Management theorists agree that decision making is one of the most important—if not
the most important—of all management activities (Lunenberg, 2011). This is the reason
why leadership is such a critical role in any organization; and with this role comes both
influence and vulnerability. The view is that leaders are mandated to design, teach,
and to steer the course of organizations regardless of the size. People in organizations
equate leadership with thinking and planning. Leaders must have the ability to translate
these thoughts and plans into action. The quality of the translation process is what
distinguishes good from mediocre leaders. There is vast array of tools available at the
leader’s dispensation to provide decision support. The ability to make a decision among
diverse and similarly viable alternatives depends on following a systematic mental and
structural models.
Many managerial decisions—regardless of their functional orientation—are increasingly
based on analysis using quantitative models from the discipline of management
science. Management science tools, techniques and concepts (e.g., data, models, and
software programs) have dramatically changed the way businesses operate in
manufacturing, service operations, marketing, transportation, and finance (Freund, Rudin,
and Vielma, 2014). However, the tools are only as good as the leader himself. A leader’s
intrinsic qualities play a significant factor in executing quality decisions.
The first component of the decision-making skill is self-confidence. Leaders with high
levels of personal mastery are confident in their abilities and can stand on their
decisions for better or worse. They do not just set out to integrate reason (from science
and decision support tools) and intuition (knowledge without evidence of rationality).
Rather, they achieve it naturally – as a by-product of their commitment to use all
resources at their disposal. Experienced managers have rich intuitions which they
cannot explain in a simple linear cause-effect language. Brilliant intuition can be taken
and converted to rationally testable propositions (Senge, 1990).
- Define the problem and the nature of the decision that must be made
The nature of the decision that must be made should be identified. The objective is to
clearly express the problem statement in a concise yet unambigous manner, seek
agreement from all stakeholders, then proceed accordingly. The root causes,
assumptions, boundaries, interfaces, and other issues are also made transparent. - Determine the strategy, goals, and requirements
The range of data management solutions and decision making alternatives can be
overwhelming. The noise can be overcome by starting with determining the strategy
and the objectives that the company wants to achieve. Objectives are statements of the
intention and desired state. The data requirements will ensue after the strategy and
objectives are determined. Requirements describe the set of feasible solutions to the
decision problem – stated in either qualitative or quantitative form, or both. - Verify the business area or component functions
Focus must be made on key business areas and critical functions, where data-driven
decisions will have the most impact. When the business areas and functions have been
identified, find out the questions that should be answered. When this is worked out,
leaders can focus on data that is really needed. There is less managerial stress when
data collection is concerted rather than all over the place. - Find the data that is needed to answer the business questions
and support the decision
Collect pertinent information before making a decision: what data is needed, where to
source it, and the methods of collection. No type of data is inherently more valuable
than others. What is more important is that they could be used to achieve the objectives.
It may be found that some of the data sets needed are already available within the
organization. - Analyze the data collected
Collected data must be analyzed in order to extract meaningful and useful business
insights. The past few years have seen an influx of systems and applications for data
analysis. A lot of organizations use MS Excel to manipulate and analyze simple data
sets. Sophisticated analytics and statistical tools are now availabe to manage complex
data analysis tasks, which obviously require specialized skills. - Normative or prescriptive model: Identify the alternatives and weigh the evidence
Alternatives offer approaches to solving the problem. In this step, all possible and
desirable alternatives are listed, bearing in mind that these alternatives must meet the
requirements. The alternatives are weighed and prioritized based on their potential to
reaching the objectives, feasibility, and value system of the decision maker or the
organization. - Descriptive model: Define the decision criteria
With a definitive list of alternatives, a criteria is then set to discriminate among
alternatives. Every objective must generate at least one criterion. It is good to group
the criteria into sets particularly when the objectives are complex. Grouping the criteria
can help when quantitative methods will be used later on to determine the weights for
each. - Select a decision making tool
Most data management software are already embedded with decision support tools. It
really depends on the problems being addressed and the objectives of the decsion
makers. Simpler methods do work but complex problems may necessarily require
complex methods. Decision support systems (DSS), for example, can help in
determining and calculating the weights for each of the decsion criteria and in the
choices among the alternatives - Take action
It is time to decide and act on the decision. The specific thought process that takes
place in the decision makers’ minds will be discussed in the section in Heuristics.
Suffice it to say, the adage from Occam’s razor stands as a viable basis.1 - Review the decision and its consequences
In this step, the results of the decision are considered and evaluated. When the
consequences are not desirable, certain steps in the process may be repeated. For
example, more data may be gathered or additional alternatives explored. - Publish the decision and the insights gained
The results are presented to the right audience at the right time to make it meaningful
and insightful. Insights gleaned from the data are used to make better and informed
decisions later and ultimately, improve performance. - Incorporate the learning into the business as a best practices
The continual publication of data from the analysis and DSS selected can be applied to
day-to-day and future problems and decisions. Acting on decisions that are better
