Gaining employee data insights to grow and develop workforce. Marta Aguilar - Accenture

1. Gaining Employee Data
Insights to Grow and
Develop Workforce

2. MARTA AGUILAR
ANALYTICS MANAGER
Experience in
Delivery
Experience in
Human Resources
Work in
Agile
Passion for
Data
marta.aguilar@accenture.com
Thanks to Neo4J for all the ideas, Arlind Nocaj for always sharing his expertise in
graphs, and thanks to my great team.

3. WHY?
BUSINESS
CHALLENGE
IDEA
DATA/ARCHITECTURE
ENABLEMENT

4. THE BASICS

5. ¿?

6. EMPLOYEE BASICS
Hi!
I am Juan.
• … is an employee
• … works as an Analyst
• … aspires to specialize in Big Data
• … follows the Agile Community
• … comments in Blockchain forums
• … volunteers in Health causes
• … works in Madrid Office
• … belongs to XYZ department
Who is Juan? Juan…
What does he like? How can we help him grow?

7. Juan is connected to other employees
• … by skills set
• … by completed trainings
• … by organization
• … by interest
• … by expertise
• … by communities
• … by projects
• … by location
• … by priorities and aspirations
• … by level
Our company is a network of connected employees

8. POTENTIAL DATA
TRAINING
SKILLS
EXPERIENCE
PRIORITIES
INTERESTS
SOCIAL MEDIA
CERTIFICATIONS
DEPENDENCIES

9. POTENTIAL USE
CASES

10. POTENTIAL USE CASES
Integrate
Join
TEAM
OPTIMIZATION
HIDDEN POTENTIAL
SKILL
RECOMMENDATION
LEARNING
RECOMMENDATIO
N
EXPERTS
CONNECTION
CONNECTIONS
RE-SKILLING
TALENT
MATCHING
HR DECISION
MAKING
UP-SKILLING

11. Use case
TALENT MATCHING
ROLE
RECOMMENDATION
Neo4J Competency Management:
https://neo4j.com/graphgist/competency-
management-a-matter-of-filtering-and-
recommendation-engines
• Collaborative ranking
• Weight based on distance
• Improve recommendation based on
additional parameters

12. Use case
HIDDEN
POTENTIAL
Person
Training
Skill B
Open
Role
Work
Exp.
Blog 2
WROTE
DEVELOPS
HAS EXP
Blog 1
Skill A
Tag I
Tag II
REQUIRES
WROTE
Neo4J as a Key Player in Human Capital Management - Graph Connect 2017 - Videos
• Explore data
• Infer data
• Connect data

13. Use case
SKILL
RECOMMENDATION
• Similar people
• Filtering out

14. Use case
UP-SKILLING
• Near skills
• Overlap Similarity
CALL algo.similarity.overlap(data, {topK: 2, similarityCutoff: 0.5, write:true})

15. Use case
RE-SKILLING
• Skills clusters
• Next Best Skills
CALL algo.louvain.stream(label:String, relationship:String,
{weightProperty:'propertyName', defaultValue:1.0})
YIELD nodeId, community - assign a community to each node id

16. Use case
LEARNING
RECOMMENDATION
Organization Learning- https://neo4j.com/graphgist/organization-learning
• Similar people
• Improve recommendation based on
additional parameters

17. Use case
CONNECTIONS
EXPERT CONNECTIONS
• Filtering out

18. Use case
HR DECISION
MAKING
• Consolidated/
aggregated insights

19. KEY POINT
Data Privacy

20. MY RECOMMENDATION
• Don’t be afraid
• Get ideas:
 Graph Connect / Graph Tour
 Subscribe to Blogs
 Connect with Neo4J experts / Neo4J
community
 Meetup.com
• Enjoy!

21. THANKS