Herbert’s hypothesis is yet significant to understand the need of strategic information in the decision making process. In the past five years, the data analytics and data scientists profession have bloomed and demand for these professionals across different industries are skyrocketed. However, the speed of companies’ adoption on this new exciting development differs. Rapid adjustments found in the industries characterized by ultra-competition, such as manufacturing, or retail. However, others such as education satisfies with best-practices and data visualization approaches, instead of deploying data analytics.
Needless to say that bounded rationality (BR) has prevented any industry, or any company to max-out their opportunities, investments or resources for some may apply less-than-optimal strategies. Making this honest mistake is natural because of BR. It prevents any organization to make optimal choice because human beings are constrained to apply their rationality fully because lack of wisdom (LW) or add (gut) feeling, rumors and owned biases in the process. Therefore, whatever the decisions that have been made in the past suffered from this natural constraint. The example has been discussed by Ricardo’s–the Law of Diminishing Return. The current example can be learned from Facebook $120 billion in 24 hours loss in market capitalization because the combination of LW and BR.
Supposed the optimal output/revenue/sales/enrollment or ROI is Π, an organization will never be able to reach Π, but (Π-ε), because of LW & BR. The gap measured by ε can be reduced by the application of data analytics where strategic decisions are made and based upon. One can call ε as the waste of resources, or the loss of chances or opportunities because the institutions fail to make important decision based on processed data. Some decision makers realize this constraint, and welcome the application of data analytics. However, many may still stick with the Neo-classical theory of profit maximization or simple rule dictated by the Wall Street.
Higher learning organizations are more comfortable with the concept of best-practices. Needless to say that these best practices are unwritten consensus among the institutions in the industry which are communicated to members through different seminars and Associations. Should it be time to think about these best practices mindset? The reason is simple for the best practices paradigm fails to recognize special or unique characteristic or core competence of an individual organization. For example, if one strategy works for Harvard, it does not automatically work for Universities in North Carolina. Or if it works for 4-year institution then it will work for 2-year colleges. Or if it works for state-owned institutions, then it will automatically work for non-profit private entities.
Having discussed the role of process data and BR, one may conclude:
- Strategic decisions need to made based on data analytics and not data visualization alone.
- Companies or higher learning organizations that have relied more heavily on data analytics will perform better in the long-run and will be able to outsmart their competitors.
- Demand for data scientists will keep increasing in the future. Please check out the following current open position. If interested, please click this link to apply.
- Decision makers will rely more on information processed by data analytics.
- Applying data visualization instead of data analytics will not produce the optimal results.
- Return or profit cannot be maxed out indefinitely. Therefore, a new paradigm need to be explored, adopted and applied in order to survive in the world of ultra competition. This new paradigm will be discussed in the up-coming BLOG article.