In the age of big data, it has become mandatory for strategic HR professionals to have strong qualiitative skills. The following presentation conducted in 2004, predicted this shift and outlined why and how HR can stay ahead of the data revolution.

1. Making
HR Count
OPRA Consulting Group
Excellence in Business.
Excellence in People.

2. Overview
• HR and Statistics - an open discussion
• Why have metrics stayed out of the hands
of HR folk?
• Hr Metrics: The myths and legends
• What makes a good metric?
• Who has a vested interest in HR metrics?
• Demonstration
• Descriptive statistics
• Simple inferential statistics
• Survey design
• Classification modelling and prediction
• Building a positive metrics-based culture

3. HR and Statistics: Discussion
• Who currently uses statistics in their day-to-day HR work?
• Who currently completes their own statistical analysis to support HR activity?
• Whose blood turns cold when their hear the word “Statistics?”

4. HR and Statistics: Discussion
• Who thinks that they would be more convincing if they were able to use
statistics in their business?
• Who thinks that they would secure more $$ for HR activity if they could show
that HR activity was returning a value to the business?
• Who would like to be more proficient with statistics as an HR professional?

5. HR CONTINUES TO EVOLVE

6. Textbook Definition
Metrics are a system of parameters or ways of quantitative
assessment of a process that is to be measured, along with the
processes to carry out such measurement.
Metrics define what is to be measured. Metrics are usually
specialized by the subject area, in which case they are valid only
within certain domain and cannot be directly benchmarked or
interpreted outside it.
Generic metrics, however, can be aggregated across subject areas or
business units of an enterprise.

7. The Business Case
“Without HR metrics, no one in management can
really put a finger on what's working or not working
with the people who make up an organisation. HR's
success at measuring "people issues" directly
contributes to informed decision-making by
executives and board members. The challenge for HR
is to measure and deliver meaningful data that is
relevant to the bottom line”.
(C. Johnson: HR Metrics: The Business Case)

8. And…….
“The future for metrics is now. Our need to measure
and to apply measurement to strategic analysis and
strategic decision-making increases every day. HR
metrics are not simply equations to think about
applying in the future when you presumably will have
more time; they should be commonplace for every
organization, regardless of size, industry; location or
success. By showing the value of assets or the return
on investment, HR metrics become key to advancing
the HR profession”.
(HR Metrics: A must, D Cohen)

9. And Again..….
“In a highly competitive world dominated by CFOs who
want to cut your budget, it is no longer optional to
demonstrate your value. Everyone is accountable! It's
interesting that when people ask me how you can
differentiate between world class and average HR
departments, the one factor that stands out dramatically is
the extensive use of metrics (or lack thereof) to measure HR
success. The very best — like Intel, Cisco and Microsoft —
are metrics fanatics, while the worst use costs, feelings, and
instincts to judge their success.
(Dr John Sullivan, 2003, “Why Metrics are Essential”)

10. Why have metrics stayed out of
the hands of HR professionals?

11. MYTH
VS
REALITY

12. Myth: Metrics are difficult
Reality:
• As more departments become paperless the
ability to sort data becomes easier with ‘point
and click’ solutions
• Most metric calculations can be done on an
excel spreadsheet or with existing software
• The best metrics are self explanatory and can
be easily defined with a simple description

13. Myth: Collecting Data is Expensive
Reality:
• Over-collecting of data is common. Whenever possible, use existing
information
• You do not have to measure every event or job.
• Instead prioritize jobs, and business units and use sampling
techniques to reduce the time and costs
• Robust software to support the collation of metrics is available for
$2,000 (per licence)

14. Myth: Our Organisation isn’t ready for this
Reality:
• Good metrics measure the things that senior
managers care about
• Build credibility by pre-testing your proposed
metrics with say, the CFO to ensure they are
robust
• In tough economic times, you prove your
business impact or you are gone

15. Myth: HR Metrics takes too much time
Reality:
• Once you get meaningful data (the hard bit!), running
the metrics is easy
• Work with other business units to build an evaluative
environment
• Once the systems are in place the time required to
generate metrics is very short

16. Myth: HR Metrics is too difficult
Reality:
• Once you get meaningful data (the hard bit!),
running the metrics is easy
• Work with other business units to build an
evaluative environment
• Once the systems are in place the time to
complete metrics is very short

17. Who has the vested
interest in HR
metrics?

18. Who has the vested interest in HR metrics?
• Consulting firms use metrics as their
“Commercial” advantage……the less you
know the more advantage they have.
• They then on-sell YOUR metrics as
“benchmarking data”, for their own
commercial gain.

19. Who has the vested interest in HR metrics?
• Where possible, organisations should
aim to move towards greater self-
sufficiency.
• You have the vested interest because it is
your organisation.

20. Why Would I Use HR Metrics?
As a HR professional, you should use metrics if you:
• Don’t want to be over-taken by a new breed of HR
professionals
• Want to demonstrate H.R’s value to the organisation
• Want to become an invaluable strategic player in your
organisation
• Want to anticipate organisational change, not just follow it

21. What is a Good Metric?
• Aligned with business: 62% of Fortune 500 companies cited "to better align HR strategy
with corporate strategy" as the number one goal for HR, but one of the most difficult to
achieve. Good metrics can bridge the gap.
• Actionable and predictive: A good metric must provide information that can be acted upon.
Too often, HR measures for the sake of measuring, without really thinking “what do I do
with this?
• Consistent: A good metric is consistent in what it measures, otherwise, the value of its
comparison is useless.
• Time-trackable: A good metric must be able to be tracked over time. It is not a snapshot of
an activity at one moment in time.

22. Internal Research Design
Identify solution
Analyse data
Identify data to collect
Identify statistics to help solve research question
Identify research question
Identify a problem

23. How long would you spend looking for a new
intranet system worth NZ$30,000?

24. Return on Investment
Does using assessments in selection save $$ ?
• Incremental costs with inaccurate selection
• Small employers = savings of US$18m per year large employers
= savings up to US$16b per year (Schmidt & Hunter, 1981)
• Higher aptitude = easier to train
• “Performance managing out” can be difficult

25. Hidden costs associated with
poor selection decisions
• Often, we can only recognise poor performers
after they have made mistakes
• If a performance issue: Training and associated
costs, but not always a guaranteed solution
• If there are attitudinal problems, these cannot
be trained

26. • Poor performers under NZ law, stuck with
individual for 3 months (minimum) or more
likely 6 months
• Poor performers  wages/salary
• Poor performers  mistakes
Hidden costs associated with
poor selection decisions

27. • Personal grievances for unjust dismissal
• $25,000 to $60,000 in court costs only
• $25,000 to $60,000 additional if company loses
• Top executives tied up in mediations for
hours – lost revenue
Hidden costs associated with
poor selection decisions

28. Cost Modelling
But do assessment tests actually “add value”?
Utility (U) - the “added valued” of using personality (or other assessment) measures
U = N * T * r (x y) * $ S d (y) * Z (x) – c
N = number of candidates selected
T = expected average tenure of those selected
r(x y) = correlation between predictor score (x) and $ value pay off (y)
$Sd(y) = standard deviation of $ pay off for selected applicants
i.e. $ profit difference
Z(x) = average standard predictor score for the group
C = total costs of selection for all applicants

29. Building a Positive Metrics Based Culture
Keep it Simple
• If a metric is not self explanatory, or can't easily be defined with a
simple description, then it is probably too complex.
Only Track Metrics you Intend to Use
• Consider those that relate in particular to such things as
performance, continuous improvement and responsiveness.
Keep Metric Goals Realistic
• Set realistic quarterly goals so the organisation can see results.

30. Building a Positive Metrics Based Culture
Keep your Metrics Visible
Post all your key vital metrics in a highly visible place. Colourful trend
charts might show:
• The metric
• Your goal
• The industry benchmark
• Your current performance
Celebrate Metrics
Metrics get a bad rap because we typically use them to show all the
things we need to improve. Develop a culture that embraces metrics
by using them to demonstrate all the things you are doing right.

31. -End-