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Exam Prep > Data & Analytics > Flashcards

Data & Analytics Flashcards

1
Q

What should be considered at the beginning of creating an analytically enabled function?

A

Early project selection for analytics

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2
Q

Success factors for an initial TD analytics projects

A
  • A business problem with a clear outcome
  • Access to required data
  • Good probability of success, isn’t too complicated, and avoids anything politically charged
  • Important to the C-suite (e.g., reducing costs, increasing sales)
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3
Q

2 qualities for ideal TD project

A
  • a quick win, such as something that improves an existing process (something that demonstrates success now)
  • uncover an insight or have a business impact that will create interest by senior-level leaders
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4
Q

Steps to gather and organize data

A
  1. Define the question that needs to be answered
  2. Set clear measurement priorities (what to measure and how to measure it)
  3. Collect the data - what info already exists; how is data stored and filed; how to collect the data
  4. Analyze the data - a number of analysis tools and software can be used
  5. Interpret the results - circle back to original question to determine if it’s been answered; data ensures a productive conclusion has been found by defending against objections
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5
Q

During which step in gathering and analyzing data would a TD professional organize the data findings around the original data question?

A

Interpret the Results

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6
Q

During which step would a TD professional create pivot tables, plot data, or find correlations?

A

Analyze the Data

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7
Q

During which step are cost, bias, and confidentiality concerns/factors?

A

Cllecting the Data

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8
Q

During which step would a TD professional visually present their conclusions from the data?

A

Interpret the Results

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9
Q

What is the value in a TD professional meeting with senior leaders/stakeholders about analytics projects?

A

shows how these projects deliver short-term and long-term value for the org

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10
Q

What should a TD professional base their recommendations on for future analytics projects?

A

level of impact
ease of implementation

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11
Q

What 3 things should a TD professional remember in developing analytics projects?

A
  • Ensure the talent strategy aligns with org success
  • Engage multiple stakeholders
  • Determine the support needed
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12
Q

Analytics projects: Engage multiple stakeholders

A

The TD professional’s key stakeholders are org leaders and business dept leaders; other may be added, which is why it’s good to consistently define the problem, measures, assumptions

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13
Q

Analytics projects: Determine support needed

A

Will require buy-in from others; building these relationships will be valuable later

e.g., critical data may be located in depts or functions outside TD, or SMEs may be needed to help gather data

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14
Q

Analytics projects: Talent strategy is aligned with org success

A

Business goals, strategy, and outcomes all have corresponding plans for how employees help support them; direct connection between TD and the org; ensures leaders will be interested in results and can demonstrate TD is a valuable resource

e.g., understand how business makes its money

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15
Q

What forms the framework for the data analysis plan?

A

The stakeholder’s purpose - their goal, need, or requirement

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16
Q

Examples of valuable info TD holds for other depts (can be aligned to the stakeholder’s purpose) - these may be aligned with the goal of an analytics project

A
  • skills needed to improve business performance
  • how to predict turnover
  • data to measure impact of LD program
  • how to determine the effectiveness of an onboarding program
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17
Q

Why should a TD professional be good at needs assessment for data analytics?

A

The stakeholder analysis may be broader and deeper than typical

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18
Q

What is the first step in a stakeholder analysis for a data analysis project?

A
  • Identifying the stakeholders
  • Determining their power and influence
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19
Q

What are three ways to segment the stakeholder group?

A

hierarchy - team leads, dept heads, directors
function or department - e.g., Sales, Marketing, Operations
decision-making authority - differs from hierarchy; may be that one stakeholder group has responsibility/authority across depts

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20
Q

What are some early ways a TD professional might do a deep data analysis?

A
  • Manipulating the data by plotting it out to find correlations
  • Create a pivot table that allows data to be sorted and filtered using different variables
  • Calculate mean, maximum, minimum, standard deviation
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21
Q

What is the value of identifying correlations, trends, and outliers early?

A

The knowledge learned helps focus the analysis and draw more accurate conclusions

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22
Q

What is one challenge/roadblock a TD professional may discover after they do early data manipulation?

A

That they don’t have the necessary data; they may need to collect more or ask different questions

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23
Q

What are common pitfalls / data analysis traps that can lead to poor data analysis? (5)

A
  • 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
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24
Q

What is the Hawthorne Effect? Placebo effect?

A

The more visible the observation process, the less reliable the data are. People will modify their behavior when they are knowingly being observed.

The placebo effect is the perception of results by learners due to correlated but not causal factors; e.g., “faux praise” may motivate learners to learn/behave correctly, even if their behavior was not correct to begin with

25
Q

Refer back to _____________ when interpreting results.

A

The original plan and purpose of the data analysis.

26
Q

What three data points should you have as you begin data interpretation?

A
  • sample size
  • response rate
  • aggregated responses
27
Q

What is the sample size?

A

Compare number of respondents to total number that describes the ideal respondents

28
Q

What is the response rate?

A

divide the number of responses by the number who were asked to complete the survey

29
Q

What is the aggregated responses?

A

the total number of responses for each question

30
Q

Should you start with quant or qual first?

A

Quant - Working with the numbers first provides an initial focus or direction for the results

31
Q

Is it easier to make comparisons with percentages or whole numbers?

A

Percentages; should turn most of your data into percentages

32
Q

Qualitative data

A
  • should be compiled after the numbers are quantified
  • used to provide a rationale for the quant data message
33
Q

What are 2 ways a TD professional can add context and meaning to their analysis through comparisons?

A
  • using benchmarks
  • making comparisons using cross-tabulation
34
Q

Benchmark

A

standards or reference points against which things can be compared or assessed

35
Q

Benchmark examples

A
  • comparison to last survey
  • other org’s data
  • best practices
36
Q

What is critical in benchmarking?

A

Compare exact questions one to another so as not to introduce misinterpretation

37
Q

Cross-tabulation

A

a multidimensional table that records the frequency ofrespondents that have specific characteristics defined in each table cell

Can be used for comparison within data analysis by breaking data out into different categories

38
Q

What is the value in comparison through cross-tabulation?

A

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.

39
Q

Correlation vs. causation

A

correlation—when two variables move at the same time
causation—if one variable directly causes a change in another

40
Q

What should a TD professional do when the results don’t “feel” right?

A

Use good judgment; question those results; dig deeper to look at correlation and causation

41
Q

T/F: It is possible to prove a hypothesis true.

A

False; you can only “fail to reject” the hypothesis

no matter how much data is collected; chance could always interfere with the results

42
Q

What questions should a TD professional ask while interpreting results to determine the legitimacy and usefulness of the conclusions?

A
  • How likely will the conclusions be beneficial?
  • Do the results answer the original research question?
  • Does the analysis explore all perspectives?
  • Does the data address any objections?
43
Q

What are 4 ways TD professionals can tell the story of the data?

A
  • 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
44
Q

What are some factors to consider/determine when deciding how to tell the data story?

A

The audience
The message they want to deliver; each tool tells a different story
Which data visualization methods provide the best aesthetic and visual effectiveness

45
Q

What are 2 guiding principles for cross-tab tables?

A

scaling and integrity:
- scaling shows proportions and relationships
- integrity focuses on the presentation’s truthfulness and accuracy

46
Q

What is a core guiding principle for graphs?

A

select graphs that present the results in the most useful format—one that clarifies the point that was intended

47
Q

6 best practices for graphs

A
  • Direct viewer to think about the message rather than methodology, graphic design, and technology used to create the graphic
  • Avoid distorting what the data say
  • Make large data sets coherent
  • Encourage the eye to compare different pieces of data
  • Reveal the data at several levels of detail, from a broad overview to the fine structure
  • Serve a clear purpose, including description, explorations, tabulation, or decoration
48
Q

What are 4 different analyses along the analytics spectrum?

A
  • Descriptive analytics
  • Diagnostic analytics
  • Predictive analytics
  • Prescriptive analytics
49
Q

What are descriptive analytics?

A
  • explains what happened
  • TD professionals are most familiar with it
50
Q

What are diagnostic analytics?

A
  • explains why something happened
  • provides correlations for focusing on the reason something did or did not happen as expected
  • can save time by knowing where to apply and concentrate next steps
51
Q

What are predictive analytics?

A
  • Uses both descriptive and diagnostic data to predict what will happen in the future
  • TD professionals can use this info to build models that prescribe support to increase success
52
Q

What are prescriptive analytics?

A
  • show how to make something happen
  • best opportunity to influence a different outcome
  • least developed analytic b/c each org has different requirements
  • can be used to personalize learning by matching a learner’s preferences to make something happen
53
Q

What type of the analytics on the analytics spectrum is data mining / data discovery?

A

Diagnostic analytics

54
Q

What type of analytics on the analytics spectrum are assessment scores, summary activities, opinions, satisfaction, and evaluation surveys

A

Descriptive analytics

55
Q

What 5 factors define a data-driven organization?

A
  • a strong company culture
  • an experimentation mindset and objectively learn from failures
  • a digital technology influence
  • a focus on the future
  • organizationally agile
56
Q

What is the value in knowing your org’s level of data-drivenness?

A

help TD professionals understand the organization’s preparedness level to use data for decision making

57
Q

What is the first step in data visualization?

A

define the outcome they would like to influence:
- consider main purpose of TD and which TD reports leadership uses to make decisions
- look for business metrics tied to performance, such as increasing sales
- where you want to impact/influence gives you a good candidate for gathering and analytics data at large

58
Q

What do you want to do before moving forward with your selection for data analytics and visualization?

A

make sure that their selection is not in conflict with what the stakeholder might be doing or planning

This requires you to know their goals and take time to predict their questions before introducing the idea to the stakeholder

59
Q

What are 7 visualization techniques?

A

distribution of a single variable
relationship
comparison
distribution of multiple variables
connection
composition of the whole
location

Exam Prep (39 decks)

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