Difference between revisions of "APA Project Overview"

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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.
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Human Resource Analytics is the idea of using data in the organizational context to understand different factors about employees such as their degree of collaboration and influence. Known researcher Rob Cross has also said “Organizational Network Analysis provides a powerful means of making invisible patterns of information flow and collaboration, visible.” These factors are generally computed based on various sets of data that are primarily collected via pulse surveys. The data collection process is slow because pulse surveys must be distributed at regular intervals to receive updated insights. However, this is not a viable option as it is not only a repetitive process but also makes it difficult for managers to view real-time insights.  
 
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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.
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This study explores and investigates whether subject lines and frequency of emails exchanged between employees can be used as a representative resource for analyzing organizational networks, specifically, the work network. We define work network as the network of employees with whom one interacts with on a daily basis for work purposes.  
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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
 
 
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Revision as of 13:44, 22 April 2017

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Motivation And Project Overview

Human Resource Analytics is the idea of using data in the organizational context to understand different factors about employees such as their degree of collaboration and influence. Known researcher Rob Cross has also said “Organizational Network Analysis provides a powerful means of making invisible patterns of information flow and collaboration, visible.” These factors are generally computed based on various sets of data that are primarily collected via pulse surveys. The data collection process is slow because pulse surveys must be distributed at regular intervals to receive updated insights. However, this is not a viable option as it is not only a repetitive process but also makes it difficult for managers to view real-time insights.
This study explores and investigates whether subject lines and frequency of emails exchanged between employees can be used as a representative resource for analyzing organizational networks, specifically, the work network. We define work network as the network of employees with whom one interacts with on a daily basis for work purposes.

Objective

  1. 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.
  2. 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.
  3. 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 Timestamp of the email
Remote IP If the email exchange is external then this column shows the external person's email
Remote The TrustSphere employee who is receiving or sending the email
Remote Domain Always TrustSphere
Local Email address of the person sending the email
Local Domain Domain of the person who is sending the email
Originator Inbound, outbound or internal (if you’re receiving the email, sending it or if the email is between 2 TrustSphere employees)
Direction Always TrustSphere in this case
Domain Group Email Header (Subject Line)
Subject Type of message: email/im (instant messaging)/voice/sms
Inbound Count Number of emails received
Outbound Count Number of emails sent
Size Size of the message (number of characters)
Msgid Encoded Message ID
Data Statistics
Number of rows 121,154
Date Range 11/26/2016 8:00 am to 02/01/2017 00:00 am


Methodology

Click on METHODOLOGY for more details.

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