© 2013 IBM Corporation
Social network analyses in organizations:
challenges and approaches for
studying work networks
Ana ...
© 2013 IBM Corporation
Agenda
 Context
 Related works
 Challenges
 Study
 Results
 Final remarks
2
© 2013 IBM Corporation
Context
 The spread of online Social Networks (SN) use is responsible for the
current high interes...
© 2013 IBM Corporation
Context
 Our work took place at a large IT service delivery organization, Big
Service Factory (BSF...
© 2013 IBM Corporation
Related works
 The foundation of the field is attributed to Jacob Levy Moreno who
conducted the fi...
© 2013 IBM Corporation
Challenges
 How to combine data sources, build a very large network, and mine it?
 Each data sour...
© 2013 IBM Corporation
Study
 108,050 tickets
 Collected from the past 10 months
 315 sysadmins
 Four features where c...
© 2013 IBM Corporation
Study
 To build the networks, we compute the cosine distance among each
sysadmin using a K-NN (K N...
© 2013 IBM Corporation
Results – Severity , Shift, Department, and Customer
9
© 2013 IBM Corporation
Results – Service Lines vs. Shift
10
© 2013 IBM Corporation
Results – Service Lines vs. Severity
11
© 2013 IBM Corporation
Results – Shift vs. Severity
12
© 2013 IBM Corporation
Final remarks
 Unveiling underlying emerging networks is a promising approach
 Integrating social...
© 2013 IBM Corporation
Thank you!
VagSant@br.ibm.com
@santanavagner
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MCPL2013 - Social network analyses in organizations: challenges and approaches for studying work networks

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  • sysadmin that work in same work shift tend to be more similar than the ones that work in different work shift connection among sysadmins during the work is higher to sysadmins that work in same department in same work shift, no matter the costumer or the tickets’ severity
  • sysadmin that solves same type of ticket tend to be similar to each other
  • most of tickets with severity one occurs in work shift 3 - night most of the tickets with severity three (Figure 3 (c) red) occurs in work shift 1 – morning
  • MCPL2013 - Social network analyses in organizations: challenges and approaches for studying work networks

    1. 1. © 2013 IBM Corporation Social network analyses in organizations: challenges and approaches for studying work networks Ana Paula Appel, Vagner F. de Santana, Rogério A. de Paula Claudio S. Pinhanez, Victor F. Cavalvante IBM Research Brazil, Service Systems MCPL 2013 - Sep 11-13, 2013 IBM Research
    2. 2. © 2013 IBM Corporation Agenda  Context  Related works  Challenges  Study  Results  Final remarks 2
    3. 3. © 2013 IBM Corporation 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 3
    4. 4. © 2013 IBM Corporation 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  … 4
    5. 5. © 2013 IBM Corporation 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] 5
    6. 6. © 2013 IBM Corporation 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 6
    7. 7. © 2013 IBM Corporation 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 7
    8. 8. © 2013 IBM Corporation 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 8
    9. 9. © 2013 IBM Corporation Results – Severity , Shift, Department, and Customer 9
    10. 10. © 2013 IBM Corporation Results – Service Lines vs. Shift 10
    11. 11. © 2013 IBM Corporation Results – Service Lines vs. Severity 11
    12. 12. © 2013 IBM Corporation Results – Shift vs. Severity 12
    13. 13. © 2013 IBM Corporation 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 13
    14. 14. © 2013 IBM Corporation Thank you! VagSant@br.ibm.com @santanavagner

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