Difference between revisions of "AY1516 T2 Team YSR"
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Revision as of 21:04, 10 January 2016
Project Motivation & Objective
Overview
Ever since the dawn of the Age of Analytics companies have used data to drive decision-making for organizational functions such as operations, sales, marketing, and finance. Human resources, however, has traditionally been an analytical backwater (only 5% of HR decisions are based on analytics). This is being disrupted by a new wave of analytics – people analytics.
Motivation
People analytics is a rapidly growing area of business intelligence and big data technology that uses snippets of people-related data to optimize business outcomes and solve business problems. People analytics replaces intuition, old-boy networks and outmoded rules of thumb with computerized tests, database searches and quantifiable performance metrics.
“People analytics transforms our understanding of socialization in the workplace, the impact of office layout, and even concepts as “soft” as creativity. In the future, we will use this knowledge to create new ways of organizing people that radically improve the way we work. Office layouts that respond to social context and real-time feedback on communication patterns and interaction styles are new levers enabled by people analytics that no one could have imagined.”
Scope
Access to TrustSphere’s datasets will allow the team to build a system from scratch using previously unused raw data to better understand turnover and attrition rules.
The minimum research points we would like to address:
- Understand the number of relationships an employee will have at different periods of time in his or her working life
- Measure the speed of growth at which employee relationships grow in a company
- Correlations between the sizes of internal and external relationships employees have
- Through social network analysis, calculate the likelihood of an employee in an informal group leaving a company upon the exit of another closely tied employee
- Identification of metrics that can help predict the likelihood of an employee leaving
It is important to note that the scope of this project is fluid and can be furthered to address additional questions TrustSphere might have regarding the dataset.
Data
The small dataset given to us was pulled from the outlook (mail server used at TrustSphere) database. The data basically is an exchange of emails. The dataset consists of 890,000 email exchanges and 13 variables namely:
- Date
- IP Address
- Local
- Remote
- Local Domain
- Remote Domain
- Originator
- Direction
- Domain Group
- Inbound Count
- Outbound Count
- Size
- MsgID
Project Timeline
References
Comments
