D356 Workforce Analytics Study
Questions & Answers
data mining can expose:
spurious or nonsensical relationships.
Relationships between variables can be identified but data mining plays no role
in determining if these relationships are:
meaningful, casual, or of any significance to the organization. Example: taller employees have
higher leadership scores.
Another drawback of data mining is that it can:
capture relationships that existed in previous patterns of relationship too.
Predictive analysis:
The creation of models of organizational systems to predict their future outcomes at some
point. It can also help in predicting how changes in the environment and planned organizational
interventions can impact key outcomes.
Benefits of predictive analysis:
Predictive analysis makes organizational planning more proactive. By knowing what to
expect, managers can act to enhance positive effects or mitigate negative ones from any
internal or external action.
Tools used in predictive analysis:.
From the simple trend analysis technique to highly sophisticated models, predictive
analysis uses a whole range of tools.
, Efforts to develop balanced scorecards are examples of: elementary predictive systems.
The quality of predictive analyses models can be enhanced through:
regular testing of assumptions in these models.
Testing and revision of assumptions lead to:
identification of additional leading indicators and better specifications about the nature of
the relationships between predictors and outcomes.
Artificial intelligence:
enhances human decision-making
Machine learning:
Refers to the use of algorithms that work with large volumes of data (moderately structured or
unstructured) to learn relationships among data elements that can be useful to improving
decision making. Machine learning tools are used along with human decision makers are
specifically useful to those who may not have a deep systematic understanding of certain fields
or problem domains.
Difference between data mining and modeling:
Modeling and optimization involve the creation of highly accurate models of key
organizational systems. Modeling varies from data mining in that the former has a more
accurate understanding of the system of relationships and interrelationships (between
variables affecting specific outcomes) than the latter.
Uses of modeling:
Accurate models can be used to assess the required input for a given level of output. They
can estimate the joint effects of environmental change or organizational action.
Using modeling:
refined theories about the effects of new or untested interventions can be generated.
Questions & Answers
data mining can expose:
spurious or nonsensical relationships.
Relationships between variables can be identified but data mining plays no role
in determining if these relationships are:
meaningful, casual, or of any significance to the organization. Example: taller employees have
higher leadership scores.
Another drawback of data mining is that it can:
capture relationships that existed in previous patterns of relationship too.
Predictive analysis:
The creation of models of organizational systems to predict their future outcomes at some
point. It can also help in predicting how changes in the environment and planned organizational
interventions can impact key outcomes.
Benefits of predictive analysis:
Predictive analysis makes organizational planning more proactive. By knowing what to
expect, managers can act to enhance positive effects or mitigate negative ones from any
internal or external action.
Tools used in predictive analysis:.
From the simple trend analysis technique to highly sophisticated models, predictive
analysis uses a whole range of tools.
, Efforts to develop balanced scorecards are examples of: elementary predictive systems.
The quality of predictive analyses models can be enhanced through:
regular testing of assumptions in these models.
Testing and revision of assumptions lead to:
identification of additional leading indicators and better specifications about the nature of
the relationships between predictors and outcomes.
Artificial intelligence:
enhances human decision-making
Machine learning:
Refers to the use of algorithms that work with large volumes of data (moderately structured or
unstructured) to learn relationships among data elements that can be useful to improving
decision making. Machine learning tools are used along with human decision makers are
specifically useful to those who may not have a deep systematic understanding of certain fields
or problem domains.
Difference between data mining and modeling:
Modeling and optimization involve the creation of highly accurate models of key
organizational systems. Modeling varies from data mining in that the former has a more
accurate understanding of the system of relationships and interrelationships (between
variables affecting specific outcomes) than the latter.
Uses of modeling:
Accurate models can be used to assess the required input for a given level of output. They
can estimate the joint effects of environmental change or organizational action.
Using modeling:
refined theories about the effects of new or untested interventions can be generated.
