mcpl2013 - social network analyses in organizations: challenges and approaches for studying work...

© 2013 IBM Corporation

Social network analyses in organizations: challenges and approaches forstudying work networks

Ana Paula Appel, Vagner F. de Santana, Rogério A. de PaulaClaudio S. Pinhanez, Victor F. CavalvanteIBM Research Brazil, Service SystemsMCPL 2013 - Sep 11-13, 2013

IBM Research


Agenda

Context

Related works

Challenges

Study

Results

Final remarks

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Context

The spread of online Social Networks (SN) use is responsible for the current high interest in complex network analyses, e.g.:

“People you may know” feature How people get connected?

SN are networks in which Nodes represent people, or groups of people Edges some form of social interaction

Social Network Analysis (SNA) allows to diagnose problems and identify opportunities in organizations

SNA allows to identify collaborative hot spots and cold spots

In organizations, connecting people with similar skills, interests, or profiles can be worth for employees in many ways

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Context

Our work took place at a large IT service delivery organization, Big Service Factory (BSF)

Employees are very specialized, often focused on specific tasks

Sysadmins are responsible for some of the most critical functions necessary to maintain the customers’ IT infrastructures

Tickets describe particular incidents

Tickets have attributes indicating work shift, department, customer, date, descriptions of the problem, among others

Challenges in the context involve Assignment time Data insertion by hand …

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Related works

The foundation of the field is attributed to Jacob Levy Moreno who conducted the first long-range sociometric study from 1932-1938

Monge and Contractor [1987] utilized social network analysis to study the communication patterns among workers

Properties identified: The power-law degree distributions as showed in Albert et al. [1999] and

Adamic et al. [2000] The diameter shrinkage present in evolving networks presented by Leskovec

et al. [2005] The Small World phenomenon studied by Milgram [1967]

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Challenges

How to combine data sources, build a very large network, and mine it?

Each data source can become a network and all networks can be combined making one large weighted or multi-edges network, but…

How do we define the weight for each graph? When using multi-edge graphs, the number of available algorithms/techniques

is an issue.

It is hard to define the mining task for multi-edge graph

Combining different type of sources to build one very large graph requires New algorithms New range of methodology and definitions of graph mining area

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Study

108,050 tickets

Collected from the past 10 months

315 sysadmins

Four features where considered Work shifts (morning, afternoon and night) Department (four departments) Customers (around 70) Problem severity (1, the hardest one to solve)

We consolidate the monthly data for each sysadmin into a percentage representing the weight of each of customers, service lines, shifts, etc.

Each sysadmin was represented by a feature vector with 80 positions

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Study

To build the networks, we compute the cosine distance among each sysadmin using a K-NN (K Nearest Neighbor) search, using k = 10

Who are the 10 most similar sysadmins?

We explored two types of network Using the entire feature vector to compute the distance Splitting the feature vector into four independent vector, each one

representing one feature, building four networks, and then merging them into one multi-edge network

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Results – Severity , Shift, Department, and Customer

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Results – Service Lines vs. Shift

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Results – Service Lines vs. Severity

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Results – Shift vs. Severity

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Final remarks

Unveiling underlying emerging networks is a promising approach

Integrating social analytics and qualitative research has the potential to reveal "hidden" practices of daily distributed complex tasks

Using network as a model to connect sysadmin is important specially in crises situation

Future work involves Field work Integrating ticket information with sysadmins’ communication patterns

New methods and tools are needed to process and display very large networks with parallel edges

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Thank you!

[email protected]

@santanavagner

mcpl2013 - social network analyses in organizations: challenges and approaches for studying work...

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