MIA Data Colin Minto, Group Head Resourcing and HR Systems, G4S Chair, Direct Employers Association (DEA)
Transcript
1. MIA Data Colin Minto, Group Head Resourcing and HR Systems,
G4S Chair, Direct Employers Association (DEA)
2. 620,000+ people 125+ countries One of the worlds largest
private employers Worlds leading international security solutions
group Recruit 200,000+ per annum Growth and turnover Recognised
innovator and multi-award winner in terms of resourcing strategy
and delivery G4S Plc
3. DEA Europe Trade Association for employers Championing the
resourcing agenda and practitioners Educate policy makers business
leaders practitioners job seekers supply chain 50+ members, 720 USA
alliance members
4. MIA Data Missing in Action? Meaningful Does it tell you
something of value? Does it help you explain something?
Interpretable Can you read it? Can you understand it? Actionable
Can you do something with it? Can you influence or make a decision
using it?
5. Candidates Find jobs easily Realistic view of role and G4S
Apply easily Be informed and engaged Customers Staff fitting unique
needs Stable workforce Smooth start ups Know we have a pipeline
Regulators Ensure equality Measure Enforce Be respected partner G4S
Managers Focus on day jobs See whats going on Different- iate G4S
Save and sell Discovery is Data Too! G4S Plc Hire very best Promote
Employer Brand Built talent pools Save and sell G4S Resourcing Be
proactive Best practice and standards Innovate and automate
Measure
6. Aggregated Job Board / Community = Internal and External
Career Centres 100000s Job Seekers ATS 1 Breaking Convention! ATS 2
ATS 3 ATS 4 Jobs People Jobs Matched Candidates Jobs Matched
Candidates Jobs Matched Candidates Jobs Matched Candidates Global
Searchable Shared Candidate Database Matched JobsApplications
Diverse, Inclusive and General Job Boards and Social Media
ChannelsPeople 88%
7. Interpretable/Actionable Data
8. Interpretable/Actionable Data
9. Interpretable/Actionable Data
10. Interpretable/Actionable Data
11. In Good Company
12. Interpretable/Actionable Data
13. keyword % male ave. age reach fresh prince 44.4 24.3 7.2m
#Milk 27.1 35.3 4.8m #Ryan Dunn 52.2 23.8 2.8m #Smoking 56.4 28.3
1.6m rip ryan dunn 20062011 52.6 23.4 1.6m #Nerd 50.7 27.5 1.5m
#Johnny Knoxville 61.5 24.0 1.0m stepbrothers 50.0 23.9 680k
rubberduckzilla 38.5 22.5 520k swag 41.2 23.8 420k eyes set to kill
39.2 21.4 360k manchester united fc 69.8 27.6 40k * Public data not
related to G4S employees! Age 18 - 49 who like Sports and Security
Guards also like.. Future Now
14. Predictive analytics (example) 1. 2,163 email addresses
loaded into Facebooks lookalike audience tool 2. Facebook matches
email addresses with a user profile >1,300 confirmed anonymous
matches 3. Extrapolated 1,300 into lookalike audience representing
a 373,500 anonymous target audience in UK 4. Socially profile and
index the audience demographics & interests
15. MIA Data - Takeaways Establish your business challenges and
if data can inform the solution If so work out what data is
Meaningful! Put in the mechanisms and processes to capture and
manage the data flow and articulation Making sure the outputs are
Interpretable! Believe in the outputs and execute accordingly As
long as they are actionable!
16. MIA Data Questions? Colin Minto, Group Head Resourcing and
HR Systems, G4S Plc Executive Chairman, Direct Employers
Association Europe [email protected] linkedin.com/in/colinminto
@colinminto @DEAEurope