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How can the company improve collaboration?

“An organization with excellent internal communication will run smoothly, allowing its members to progress toward a mutual goal, which will ultimately affect the quality of external communication.” ― Scribendi

📖 Introduction

A company's success in its mission and ability to overcome unseen obstacles largely depends on the communication infrustructure within the company. HR has provided data on messages between employees as well as employee data. We'll look to map out the company network, with the ultimate goal of finding ways to improve collaboration throughout the company.

🎯 Targets

  1. Which departments are the most/least active?
  2. Which employee has the most connections?
  3. Identify the most influential departments and employees.
  4. Using the network analysis, in which departments would you recommend the HR team focus to boost collaboration?

✍️ Executive Summary

Using message counts between departments, we are able to find which departments are most central within our company network. Out of the six departments, Sales has been the most active while Marketing is the least active.

Based on this network analysis, it is recommended for HR to focus on boosting collaboration from Sales, Operations, and Admin departments with IT, Marketing, and Engineering. In order to increase the strenghth of our company network, we need to improve collaboration with the IT, Marketing, and Engineering departments as they are currently not very influential within the network.

It is imporant to decide which department connections between the top 3 influential departments (Sales, Operations, Admin) and the bottom three (IT, Marketing, and Engineering) are of the highest importance for improving. Otherwise we could see our company network separating if the IT, Marketing, and Engineering departments only focus on improving communication with each other.

💾 Understanding The Data

The company has six months of information on inter-employee communication. For privacy reasons, only sender, receiver, and message length information are available (source).

Messages has information on the sender, receiver, and time.
  • "sender" - represents the employee id of the employee sending the message.
  • "receiver" - represents the employee id of the employee receiving the message.
  • "timestamp" - the date of the message.
  • "message_length" - the length in words of the message.
Employees has information on each employee;
  • "id" - represents the employee id of the employee.
  • "department" - is the department within the company.
  • "location" - is the country where the employee lives.
  • "age" - is the age of the employee.

Acknowledgments: Pietro Panzarasa, Tore Opsahl, and Kathleen M. Carley. "Patterns and dynamics of users' behavior and interaction: Network analysis of an online community." Journal of the American Society for Information Science and Technology 60.5 (2009): 911-932.

⬇️ Loading Datasources

Messages Data

# read in messages from csv and save as dataframe
suppressPackageStartupMessages(library(tidyverse))
messages <- readr::read_csv('data/messages.csv', show_col_types = FALSE)
messages

Employee Data

# read in employees data from csv and save as dataframe
employees <- readr::read_csv('data/employees.csv', show_col_types = FALSE)
employees

Joined Data Sources

For ease of use, we've combined our data sources into one table/dataframe as seen below.

# join messages and employees into one dataframe while reflecting attributes from the
# employees df for both sender and receiver
df = messages %>%
	left_join(employees, by=c('sender'='id')) %>%
	rename(sender_department=department, sender_location=location, sender_age=age) %>%
	left_join(employees, by=c('receiver'='id')) %>%
	rename(receiver_department=department, receiver_location=location, receiver_age=age)
head(df)

1. 📝 Which Departments Are the Most/Least Active?

We'll begin analyzing the company network by assessing the strength of the network on a more macro level. Looking at messages sent and received by each department provides us with an overview of how strong cross-team collaboration is at the company.

a. Message Count by Department

Using the dataframe below, we see that out of the six departments, Sales is the most active with 2,780 messages (1,551 sent and 1,229 received). Marketing is the least active department with 16 messages sent and 140 received.

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