Motivation And Project Overview
People Analytics has been rated as the second-biggest overall capability gap in organizations by the Deloitte university press in 20151. Through people analytics, companies are able to find better hires, improve retention, and find more suitable leaders. This has a direct impact on direction of the organization and hence its growth. Our team has a great opportunity to delve into Social Network Analysis, a fast-growing research field in Analytics through this project.
In this project, our focus is to develop various metrics that would quantify the collaboration between employees, identify the most influential employees and give managers a high-level view of these statistics to maintain a collaborative and efficient workplace. Currently at the company, these metrics are computed based on various sets of data that are primarily collected via pulse surveys. The survey data collection process is slow and makes it difficult for managers to view real-time insights. As an alternative, our team would be computing these metrics based on only email communication data. Since the data is always present in the IT system, an automated data pipeline can be created to compute the metrics and view them on a custom dashboard. We would also be involved in feature engineering to create an unbiased email network before the calculation of metrics.
A primary metric that our team would explore and test for value is a hybrid centrality to calculate an influential score. We are exploring a new equation that combines various
Objective
- Perform Feature Engineering to create a new ‘Trust Score’ algorithm. A trust score is an aggregate weightage shows the strength of communication tie between two employees in a social network.
- Develop a dashboard that displays various metrics that would quantify the collaboration between employees, identify the most influential employees and give managers a high-level view of these statistics to maintain a collaborative and efficient workplace.
- Research and validate the potential of a Hybrid Centrality (potentially a combination of betweeness and degree) calculated from email communication data as a measure of influence score.
Data
We are provided with an excel sheet containing a huge set of email exchange log via the TrustSphere domain. The data consists of 14 columns as described below:
Column Explanations
| Date
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Timestamp of the email
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| Remote IP
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If the email exchange is external then this column shows the external person's email
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| Remote
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The TrustSphere employee who is receiving or sending the email
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| Remote Domain
|
Always TrustSphere
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| Local
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Email address of the person sending the email
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| Local Domain
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Domain of the person who is sending the email
|
| Originator
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Inbound, outbound or internal (if you’re receiving the email, sending it or if the email is between 2 TrustSphere employees)
|
| Direction
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Always TrustSphere in this case
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| Domain Group
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Email Header (Subject Line)
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| Subject
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Type of message: email/im (instant messaging)/voice/sms
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| Inbound Count
|
Number of emails received
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| Outbound Count
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Number of emails sent
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| Size
|
Size of the message (number of characters)
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| Msgid
|
Encoded Message ID
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Data Statistics
| Number of rows
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121,154
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| Date Range
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11/26/2016 8:00 am to 02/01/2017 00:00 am
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METHODOLOGY
SCOPE OF WORK
1. Create a hybrid centrality score as an overall comprehensive measure of the network
2. Identify Silos
3. Assess Collaboration
a. Within departments
b. Within different geographical regions
c. Within projects
4. Assess Influence, Network Strength and Email collaboration
5. Develop a dynamic dashboard to visualize relevant measures
6. The Scope is fluid and will become more specific as the project progresses
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