3.7.A/B/C/D Data & Analytics Flashcards
True or False?
TD professionals should have an understanding of how data driven their organization is prior to planning projects and selecting or using data visualization techniques.
True
Five factors define a data-driven organization. Name 4 of the 5.
- a strong company culture
- an experimentation mindset and objectively learn from failures
- a digital technology influence
- a focus on the future
- are organizationally agile (Sinar 2018).
3.7.6.1 Presenting Data to Stakeholders
Which visualization techniques can be used to show distribution of a single variable?
columns, histogram, scatter chart, bar chart
3.7.6.2 What to Display
Which visualization techniques can be used to show relationship?
bubble charts, scatter chart
3.7.6.2 What to Display
Which visualization techniques can be used to show comparison?
bars and columns, timeline, line chart, scatter plots
3.7.6.2 What to Display
Which visualization techniques can be used to show distribution of multiple variables?
heat maps, bubble charts
3.7.6.2 What to Display
Which visualization techniques can be used to show connection?
relationship or connection maps, heat maps, Venn diagrams
3.7.6.2 What to Display
Which visualization techniques can be used to show composition of the whole?
pie chart, stacked bar chart
3.7.6.2 What to Display
Which visualization techniques can be used to show location?
maps, building diagrams, processes
3.7.6.2 What to Display
3.7.B Skill statement:
I. Developing a People Analytics Plan
TD professionals should be skilled in identifying stakeholder requirements so they can develop a people analytics plan.
3.7.3 Skill in Identifying Stakeholders’ Needs, Goals, Requirements, Questions, and Objectives to Develop a Framework and/or Plan for Data Analysis
When working with a stakeholder, what is their desired purpose?
When working with a stakeholder, the purpose is what the stakeholder wants and needs to know—their goal, need, or requirement. This forms the framework for the data analysis plan.
3.7.3.1 The Stakeholder’s Desired Purpose
When conducting a stakeholder analysis, what are some the three ways that a TDP might segment the stakeholder group?
- hierarchy, such as team leads, department heads, or directors
- function or department, such as sales, marketing, or operations
- decision-making authority, which differs from hierarchy; for example, if there is a unique situation where the stakeholder group has responsibility across departments (Anand 2017).
3. 7.3.2 Conduct Stakeholder Analysis
3.7.C Skill statement:
I. Analyzing Data and Interpreting Results
TD professionals should be skilled in analyzing results so they can identify trends and relationships among variables. They do this in two steps: analyzing data and interpreting what it means.
3.7.4 Skill in Analyzing and Interpreting Results of Data Analyses to Identify Patterns, Trends, and Relationships Among Variables
Once questions have been asked and the right data collected, TD professionals should use a deeper data analysis to identify useful information and initial conclusions. What types of data analysis makes sense as a good place to start?
- plotting it out to find correlations or creating a pivot table that allows data to be sorted and filtered using different variables.
- calculate the mean, maximum, minimum and standard deviation of the data.
3. 7.4.1 Process for Data Analysis
There are 5 most common pitfalls of poor data analysis. What are they?
jumping to conclusions—or worse, starting with the conclusion
unconscious bias
overusing the mean and avoiding the mode and median
incorrectly defining the sample size
hypothesis testing without accounting for the Hawthorne effect or placebo effect.
[See 2.8.6.2]
3.7.4.2 Pitfalls of Initial Data Analysis
What is the Hawthorne effect?
The more visible the observation process, the less reliable the data are.
the alteration of behavior by the subjects of a study due to their awareness of being observed.
What is the placebo effect?
the placebo effect, describes an actual physiological or psychological response to an inert intervention (behavioral or pharmacological) regardless of observation
When making sense of quantitative and qualitative information, which data set is best to begin working with as a starting point?
Quantitative data
3.7.4.3 Interpret Results
True or False?
When working with numbers, TD professionals will want to turn most of their data into percentages.
True
When working with numbers, it is easier to make comparisons of percentages than whole numbers, so TD professionals will want to turn most of their data into percentages.
3.7.4.3 Interpret Results
When intepreting results, what is the best way to use qualitative data?
Qualitative data should be compiled after the numbers are quantified. This will be used to provide a rationale for the quantitative data message.
What is one way a TDP can provide context and meaning to their analysis?
benchmarks
Potential benchmarks include a comparison to the last survey, other organization’s data, or best practices.
3.7.4.3 Interpret Results
If a TDP wanted to make comparisons within the data, what technique would they use?
A) qualitative data
B) mean, median, and mode
C) cause-and-effect
D) cross-tabulation
D) cross-tabulation
Crosstab or cross-tabulation is a multidimensional table that records the frequency of respondents that have specific characteristics defined in each table cell. These tables show valuable data about the relationships of all the variables to one another and help to analyze cause-and-effect or complementary relationships. For example, the cross-tab table between a question about age and a question about professional development might lead to a conclusion that 20 percent of employees over age 50 want more professional development opportunities. [See 3.7.4.4]
3.7.4.3 Interpret Results
True or False?
As TD professionals interpret their analysis, they must remember that it is possible to prove a hypothesis true.
False
As TD professionals interpret their analysis, they must remember that it is not possible to prove a hypothesis true. Instead, it can only fail to reject the hypothesis. This means that no matter how much data is collected; chance could always interfere with the results.
3.7.4.3 Interpret Results
Although the numbers are important, listeners will want to know the story the data tells. TD professionals can create a story by:
(4 possible answers)
- using the percentages to create a narrative
- providing context with the statistics, such as comparing to a previous year
- showing which benchmarks were used for comparison when interpreting results
- including quotes from open ended questions or interviews, if possible, to help interpret numbers.
3. 7.4.4 Using Data Visualization to Tell the Story
