Effective Knowledge Management using Big Data and Social Network Analysis

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1 Effective Knowledge Management using Big Data and Social Network Analysis Andry Alamsyah 12, Yahya Peranginangin 1 1 ICT Research Group, Telkom Economy Business School, Telkom University 2 School of Electrical Engineering and Informatics, Institut Teknologi Bandung andry.alamsyah@imtelkom.ac.id, yahya.peranginangin@gmail.com Abstract Knowledge management consists of identifying, creating, representing, distributing, and enabling adoption of insights and experiences in an organization. One approach of modeling knowledge management is using network model. Big Data is one of important ICT technological roadmap, which main function is modelling behaviour and helping organization decision support. Social Network Analysis is a micro version of Big Data where we can model and establish social network quantification. In this paper we will show how Social Network Analysis can help organization applying Knowledge Management strategies and practices by experiment using real-world large dataset contains exchanges between employees inside in an organization. Keywords: social network analysis, big data, knowledge management, decision support, social computing, and organization 1. Introduction Knowledge Management comprises a range of strategies and practices used in an organization to identify, create, represent, distribute, and enable adoption of insights and experiences. Growth of interest in both learning in organization and knowledge management occurred at very similar time. This is to a large extent no accident, and indicated the interrelatedness and interconnectedness of both issues [5]. The new way of acquiring and spreading knowledge, i.e. exchanging ideas, values, information flow, and behaviors is developing rapidly, by the influence from outside the organization such as Internet or social media, and inside the organization. Although, we can say that it is contagious, the more an organization has knowledgeable elements, the better their competitive advantage [4]. As it is said by Drucker (1993) The basic economy resources is no longer capital nor natural resources, nor labor, but it is and will be knowledge [3]. Often, there are differences between formal organization structure and informal organization structure in a context of information flow, leading to inefficient organization operations as shown in Figure. 1, a reproduce scenario from [2], where the position of an employe name Cohen in informal structure are more efective in spreading the information, yet in formal organization structure, his location is not easily reachable by others. The problem of conventional approach of collecting data in knowledge management and social sciences studies in general, that they rely heavily on approaches such as questionnaire, interview, snowball sampling, contact tracing, random walks and direct observations [1]. We mostly consider those approaches are good enough to implement, if we deals with small-medium number of data in an organization, typically with population under peoples. In the case of larger corporations, the drawbacks of such approaches are very expensive, time consuming, and having the accuracy issue. These shortcomings are 1

2 typical to offline data collection approach, where they mostly suffer when population is getting bigger. Otherwise online data collection approach using Internet are cheap and today large volume s data can predict individual and social behavior in their network. (a) (b) Figure 1. (a). Formal organizational structure. (b) Informal organizational structure of information flow. Data Analytics is powerful tools to process, cleaning, transforming and modeling data with the objective to discover useful information, insight, suggesting conclusions and support decision-making. Today, the data from online conversations growth exponentially because of increase usage of User Generated Content and Web 2.0. This phenomenon leads to the birth of Big Data, which basically Data Analytics with several distinct features called 4V: the big Volume of traffic data, the Variety of data format to be exchanged and stored, the speed / Velocity of data creation is faster then the ability to process data, the insight / Value that we get from different type of data in order to get comprehensive understanding about an event. Big Data is widely anticipated in many big coorporation with objectives to increase their performance on business intelligence, customer-relationship management, supply chain management, target marketing, competitive advantage, and others. Social Network Analysis (SNA) is a micro version of Big Data which focus on relationship / interaction between actors in an online social network. SNA models the relation using mathematics branch of Graph Theory, where nodes represent actors or entities while edges represent relations between actors or entities. Social structures is built from social links contains individual or organizational connected one and another by means of friendship, kinship, interest, financial transaction, dislike, trust, beliefs, sexual relationship, knowledge, prestige and many others. SNA study views those relationship as a graph representation either symmetric or asymmetric relations in a form of ties, links, connections. Research in a number of academic fields has shown social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organization are run and the degree to which individuals succeed in achieving their goals [6]. SNA has several tools and metrics for mapping important knowledge relationship between peoples or departments that to be particularly helpful for improving collaboration knowledge creation and knowledge transfer in organizational settings [2]. In this paper, we will show how to implement SNA in forming efficient organization using real large dataset, which consistent with this paper theme about Big Data. We will use metric in SNA to answer several question such as: what is the most efficient network formation for knowledge management, who is the most influential actor in certain context and where we should relocate this actor in order to boost his/her influence, how we detect a 2

3 communities/groups inside an organization and some others importants questions based on network model. This paper is an upgrade works from our previous paper Role of Social Network Analysis in Knowledge Management [8] with the addition of experiment using large-scale data, a near Big Data approach. 2. Network Model and Social Network Analysis Roles in Knowledge Management SNA models relations/interactions between actors/group inside an organization into nodes and edges as mentioned in previous chapter. Nodes are representing actors or peoples and edges are representing relationship among the actors. In Figure 2, we show example of SNA model of relations between actors in famous play of les miserables [7]. The larger nodes means the actor have more connections thus they are the most important actors in the network. The same color means the nodes are belong to the same community / group based on their overall interactions pattern. Fig. 2. Network models of relationship between actors in Les Miserables [7] We elaborate several measurement in SNA that suit for knowledge dissemination scenario. Those metrics explained as follows: Centrality: to measure importance or influence an individual or group, there are several type based on the approach: Degree Centrality, Betweeness Centrality, Closeness Centrality, Eigenvector Centrality, Alpha Centrality, Katz Centrality, Pageranks. Bridge: an individual or group that connecting two distinct group or department, without them the two distinct group or department will be separated, this approach is operated at the same ideas with Structural Hole. Density and Distance: a topological measure useful for predicting the scale of our organization. Tie Strength: to measure how strong the relationship between individuals or groups, the stronger tie the better environment for collaboration knowledge creation, although weak ties have their own advantage. Another measures which also useful is measuring connections which includes: Homophily: groups of individuals with same interest, job descriptions or other factors form a ties together versus dissimilar others, Reciprocity: the extent to which two individuals reciprocate each others friendship or other interaction leading to the strength of tie, Mutuality, Transitivity. Network segmentation can also be measured by SNA using the metrics such as: 3

4 Clustering Coefficient: a measures of likelihood that two associates of a node are associates; in practical we measure complete mutuality between nodes that we called Clique. Applying SNA metric for knowledge management in an organization can be described based on several cases often occur in real-world application. We expand our categorization based on [8] into the Table 1. Table 1. Cases in a business organization that can be resolved by the help of SNA Case Questions SNA Tools Leader Selection Ranks Task Force Selection Mergers and Acquisition Competitive Advantages Advertising Attachment Market Segmentation Information Dissemination Strength out the organization Dynamics of Organization Who is the central in the trust and respect network? How do we rank our top performer individuals in the organization? How do we put together a team that maximally connected through out the organization? How to merge separate cultures / networks? What is the missing links between supply and demand? How strong the impact of our advertisement effort? How segmented our market is? How is the information / knowledge spreading? How to increase redundancy and interconnectedness? How dynamics our organization is? Degree Centrality, Betweenness Centrality Eigenvector Centrality, Pageranks Closeness Centrality Homophilly, Reciprocity, Mutuality, Transitivity Structural Holes, Bridge Tie Strength, Community Detection Clustering Coefficient, Clique, Cohesive Random Walks, Hits Algorithm, Temporal / Dynamics Network Bridge, Overlapping Communities Temporal Networks The extent of SNA roles in helping organization analyze their knowledge environment is far richer than we previously imagine that it is only limited to the graph visualization and simple metrics. SNA helps determine measure and metrics we mentioned above regardless network formation / structure such as whether it's a dense or sparse network, whether it consist sub-cluster or sub-component inside overall organization network, whether it consist several bridges in strategic location that catalyst faster knowledge dissemination. Several interesting scenario also mentioned in [2] regarding real-world application that can be explained by network model / SNA. Bottleneck / Overload: central nodes that provide the only connection between group / parts / components of the network Number of Links: insufficient and excessive links between department can lead to ineffectiveness performance 4

5 Average Distance: the degree of separation connecting all pairs of nodes in the groups, short distance transmits information accurately and in timely way, while long distances transmit slowly and can distort the information Isolation: peoples that are not integrated well into group and therefore, represent both untapped skills and a high likelihood of turnovers Highly Expert People: we need to make sure that are being utilized properly Organization Subgroups: they can develop their own subcultures and negative attitude towards other groups 3. Network View of Knowledge Relationships The importance of social network means more than just communication map or information flow perspective. Effective interventions for improving specific networks of people often have more to do with helping groups to know what the others know and ensuring safety and easy access to information among people. Based on this fact, we focus less on communication and more on knowledge based dimensions of relationships that make useful in sharing and creating knowledge. A study from IBM [2] about key relationship, they found four dimensions tended to be critical for relationship to be effective, in terms of knowledge creation and use: 1. Knowing what someone knows, this is related to analyzing their understanding of each other s knowledge, skills, and abilities to evaluate the overall cohesion of the group. It is called the know network. 2. Gaining timely access to specific person, this is related to identifying the central people in a specific network, of which their skills and knowledge are the most influential in terms of knowledge creation and use. 3. Creating viable knowledge through cognitive engagement, this is related to assess those who are not well connected in the network, this people probably represent underutilized asset. 4. Learning from trust relationship, this is related to analyzing the network to highlight ties between people who we will trust in knowledge sharing environment. Figure 3. Network representation of knowledge transfer inside an organization with 34 employees 5

6 By applying these dimensions to important groups of people inside an organization, we can better analyze and intervene in critical points of knowledge creation and sharing. The four key dimensions can be viewed separately to illustrate different aspect of an organizational network, but they can also examined together and cumulatively. For the illustration we construct data relationship about knowledge transition inside a network of 34 employees regarding issues about IT subject, who should they turn to ask for knowledge / information among their colleagues. The network representation is in Figure 3. We can see employes 1, 33 and 34 emerged as central of this group, which means they are the most likely peoples to turn for advice regarding any subject about IT. We also found that there is a subgroup that contain of 6 employes on the right side of the network, which means without the relationship to 1 and 18, they would disconnected from the main network. The existence of groups implies inefficiency in knowledge utilization; members of the group are not utilizing expertise of the network. Strengthening out ties between individuals is probably one solution to broader the information access. Trust Network and Recommender System [9] also play an important roles in mapping who we trust, who we recommend and how our recommendation accepted and accessible to the rest of the network. 4. Experiment and Dataset Our experiment is using Enron dataset from [10]. After filed for bankruptcy, Enron Corporation faced heavy investigation for its financial scandal. A set of corpus, i.e. internal s between Enron Corporation employes, is one of important data sources used in investigation conducted by Federal Energy Regulatory Commission. Later on the corpus is made available for free by Massachusetts Institute of Technology s researcher (the corpus data set is available at In this paper the corpus is used as object implementation of SNA tool. The purpose of this experiment is to show how we use large-volume data and interactions, a similar scenario to Big Data, to implement SNA metric in understanding knowledge management in an organization. The data set consist of employes (nodes) and exchanges (edges). We construct an undirected network model / unweight network, which means we don't consider the weight of relations thus it consist nodes and edges. We use Gephi to calculate SNA metric and measurement. On the first step we visualize overall network of exchange, the result is shown at Figure 4. On macro level, we can see that network is very dense in the middle, which is the biggest component of the network, and many smaller components in the periphery of the network. We can zoom-in to the level that we wanted in order to get closer look of the pattern, especially in dense area / component. To find the most influential / important employe or group of employes in the network, we use Centrality metric. Degree Centrality (DC) measures how many interactions that an employe made, Betweenness Centrality (BC) measures how likely an employee become bridge or act as trusted person to forward the information or knowledge, Closeness Centrality (CC) measures how fast an employee reach anyone in the company, it is also called as based on the location of the employee. BC and CC value is normalized between value [0,1], with 0 is the minimum value and 1 is the maximum value. We rank top-10 of each DC, BC and CC measurement. We found similar pattern for DC and BC that show nodes number 5038 as the most influential employee in both metric categories (Table. 2). However CC measurement revealed that the biggest value coming from lowly connected employee, which means this measurement is not accurate representation and can be ignored for now. Community Detection measures how many communities or groups inside the network and how clustered the network is. By knowing the structure of community, we can understand how interaction between group of friends, between department, formal and informal. Our measurement found that the network have 1294 communities. In Figure 4, node colours 6

7 shows nodes categorization into communities, same colour means nodes belong to the same community. Table. 3a shows percentage rank of 10 biggest communities in the network, surprisingly node 5038 does not belong to the biggest community that is community 57 which its size is cover 17.88% of the whole network. Figure 4. Map of full network exchange between employes in Enron Table 2. Centrality metrics (a) Top 10 Degree Centrality (b) Top 10 Betweenness Centrality and (c) Top 10 Closeness Centrality (a) Top 10 Degree Centrality (b) (c) Top 10 Betweenness Centrality Top 10 Closeness Centrality 7

8 Our intention in this experiment is doing data analyzing on high-level abstraction with only top-10 centrality and community detection. In the real life however we could process as much detail as we can, provided we have considerably sufficient time to process. In other words, we can zoom-in to very detail process and also we can zoom-out only to several important nodes / parts in the network. In this experiment, we focus on employee 5038 as our primary since he is the most influential employee in the entire network belong to community 88, which cover only 6.81% of network size; it means that community 88 is close-tight community, where the members have very intense interactions among other community. The fact that several other high centrality employees such as 566 and 588 are also in the community 88 strengthen the fact that community 88 is very dense or at least denser than any other communities. If we zoom to the 1-level ego-network of 5038, we see that he interract with many peoples across the groups and structure (Figure. 5) We now revisit CC to get the more accurate description on how fast knowledge disseminate. We filter the network to the based on certain level of DC. The reason of this treatment is to filter out the peripheric nodes, which are the nodes located on the edge of the network. It is clear that peripheric nodes have little effect on the speed of knowledge dissemination thus we can ignore them. The degree level that we choose is DC value above 111, we get the result on Table 3b. From the result we can see that community 33 have higher value of CC, with employes 136, 140, 292 and some others from community 33 dominating the rank, This mean community 33 have more shortest path reaching others part of network comparing to others. It is interesting that employes and communities dominating in DC and BC do not contribute to higher CC value. Table 3 (a) Community inside enron network and their size (percentage of network) (b) Closeness centrality after applying degree centrality filterization (a) (b) Conclusion from the experiment, 5038 is the most connected employe, he connect with more employes in Enron than any other employe. His community or we can say his department, which is 88 however is not the most active department in Enron network. Surprisingly the most active department, which is 57 have not any single employe in the top- 10 most active employe. The fact that department 33 have most shortest-path concludes that we have several scenario to retain the importants employes and communities depends on whether we want to reach out as many as possible (DC) or whether we want to see who is the important people in forwarding knowledge (BC) or whether we want to spread the information as fast as possible (CC). 8

9 Figure 5. 1-level ego network of employee Conclusions Many organizations put their effort largely on hiring quality individual or consultants for their day-to-day operational. However their attention on attract, develop and retain highly skilled individual alone is not sufficient. There is a little effort into systematic ways of providing knowledge that embedded in people and relationships, the significant shortcomings is the facts that people rely on their knowledge and the knowledge of their colleague to solve the problem. Social Network Analysis allow us to understand better how an organization create and share knowledge, with this approach we will better equipped to move beyond this approach alone. This paper shows how we can effectively manage knowledge inside organizations with the help of Big Data and Social Network Analysis approach. With the coming of Big Data era, we predict more and more data available online as a part of social interactions. Organization can look their employee interactions using inside social media such as , notes, formal/informal meeting or outside social media such as facebook, twitter, etc. Our experiment represent high-level solution and give the overall pictures on how we detect the most important employee and how many communities inside the organizations. We also compare the experiment result, logical explanations with empirical observations on what roles of certain employees / communities in overall knowledge network. For future research, we recommend taking a look to others metric and their meaning in knowledge management that are not covered in this experiment, such as Structural Holes, Bridge, Tie-Strength, Eigenvector Centrality, Homophilly. 9

10 REFERENCES: [1] Newman. M.J. Network: An Introduction. University of Michigan and Santa Fe Institute. Oxford University Press 2010 [2]. Cross. R, Parker. A, Borgatti. S. A Bird Eye s View: Using Social Network Analysis to Improve Knowledge Creation and Sharing. IBM Institute of Business Value [3]. Drucker, P. F. Postcapitalist Society. New York: HerperCollins Publishers [4]. Porter, M. Competitive Advantage: Creating and Sustaining Superior Performance. The Free Press [5]. Hislop, D. Knowledge Management in Organizations. Oxford University Press [6]. Scott, J. Social Network Analysis Theory and Applications. Sage Publications 2000 [7]. Knuth, D.E. The Stanford GraphBase : A Platform for Combinatorial Computing. Addison-Wesley, Reading, MA, 1993 [8]. Alamsyah, A. Role of Social Network Analysis in Knowledge Management. Jurnal Manajemen Indonesia Vol 12 No 4, April 2013 [9]. Easley. D, Kleinberg. J. Network, Crowds, and Markets: Reasoning about Highly Connected Network. Cambridge University Press [10]. Klimmt. B,Yang. Y. Introducing the Enron Corpus. CEAS conference, 2004, taken from Stanford Network Analysis Project : 10