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HR AnalyticsPresented by Melissa Asher, Director of Training &

Development and Talent AcquisitionCPS HR Consulting

October 16cpshr.us

What brought you here today?

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Agenda

What does “analytics”

mean?

Why is this important for

HR?

Getting started

Analyzing the data

Presenting findings

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What does “analytics” mean anyway?

1

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Basic Terms

Data

Metrics

Analytics

What is the difference?

cpshr.us

Metrics

Actionable info to improve outcomes

Analytics

Data

6

In order to improve turnover

Examples ofData:Metrics:Analytics:

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IdentifyRoot Cause

ImprovementOpportunity

Why Analytics?

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Identify Need to Improve

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Why is this important for HR?

2

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HR as Business Partner

Improved Outcomes

HR & Ops as

partners

Has org-wide

orientation

Work proactively

on dept. challenges

Share credit for results

Work together on Ops and HR

issues

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Alignment with Strategy

Align people metrics with business strategy Not letting the metrics take the place of strategy Keep purpose (outcomes) in the forefront

Case Study

Can Connect the Dots

Connecting the dots

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Getting Started

3

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• What are we trying to solve?• What are people complaining

about?• What is the business problem?

Clarify

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Plan What Data Is Needed

Understand goal or problem

Understand what types of data

Identify key HR functions

Make initial hypotheses

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For Example: Two Depts. in Same Agency

16

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Common Data Sources

HR Files (e.g. applications, performance reviews) HR Information System (e.g. NeoGov, Cornerstone) Financial System (e.g. salary, benefits)

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Common Metrics

HR Metric Basic FormulaTurnover (annual) # employees exiting during 12 months/average actual

# employees during same periodCost per hire Recruitment and selection costs/# new hiresTime to fill # days positions are open/number of positions filled

during same periodEngagement % of employees stating engaged with a given aspect of

the workplace on surveys or other data collection toolsWorkers’ comp cost per employee

Total workers’ comp cost for year/average number of employees

Training participation # employees participating in training opportunity/# employees eligible

Age to retirement # employees in age range/total employees

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Age to Retirement Metric Example

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Analyzing the Data

4

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Data Accuracy & Assumptions

And what do YOU mean by. . .

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Working with Qualitative Data – Basic Steps

1• Review

data vis-a-vis goals

2• Organize

data

3• Explore

& sort

4• Identify

themes

5• Analyze

& draw conclu-sions

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Example – Word Cloud

Open-ended survey question from a training effectiveness survey:

What could we have done to improve your

Risk Management Practitioner’s Program

experience?

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Working with Quantitative Data – Basic Steps

Look for missing data and errors

Look for outliers

Check quality of collection

Check possible intervening variables

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Descriptive Statistics – Two Types

Central Tendency• How data is grouped together

Spread or Variability• How different are data points within the

population

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Central Tendency – How Data is Grouped

Mean = average

Median = point with 50% data points above and 50% below

Mode = most frequent score

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Example: Means and Medians

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Variability – How Different Within Population

Range = difference of highest to lowest

Standard Deviation = defines the magnitude of deviations from the mean

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Analyze & Draw Conclusions

Look for patterns and themes

Can you explain deviations?

Are results significant &/or meaningful?

Reality check. Make sense?

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Presenting Findings

5

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Presenting Your Findings

Know your audience

Know your time

Create a story

Avoid powerpointlessness

Visualize your data

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Traditional Visualizations

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Visualize Results – Gauge Charts

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Visualize Results – Infographics

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Visualize Results – Dashboard

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How would you present this?Recruitment Date Closed

AppsReceived

Days Open

Test Date QAIHiring

InterviewJob Offer Start Date

Days to fill

Accounting & Financial Analyst II 8/11/2017 100 25 8/21/2017 8/31/2017 9/7/2017 9/20/2017 10/16/2017 91Laboratory Director 9/15/2017 41 42 9/25/2017 10/4/2017 10/19/2017 11/13/2017 101Technical Writer Intern 9/22/2017 21 32 10/2/2017 10/4/2017 10/23/2017 63Admin Specialist (FMC) 9/12/2017 106 6 9/28/2018 10/11/2018 10/25/2017 11/7/2017 11/12/2017 67Collection System Worker I 9/19/2017 1 7 10/4/2017 11/13/2017 62Admin Specialist (CIP) 9/25/2017 92 10 9/28/2018 10/12/2018 10/25/2017 11/13/2017 11/27/2017 73Casual Receptionist 10/12/2017 60 10 10/27/2017 11/2/2017 11/9/2017 11/21/2017 11/28/2017 57Engineering Technician II/III 10/24/2017 73 20 11/3/2017 11/16/2017Plant Operator III Trainee 10/31/2017 120 15 11/17/2017 11/29/2017 12/7/2017 12/27/2017 1/23/2018 99Plant Operator III 11/3/2017 26 18 11/13/2017 12/7/2017TPO Trainer 11/8/2017 7 11/15/2017 11/21/2017 1/18/2018 78Receptionist 11/13/2017 100 3 11/27/2017 12/14/2017 12/19/2017 1/4/2018 1/29/2018 80Engineering Technician I/II 1/25/2018 104 17 2/5/2018 2/13/2018 2/20/2018 3/5/2018 4/16/2018 98Purchasing Agent 3/9/2018 57 25 3/20/2018 4/3/2018 4/23/2018 70Assistant/Associate Engineer 4/26/2018 61 42 5/8/2018 5/30/2018 6/16/2018Collection Services Coach 6/1/2018 18 18 6/15/2018 6/27/2018

Averages 65.33 18.56 78.25

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A Real Example From the Data

78.25

16

21 21

2322

16

10

15

20

25

Number of Recruitments Since 2013

67

62

73

57

99

78

80

98

70

0

0

Admin Specialist (FMC)

Collection System Worker I

Admin Specialist (CIP)

Casual Receptionist

Plant Operator III Trainee

TPO Trainer

Receptionist

Engineering Technician I/II

Purchasing Agent

Assistant/Associate Engineer

Collection Services Coach

Days to fill

Average Recruitments

Per Year

19.83

AverageDays To Fill

78.25

https://www.youtube.com/watch?v=e2wDqspleNk

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Where We Went!

What does “analytics”

mean?

Why is this important for

HR?

Getting Started

Analyzing the Data

Presenting Findings

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Melissa Ashermasher@cpshr.us916-471-3358www.cpshr.us

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