This document discusses using HR data and analytics to gain insights. It provides examples of moving from raw data to metrics, analysis, intelligence, and insights. HR professionals are encouraged to focus on business priorities, integrate data sources, and build skills in data analytics. Example metrics that organizations measure include workforce profile, performance, productivity, rewards, talent, and how these have changed over time when compared to competitors or past periods. Getting started may involve downloading some HR data and exploring what patterns and insights can be discovered.

1. HR data - it’s about insights
“We need to find a way to make our
intuitive decisions more data-based
and our data-based decisions more
intuitive.” - Kevin Ball, VP HR, CDK
Global
“Analytical people don’t live in HR.
HR people are better at managing
ambiguity than analysis.” – Peter
Turner, Ricoh (Quoted in CIPD
report)

2. But “the people analytics revolution is gaining speed…in 2016 we see a major leap
forward in capabilities”
A lot more being said than done

3. From data to insights
• Data: Coded values collected from business processes, raw facts, eg
headcount and revenues (ambient data – stuff just lying around).
• Metrics: Measurement created by combining data, eg revenue per FTE.
• Analysis and information: Compared to what? Since when? Eg Has our
revenue per FTE gone up or down? How does that compare with our
competitors?
• Intelligence: What else do we know? Eg Do we have a lot of non-revenue
generating employees due to recent investment? How does this compare to
our competitors?
• Insight: Whole story, eg our revenue per FTE is still ahead of our
competitors, even after our increased research headcount.

4. 3 levels of analytics
• Level 1 - basic analytics: The use of descriptive data to illustrate a particular
aspect of HR. This covers most of the standard reports from the HRIS, for
example, headcount, turnover, absence rates and age profiles.
• Level 2 - using multidimensional data: Combining different data sets, or
types of data to investigate relationships between them. This involves cross
referencing HR data with information from other systems, for example
looking at the relationship between absence and financial or sales
performance.
• Level 3 - predictive analysis: Using data to predict future trends or to
anticipate events and scenarios. For example, forecasting attrition rates or
modeling future remuneration costs. Some organisations claim to have used
HR analytics to identify probable leavers and predict future team
performance.

5. Data Metrics Information/Analysis Intelligence Insight
From data to insights
Number of
successful
calls
Number of
staff in each
location
Supervisor
development
scores
Training
investment
Successful
calls per
employee
Aggregate
scores by
location
Average
supervisor
rating
How do
locations
compare?
How has this
changed?
What is
difference
between
performance
of
supervisors?
Business
circumstances?
Seasonal
effects?
What else was
going on?
Where we
invested in
supervisor
development,
team
performance
improved
Quantitative Quantitative & qualitative
Is our supervisor development working?

6. Getting started
Focus on business priorities:
• To achieve our business goals, what information will we need over
the next 3 years?
• What is the CEO interested in?
• What stories do we want to be able to tell the exec team?
• How does the company make its money and how will these
measures help?
One version of the truth:
• Single point of data entry for all people data
• Integrate with other systems
• Clean your data
• Agree common coding with other functions
• HRIS becomes source for all people data on other systems

7. Getting started
Build your skills:
• Data and analytics team
• Critical thinking skills
• Ability to understand patterns in data - what it is telling us and what
it is not
• Consultancy skills - people who can understand the business, get HR
and can do data
Work with other functions:
• IT
• Finance
• Marketing
Just start!
• Download some data, mess about with it, see what you find

8. Poll – what do you measure?
1. Absence
2. Workforce costs
3. Employee turnover and retention
4. Workforce productivity
5. Cost of the HR function
6. Employee engagement
7. Remuneration relative to market
8. Metrics that link people management
to financial performance
9. None

9. What do organisations measure?
Source: Partnering for performance – EY

10. Working with finance
“CFOs and CHROs at high-performing companies collaborate better and
differently”
Source: Partnering for performance – EY

11. Example metrics
Workforce profile:
• Heads and FTE
• Age and length of service
• Type, eg sales/operations/development/support
• Grade and span of control
Performance and productivity:
• Revenue per FTE
• Human Capital ROI
• Human Capital Value Added (HCVA)
• Absence
• Performance ratings relative to business performance
Reward:
• Total reward as % of revenue
• Compa ratios
• Percentage of employees paid above or below market rate
Talent:
• Capability gaps
• % new hires at mid-performance point by first review
• Succession risk - % of key roles with no clear successor
• Replacement impact (difference between reward of new hires and those who have left over same
period)

12. Compared to what?

13. Since when?
Source: NTPC

14. 4 ways to show changing profile
Source: Institute for Government
Source: National Audit Office