HR analytics - summary
, HR analytics - summary Emma Hamm, 2078889
TABLE OF CONTENT
1 - introduction to hr analytics 3
hr and the new world of work 4
what is hr analytics (2) 5
from reporting to analytics 6
measuring the return on people - it is about intangible value (1) 6
hr analytics and predictive modeling (1) 8
the evolution of hr function 8
hr analytics - benefit 8
hr analytics - challenges (1) 9
hr function (1) 9
strategic hr function 10
what is the role of the strategic part of hr? 10
selling the people function to top management 10
the people function as a profit center 11
hr analytics - its position in the hr function (1) 11
summary
12
2 - the hr analytics frameworks 13
case example: 35 dollar million misinvestment 13
what are frameworks? 13
why use frameworks? 13
challenges of using frameworks 13
a. crunchr maturity model for people analytics 14
case example: turnover of project managers 15
journey from reporting to analytics 19
hr from a decision science lens: efficiency, effectiveness, impact 19
the problem in people management: hitting the wall 20
b. lamp framework 20
c. the people analytics effectiveness wheel 21
governance 22
summary
23
3 - the analytical toolset and big data 24
hr information systems and data (1) 24
data, information, knowledge, wisdom 24
types of data (1) - categorical variables 25
types of data (2) - continuous variables 25
information sources 25
how to measure data? 27
what is a good measure? → validity 28
analysis software options 29
big data 30
types of big databases 30
hr analytics and big data 31
building business cases with data and technology 32
stage 1: the diagnosis 32
1
, HR analytics - summary Emma Hamm, 2078889
stage 2: outlining the proposed solution 32
stage 3: determining the desired results 33
an example of a business case 33
a simplified example of stage1/2 34
comparing ethnicity across two functions in a uk bank 34
summary
36
4 - presenting results and the road ahead 37
visualization 37
approaches for presenting data 38
storytelling with analytics 38
practical example 39
(1) understand the context 39
(2) choose an appropriate display 40
(3) eliminate clutter 41
(4) draw attention where you want it 42
(5) think like a designer 42
(6) tell a story 43
toolbox 46
the future of people analytics 46
hr analytics - the present 46
hr analytics - the present and future 47
where are we going 48
organisational network analysis (ona) 49
some applications of ona 49
an example 50
network diffusion 51
summary
51
appendix 52
a. statistical toolbox 52
b. sv testen en analyses (chat) 53
c. toolkit: types of statistical tests explained in edwards & edwards (2019) that you might use to
support your plan 55
🧩
d. chat: definition comparison between authors 57
🧩
1. definition & purpose of people analytics 57
🧩
2. hr analytics maturity models 57
🧩
3. hr analytics as a “fad” 58
🧩
4. ethics & data governance 59
🧩
5. the analytics translator role 59
🧩
6. management support and organizational enablers 60
🧩
7. hr’s peripheral position & strategic integration 60
🧠
8. frameworks for effective people analytics 61
core theories, concepts & terms for the exam 62
2
, HR analytics - summary Emma Hamm, 2078889
1 - introduction to hr analytics
- edwards & edwards (2019): chapter 1
- khan & millner (2020): chapter 1 & 2
WHY IS HR ANALYTICS HOT?
HR IS ABOUT DECISIONS
- Do we use assessment centers or structured interviews in the selection procedure for
job A?
- What procedures shall we use for socializing new employees? Are institutionalized
tactics in this process really more effective than individualized ones?
- Will the group of new employees require additional training. If so what kind of training
is more effective?
- All our competitors apply pay for performance, do we need to do that as well?
- What’s the optimal team composition (Starbucks)?
- Are commitment HR practices really the best option for our R&D department or shall
we use a mix of the former and compliance HR practices?
- But how can we get an answer to previous and other possible questions HR
practitioners and management faces on daily basis?
STATISTICS…
Bad decisions and bad statistics → all questions can be answered with some data
- high correlation between two random things (films appearance and number of people
who drowned in the pool)
→ In statistics, a spurious relationship or spurious correlation is a mathematical
relationship in which two or more events or variables are not causally related to each
other, yet it may be wrongly inferred that they are
you can make a very bad decision if you don’t know what you’re doing!
→ based on this data, would you support the GM’s decision? What data and analysis do we
need to have in order to prove that authentic leadership can decrease turnover intention?
→ NO! Correlation is not the same as causation!
3
, HR analytics - summary Emma Hamm, 2078889
TABLE OF CONTENT
1 - introduction to hr analytics 3
hr and the new world of work 4
what is hr analytics (2) 5
from reporting to analytics 6
measuring the return on people - it is about intangible value (1) 6
hr analytics and predictive modeling (1) 8
the evolution of hr function 8
hr analytics - benefit 8
hr analytics - challenges (1) 9
hr function (1) 9
strategic hr function 10
what is the role of the strategic part of hr? 10
selling the people function to top management 10
the people function as a profit center 11
hr analytics - its position in the hr function (1) 11
summary
12
2 - the hr analytics frameworks 13
case example: 35 dollar million misinvestment 13
what are frameworks? 13
why use frameworks? 13
challenges of using frameworks 13
a. crunchr maturity model for people analytics 14
case example: turnover of project managers 15
journey from reporting to analytics 19
hr from a decision science lens: efficiency, effectiveness, impact 19
the problem in people management: hitting the wall 20
b. lamp framework 20
c. the people analytics effectiveness wheel 21
governance 22
summary
23
3 - the analytical toolset and big data 24
hr information systems and data (1) 24
data, information, knowledge, wisdom 24
types of data (1) - categorical variables 25
types of data (2) - continuous variables 25
information sources 25
how to measure data? 27
what is a good measure? → validity 28
analysis software options 29
big data 30
types of big databases 30
hr analytics and big data 31
building business cases with data and technology 32
stage 1: the diagnosis 32
1
, HR analytics - summary Emma Hamm, 2078889
stage 2: outlining the proposed solution 32
stage 3: determining the desired results 33
an example of a business case 33
a simplified example of stage1/2 34
comparing ethnicity across two functions in a uk bank 34
summary
36
4 - presenting results and the road ahead 37
visualization 37
approaches for presenting data 38
storytelling with analytics 38
practical example 39
(1) understand the context 39
(2) choose an appropriate display 40
(3) eliminate clutter 41
(4) draw attention where you want it 42
(5) think like a designer 42
(6) tell a story 43
toolbox 46
the future of people analytics 46
hr analytics - the present 46
hr analytics - the present and future 47
where are we going 48
organisational network analysis (ona) 49
some applications of ona 49
an example 50
network diffusion 51
summary
51
appendix 52
a. statistical toolbox 52
b. sv testen en analyses (chat) 53
c. toolkit: types of statistical tests explained in edwards & edwards (2019) that you might use to
support your plan 55
🧩
d. chat: definition comparison between authors 57
🧩
1. definition & purpose of people analytics 57
🧩
2. hr analytics maturity models 57
🧩
3. hr analytics as a “fad” 58
🧩
4. ethics & data governance 59
🧩
5. the analytics translator role 59
🧩
6. management support and organizational enablers 60
🧩
7. hr’s peripheral position & strategic integration 60
🧠
8. frameworks for effective people analytics 61
core theories, concepts & terms for the exam 62
2
, HR analytics - summary Emma Hamm, 2078889
1 - introduction to hr analytics
- edwards & edwards (2019): chapter 1
- khan & millner (2020): chapter 1 & 2
WHY IS HR ANALYTICS HOT?
HR IS ABOUT DECISIONS
- Do we use assessment centers or structured interviews in the selection procedure for
job A?
- What procedures shall we use for socializing new employees? Are institutionalized
tactics in this process really more effective than individualized ones?
- Will the group of new employees require additional training. If so what kind of training
is more effective?
- All our competitors apply pay for performance, do we need to do that as well?
- What’s the optimal team composition (Starbucks)?
- Are commitment HR practices really the best option for our R&D department or shall
we use a mix of the former and compliance HR practices?
- But how can we get an answer to previous and other possible questions HR
practitioners and management faces on daily basis?
STATISTICS…
Bad decisions and bad statistics → all questions can be answered with some data
- high correlation between two random things (films appearance and number of people
who drowned in the pool)
→ In statistics, a spurious relationship or spurious correlation is a mathematical
relationship in which two or more events or variables are not causally related to each
other, yet it may be wrongly inferred that they are
you can make a very bad decision if you don’t know what you’re doing!
→ based on this data, would you support the GM’s decision? What data and analysis do we
need to have in order to prove that authentic leadership can decrease turnover intention?
→ NO! Correlation is not the same as causation!
3
