In my last post, I pondered the question, is faster decision making the same as “better” decision making? Answering that question might be a little tricky when there are no clear specifications for assessing the quality of a decision. Therefore, as a thought experiment, it might be worth considering some potential characteristics of different types of decisions and then infer timeliness expectations with respect to the quality (or perhaps, “effectiveness”) of the decision-making process
Qualifying “Decision Making”
On Tuesday’s blog, I pondered the question, is faster decision making the same as “better” decision making? Answering that question might be a little tricky when there are no clear specifications for assessing the quality of a decision. Therefore, as a thought experiment, it might be worth considering some potential characteristics of different types of decisions and then infer timeliness expectations with respect to the quality (or perhaps, “effectiveness”) of the decision-making process.
The scale of the decision characterizes the level of planning and need for resources for execution within the organization:
- Strategic decisions are ones that focus on broad corporate objectives that have a potentially wide effect and require a heavier set of resources for execution. An example would be the decision to launch a series of new product lines within the next 18 months.
- Tactical decisions are used to evaluate the results of strategic decisions and prioritize them into a plan of action, and those decisions require directed and dedicated resources for execution. An example would be to consider the costs of launching each of the new products in comparison to the expectation.
- Operational decisions that are intended to execute specific tasks regarding the corporate strategy and prioritized tactics can be integrated directly into operational processes and may only require a small amount of resources to execute. An example is guiding a prospective customer to the best product to purchase for their specific needs.
The breadth of the decision has to do with the perspective impact across an organization:
- Wide impacts that affect many (or all of the) divisions or departments or many individuals.
- Medium impacts that affect a “neighborhood” or subset of the organization, such as organizational restructuring within a corporate division or affect a medium-sized constituency.
- Local impacts associated with a small or limited number of individuals.
Are the results of the decision intended to drive action in a short time frame or over a longer period of time? These windows of execution against a decision can provide some insight into the determination of quality as well:
- Immediate decisions have actions that need to be executed in a very short time frame (seconds to days).
- Medium-term decisions have some flexibility in time of execution (weeks to months).
- Long-term decisions cover longer periods of time for execution (months to years).
Visibility into the Data
Some decision makers expect to have access to all the underlying data associated with a report or an analysis to be able to drill down to get a better understanding, while in other cases there is no need to have access to that data to justify aspects of the decision. Data visibility can be:
- Transparent (with the ability to drill through and down into supporting data), or
- Opaque (with no need for increased drill-through capability).
Value at Risk
The term “value at risk” is used in many contexts, but here we use it to consider the degree to which corporate value changes, based on the actions taken after a decision. Some potential scaling factors might be:
- Grand, in which a large amount of value can be affected by the decision (such as an investment in building a new factory).
- Medium, in which a moderate amount of value can be affected, such as deciding to open a new retail location.
- Small, in which small value is created or affected.
The Need for Speed
These dimensions can be used to ascertain whether the rapidity of decision making skews its quality. For example, when it comes to the quality of strategic decisions, the measures might be slightly obtuse; an organization might not recognize whether that strategic decision was a good one until some time in the future because the decision might have a broad affect and require a long time frame for execution. Under those circumstances, making a fast decision might not be in the best interests of the organization. On the other hand, that same strategic decision may have a high need for data transparency, in which case have rapid access to the data is important.
In a different scenario, a localized operational decision may be informed in an opaque manner by integrating the decision process directly into the application. An example is real-time scripting for call center representatives to help in reducing attrition. As the call center representative asks questions and transcribes the answers, real-time analysis may adjust the types of offers that can be made based on customer profiles and predictive models, but all the call center representative sees are suggestions to his/her own decision-making process. In this case the decision-making is operational, local (focusing on a single customer at a time), with a short time frame, yet may have a wide scale (a large constituency of customers!). Interestingly, again, the decision-making process is better informed through better performance when it comes to delivering information.
There are other examples, but one constant theme seems to emerge: many of these scenarios demand rapid access and integration of data. So from this perspective, perhaps instead of equating “faster” decision making with “better” decision making, it might be more reasonable to suggest that “faster data access and integration” informs the decision-making process to help maximize the value of the different kinds of decisions.