GraphConnect Europe 2017 Claudio Borile, aizoOn

1. moOn
A Multidimensional Graph Approach
to Human Resources Analytics
CLAUDIO BORILE
GRAPHCONNECT
MAY 2017 – QEII CENTER LONDON

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GraphConnect London
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WHO WE ARE
Our vision: To apply widely the
scientific and quantitative
approach, for a more responsible
and sustainable society
Our mission: To sustain the future
of our customers in the digital era,
bringing knowledge in
technology and innovation
AIZOON IS
aizoOn is a technology consulting
agency for innovation, is
independent, and operates at a
global scale

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WHO WE ARE
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GraphConnect London
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We follow our customers in all continents: Africa, America, Asia, Europa, Oceania
GLOBAL FOOTPRINT
AUSTRALIA
Sydney NSW
EUROPE
Torino ITA | Cuneo ITA | Milano ITA | Genova ITA
Bologna ITA | Roma ITA | Bari ITA | Sheffield UK
USA
New York NY | Troy MI | Cambridge MA | Lewiston ME
aizoOn USA
aizoOn AU
aizoOn EU
Direct presence
Areas of intervention

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“Standard” HR
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GraphConnect London
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Historically the organization is
resumed in a pyramidal chart,
from the CEO/manager to the
working base and neatly
divided into departments,
groups, hierarchical chain, etc.

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GraphConnect London
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“Standard” HR
Reality is different,
and messier

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“Standard” HR
A company is a complex
organism, composed of
many different interacting
components.

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Data-driven HR
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GraphConnect London
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In the last few years data analytics and quantitative
methods have been applied to many areas of business,
marketing and production to help making better choices.
People are rightfully considered the most important
assent in an organization, and why shouldn’t we exploit
data to better know this asset?

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Data-driven HR
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GraphConnect London
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In the last few years data analytics and quantitative
methods have been applied to many areas of business,
marketing and production to help make better choices.
People are rightfully considered the most important
assent in an organization, and why shouldn’t we exploit
data to better know this asset?

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People Analytics
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People Analytics is a data-driven approach to the management
of the workplace for a better knowledge of the real organizational
structure, practices and processes.
• Organization
• People Review
• HR Transformation
• Talent Management
• Perception vs. Reality
• Monitoring
• Early detection
• Top and weak performers

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The moOn approach
moOn: MultidimensiOnal cOmpany Navigator
is a People Analytics tool imagined not to
substitute, but to support the traditional HR
methodologies with the help of quantitative
measures.
Multidimensional: We use diversified sources
of data to highlight different aspects of the
organization and the people in it, and a
coherent framework allows to navigate
through these many layers of information.

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Data
Data will come from:
• E-mails
• CRM, Organizational and personal
registries
• Surveys
• Meetings
• Resume
• …
The project is now TRL 6

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Data
In this presentation:
• E-mails
• CRM, Organizational and personal
registries
• Surveys
• Meetings
• Resume
• …
The project is now TRL 6

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Components
Network Analysis
Machine learning Techniques
Dashboard & control panel
Network visualization and exploration
Storage
Analysis
Data
Viz

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Mail Network – raw data
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One year of e-mail logs from a standard Microsoft exchange mail server
~ 2 million rows csv file
Data format:
Data is anonymized for privacy reasons
timestamp sender recipient(s) Subject

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Mail Network – Graph construction
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We keep only “relevant” communications (~30% of total)
Atomic resolution for internal addresses, domain level for externals
A directed and weighted graph is extracted where nodes are
internal people or external domains and edges represents single
mail threads

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Database – E-mails
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The Neo4J graphDB stores all the
information from the
preprocessed dataset.
The schema allows to easily and
rapidly gather all time-
dependent, aggregate data for
later visualization, and the
navigable graphs, with standard
queries.

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Database – E-mails
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All the queries to the DB are
done directly from moOn’s
Python core using Cypher for
later manipulation

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Mail Network – Overview
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~1200 Vertices
500 internal nodes,
700 external domains
~16000 Edges

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Mail Network – Departments
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Inter-departments
communications
and silos

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Mail Network – People descriptive
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People workload
and total contacts

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Mail Network – People descriptive
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People workload
and total contacts

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Mail Network – People descriptive
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People workload
and total contacts

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Mail Network – Socialization
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Socialization
process for newly
hired people

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Mail Network – Socialization
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Socialization
process for newly
hired people

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Mail Network – Stress
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We can easily monitor
stress components like
working after office
hours, or working during
weekends. Also, we can
have suggestions on the
daily routine of the
person

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Mail Network – Stress
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We can easily monitor
stress components like
working after office
hours, or working during
weekends. Also, we can
have suggestions on the
daily routine of the
person

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Mail Network – Communication network
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Subdivision of contacts
by single addresses,
departments, external
domains.

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Mail Network – Communication network
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Subdivision of contacts
by single addresses,
departments, external
domains.

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Mail Network – Communication network
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GraphConnect London
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Subdivision of contacts
by single addresses,
departments, external
domains.

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Mail network – Graph exploration
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Complete
network with
internals and
externals
separation

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Mail network – Graph exploration
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Egocentric
network of a
specific user

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Mail network – Graph exploration
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Departments’
network

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Mail network – Graph exploration
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Intergroup
bridging
and
gateways to the
exterior.
Individual or
departmental level
of “frontier” towards
the exterior

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Surveys
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GraphConnect London
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Surveys allow us to add a layer of informal and personal network
of connections between people in the organization.
Easily compiled and submitted through a ad hoc web platform,
surveys are automatically integrated in moOn and elaborated.

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Surveys – Graph exploration
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GraphConnect London
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All methods and graphic
interfaces are similar to
the mail part, but they
carry very different
information

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Multidimensional navigation
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We look at the same
people from different
perspectives, to capture
all the complexity of the
workplace

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Network Metrics
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The topological structure of the networks yield relevant information,
that we translate in easy-to-interpret visualizations

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Network Metrics
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Depending on which level of network we are focusing on, we can
exploit its topological structure to infer information on the behavior
and characteristics of people. Reference figures (e.g. innovators,
mentors, etc.), top performers, bridges, or the frontier and bulk
people with respect to the exterior of the organization or of a
department. We can see the real interaction and composition of
working groups, or how to easily reach a customer. For the
succession problem we can compare the network structure of
similar workers, compare it with other data like skills, seniority,
contractual level, etc. to have the best replacement.

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Conclusions
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• Simple sources of information and data can help us know and
manage our organization better.
• Multidimensionality gives us the opportunity to scrutinize our
organization on various levels, from the formal to the informal,
and compare them to the official structures and hierarchies.
• Fine and coarse grained analytics give us ready-to-use
information from single employees to the whole business.
• The specific choice of the Neo4j graphDB helps us to organize
and query the great amount of data that we obtain for later
processing.

40. www.aizoongroup.com
claudio.borile@aizoongroup.com
Claudio Borile
@aizoongroup
AUSTRALIA
Sydney NSW
EUROPE
Torino ITA | Cuneo ITA | Milano ITA | Genova ITA
Bologna ITA | Roma ITA | Bari ITA | Sheffield UK
USA
New York NY | Troy MI
Cambridge MA | Lewiston ME
THANK YOU!