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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. It uses various facets of people-related data to optimize business outcomes and solve business problems. The application of people analytics with new techniques such as predictive behavioural analytics has helped organisations to save millions of dollars while improving attrition rates, employee engagement and identify underlying training requirements.

TrustSphere is the widely recognized market leader in Relationship Analytics. TrustSphere enables forward thinking organizations to unlock the inherent value of their own networks using next generation technology. The solutions provide real-time intelligence and insights which help clients across the globe improve salesforce effectiveness, enterprise-wide collaboration and corporate governance.The motivation behind this project is to assist TrustSphere in verifying the effectiveness of their product through other statistical techniques.https://www.crunchbase.com/organization/trustsphere#/entity

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

http://www.mckinsey.com/insights/organization/power_to_the_new_people_analyticsa

https://en.wikipedia.org/wiki/People_analytics

https://www.crunchbase.com/organization/trustsphere#/entity

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