In the previous post “Growth and Scaling Downfalls – Part III” we discussed strategy aspects of a scaling project. The next topic on the scaling preparation “to do” list is measuring success and failure.
Scaling and growth both depend a great deal on experimentation: be it at tactical level deciding who will do what to strategic level defining success or failure. That being said that kind of decision making naturally requires a great deal of analysis; qualitative or quantitative.
Data driven quantitative analysis is or should be the basis of virtually all business decisions. Though an established field, the quantity of data that has been previously inaccessible or impractical for usage has changed the field. The same quantity of the data sets that are now available have also created several other side effects for small and mid-size organizations; ranging from increased cost for proper analysis to “analysis paralysis”. Hence, the usage has to be defined in terms of practicality: both the collection and analysis of data have to be defined within the context of cost and impact.
In a previous discussion about decision making we discussed the usage of qualitative decision making. Those parameters previously discussed i.e. strong pattern recognition as part of the qualitative decision making are particularly applicable when it comes to growth and scaling. In practical terms it translates to a combination of using practical experiences both industry related as well as general business experiences to decide on both tactical and strategic level: the industry know-how combined with generic business experience will provide the sort of “umbrella” coverage that will leave little room for “guessing”.
On the front line
Interestingly enough there are some unique aspects to data usage when it comes to scale and growth: though the basic methodology of collection and analysis is the same, the decision making direction should entail a more dynamic version of “bottom to top” or “top to bottom”: Micro decisions vs. Macro decisions:
When it comes to scaling and growth, micro decisions made in the “bottom to top” model are substantially more beneficial: front line experiences and nuances that may be unknown or unpredictable dictate a great deal of the success needed to build on. Those nuances, irrelevant from the cause, cannot and should not be decided on from the “top” in order to avoid long decision making process as well as preventing the systematic delay and discouragement of the front line teams.
Much like the micro decisions impact, macro decisions can equally benefit from procedural input from frontline, with one major difference: macro decisions may have additional considerations that may or may not be obvious to those at the front line. To mitigate that, a dynamic approach that includes compartmentalized decisions combined with a horizontal feedback loop should be sufficient.
What does it all mean?
When it comes to growth and scaling projects, the decision making in terms of success or failure is equally science and art. The traditional data based decision making models may have to be adjusted to account for the unique nature of extensive trial and error that are inherit in growing or scaling a business.
Revisiting the old adages that “there are no right or wrong decisions, only trade offs” would dictate an individualized approach that would preferably include a mixture of input and dynamic actions that are non-traditional yet inclusive of virtually all level of the organization.