Social Network Analysis as a Tool for Improving Enterprise Architecture

Transcription

1 Proceedings of the 5 th International KES Symposium on Agents and Multi-agent Systems, KES-AMSTA Manchester, UK, June 29 - July 1, 2011 Lecture Notes in Artificial Intelligence LNAI, Volume 6682, 2011, pp DOI: / _67 Social Network Analysis as a Tool for Improving Enterprise Architecture Przemysław Kazienko, Radosław Michalski, Sebastian Palus Institute of Informatics, Wrocław University of Technology Wybrzeże Wyspiańskiego 27, Wrocław, Poland {kazienko, radoslaw.michalski, sebastian.palus}@pwr.wroc.pl Abstract. The paper provides the overview of essential analyses and methods, helpful for enterprise architecture improvement and based on social network approach. The ideas presented in this paper focus on social network, that is built with the use of real-life manufacturing company data. It has been shown that corporate social network analysis, as a decision support system, may be influential for managing a company. Several ideas, measurements, interpretations and evaluation methods are given and discussed, in particular centrality degree, social network extraction, process management. 1 Introduction Nowadays, in the times of strong competition, business organizations constantly look for tools and techniques to beat market opponents and become leaders among other companies. This paper focuses on corporate social network analysis as a possible way to improve enterprise architecture leading to the above mentioned goals. However, the proposed methods are suitable for all kind of organizations with the stable organizational structure, not only the commercial ones. Social network analysis is the well-established way of analyzing relations between persons in the social network. These methods focus on determining user position, extracting groups of users, describing and analyzing changes in such kind of networks [12]. Among many types of social networks, like social web sites, instant messaging systems, it has been found useful to build and analyze corporate social networks [1], [17], because results of such analysis may be found very helpful in improving corporate management. This paper presents findings achieved by using corporate social network analysis in the mid-sized manufacturing company and possible ways of their interpretation as well as further usage of these results. The source data gathered for building the social network were unique and novel in the field of corporate social network analysis, because in other papers [17], only some assumptions about organizational structure were made due to lack of full information. This time, the achieved results have been compared to reliable information about company structure to depict that it is possible to improve organization management using proposed set of techniques as a decision

2 2 Przemysław Kazienko, Radosław Michalski, Sebastian Palus support tool. It has also been shown that only some metrics used in social network analysis are valuable and suitable in the process of the evaluation of a company. Section 2 provides introduction to social networks in the enterprise describing possible ways of gathering and extraction of social network data. Section 3 relates to the idea of corporate network analysis. In Section 4, authors present their results from social networks analysis in the above mentioned company, while Section 5 focuses on discussion regarding these results. Conclusions and future work directions are presented in Section 6. Overall, authors focused on analysis of employee position and process management implications of the achieved results. 2 Social Networks in the Enterprise 2.1 Source Data for Social Network The data for the social network can be gathered from a vast variety of sources. One of the most often used methods is based on questionnaires and interviews. People are asked to identify the frequency of communication with others and the channel of interaction, e.g. face-to-face meetings, telephone, , paper letters, etc. [8]. They need to recall their behavior that has taken place over the long time frames in order to capture as much information as possible. However, if the time frame is too long or the information is too detailed, accuracy and reliability of such a method can be very low. It has been proven in [2], [3] that the data based on recall, although widely used, may be much less reliable than the data gathered from observation. Moreover, recall may be better for perceptions of media usage, while the electronic data may be better for measuring actual usage. Nowadays, when social networking sites have become an essential part of everyday life, questionnaires are no longer needed. These sites allow users to construct an online profile with explicitly defined connections with other users (usually called friends ) [5], [19]. Since web 2.0 sites like Facebook, LinkedIn, MySpace and many others have been established, they have attracted more than one billion users worldwide. People use social networking sites in their daily lives, to communicate with friends, share interests, organize events, find jobs and many more. However, connections in such systems are self-defined by users, which means that it can be subjective. Instead of asking people to provide details of their communication, parallel collected data can be used. Recently, people communicate with each other electronically (by [17], phone [16], or Instant Messenger, etc.), they leave a trace behind them in the form of server logs, phone billings, IM logs, etc. The more facts of communication between users, the stronger they are connected within the social network. Corporate information systems introduce even more possible sources for social network extraction, like directory services (e.g. Microsoft Active Directory [9]) or ERP systems [20] which allow to analyze not only pure communication but users` business relations as well. Figure 1 presents few of all possible data sources for extraction of the social network.

3 Social Network Analysis as a Tool for Improving Enterprise Architecture 3 Observation Phone billings IM logs Questionnaires Social Networking Sites Data sources logs ERP systems Directory services Fig. 1. Data sources for social networks extraction in an enterprise 2.2 Social Network Extraction Social network extraction should be considered as the most important preliminary step in all further activities on the obtained social network. The final results may differ depending on the approach chosen at this stage. As previously stated, there are many diverse source data for enterprise social network extraction. However, when deliberating enterprise architecture improvement ideas, it should be noted that it is essential to obtain the scheme of organizational structure and to gain at least one type of source data for the company social network extraction. Overall, the organizational structure should be treated as the snapshot state of the enterprise representing formal dependencies between individual employees. Those dependencies may vary - from simple ones, as functional structure, through matrix structure, up to complex horizontal designs oriented mostly to processes in the enterprise [6]. Paradoxically, such kind of relatively well-defined corporate relations, i.e. the organizational structure can be hardly extracted automatically, because, depending on size and profile, the companies may have no need to maintain full company structure drill-down from board through departments up to a single employee in their IT systems. That is why, it may be necessary to convert organizational structure, taken from official documents, into graph, where nodes are representing employees and vertices employee-supervisor relations. This was also partly necessary in the analyzed company. Additionally, each node, i.e. each employee, is labeled with the department ID and function what is especially useful in business process analysis. It is also important to have the opportunity to track all the organizational changes during the analyzed period, i.e. employment or dismissals, position or department changes, etc. The company structure, just like real social network, also varies over time and that fact may be crucial in further analysis. Obviously, the process of the social network extraction may differ due to accessibility of the data. The most common case refers a single-layered social network, in which the network is extracted only from one simple data source, e.g. logs about communication [4], IM communication [18], etc. An ERP system is the more sophisticated example of social network source and analysis of transaction logs

4 4 Przemysław Kazienko, Radosław Michalski, Sebastian Palus in such case can be valuable for business process improvement [1]. If there are more data sources available, it is worth building a multi-layered social network, in which each layer represents relations inside one channel of communication or common activities [10], [13]. Further social network analysis (SNA) and enterprise architecture improvement considerations (see Sec. 3 and 4) can analyze those layers separately or flattened with weights assigned to each layer, depending on purpose. It is worth mentioning that the corporate structure can also be used as an additional layer in SNA. In both cases a single-layered and multi-layered social network - there is one more consideration: should a social network be limited only to company employees or should also outside connections matter? In the first case, the number of nodes in the social network will be equal or less than the number of nodes in the corporate structure network. In the second case, it is proposed to limit the output social network to company employees, but instead of including outside nodes, one or more additional labels are added to employee nodes informing about count of external connections or count of s sent outside, etc. Such information can be included further in social network analysis as another factor for calculation. While extracting a social network from one or multiple sources, yet another difficulty may appear user identification. In a single layer, one user can use multiple aliases [14], [4] and in case of many layers a need to identify the same user across the layers is even greater. The suggested way to start such pre-processing is to use the company structure node list first, because such network has only one occurrence of a single employee. All further layers should be mapped and reduced to the company structure nodes list, because in that case all users will be uniquely identified, as shown in Figure 2. Multiple instances of the same employee in a layer should be reduced to one at this stage. Without proper pre-processing the social network may be hard to analyze. n 1 n 2 n 3 n 4 n n Layer 1: s e 3 e 1 e 2 e 4 e n Employee list n 1 n 2 n 3 n 4 n n Layer 2: ERP logs Fig. 2. Mapping social network actors to employee list There are also special cases in the mapping process, strongly depending on company operating model. For example, if a group of employees uses one login in ERP system or a single account, such a functional account cannot be simply mapped to a single employee and sometimes additional internal company knowledge is required to perform mapping correctly. In some cases it is even impossible to merge...

5 Social Network Analysis as a Tool for Improving Enterprise Architecture 5 layers containing functional entities with layers including individuals and then each layer could be analyzed separately. 2.3 Multi-agent Architecture of the System As presented in the previous section, the data used for building the corporate social network may be stored in various, independent systems. That is why the multi-agent architecture is very helpful in the process of obtaining the data and generating the social network. It is proposed to build an agent architecture, where each agent or agent set is responsible for one source representing one social network layer. For example, after identification of possible source data location, one set of agents may gather the organizational structure data in Active Directory catalogue, the other will analyze mail logs, while some others will focus on contacts and meetings data stored in the Microsoft Exchange server. The result of the proposed approach will be a multi-layered social network built effectively and independently. 3 The Idea of Corporate Social Network Analysis The idea of corporate social network analysis consist of application of social network analysis methods (SNA) to social networks built on various company communication and event data. Such analysis can provide an additional information on groups, relations and information flow in a company which may be further used for improving company management in various ways [21]. Also regularities and anomalies in processes, key persons extracted from the social network and many other factors may be found and all these can be considered crucial in finding competitive edge for organizations. Typically, social network actors are persons, i.e. customers, social networking sites users, employees, etc. What may be interesting is that corporate SNA can consider other type of actors functional actors, i.e. warehouse workers threat as one entity or even non-human actors, like IT system, depending on data source availability. Thus, there may be different networks built based on the same data source and analyses may be performed using various combinations of entities. That makes corporate SNA task interesting, but, in some aspects, also complex and challenging. As stated, corporate SNA uses mostly same tools as typical SNA centrality metrics, clustering, group analysis, etc. However, due to its nature, corporate SNA takes into account also some other company information, such as process definitions and HR information. Conjunction of them can facilitate improvement of company organization and management, however, it also requires development of new combined data analysis methods. Interpretation of SNA results in the organization and especially corporate management changes based on these results should be made very carefully, because their straight implementation may be often risky and more disruptive than helpful for organization. This aspect will be discussed further in Section 5.

6 6 Przemysław Kazienko, Radosław Michalski, Sebastian Palus 4 Experiments 4.1 Company Description Researchers gained access to the organizational structure data, logs and (very limited) process definitions in the mid-sized manufacturing company located in Poland. Company employs 300 persons, whereas 1/3 are clerical workers, the rest laborers. The period analyzed was half a year. The type of organizational structure is functional [6]. However, due to organization operating model and its consequences to organizational structure clarity as well as logs interpretation possibility, only a subset of organization have been chosen for analysis: 49 clerical employees not directly related to manufacturing process. There exists three-level management structure in the selected company part: management board (2 persons), managers (11 persons) and regular employees (36 persons) and they work in twelve different departments. 4.2 General Assumptions Social network has been build using logs with the following assumptions: 49 internal identities (employees) analyzed (the reasons described above) s addressed to self, auto responses ( delivered, out of office etc.) and correspondence with IT systems (ERP and helpdesk system) were omitted resulting graph is directed and weighted with the weight of an edge between node i and j is as follows: where is the number of s sent by node i to node j and is a total number of s sent by member i. It means that weight w ij focuses on local neighborhood of an employee rather than on global network characteristic. Because of logs structure, there was no distinction between To, CC and BCC recipients. The resulting set of data contained 11,816 s in total. (1) 4.3 Social Network Profile Basic characteristics of resulting social network are shown in Table 1. Property Value Number of nodes 49 Number of edges Average number of edges per node Range of incoming edges per node <9;32> Range of outgoing edges per node <2;48> Table 1. Characteristic of the social network based on company interactions

7 Social Network Analysis as a Tool for Improving Enterprise Architecture 7 The histogram of node degrees is shown in Figure Fig. 3. Histogram of node degrees Outgoing degree of a node Incoming degree of a node 4.4 Results The analysis focused on opportunities to improve enterprise architecture by means of SNA techniques and, in some cases, comparison to organizational structure or company processes definitions. The first step of social network analysis entities (node) identification showed the difference in number of nodes between the employee list (organizational structure) and social network. As concluded, it was caused by an expatriate delegated to work from other company (part-time), however, still using the previous address. Moreover, the position, which the expatriate occupied, was an important one (head accountant). The lesson learned shows that such situations should be taken into account as a preliminary step before building the social network. The other possible reasons of the same effect: supervisory boards, self-employment, etc. As regards node degree histogram (Figure 3), six persons were contacting with less than ten recipients and these persons were potentially interesting from the process management point of view. Hence, after acquiring information about processes those employees are involved into, it has been found that the process was carried out in non optimal way in three cases. If these persons would use proper IT system or instead of paperwork all of those processes may be improved. Another analysis headed towards finding key persons in each department (partition of social network) and further comparison with the organizational structure. It has been found that among many centrality measures [7], [15], a variant of in-degree centrality IDC w (j) provides very good results in finding department managers in the social network. It is computed in the following way: ( ) (2) where n is the number of all nodes in the social network. In comparison with outdegree centrality, where weight is based not on the number of incoming mails, but on the number of outgoing mails, it has been found, that most managers are rising in indegree centrality rankings (Figure 4). It means that managers are becoming identifiable not due to their relations to the others, but due to the others' relations to these managers.

8 8 Przemysław Kazienko, Radosław Michalski, Sebastian Palus What may be interesting, two non-managers have been found in the top ten nodes of the IDC w rank compared to the organizational structure, but after consultation with the company these two members were also managers at that time: the first person was the formal assistant manager of the before mentioned part-time expatriate and the second one was the executive temporal manager because the original manager was on long-time sick leave (longer than the analyzed period). It is visible in Figures 4(a) and 4(b), where the first step is based only on the social network compared to the organizational structure and the second step reflects those two special cases. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% (a) rank of identity using IDC w metric 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% (b) rank of identity using ODC w metric Percent of managers identified (the first step) Percent of managers identified (the second step) Department manager Top 10 managers identified (second step) Fig. 4. Managers identified in company social network 5 Discussion on Enterprise Improvements It has been shown that corporate SNA can provide diverse findings. The question is how companies should use the results in a reasonable manner. As previously stated, companies should not base only on SNA, because it is only a support tool. All the results suggesting that some changes can be made should be carefully analyzed and consulted with the management, quality control department, HR department and other authorities involved in the discussed topic. The reason for a such broad discussion is quite simple some facts may be simply hidden for SNA (not stored in any IT system) and consultations can either confirm SNA results or reveal this latent knowledge. For example, the analysis performed on one available layer mail server logs - will prove that one company manager is extremely separated from other department managers or even company board. It may suggest that he is alienated from others, what, in general, is undesired on that type of position. Despite of that, further consultations showed, that he prefers direct meetings with co-employees rather than electronic communication and, for that reason, he should not be relocated or dismissed. That situation is also worth deeper analysis, because it may suggest that there is a need to gather information from more sources in that particular company, i.e. PIM appointment lists as a new layer. Another benefit is that this SNA result may also suggest some changes in case of direct meetings not all decision contents may be written and if there will occur some controversy regarding one of decisions, there would not be a trace to the final decision maker. That is why company management may suggest using communication to that particular manager in order to improve transparency of decision making process.

9 Social Network Analysis as a Tool for Improving Enterprise Architecture 9 The other example worth discussion is the above presented in-degree centrality metric which showed that it is possible to find department managers using SNA. However, why should this metric be considered as the most valuable one? And what decisions should be undertaken by top management if some real managers were found below their subordinates? Or, maybe, if more layers in the same company were analyzed, the rank would differ significantly? There are some more elemental and easier to interpret findings possible. The example of expatriate revealed that some information may be not properly protected, because internal s were stored outside the corporate infrastructure where security policies may be not on desired level. It may lead to some changes in internal communication, improving overall company security. Nodes degree analysis was also valuable in terms of process management shortening the delays in company information flow. Despite all the techniques regarding core data analysis that may be very challenging for SNA experts that the real challenge for companies is to properly interpret and make valuable use of the achieved corporate SNA results. 6 Conclusions and Future Work The analyzed real-world case has proved that corporate social network analysis may be a way to get another point of view on the company. The different channels of communication between employees may be used as a data source to extract corporate social network and results of such network analysis can be used as a valuable decision support tool leading to company architecture improvement. Albeit, as stated in Section 5, the output of this tool should not be used uncritically. The research undertaken shown that some metrics and techniques used in corporate SNA are more valuable than others, especially in-degree centrality may be considered as an important metric in further corporate network analysis. Further work on this topic will include in particular: time-oriented dynamic communication analysis, including sliding windows and weights adapted to time [11], comparison of graphs built separately using different layers as well as the merged ones to analyze metric ranks changes and social network surrounding analysis. Acknowledgments. The work was supported by The Polish Ministry of Science and Higher Education, the development project and the research project References 1. Aalst van der, W.M.P., Song, M.: Mining Social Networks: Uncovering interaction patterns in business processes, In Weske, M., Pernici, B., and Desel, J., (eds.) International Conference on Business Process Management LNCS, vol. 3080, pp Springer-Verlag, Berlin (2004) 2. Bernard, H. R., Killworth, P., Sailer, L.: Summary of research on informant accuracy in network data and the reverse small world problem. Connections, 4(2), pp (1981)

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