This week’s theme is the power of analytics and how they can help organizations establish powerful strategies to compete in an otherwise competitive world. I find this week’s readings to be particularly relevant to Mikes Bikes as it is literally what we have been trying to accomplish since the beginning of the activity. We are very lucky to have the different parameters and relationships of a simulated business/economy explained to us in detail in the game’s introductory manual. This is essentially the equivalent to a multi-million dollar investment into either in-house statistical reporting or expensive marketing research. As it was mentioned by Davenport (2006) the power of analytics cannot be underestimated as it enables organizations to identify and make business decisions down to a science. This can be seen within the car manufacturing industry as well as the dairy industry where almost every aspect in the value creation chain is recorded to determine what little changes in variables would impact the output. For instance, the dairy industry record every cattle since birth to slaughter with the amount feed, medicine, biometrics and even climate in order to quantify the perfect balance for optimal yield. None of this is possible without the integration of analytics into the industry.
However; in the context of mikes bikes, although we are provided with such powerful tools to make our decisions, we must also realize that every competing team have equal access to all this information. As both Davenport, Baghai, Smit and Viguerie (2006, 2009) suggested; analytics are a good tool to formulate powerful strategies and accurate decisions, it is often those who have the best analytics that comes out on top. So in a sense, by providing everyone with the same statistics, there is no real advantage, so how is that any different from firing blindly anyway? After all, it is infeasible for a small-medium sized organization to finance such endeavors in the first place let alone establish such a comprehensive system. Personally I feel like making decisions based on stats alone isn’t enough; that it is only a small part in the equation for success. I draw upon last week’s readings by Katz (1955) and the importance of the conceptual skill. Just because everyone has the same access to the same information, it is obvious just from the wide range of SHV across all teams that not every team has successfully applied these reporting into viable strategies. Despite the slow start we experienced in the beginning, I feel like that was not a wasted opportunity as it allowed us to better understand just how much impact our decisions had on our profitability and how changing values by X amount will affect the various different dimensions of the simulation. However, this was not enough. Despite continuous growth, we understand that our strategies are only utilizing only a fraction of the potential of analytics and that the analysis of number on a superficial level will only provide us with weak, reactive strategies for short term solutions of symptoms rather than realizing the fundamental problems in our business model.
After several heated arguments and debates with the team, everyone upped the ante in the recent weeks and essentially tried to look beyond the numbers and find discernable patterns that could lead us to even greater success. It paid off. Rather than focusing on what the current problems are, we were able to predict competitor actions, market fluctuations and establish a strategy that I feel personally owed to the utilization of analytics on a holistic scale. This is not to say that there is no element of risk involved in the decision making process. As it was covered in weeks 4 and 5, there will always be the element of risk in every decision made. However as Davenport (2006) have mentioned, these risks can be significantly mitigated through the utilization of analytics. Having said that, luck still plays an important role, especially in the beginning phases of an organizations life cycle where resources are tight and important decisions may have to be made on the bases of experience and gut instinct which could result in either a huge success or failure of the endeavor. But that is a problem more associated with a strong business model than analytics even though the two are so closely interlinked.
This leads to my biggest concern with the readings this week: the applicability of this strategy for small businesses who cannot afford such investments into their projections. An argument can be made that these detailed analytics would only be profitable for mature/large organizations due to the fundamental difference in needs between small and big business due to the economies of scale. For example, a bakery down the street might be concerned with the number of sales, inventory, weather, price of competitors etc etc because the processes involved are primarily accomplished by a few operators and they do not need to focus on things like baking power price and optimal baking time as any variance from optimal would only incur minute costs whereas the bread factory could potentially lose hundreds of thousands in utility bills just for baking bread for an extra 10 minutes per day. But in NZ, a significant number of businesses are small and whether or not these practices in analytics would benefit those remains. Another concern arisen from this problem revolves around the application of innovation in analytics. The readings tend to stress that innovative methods and procedures can be established to help reduce unnecessary costs derived from analytics however, this feel a bit stifling in terms of growth. There are cases where innovation in cost reduction tech/process have resulted in new industries themselves such as Toyota however I am a bit concerned at potential of over emphasizing the power of analytics and completely ignore entirely new products or ingenuity. This obviously is not a critical concern in the grand scheme of things, but it is something that’s worth a thought depending on the goals and objectives an organization strives towards.
Baghai, M., Smit, S., & Viguerie, P. (2009). Is your growth strategy flying blind? Harvard Business Review, 87(5), 86---96.
Davenport, T. H. (2006). Competing on analytics. Harvard Business Review, 84(1), 98--107.
Katz, R. L. (1955). Skills of an effective administrator. Harvard Business Review, 33(1), 33--42.