more creative structure on top of it.
Building on a solid infrastructure of data and established fact patterns, the strategy you develop will respond to the voices, perspectives and relevant institutional experiences of those who will participate in the creation and execution of a solution. If the data seems disconnected, incorrectly interpreted or misaligned, questions start to emerge regarding the utility of the effort. You may hear things like: “how does this relate to what we heard?”, “how are these things connected?” or “how does this respond to the problem we are trying to solve?” Questions that could be avoided.
A cubic spline is a solution designed to “smooth out” or create a true correlation between input and action — the idea that those who you speak with can contribute useful data — and that data can be honed into a cogent and evidence-based strategic plan.
Interpolation is one of the most powerful tools you have as a strategic planner. But, as with any tool, if it is misused, it can create false or atonal outcomes that will leave your client organizations questioning the value or efficacy of the strategy or process you are trying to employ. We should avoid Runge’s phenomenon. Doubt at this level can be existential and can be avoided by asking the right groupings the right questions, leading to the best possible result.
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