Decision Framework means a guideline containing approach or model, methodology, process, and the scope of the activities.
The decision framework is defined as a set of principles/models that underpins the governance of decision management. It can mean the structural skeleton which defines building blocks of decision management such as structured processes or methodologies, roles & rules, standards, etc, and how these blocks relate to each other. It also describes the best practices of how decisions should be done from the methodology, process, and technical perspective.
Decision frame: The purpose of decision making is to solve problems large or small. The decisions to act or not and how, are by fully understanding the situation, and using a combination of the least bad solution compared to effort and time availability. Many decision-makers fail to fully grasp and accurately perceive what leads to problems and difficulties. Trying to fix the wrong cause of a problem will waste time and resources, increase anxiety, etc.
Decision making is in less mathematical or fancy methodological consideration but as a sociological problem. An essential part of the “decision framing process” is to understand what your high-level outcomes are related to the issue, opportunity, or problem. Until you have made the first cut at defining the opportunity, issue, or problem that needs a decision ("framing the issue"), you are not ready to start on decision making.
Decision criteria: There is fuzziness in the decision because there is fuzziness in conflicting criteria. Human decision making becomes important and essential when there are multiple criteria; as in multi-objective optimization, and the decision changes according to which criteria have priority.
Framing the analysis in terms of the span from worst to the best on each criterion as a decision management practice. “SMART” decision techniques solve the problem by using weights based on moving from the worst to the best in various criteria. It works well even for a combination of rational and emotional criteria.
Decision analytics: The whole purpose of decision analytics is to make better decisions based on data big and small. The typical challenge seen with the traditional decision-making analytics approach is to arrive at insights, but not necessarily affect actual business decisions; the better decision engineer approach is to embed analytics in decision making.
Leverage good information, processes, and other decision tools as a "corporate knowledge base" to help make better decisions. How you frame a decision largely determines the kinds of alternatives that will emerge from the choice process of decision analysis. The key is to orient the analysis around decision variables tied to forecasted outcomes with uncertainty.
Decision resource (information, knowledge, etc.): The intention of eco-information life cycle management (Data-Information-Knowledge-Insight-Wisdom) is to improve decision effectiveness. Knowledge is power, you have to apply it at the right time in the right circumstance with the right attitude. Decision performance is based on the effectiveness of managing the life cycle of data -> analysis -> decisions -> performance. For complex decision making, the team works because they bring different perspectives and collective knowledge to the table; they help to generate more of everything (viable alternatives, criteria, etc.); they help to balance out the biases that we all suffer from, all of which are shown to improve the quality of decisions.
Keep in mind though, there are a number of contrary factors at play in a team making a decision. In an Abilene paradox, a group of people collectively decide on a course of action that is counter to the preferences of many of the individuals in the group. Also, timing is critical in decision- making. The other issue with group decision is tied up to which is going to be the process to come up with the final decision, weighted voting, consensus, unanimity, etc. All those can slow down the entire process and hinder the quality of the final decision.
Decision variables: Improving decision quality is about reducing the uncertainties of the most variable elements; and estimates of future consequences of choosing various alternatives. There is the process of working with decision-makers to support their thought processes, and how they leverage variables and judge tradeoffs between choice criteria.
The biggest challenge in decision making is knowing what you don't know, it is a reasonable moniker for identifying blind spots and biases in decision making. Finding acceptable tradeoffs between these competing objectives; which at the very least is a sensitivity analysis of the optimization parameters. Visualization tools and approaches help to explore the trade-offs between different objectives and improve decision effectiveness.
Decision outcomes: In a world of uncertainty and ambiguity, the decisive mind can, by definition, not control the outcome. However, focus on making good decisions and the best chance for a good outcome is to make a good decision. Always attempt to identify areas in which measurable improvements can be realized via practicing, providing demonstrable progress is essential for improving decision maturity.
There is a clear distinction between a good decision and a good outcome. A good process can still get adverse outcomes. All good processes do decrease the risk of the wrong choice, not eliminate it. Too many decisions do not have the ambitious performance outcomes that were intended. The decision is not necessarily bad, but the flow and sequence of the subsidiary decision implementations all encounter a different set of context dynamics.
Decision Framework means a guideline containing approach or model, methodology, process, and the scope of the activities. An effective decision can be defined as an action you take that is logically consistent with the alternative you perceive, the information you get, and the preference you have.
Decision making is in less mathematical or fancy methodological consideration but as a sociological problem. An essential part of the “decision framing process” is to understand what your high-level outcomes are related to the issue, opportunity, or problem. Until you have made the first cut at defining the opportunity, issue, or problem that needs a decision ("framing the issue"), you are not ready to start on decision making.
Decision criteria: There is fuzziness in the decision because there is fuzziness in conflicting criteria. Human decision making becomes important and essential when there are multiple criteria; as in multi-objective optimization, and the decision changes according to which criteria have priority.
Framing the analysis in terms of the span from worst to the best on each criterion as a decision management practice. “SMART” decision techniques solve the problem by using weights based on moving from the worst to the best in various criteria. It works well even for a combination of rational and emotional criteria.
Decision analytics: The whole purpose of decision analytics is to make better decisions based on data big and small. The typical challenge seen with the traditional decision-making analytics approach is to arrive at insights, but not necessarily affect actual business decisions; the better decision engineer approach is to embed analytics in decision making.
Leverage good information, processes, and other decision tools as a "corporate knowledge base" to help make better decisions. How you frame a decision largely determines the kinds of alternatives that will emerge from the choice process of decision analysis. The key is to orient the analysis around decision variables tied to forecasted outcomes with uncertainty.
Decision resource (information, knowledge, etc.): The intention of eco-information life cycle management (Data-Information-Knowledge-Insight-Wisdom) is to improve decision effectiveness. Knowledge is power, you have to apply it at the right time in the right circumstance with the right attitude. Decision performance is based on the effectiveness of managing the life cycle of data -> analysis -> decisions -> performance. For complex decision making, the team works because they bring different perspectives and collective knowledge to the table; they help to generate more of everything (viable alternatives, criteria, etc.); they help to balance out the biases that we all suffer from, all of which are shown to improve the quality of decisions.
Keep in mind though, there are a number of contrary factors at play in a team making a decision. In an Abilene paradox, a group of people collectively decide on a course of action that is counter to the preferences of many of the individuals in the group. Also, timing is critical in decision- making. The other issue with group decision is tied up to which is going to be the process to come up with the final decision, weighted voting, consensus, unanimity, etc. All those can slow down the entire process and hinder the quality of the final decision.
Decision variables: Improving decision quality is about reducing the uncertainties of the most variable elements; and estimates of future consequences of choosing various alternatives. There is the process of working with decision-makers to support their thought processes, and how they leverage variables and judge tradeoffs between choice criteria.
The biggest challenge in decision making is knowing what you don't know, it is a reasonable moniker for identifying blind spots and biases in decision making. Finding acceptable tradeoffs between these competing objectives; which at the very least is a sensitivity analysis of the optimization parameters. Visualization tools and approaches help to explore the trade-offs between different objectives and improve decision effectiveness.
Decision outcomes: In a world of uncertainty and ambiguity, the decisive mind can, by definition, not control the outcome. However, focus on making good decisions and the best chance for a good outcome is to make a good decision. Always attempt to identify areas in which measurable improvements can be realized via practicing, providing demonstrable progress is essential for improving decision maturity.
There is a clear distinction between a good decision and a good outcome. A good process can still get adverse outcomes. All good processes do decrease the risk of the wrong choice, not eliminate it. Too many decisions do not have the ambitious performance outcomes that were intended. The decision is not necessarily bad, but the flow and sequence of the subsidiary decision implementations all encounter a different set of context dynamics.
Decision Framework means a guideline containing approach or model, methodology, process, and the scope of the activities. An effective decision can be defined as an action you take that is logically consistent with the alternative you perceive, the information you get, and the preference you have.
0 comments:
Post a Comment