ONDERZOEKSRAPPORT NR 9408 An Integrated Model for the Scoping Decision of Automated Help Desks by Patrick E. MERLEVEDE Jan J. VANTHIENEN D/1994/2376/10
An Integrated Model for the Scoping Decision of Automated Help Desks Patrick E. MERLEVEDE Consultant KBS solutions W armoes NV (Belgium) Jan J. V ANTHIENEN Katholieke Universiteit Leuven Department of Applied Economic Sciences Dekenstraat 2, B-3000 Leuven (Belgium) Tel.: (32)16-28.58.09, Fax: (32)16-28.57.99, E-mail: fdban06@bleku111 Abstract Help desk automation is for many companies the first application area of knowledge based systems. In a lot of cases, however, insufficient attention is paid to the scoping question, advocating the use of advanced technology, without thinking in terms of appropriate solutions. Several approaches in help desk automation are being taken nowadays, with different degrees of computer involvement. Typical applications can be classified into the following common views, based on perception of underlying technologies: (intelligent) information sharing & retrieval, call administration, case storage & lookup, expert systems. A lot of help desk systems, however, only deal with a partial aspect of the problem, without justification or even awareness of the total picture. In this paper a global framework of help desk automation is presented. It is argued that, from a scoping point of view, such a framework must be considered first. Only then, and based on the evaluation of needs and opportunities, the decision to implement a total or partial solution can be made on safe fundamentals. The use of a general help desk automation model is advocated as a basis for the system inception phase, in order to set organizational priorities and avoid nice solutions to the wrong problem. Keywords Track: Deployment of Intelligent Systems; Topic: Organizational Issues, Justification for intelligent systems projects; Subtopics: help desk, knowledge based system, expert system, case based reasoning, machine learning, knowledge technology, intelligent information system, text retrieval
-2-1. Introduction In information systems development, the scoping decision is of major importance. Many projects have failed because of a too narrow (trivial) or too broad (infeasible) focus. When it comes to knowledge based systems, however, this crucial system inception phase is often treated with less respect than it deserves. In recent papers, the stress is put on a formal analysis of the situation and the needs of the help desk. Citing Avron Barr [92]: "A good analysis of your operation will uncover the underlying causes of your help desk problems and determine whether they are likely to be treatable by automation. This "systems analysis" should be done before you spend any money on a computer system of any sort. " In the following sections a general model of help desk automation will be presented as a basis for the system inception phase. It is argued that the decision to implement a total or partial help desk solution can only be made based on this global picture, after evaluating needs, opportunities, and priorities of the various components. 2. Some Common help desk views Several approaches in help desk automation are being taken nowadays, with different degrees of computer involvement. More and more, software becomes available to support these [sometimes partial] approaches. Based on previous experiences [Vermeulen & Merlevede, 90], we would like to classify the typical 'solutions' into 3 common views: Information sharing & retrieval Under this common denominator reside elements (technologies) like: on-line documentation (e.g. manuals, shortlists, procedures), E-mail, groupware (workflow automation), etc. Some advanced extensions might be present, e.g. intelligent text retrieval, multimedia databases, hypertext, fuzzy systems,...
- 3- Call administration & case lookup Keeping track of cases at the help desk mainly implies two types of data storage: administrative call tracking databases and problernfsolution storage. Both approaches require detailed storage of individual calls, although for different purposes. The former will be used for help desk management purposes (follow-up, statistics, accounting,...). The case history, on the other hand, is an invaluable source of information. Instead of being a dead archive, it may be used to retrieve identical or similar problems from the past, potentially leading to such advanced issues as machine learning, database mining, case based reasoning. Expert systems technology Several advanced AI technologies have found their way to the help desk. The basic issue here is to provide intelligent support (assistance, advice, decision making) to the solution of the problem, based on domain knowledge. The expectations of the results when applying one of these technologies are quite high. Several commercial tools exist such as decision tree editors, simple rule based systems and model based tools (deep knowledge, simulation). 3. Positioning the help desk project: goals, partners and major components The help desk is more than an office answering end user questions: several other parties are involved; the goal is larger than strict problem solving and major components range from traditional administrative matters, over information retrieval to intelligent problem solving. 3.1. HELP DESK GOALS IN THE PROBLEM LIFECYCLE The first and major task of the help desk is to solve incoming problems as soon as possible, in an efficient and effective way (avoiding a backlog of unsolved problems is an important objective here). But when a call comes in, two observations can be made:
-4-1. the user has a problem, and 2. he or she is not able (or not willing) to solve it himself. Based on these observations, the role of the help desk can and should be extended to incorporate both stages of the problem lifecycle: 1. avoiding the problem situation in the first place or, 2. if a problem has occurred, enabling the user to solve it without extra help. In a similar fashion, the task of the help desk is not finished when the problem is solved: the user(s) must be informed about causes and consequences in order to avoid similar problems in the near future. The help desk time scope, therefore, may range from call based problem solution only (the narrow help desk) to the full trajectory from error avoidance (which is usually beyond the responsibility of the help desk, e.g. bug free software design), problem avoidance (correct use by training), call avoidance (the help yourself approach), up to repetitive call avoidance (distributing problem solutions): the extended help desk (figure 1). When the time scope of the help desk is extended in this way, more emphasis is put on training, teaching and giving feedback, on top of actual problem solving. In accordance with this time scope, the role and goals of the help desk will range from information retrieval (librarian), support (assistant), advice (adviser), education (instructor) to problem solving (expert).
-5- I install I problem encountered call I I problem solved design ( train teach EXTENDED I ool~ p.ooiom j ""'""" ( narrow ) ) Figure 1: The extended versus narrow help desk in the problem lifecycle 3.2. PARTNERS INVOLVED IN THE HELP DESK Since the primary goal of a help desk is to have qualified people solve problems of others, a considerable number of partners are involved in a help desk project: End Users (of different levels) They are the final target of the system. Their problems must be solved in the most efficient ways. This can be training, to prevent questions, or provide manuals so that they can find the solution themselves. Of course, in most of the cases it will involve communication with experts. Experts This group should be able to solve the problems. To prevent a work overload, easier or routine tasks can be taken away from them, e.g. by distinguishing between senior & junior experts. The senior experts will be shielded so that they only have to solve the more complex problems. This gives them time to increase their know-how and -eventually- to train the end user. [Help Desk] Management Management will try to have the experts provide the best service at the best price. Apart from its choice where the trade-off level is put, it will aim at maximizing the efficiency of the help desk. It has to manage issues of providing full access to the
-6- in-house knowledge, ensuring its quality and guarding it. If managed well, the knowledge can even become a strategic weapon. Developers Developers are often considered to be an "external party". Management will call them in to improve the efficiency of the help desk staff, by means of automation. They will have to evaluate the needs of the other parties and provide the best solution, whatever that might be. If the necessary experience with knowledge based solutions is not available internally, an external consultant may be called in. Information Systems The interfaces of the knowledge based system are the communications link between the help desk and other information systems. It would be hard to imagine an automated help desk without links to data processing, configuration and user databases, corporate data and applications, etc. In an increasing number of cases, the help desk is the end user's main contact with the information systems department. Education/Training Gathering and modelling corporate knowledge is an invaluable source of information for training purposes. The availability of computerized knowledge adds to the consistency, efficiency and quality of the training process throughout the organization. Training new help desk personnel, when automated help desks are available, will relieve the expert from a heavy burden. Knowledge Quality Circle A project implying knowledge can only be successful if the different parties work together, e.g. to improve the knowledge. Automating the help desk provides an ideal environment for knowledge improvement. The experts start to work more explicitly with their knowledge; the users can give advice to improve the knowledge models and to make the systems more user-friendly. In most cases, knowledge acquisition leads to knowledge improvement. Once a system is taken into production, the knowledge will be better used through its distribution. Meanwhile the experts get more time to deal with more complex
-7- problems of which the solutions can be automated as well, giving even more time, etc. 3.3. PRINCIPAL COMPONENTS OF THE HELP DESK Basically, the [automated] help desk consists of 4 major components, which in several ways interact with the help desk partners, as they were described above. The basic components of the automated help desk are outlined in figure 2. ADMINISTRATIVE Cat! Hlstory idb PROBLEMS AND CASES KNOWLEDGE TECHNOLOGIES INFORMATION SYSTEMS Figure 2: Major help desk components (details dimmed intentionally) Administration First, one will want to keep track of the results of the help desk. In most cases, help desk staff will not be very cooperative in registering the necessary details about past cases (waiting times, success ratios, time spent on solution,...). It is often considered as a form of control that does not add anything to the service the help desk provides.
- 8- Problems & Cases Above the mere storage of administrative data on past cases, one will want to extend this facility to become more useful for dealing with future problems. If the case base is linked in one way or another to the administrative system, it will also help to motivate the help desk staff to spend some of their precious time to keep track of cases. Information Systems As the help desk usually has to handle computer problems, one can find much relevant information in the existing information systems of the company. This information can come from diverse sources, from [on-line] manuals provided by the vendors, from corporate databases and repositories, and from a - more or less well structured - configuration database. Also the help desk should be able to access and be accessed from other (data processing) applications. Knowledge Technologies Trying to solve help desk problems using knowledge based systems has always been appealing, as it reduces the burden of routine tasks for highly skilled experts. Having the knowledge available at all times and with a constant quality is highly valuable. Knowledge management can start from writing down procedures for dealing with common problems, in order to keep the knowledge in-house and to have some standardized solutions for these important problems. As the amount of written procedures tends to occupy large volumes, the maintainability and the overview of this 'solution' become a bottleneck. At that point, organizations often start thinking about automating the knowledge. A central dispatcher takes care of problem identification and routing, when questions arrive at the help desk. It will direct them to the appropriate component, in a manual or automated way.
-9-4. A general help desk model The following figure (figure 3) gives a complete overview of the elements from which the automated help desk can be assembled. In the ideal case all elements will be present. This section defines each of these elements and explains its function in the model. HELP DESK PARTIES Knowledge Quality Circle ADMINISTRATIVE Administrative Call History Case History Response System Domain Information Corporate Data KB KNOWLEDGE BASE Configuration Users Repository PROBLEMS AND CASES KNOWLEDGE TECHNOLOGIES INFORMATION SYSTEM~ Figure 3: A general help desk model
- 10-4.1 I NFORMATION & DATABASES A first step in help desk and knowledge management will be keeping track of everything that happens. All information available should be stored. For these purposes, several databases can be set up, each with their own role. Administrative Call History This database will store information about the problem history. It will be used to answer questions like: when was the call made; is the problem solved, how fast was it solved? This information will be the basis for statistics and follow-up, and can help to convince management of the importance of the help desk. Case Base Solved problems can be stored in the case base. This database contains structured information about typical symptoms, the steps to take, etc. From this database, one can extract information about solutions for previous cases. Extracting knowledge from a case base implies that its filling should be handled carefully. Filling the case base is often regarded as a burden, as the help desk staff fails to see its potential use. Domain Information Ideally, a (hyper)text & multimedia database will contain all available information about the domain to be handled by the help desk. This means that this database can serve as the only source of information, replacing all other documentation sources, acting as a large automated library with all information available on-line... Several vendors sell pre-packaged electronic information, such as CD-ROM libraries, on-line documentation, etc. Corporate Databases Using the corporate database will prevent creating redundant information. Indeed, these databases contain a lot of information which is already available. Wellorganized corporate databases will prove to be the easiest sources to get the correct information about a user, the software and the equipment he uses and his connections to the rest of the company:
- 11 - Configuration database As its name implies, this database contains information about the configuration that has to be monitored. Configuration can be interpreted in the largest sense: the system can be everything from a computer system to a nuclear plant. Whatever the help desk is about, it will be important to achieve a sufficient level of detail in this database. User Information This database contains information about the user. It should be able to give us answers such as: what is the user's job-description, what [applications] does he need for this, what is his available hardware, what are the access profiles? Repository Meta-information is stored in the repository. We can use it to find out which data is available in the company, where it can be found, who is responsible for specific systems, etc. 4.2 I NFORMATION RETRIEVAL Once information can be stored, one should think about ways to retrieve it. This section discusses some classic retrieval solutions. Tracking & Logging Subsystem The role as retrieval instrument from this help desk module consists mainly of reporting. This includes tasks like incident reporting, statistics and monthly reports. Response System Questions asked to the help desk cari often be answered by consulting the databases or by putting the question in e-mail. Coordination of these sources of information will be done by the response system. In turn, this system consists of three main elements:
- 12- Systems Interface (S.l.) The help desk will need links to other parts of the corporate information systems. One could think about links to the mainframe (e.g. to monitor it), network servers, etc. The system interface is the communications link between the help desk and the rest of the computer system. It should offer facilities to allow the system to run other applications concurrently, to embed other applications, etc. Intelligent Document Retrieval (l.d.r.) The IDR is the link between the (hyper)text/multimedia database and the outside word, in this case the help desk. Its goal is to help the user find the correct documents to answer the question. Several solutions are available in this domain, from [text] retrieval based on keywords, topics or search patterns, to hypertext facilities that allow browsing through documents. Data Interface (D./.) This part of the response system contains the access mechanisms (retrieval & storage) to the corporate databases. This mechanism should provide a transparent access path to the information, on whatever computer system it might be stored. This implies that this interface will have to manage complex architectures, like client-server or distributed databases. 4.3 ADDING INTELLIGENCE As a subsequent step, knowledge can be added to the system. Building an intelligent system usually does not mean to replace the experts. An intelligent system is a performance support system [Carr, 92] that helps the users and the experts to solve the more common problems. The technology behind the "intelligence' is not the goal, but means of the project. Case Based Reasoning (CBR)/Learning/Database Mining One can use the cases stored in the case base to solve new cases that are coming in, based on the similarities between the new case and cases stored in the case base. The case base can also be analyzed (using learning-technology) to extract more general solving strategies from it. Research in this area resulted into solutions
- 13- ranging from decision tree generation (e.g. the ID3-algorithm) to artificial neural networks. Knowledge Based System (KBS) Both CBR and learning systems act upon the stored information (the case base). A KBS will go further and will include other sources of knowledge. The KBS is an integral part of the response system. It works analogously to the other interfaces: it acts as the link to the knowledge base. The knowledge base is the heart of the knowledge based system. It contains the detailed knowledge supplied by the expert(s). The knowledge base contains both factual knowledge and logical, procedural knowledge to manipulate the facts. This knowledge can be stored using different representations, ranging from decision trees and rule bases to more complex models [V anthienen, 91]. 4.4 BRINGING IT TOGETHER Until now, all elements have been described independent from each other. This section describes the missing links that bring these modules together into a working system. One of the main tasks of these modules will be to couple the (human) help desk parties to the highly automated back office. Acquisition & Maintenance Special systems can be developed to "elaborate" the knowledge. This goes from having a developer acquiring the knowledge and building a system to store it, to a system in which the expert can store the knowledge himself, in a (slightly adapted) natural form that he can understand. When building such an expert interface, one should realize that there is a trade-off between simplicity and expressive power. Expert systems are often being marketed as ideal tools for non-programmers to develop complex knowledge-based applications. One should be very cautious, however. There exists a wide variety of tools for the development of expert systems and only the simple systems are suited for people with a limited knowledge of computers and expert systems development methodologies. [Merlevede & Vanthienen, 91]
- 14- User Interface As with most advanced information systems, the acceptance of the automated help desk will depend heavily on its front end: the user interface. Building the user interface means that the user has to be taken into account. The user interface should be able to deal with varying levels of computer experience among the users. The stored information should be accessible in numerous ways. The automated help desk has to be more than an advisor. It should also be a library to retrieve information from and an instructor to learn from. Central Dispatcher The central dispatcher manages the problems arriving at the help desk. This management consists of problem determining and problem routing. It will help to decide which piece of software or which help desk staff member or expert is best suited to answer the question, so that the problem is routed to the right part of the help desk infrastructure. The help desk might be organized into different lines of involvement: first line help by the dispatcher, then a limited amount of time by an expert, and finally, if the problem remains unsolved, an intensive study by the field expert or even a new design proposal. The dispatcher can be automated or human. Its form will depend on the level at which the user-interface with the end-user is situated. There are two possibilities: the end-user can communicate directly with the available tools, or the communication will happen trough the help desk staff. 5. Applying the framework to the scoping decision Help desk automation is for many companies the first application area of knowledge based systems. "The help desk is to automated knowledge distribution what payroll was to the automation of record keeping... a universal application that fits the new technology like a glove." (Avron Barr [92]). Back in the early days, when knowledge based systems were research objects and when private persons and companies took their first steps in the world of AI, there was no need for extensive communication with other systems. Now that
- 15- prototypes are leading to fully operational systems, the need for integration and communication with the other facilities is critical. A knowledge based system, and particularly a help desk, must offer a number of possibilities to interface with the rest of the computer infrastructure. Tools often focus on technology rather than on the solution of the problem the user or organization really has. Rather than advocating the use of advanced technology, the implementation of knowledge solutions should be considered first [Merlevede, 91]. Before help desk automation can be pursued, real bottlenecks and opportunities should be examined. The help desk scope can then be directed to the most relevant items. In this analysis, the global help desk model will be of valuable assistance, as it identifies major components and relations. Every partial solution has its drawbacks. But if the scoping decision is based on extensive examination of alternatives/extensions, no surprises will pop up and users or management will not be deceived in their false expectations. Based on the general help desk model, the scoping question can be put in a more general context: What is the goal of the help desk in the problem lifecycle (narrow versus extended)? What type of knowledge will the help desk contain (causal model, experience, h eunstics,..... )? What are current weaknesses, priorities and opportunities? If not every component of the general model can be realized, what will be lacking? The make or buy decision: do we build a help desk tool and fill it with knowledge; buy a tool and develop our own knowledge; buy knowledge with or without a tool; outsource the building process, or even the entire help desk operation;
- 16- This evaluation of the help desk needs and opportunities should take place before starting to build prototypes. Only after the evaluation and scoping process, we can decide to which degree advanced technologies, tools or languages are really needed, or other elements should be considered first. 6. Conclusions At the beginning of this paper, some common help desk views are listed, which are typically limited to a subset of a more general help desk model. These limitations are part of the problem: one should not use a partial solution when it is not satisfactory. Another danger consists of using technology for the sake of technology. The previous section has shown the relations between the different elements: one technology as such will not solve the help desk problems. We advocated the use of a general help desk model as a basis for the system inception phase of the help desk automation project. This will provide an overview and avoid unintended partial solutions. References BARR, A., Software Trends at the Help Desk, Intelligent Software Strategies 8(8), August 92, pp. 1-13. BRAMER, M.A., Practical Experience in Building Expert Systems, John Wiley & Sons Ltd, Chichester, England, 1990, 230 pp. CARR, C., Performance Support Systems: a New Horizon for Expert Systems, AI Expert 7(5), May 1992, pp. 44-48. HARMON, P. & HALL, C., Intelligent Software Systems Development, An IS Manager's Guide, John Wiley & Sons, Inc., 1993, 472 pp. LICKER, P.S., Getting Advice From a Computer, Data Base 22(3), Summer 1991, pp.1-13. MERLEVEDE, P.E., Guidelines for Structuring and Implementing Knowledge, Master Thesis, K.U.Leuven; 1991, 30 pp.
- 17- MERLEVEDE, P.E., SCHOONVLIET, P. & VANDAMME, P., Automating a Decision Support Desk, Kennistechnologie '93, Amsterdam, pp. 233-237. MERLEVEDE, P.E & V ANTHIENEN, J., A structured Approach to Formalization and Validation of Knowledge, IEEE/ACM International Conference on Developing and Managing Expert System Programs, Washington, DC, 30 SEJ.Yf-2 OCT, 1991, pp. 149-158. NASH, J., Expert Systems: A New Partnership, AI Expert, 7(12), December 1992, pp. 36-39. NEWQUIST III, H.P., Moving away from the middle, AI Expert, 7(9), September 1992, pp. 53-55. V ANTHIENEN, J., "Knowledge Acquisition and Validation Using a Decision Table Engineering Workbench", The World Congress on Expert Systems, Pergamon Press, Orlando, 16-19112/91, pp. 1861-1868. VERMEULEN, S. & MERLEVEDE, P.E., Het ontwikkelen van een Help Desk Toolset, Seminar on Computer Applications in Management, K.U.Leuven, February 1990, 45+56pp. WARMOES, S., Kennissystemen en Kennismanagement in de Praktijk, Garant, Leuven, 1991, 128 pp. XEPHON USER SURVEY, The Help Desk in Action, Xephon Technology Transfer Lim. Berkshire, England, 1989, 172 pp.
Contents ABSTRACT............................................... 1 KEYWORDS...............................! 1. INTRODUCTION......................... 2 2. SOME COMMON HELP DESK VIEWS.......................... 2 3. POSITIONING THE HELP DESK PROJECT: GOALS, PARTNERS AND MAJOR COMPONENTS...................................................... 3 3.1. Help desk goals in the problem lifecycle... 3 3.2. Partners involved in the help desk... 5 3.3.Principal components of the help desk... 7 4. A GENERAL HELP DESK MODEL.................................... 9 4.1 Information & databases... 10 4.2 Information Retrieval...... 11 4.3 Adding intelligence... 12 4.4 Bringing it together... 13 5. APPLYING THE FRAMEWORK TO THE SCOPING DECISION... 14 6. CONCLUSIONS... 16 REFERENCES... 16