A Professional's Guide to Decision Science and Problem Solving: Define the Objectives and Identify Metrics
- 1.1 Chapter Topic
- 1.2 Key Corporate Participants
- 1.3 Management Steps Required to Execute the Approach
- 1.4 Solving the Right Problem
- 1.5 Developing an Understanding of the Problem
- 1.6 Defining Goals and Objectives of a Company or Organization
- 1.7 Defining the Framework for the Decisions Being Made
- 1.8 Metrics for Measuring Success
- 1.9 Definition of a Metric
- 1.10 Developing Decision Criteria and Metrics
- 1.11 Data Used to Support Metrics
- 1.12 Structure and Definition of the Problem
- 1.13 Key Concepts in Defining the Objectives
1.11 Data Used to Support Metrics
You can use either objective or subjective data to represent metrics used in the decision process. Objective data usually can be described as data that can be quantified by some measure of known commonality. This may be data such as the number of items produced, number of trucks in a location, population of a city, and so on. This data is usually available in some form in company databases and information systems. Typically, statistics such as averages and trends are generated based on a record of this objective data over some period in time. Objective or quantitative data represents a history of activities of a company that has been operating during a given time period.
Qualitative or subjective data can be easily used in a number of different situations. Surveys are good examples of subjective data used to represent a rating of a product or service. Use scales from 1 to 5 or 1 to 10 to represent high, medium, and low assessments for given metrics. Use assessments such as red, yellow, and green in other situations in which individuals (such as military personnel) might find more meaning in rating conditions. Numerical values with their verbal description provide the type of information that can be captured and utilized in a decision model when other information is not available.
Subjective data is data based on someone's opinion or best guess of a condition or a future event. Subjective data is more qualitative in nature in that it defines a situation or condition without specific data points. Subjective data can be generated by individuals within or outside of a company or experts within a given field of operation.
Typically, subjective data or opinions provide insights into a subjective assessment of a metric. Subjective data and expert opinions are typically forward looking in nature trying to predict what will happen in the future. Individuals make assessments based on what has happened in the past and what may happen in the future. Objective data, especially in the form of statistics, however, is based on historical data, thus projecting the future, what has happened in the past, which assumes that the future will behave much like the past. The entire business environment may have changed; thus, what has happened in the past may be a poor representation of the future; thus, a new source of data is required.
Following is a simple scale example that you can use to represent the assessment of the future development potential of a given market.
Future Market Potential
Definition: The projection that this market will become a substantial market in future corporate activities.
This type of information is good information to capture from the experts and decision makers. Utilizing this type of information along with statistics fills gaps that exist to get a better representation of factors that influence future activities.
In developing goals, decision criteria, and constraints, consider a number of parameters in the development process to ensure a set of well-structured, well-represented goals and decision criteria. These development parameters are as follows:
- Goals and decision criteria must represent actual and important considerations in making decisions. Examples would include reducing logistics costs, improving call center response times, and so on.
- Decision criteria must differentiate one project from another in terms of higher or lower priority. This would involve capturing key project characteristics that differ among projects, such as impacts to different functional areas, costs, completion time, and so on.
- Decision criteria must be independent, not overlapping in content or intent, to avoid accounting for the same thought or idea more than once. This tends to overweight the importance of certain criteria. Instances may occur in which both a component cost and a total cost are considered. The component cost would overlap with the total cost value.
- Decision criteria must be defined as clearly as possible to ensure that the decision criteria in the evaluation process are viewed in the same context. Individuals have different perspectives associated with various terms and definitions. The definitions must be clear.
- Measures and scales developed for the decision criteria must be meaningful in the evaluation process and the data to perform the evaluation easily accessible. Objective data provides a basis for a relatively clear scale or measure. The use of subjective criteria requires that the scale components are clearly defined and represent a natural language intention and meaning.
- Constraints that represent types of mixes, qualifiers, and conditions that would be applied to a prioritized list of items must be identified and differentiated from the evaluation decision criteria. You must define the operating parameters. This may be total budget, capacity constraints, and manpower availability. All of these components put bounds around the issue addressed.