Models, Social Tagging and Knowledge Management - A fruitful Combination for Process Improvement
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Prilla, M. (2010): Models, Social Tagging and Knowledge Management? A fruitful Combination for Process Improvement. In: Proceedings of 2nd Workshop on Business Process Management and Social Software in Conjunction with the Business Process Management Conference 2009. Models, Social Tagging and Knowledge Management – A fruitful Combination for Process Improvement Michael Prilla Information and Technology Management Institute for Applied Work Science, Ruhr University of Bochum Universitaetsstr. 150, 44780 Bochum, Germany firstname.lastname@example.org Abstract. Process Models are the tools of choice for capturing business processes and communicating them among staff. In this paper, an approach focusing support in creation and usage as well as the dissemination of process models in organization is described, intending to improve business processes. To accomplish this, the approach makes use of social tagging as an approach to integrate process models into knowledge management (KM). In the paper, the empirical foundation of the approach is described and a corresponding prototype implementing a tagging mechanism for process models is discussed. Topics: New possibilities for the design of business processes by social software (1), phases of the BPM lifecycle affected by social software (2), use of social software to support business processes and new kinds of business knowledge representation by social production (3). 1 Introduction: Processes, Models and Knowledge Management Process models are well-established tools in business. They capture business processes as well as related knowledge and are used for a multitude of purposes . However, the active usage of process models in organizations is usually limited to a small group of people and models are usually not well known as resources in organizations . This paper argues that the dissemination of models and their active use by more users can help to get input from those both interested and competent enough to improve processes: people involved in the conduction of processes . The value of models aside from being expert tools for the documentation, creation and maintenance of processes in organizations is widely neglected. It can be found in models capturing knowledge related to processes, mediating its acquisition  and helping to solve related problems . Nevertheless, because of poor findability and acceptance of models , they are scarcely used. Additionally, modelling as a knowledge intensive task  can obviously benefit from KM providing relevant information and a context for understanding . Therefore, research questions concerned with the work presented here are which needs are imposed by the current situation of neglected process models, how these needs can be diminished and how the support needed can be implemented.
2 Michael Prilla Overcoming scarce usage and supporting model creation mean shifting attention towards models and intertwining them with other content. In this paper I argue that this can be done by semantic integration of process models into KM. In a previous analysis, formalized semantics were identified to not suit the needs of this purpose . Therefore, I use social tagging for models and other content in order to abstract from content types and focus on relevance instead in KM. To reach the goals described above, models as the best way to capture processes ,  have to be considered an important factor in process improvement. This is an observation backed up by earlier findings on the role models play in business process improvement . I argue that the approach presented here provides a step towards in making models artefacts of everyday use and therefore helps to improve business processes. The approach contributes to the improvement of business processes in multiple ways. First, social tagging provides access to processes for all stakeholders and thus disseminates models in organizations. Second, by making people aware of models, it increases the chance that those formerly excluded will give valuable feedback to business processes. Third, it supports the creation of models by providing relevant information and therefore improves the quality of models and processes. The concept of the approach has been described in  and basic requirements of it have been presented in . This paper focuses on an empirical study to analyze tasks and respective requirements. As an outcome of that, the paper presents a prototype of process model tagging, which is tailored to the needs found in the study. In what follows, section 2 gives an overview of the approach‟s background. In section 3, the empirical study is described and the resulting fields of support are analyzed for requirements. Section 4 then describes the prototype. The paper concludes with a discussion of related work (section 5) and an outlook to further work. 2 Social Tagging for the Integration of Process Models into KM Knowledge Management aims at “capture, validation, and subsequent technology- mediated dissemination of valuable knowledge from experts“. This aim makes no difference between content types. Thus, if knowledge is supposed to be shared, we should not rely on separate systems for different content, and we should not favour one content type over the other, be it text or models. The current situation of KM favouring textual content while neglecting process models and the existence and usage of specialized management tools for models counteracts this demand. Thus, we should aim for an integrative solution capitalizing on the potential of models in KM. Fig. 1. Potential of process models in KM (adapted from ).
Models, Social Tagging and Knowledge Management – A fruitful Combination for Process Improvement 3 The potential benefits of process models being visible and accessible in KM applications is grounded in the distinction of tacit and explicit knowledge . Tacit knowledge is in the head of people and not codified anywhere, whereas explicit knowledge is formalized by e.g. writing it down. In Fig. 1, this distinction and the transitions between knowledge being tacit or explicit are shown with respect to the potential benefits of models in KM. First, as shown in the upper right corner, models capture tacit knowledge related to processes. Therefore, neglecting them means leaving out relevant knowledge. Second (lower left), models should be usable to acquire process related knowledge. Third, models should be available for users in order to combine different content types (Fig. 1, lower right), which, as Nonaka  states, is what “can lead to new knowledge”. Neglecting existing model content hinders this process1. Therefore, the integration of models into KM bears potential for publicity and improvement of processes in organizations (see also ). 2.1 Basic requirements for the Integration of Process Models into KM Currently, to my knowledge there is no KM system properly supporting process models as its content. In a prior analysis , I found some basic requirements for the integration of models into KM: First, semantic content description to overcome the “complexity gap”  by providing homogeneous access to different content types such textual content and process models. Second, semantic content description must not be implemented at the expense of user effort. Such a mechanism has to provide a low usage burden while maintaining a high ceiling to provide a sufficient surplus in content handling. This makes formalized semantics such as Ontologies less applicable for this task. Third, all stakeholders of processes have to be integrated, bringing together their perspectives of how process can be improved . Fourth, such functionality has to be integrated into daily work tasks, meaning that these tasks must be tightly integrated into existing tools and give users a benefit for their sharing behaviour . In , these requirements are analyzed and as the result of that, social tagging is proposed as a mechanism fulfilling all requirements. 2.2 Social Tagging for Process Models The approach to integrate process models into KM proposed here is based on the mechanism of social tagging. Tagging means assigning unrestricted keywords to all kinds of content. It becomes social when tags are shared among users and different users are allowed to tag the same content unit. The key learnings from social tagging applications are that they provide an easy to use mechanism and the bottom-up integration of relevant stakeholders  with proper means of semantic content description  and make all content accessible despite its immediate popularity. 1 It should be noted that the analysis given above can also be done with similar results for systems managing business process models, which prefer models over textual content and are usually used by only a small number of people.
4 Michael Prilla Our analysis showed that tagging mechanisms are in applicable to process modelling tools and impose mediocre technical challenges . Comparing the characteristics of tagging to the requirements described in section 2.1 shows that tagging can fulfil each of them. However, questions such as which demands a resulting approach has to cover and how tagging can be applied to process models remain unanswered. The remaining paper will be focused on this question. 3 Model Knowledge Usage in Practice: An Empirical View To analyze the daily practices and KM needs of people using models, a series of six interviews with practitioners was conducted2. The participants worked in different business such as call centre organization, public energy supply and software development. All participants had a graduate degree and their age varied from 36 to 53. With the exception of one interviewee, they had more than ten years of experience in using models, making them viable candidates for the interviews. The interviews covered the entire lifecycle of models, including their creation, the integration of knowledge into models, their exchange, their understanding by users and their reuse. Afterwards, the interviews were transcribed and a catalogue of codes was developed out of the resulting material. The interviews were then analyzed according to patterns of support needed in the work with models and seven fields of support were identified. In this section, these fields are described and analyzed. 3.1 Observations from Practice: Seven Fields of Support In the interviews with practitioners, a detailed set of requirements complementing the basic ones described in section 2.1 could be identified. In this set, the abovementioned problems of lacking support in model creation and usage, neglected content and inadequate support for the acquisition of knowledge are present as cross cutting concerns. The set consists of seven fields of support: creating models, ensuring understanding and quality of models, using models together, using models for communication with others, finding and contextualizing models, connecting models with other content and facilitating and extending model usage. In what follows, these fields are described including sub-tasks, observations and resulting requirements3. Table 1. Support field „Creating Models“ Task Observation Requirement Information research and Hard to find matching content Provide a means to match available integration and competent partners needed content in KM, the current model during the modelling process. and expertise. Model reuse Hard to find similar models for Provide a means to find models by reuse. content similarity. 2 To ensure anonymity, I will refer to the interviewees as I1 to I6 in this section. 3 Please note that for the sake of brevity, the description of the analysis can only cover a choice of observations and requirements here.
Models, Social Tagging and Knowledge Management – A fruitful Combination for Process Improvement 5 The first field identified is model creation (Table 1). In the interviews, respondents mostly reported on information research and its integration into models for the preparation of modelling as well as model reuse during the modelling process. For the first task, interviewees described the process of modelling as preceded by collecting information on the respective processes and that their sources for this are people working in processes and documents describing the process. They stated that it was often hard to find the right people or content for the preparation and model reuse: “(…) for a co-worker in a subsidiary, there is no occasion in which he becomes aware of models, (…) diagrams drown in the depths of IT” 4 (I2). From a requirements perspective, model creation and reuse need to be supported by mechanisms to find relevant content and people. This results in the need to match content available in KM systems, models and a description of users‟ expertise. Integrating this into everyday work means coupling such mechanisms with modelling tools. Table 2. Support field „Ensuring Understanding and Quality of Models“ Task Observation Requirement Ensuring Understanding Hard to find relevant Provide a means to retrieve information when encountering information relevant for problems in understanding. understanding. Assuring Quality Relevant people for approval Provide a means to distribute hard to reach. models for expert approval according to their content. In the interviews, participants put an emphasis on means to ensure both understanding and quality of models for their later use (Table 2). For better understanding and higher quality of models, they combined models with additional textual descriptions, named model elements carefully and tried to get their models approved by stakeholders: “(…) the quality of a model is closely related to the amount of people that have talked about the model” (I3). However, they felt badly supported by existing tools in this task: “It would be nice if we could find additional content for models” (I5). They reported that they had a hard time to reach acceptance and find people to approve models. These observations result in two requirements. First, the understanding of models should be fostered by providing relevant content to users encountering these problems. Second, for approval a mechanism to reach people both competent and willing to give feedback on a model should be available. Table 3. Support field „Using Models together“ Task Observation Requirement Model exchange Task-specific distribution Provide a central repository for hinders availability. models with task-specific notifications. Hard to share and sustain Provide a means for context descriptions of models. descriptions sticking to models. 4 The statements of interviewees have been translated from German to English by the author.
6 Michael Prilla Interviewees reported several means they use for the exchange of models (Table 3) with others such as email, shared folders and content repositories, which worked reasonably well but had some shortcomings: “(...) and then it is present somewhere, because you sent it by email and it is bound to a certain sent-folder” (I1). They stated that it was difficult to properly describe models to make others aware of their relevance and that additional text in emails was not sufficient as it is bound to the email and provides no help if the model is used in practice. Most interviewees reported that the result of this situation is a lack of transparency concerning which models are available and thus sharing is difficult. From a requirements point of view, model sharing should be supported by a centrally accessible repository supporting users willing to share content to point people to models relevant for specific tasks. Additionally, the description of model content has to be attached directly to models. Table 4. Support field „Using Models for Communication with others“ Task Observation Requirement Using Models as Lacking awareness of models Make models as findable in communication artefacts as information sources repositories as textual content is. Notion of models as technical Provide a content description of artefacts. models in order to demonstrate their relevance. Most interviewees regarded models as a means for communication (Table 4). They reported different variants for this, including models as guidance in discussions and models as a specification for work processes. They also reported that models were not as frequently used by people as they intended them to be because people were not aware of models as relevant information or do not accepted them: “(…) they are mostly regarded as my artefact” (I2). There are two requirements stemming from this. First, to make people aware of models, they have to be able to find them as easy as they can find textual content. Second, in order to show that models contain valuable content the content of a model has to be made explicit to users. Table 5. Support field „Finding and Contextualizing Models“ Task Observation Requirement Searching and Finding Search engines cannot use the Provide a means to make a model‟s content of a model. content description accessible to search engines. Naming and structuring Model names are not sufficient Provide a means to give content for describing models. descriptions extending model names. Interviewees reported that finding and contextualizing models (Table 5) was hard to accomplish due to the lacking fit of existing retrieval methods. Rather than searching models for a long time, they would usually redo a model: “if I can‟t find it quickly, I stop searching” (I3). Even for corporate naming conventions, they stated that they were of no help: “these conventions should be adapted continuously, but this is not done properly, making them hard to use” (I4). The basic requirement stemming from these observations is that if models are to be found, a retrieval engine has to include a
Models, Social Tagging and Knowledge Management – A fruitful Combination for Process Improvement 7 description of their content, which must not rely on proper naming, as „proper‟ is dependent on both search intention and context. On the contrary, there has to be a means to provide context information to a model besides its name. Table 6. Support field „Connecting Models with other Content“ Task Observation Requirement Connecting models with other Manual linkage of content is Provide a mechanism handling content costly and erroneous. models and other content equally, identifying possible relationships and proposing them to a user. Interviewees reported that for the usage of models by others, they need to relate models and other content to e.g. create the documentation of their work and make it accessible to others (Table 6). They also reported that this was poorly supported in their companies and took a lot of time: “I wish there was a more lightweight way of linking content to models” (I2). This observation raises the requirement of easing the linkage of models and other content. For this, a mechanism handling models and other content equally and identifying possible relationships by their content and proposes these to a user should be provided. Table 7. Support field „Facilitating and Extending Model Usage“ Task Observation Requirement Extending the user group Scarce usage of models in Provide a mechanism pointing out organizations. models as relevant sources of information. Supporting target groups Specific models versions Provide a mechanism to generate needed for each target group. views from existing models. Concerning the facilitation and extension of models use (Table 7), the interviewees stated that models are usually bound to a small group made up by e.g. analysts and developers. They explained this by the poor acceptance of models and stated that they needed users to see the relevance of models. Additionally, they reported that they needed adequate models for different target groups such as clients, developers and users, but had no tool support for this task: “I don‟t see any need to discuss design details with a client” (I3). Two requirements result from these observations. First, for promoting model usage in organizations, people should be supported in perceiving the relevance of models. Second, it should be possible to generate versions of models for target groups and provide these versions to them in a KM application. 3.2 Discussion As can be seen from the analysis above, topics like knowledge acquisition, preventing the loss of knowledge and supporting the creation and active use of process models are present in nearly all fields of support. Moreover, the analysis shows the potential the approach bears for the improvement of business processes. As an example, for
8 Michael Prilla creating models, it is obvious that if a modeller is provided with relevant information in preparation and modelling, the quality of the model and the corresponding process will increase. Other fields of support such as using models for communication or finding and contextualizing models underpin this – if people in organizations have a better chance to find models and learn about processes from them, the quality of processes captured in these models is likely to benefit from their input. 4 Applying Social Tagging in a Modelling Tool From a technical perspective tagging mechanisms are not hard to integrate into existing applications due to manageable complexity and their straightforward mode of operation. After I integrated it in conformance to the requirements , it had to be tailored to the needs of the fields of support described in section 3. To face this challenge, a series of participatory design workshops was conducted, including potential users and experts in the field. In the workshops, we iterated through the fields of support described in section 3. The resulting prototype consists of the modelling tool SeeMe  and the KM application Kolumbus 2 . In what follows, some features of the resulting prototypes will be demonstrated and related to the fields of support described above. These features represent a choice of the overall design and are restricted to the work on the process modelling tool. It should be noted, however, that tagged process models are analyzed in the KM application, which possesses a tagging mechanism and corresponding functionality to search and structure content by tags (see  for more details). 4.1 Prototypical Implementation: A Tagging Mechanism for Process Models The integration of a tagging mechanism into a process modelling tool has to start with enabling the assignment of tags to process models. Considering the structure of process models, this has to be done on three levels: elements, groups of elements or sub-procedures and models. Fig. 2 shows an example for basic tagging support in process models. In the figure, the element “Action 3.1” is tagged as a single element and the elements “Action 3.2” and “Action 3.3” are tagged as a group of elements (indicated by a box around them). While the former is important for the reception of information from the KM application, the latter provides a means to mark up groups and share them with others. Fig. 2. Tagging in Process Models.
Models, Social Tagging and Knowledge Management – A fruitful Combination for Process Improvement 9 Considering the basic requirements described in section 2.1, a tagging mechanism has to be smoothly integrated into a process modelling tool. Therefore, tagging was not implemented as an isolated feature to be reached from an extra menu but integrated into existing dialogues used for e.g. naming elements (Fig. 3). Fig. 3. Smooth integration of tagging into the modeling tool. An important part of the design was focused to the connection of the modeling tool to the KM application. The resulting prototype currently supports adding tags to models and using these tags for storing the models in the KM application (Fig. 4, left side). The other way round, models and other content can be explored with tag-based navigation from the application (Fig. 4, right side). This e.g. enables a user to perform contexualized searches for models. The features shown in Fig. 4 correspond to the fields of support using models together, using models for communication and finding and contextualizing models. Work on the next generation of prototypes will include tag proposals for storing and searching models as well as generating proposals for adequate locations to store a model to based on its tags. Fig. 4. Tag-based support for searching and opening models in a KM tool. Another important part of the design was supporting the modeling process. For such support, the prototype features contextual content retrieval from the KM application (Fig. 5). The retrieval is based on tags available anywhere in the model and matching these tags to similarly tagged content in the KM application. Additionally, the names of elements are parsed and used as tags for the search. Figure 4 shows this function in the prototype. In the figure, similar content to the tags assigned to „Activity 3.1“ is displayed and offered to the user. This feature corresponds to the field of creating models, enabling modelers to find and intergrate
10 Michael Prilla existing knoweldge on processes in a model. It also applies to the field of ensuring understanding of models as well as connecting models with other content by interrelating similar content found by tags with the model currently viewn. Fig. 5. Tag-based contextual content retrieval from the KM tool. Besides the integration of tagging into a process modeling tool, the mechanism is intended to combine the tool with a KM application in order to foster the findability of models and contextually share models with others. Corresponding fields of support such as facilitating model usage and aspects of these fields not covered in the description above such as ensuring the quality of models are covered by functionality implemented in the KM application Kolumbus 2. This system, as described in , , also has a tagging mechanism and is able to handle content on this basis. 5 Related Work There are several research areas related to and influencing the approach presented in this paper. In the following, some of these areas are briefly sketched. A more detailed discussion and a comparison to the approach presented here can be found in . Familiar research areas can be found in approaches aiming at the management of process models by either creating model catalogues  or building applications for maintaining and editing models . Another familiar area can be seen in (semantic) business process management, which is focused to managing process execution and monitoring , . Additionally, in KM there is an area of research on process oriented KM, which uses process models for navigational and structural purposes . As shown in , all of these approaches are valuable for the problems they work on, but these problems differ from the one tackled here and therefore, these approaches do not provide a solution to the problems described above. Recently, approaches using social software in combination with model management have appeared. In , the authors describe an approach using social networks to support the work with process models by providing recommendations for processes to people and supporting the collaboration among people and processes. The approach described in  puts forward the idea of tagging process models for their management, but while the authors provide a solid ground for this idea, they do not show a system implementing it. What can be learned from both approaches is that social software can provide benefits in the management of process models and therefore improve business processes in organizations. The second approach also
Models, Social Tagging and Knowledge Management – A fruitful Combination for Process Improvement 11 shows that tagging process model is worthwhile. It corresponds with an early description  of the approach presented in this paper. 6 Conclusion and Further Work The basic argument pursued in the paper is that support for creation and usage of processes models as well as for their dissemination are decisive factors in the improvement for business processes. I argue that with process models being neglected content, relevant knowledge of and in processes is lost and therefore, potential for process improvement is wasted. My work is backed up by an empirical analysis of process model related tasks and resulting requirements. It covers the whole lifecycle of process models in organizations and therefore identifies needs and problems in various aspects. This paper presents tagging as a lightweight mechanism for semantic description and shows it that can accomplish the task of integrating process models into KM and therefore improve work with process. The resulting prototype shows how such potential benefits can be implemented in prototype aiming to tackle the problem of business process improvement and the involvement of stakeholders from a different side than existing approaches do. The approach presented here represents a concept of integrating process models into KM and should not be seen as the only way for that. Rather than that, there is potential for synergies with existing solutions, including combining it with business process management systems and other modelling and KM tools. Including such potential in future generations of the prototypes provides a both a challenge and huge potential for generalizing the approach and making it applicable in organizations. Right now, we are developing the next generation of the prototypes and experiments to explore its impact on process model usage and process handling in organizations. This generation will use tags as guidance for storing and finding processes models as well as mechanisms to match people, content and information needs in the modelling process. Additionally, the prototype will be tailored to all needs found in the empirical work. In general, we are confident to make the approach a successful step towards the dissemination of process models in organizations and therefore to business process improvement. Concerning its benefits, the approach will have to prove whether it eases process knowledge acquisition, supports the acceptance of models and improves the quality of models created with its support. References 1. Aurnhammer, M., Hanappe, P., & Steels, L.: Integrating Collaborative Tagging and Emergent Semantics for Image Retrieval. Collaborative Web Tagging Workshop at WWW2006, Edinburgh.(2006). 2. Brooks, C. & Montanez, N.: Improved annotation of the blogosphere via autotagging and hierarchical clustering. Proceedings of the 15th international conference on World Wide Web, pp. 625-632 (2006).
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