=Paper=
{{Paper
|id=Vol-34/paper-8
|storemode=property
|title=Knowledge Management in the Service and Support Business
|pdfUrl=https://ceur-ws.org/Vol-34/delic_dayal.pdf
|volume=Vol-34
|dblpUrl=https://dblp.org/rec/conf/pakm/DelicD00
}}
==Knowledge Management in the Service and Support Business==
https://ceur-ws.org/Vol-34/delic_dayal.pdf
Knowledge Management in the Service and Support Business
Kemal A. Delic Umeshwar Dayal
Hewlett-Packard Hewlett-Packard
kemal_delic@hp.com umesh_dayal@hp.com
measurement methodology, and there are few reliable
reports of the impact of knowledge management on
Abstract business metrics [Del00].
As we are entering the New Millenium, we are We believe that knowledge management (KM) today
witnessing a global shift toward a society of [Dav00] plays a crucial role in the IT support and service
services. The evolution of the world's workforce environment. It directly impacts the productivity and
structure indicates clearly the magnitude and training of support personnel, and this in turn justifies
importance of this mega-shift. The introduction investments in knowledge management systems, programs
of new, exciting technologies has led to a kind of and associated processes. To give an idea of the potential
New Economy that is based on the huge and market size, industry estimates indicate that for each
rapid flows of data, information and knowledge. dollar spent on hardware and software products, seven to
The Internet has had a most profound impact on fourteen dollars are spent in associated training, support
business and society, enabling the quick spread and services.
of service industries and technologies. We
observe that cost reduction and acceleration of From the business point of view, KM impacts profit by
business processes are the most obvious reducing the costs of doing business and creates new
consequences of this singular phenomenon. In streams of revenues through the introduction of new,
such an environment, the management of IT innovative services. From a strategic point of view, it is
support services is becoming critical for business seen as a potentially strong business growth generator (>
profitability. 50%). Unfortunately the KM field is saturated with hype
and buzzwords, so that real, documented KM success
stories are rarely published. We will focus our attention
1 Introduction on deployment of KM in IT support services as one of the
most important practical domains, and we will describe
The management of support services includes the efficient our experience at Hewlett-Packard with the deployment of
deployment of people, processes and technologies so as to one KM system for IT support services.
improve operational business parameters and financial
indicators. Knowledge Management (KM) is a general IT support and services are typically organized as a multi-
umbrella term that encompasses several techniques and tiered operation, consisting of help desks or service desks,
processes whose main common objective is to deal and operational service centers supporting the IT
successfully with various inefficiencies in operational infrastructure and delivering various services. Customers
business processes and to create business value. obtain support and services by contacting the first line
service desk. Problems that cannot be resolved in the first
Intuitively, it is clear that knowledge plays a crucial role line are escalated to higher, but more expensive, levels of
in human business activities, and that it has significant expertise. In addition to telephone or email-based help
monetary value to an enterprise. However, measuring the desks, enterprises are moving towards Web-based service
impact of knowledge management has proved to be portals, where customers can directly obtain access to IT
difficult, since there is no commonly accepted support and services. In this paper, we will show in more
detail how knowledge management can enhance the
The copyright of this paper belongs to the paper’s authors. Permission to copy
efficiency of traditional IT operations and enable the
without fee all or part of this material is granted provided that the copies are not delivery of new types of services.
made or distributed for direct commercial advantage.
Proc. of the Third Int. Conf. on Practical Aspects of In the second section of this article, we introduce
Knowledge Management (PAKM2000) Knowledge Management. The third section deals
Basel, Switzerland, 30-31 Oct. 2000, (U. Reimer, ed.) specifically with KM in the Information Technology (IT)
http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-34/ domain. In the fourth section, we outline the architecture
of an IT KM system being deployed within Hewlett-
K. A. Delic, U. Dayal 7-1
Packard Corporation. In the fifth section, we sketch the ontology consisting of contexts, subjects, and topics. The
evolutionary architecture of IT support and service third stage includes a battery of automated refinery
systems, concluding with a few insights about the future processes that include on-line analytical processing
role of KM in the service industry. (OLAP), data mining and knowledge discovery, and
information visualization techniques [Fay96, Min99]. The
results of this stage are usually in the form of actionable
2 Knowledge Management Defined knowledge that can be disseminated, shared, and delivered
to knowledge workers for decision making. Today, much
of this dissemination is Web-based.
Knowledge is primarily embodied in human expertise and
experience. It has first to be captured and expressed We will try to classify typical KM scenarios as reported in
explicitly, then transformed and represented in various industry and academia. The most frequent situation is
repositories, then disseminated and shared by knowledge known as Knowledge Sharing (Figure 2. - left) in which
workers who exploit it for making business decisions, and KM is seen as the technique to transfer expertise from the
finally these actions may in turn lead to the creation of top performers or domain experts (20%) to the rest of the
more knowledge. Knowledge Management can be defined population (80%). In the IT support and services context,
as a set of generic processes aligned with the four for example, this involves the transfer of problem solving
principal transformation phases: gathering, organization, knowledge from the more proficient service desk agents to
refining, and dissemination of knowledge (Figure 1). For other agents, thus improving the productivity of the whole
the purpose of this article, we will consider Knowledge population.
Management as a combination of several disciplines and
techniques. The next common scenario is known as Knowledge
Harvesting (Figure 2. - right) where we collect knowledge
In Figure 1, we summarize a generic KM methodology codified in various repositories. A user harvests
and process that can be applied to the majority of known knowledge by querying the various repositories. In
KM approaches in various domains such as consulting, response, a list of solutions and answers is offered to the
customer service & support, training, human resources, user, who will thus experience augmented problem-
finance, product creation, sales and marketing, and
strategic management. L
H
KNOWLEDGE
HARVESTING
L
KNOWLEDGE H
KNOWLEDGE
Collect SHARING PROBLEM
SHARING
SOLVING POWER
Data & Introduce Context,
PRODUCTIVITY
Information Subjects, Topics
20 %
1
1 2 QUERY
KNOWLEDGE 2
MANAGEMENT
Gathering Organizing
ORDERED LIST
- SOLUTIONS 3
File System, Indexing, 80 % - ANSWERS
Database, Classification, - ADVICES
Spreadsheets Categorization - HINTS
USER 4
POPULATION REPOSITORIES
Web, Java,
Data Mining, OLAP,
Messaging,
Knowledge Discovery
Brokering
Refining Disseminating solving power.
3 4
Pack & Deliver
Measure & Adapt
Discover Relationships, Figure 2. Knowledge Sharing and Knowledge Harvesting
Synthesize, Abstract,
Aggregate
In the first two scenarios, the knowledge management
system does not differentiate among users. However, the
Figure 1. Generic Knowledge Management Process ultimate objective of IT tools is to be able to learn and
adapt to individual users’ habits, preferences and evolving
The first stage is the gathering or capture of raw data or needs. There are several products that are able to capture
information collected from operational processes. This interactions with users via sophisticated adaptive
information may be in the form of structured data (e.g., algorithms in which the key asset is Captured Knowledge
relational databases, log files, event traces), or in the form (Figure 3, left). Since this knowledge is captured in a
of semi-structured or unstructured documents. The second suitable user model, it can be used to provide a user with
stage organizes the information through indexing, solutions that are customized to his needs, and hence
categorization and classification into a domain-specific directly impacts the user's productivity.
K. A. Delic, U. Dayal 7-2
insights from knowledge gained over a huge population of
L
H
IT systems and over very extended periods of time creates
KNOWLEDGE
DISCOVERY
wisdom, e.g., “It is cost-effective to invest in a high
L DECISION MAKING
KNOWLEDGE
KNOWLEDGE
SHARING
CAPTURING
H
QUALITY availability solution with server fail-over capability for the
PERSONAL production department, because a server outage there
PRODUCTIVITY
BUSINESS
INTERACTIONS
CALL LOGS
CALL LOGS INTELLIGENCE results in a loss of 500 person days of work.” Business
enterprises that are able to efficiently manage these kernel
DATA MINING
WEB LOGS
entities (data, information, knowledge, wisdom) are
ADAPTATION
LEARNING
CUSTOMER
typically market leaders and consistent business winners.
RELATIONSHIP
INTERACTION
MANAGEMENT
LOGS
SYSTEM - TOOL
FRONT END BACK END Business enterprises are huge generators and consumers
of data, information, and knowledge. Terabytes of data are
Database
Figure 3. Knowledge Capturing and Knowledge GROSS AVERAGES
Number of Events within
Discovery Non Human
Enterprise per Day
Trigger 5000 - 15000 Events - Burst
Low Disk
Space $ 100s
Finally, the most recent KM paradigm is known as Knowledge Item
Created
$ 10s
Knowledge Discovery (Figure 3, right) wherein many Event
Data Item
$ 1s Information
Knowledge
Document
Item Created
different types of information about user interactions (e.g., Blue Screen
Created
Trouble Ticket
Case $ 1000s
Death
transaction logs, case histories, web logs, call logs, traces Human
Wisdom
Service
Initiated Sizing & Architecture
of problem solving sessions) are amassed and analyzed Number of Objects
Creation &
Delivery
GROSS AVERAGES Number of Events
with sophisticated large-scale algorithms. These create 1000 PCs having
Number of Users &
Operators Topology Knowledge Base
40 Problems per Day causing Data Store
insights and recommend optimal business actions aimed at 120 Calls
improving the quality of decision making.
3 IT Knowledge Management Figure 4. Event and Value Chain within Enterprise IT
Different types of knowledge are encountered in IT created by machines and humans in their daily operational
domains: product knowledge, procedural knowledge, legal tasks. Generic event creation rates within the
knowledge, behavioral knowledge, customer knowledge infrastructure of a typical enterprise are illustrated in
and topological knowledge Within the domain of IT Figure 4 in the context of PC support and service. One
support and services, knowledge management can be must take into account these figures when planning and
regarded as a process that impacts productivity and sizing an enterprise architecture. From experience, we can
learning. It is typically achieved through the capture, attach a dollar value to each item in the chain and talk
articulation, and reuse of relevant domain knowledge. about its positioning in the value chain. So, if we spend $1
This strategy fits domains in which the problems to capture event traces, then additional processing might
encountered are simple, repetitive in nature, and for which bring us into the $100 range per knowledge item (e.g.,
standard solutions exist. document) if we are able to create one in a cost efficient
manner. Some companies will still earn money by
For the purpose of this article, we will define key IT exploiting even a simple data capture process. Others will
terms: data, information, knowledge and wisdom. In the cash in on the wisdom acquired from careful knowledge
typical IT service domain, data is a collection of observed management and transforming knowledge into the ability
facts or events, such as "3 disk errors from server xyz to deliver superior end-to-end services.
have been recorded in the last 10 minutes". Information
is derived from data by summarizing or aggregating data In the generic IT landscape, one typically encounters a
from several sources and over a period of time, e.g., "the multi-tiered operation consisting of help desks or service
failure rates of systems with a configuration similar to desks, and operational service centers supporting the IT
server xyz is 5% over a year; or, server xyz has been infrastructure and delivering various services (Figure 5).
down 20% of the time in the past 3 months, and such
failures affect 250 users in the production department". Activities in the help desk and service desk require
Knowledge is in the form of business rules or patterns knowledge sharing and reuse. One analyst usually covers
derived from large collections of data and information, 200-300 desktops, handles daily 20-30 phone calls, whose
e.g. "3 disk errors within 15 minutes from systems similar duration is typically 20 minutes and whose average cost is
to server xyz are predictive of server failure with 90% around $50. Service Desk analysts are focused on simple
confidence”. Deriving actionable business decisions and information inquiries, desktop problems, and application
K. A. Delic, U. Dayal 7-3
usage problems. Their work is usually supported by a discovered knowledge is to create business insights (for
problem-solving knowledge base. This knowledge base example, why are users from one line of business more
typically consists of a large collection of documents (case efficient than others). We believe that certain core
histories of problems encountered before and their technologies such as data mining and machine learning,
resolution, product documentation, and the like), and Bayesian reasoning, neural networks and genetic
other troubleshooting and diagnostic tools. algorithms, information retrieval and natural language
processing, information visualization, and intelligent
Operational centers within IT departments in big agents, some of which have being actively investigated
enterprises take care of more complex problems, for during the last 50 years, will play an important role in the
which unique solutions are created through the future development of decision support services.
communication and cooperation of several human experts. Accumulated experience and insight from these large
(A typical enterprise has $3 billion in annual revenues, fields of research are beginning to appear in innovative
9000 employees, 500 IT professionals, 4300 desktops, applications and tools. The notion of "knowledge"
470 servers spread over 40 sites.) Knowledge provides the glue for combining and exploiting techniques
management here usually involves indexing of human from these different research disciplines.
expertise to enable collaboration. IT personnel in the
operational center are usually focused on more complex Enterprise information systems should be designed with
domains such as network management, server the user model explicitly included into all aspects of the
management, and system management. They normally design. The primary point of focus for user interaction is a
encounter complex but non-repetitive type of problems user view or portal, which provides the working context
that are frequently specific to the particular set-up and the for the user, guides the user through his tasks, serves up
particular enterprise. relevant data, information and knowledge to aid him in
decision making, and (ideally) evolves and adapts to the
All IT processes are supported by computerized systems, user’s needs. The machine emulates intelligent activities
so that huge volumes of data are created and stored daily. such as answering questions, giving advice, solving
The process of transforming data into information and problems, and playing what-if scenarios.
knowledge is not completely automated, and recent
investigations in text mining and business intelligence Frequent, Tactical, $
Rare, Strategic, $$$
represent efforts in that direction. Knowledge is extracted
Top Management
and captured in a model able to emulate certain human
Decisions
behaviors. Such knowledge could be embedded in Senior Management Knowledge
products (giving differentiating or innovative features) or
Data Mining
it could drive various processes. Management Decision
Support Document Collections
System
Document
Knowledge Workers Management
Work Desk Net Server Text Collections
Contact
Control Console Application
Service Supported Population
Intangible Desktop Support Collections
Management Service
Events Operational
Services Calls Service Center
Strategy
Tools
Efficiency
Infrastructure
Problem
Resolution
Figure 6. Cooperative Systems: Users and Collections
Dashboard Time
Information
Service Desk 3
In Figure 6, we sketch segmented user populations and
Tangible
Management
repositories used to support decision making. All users
Calls are considered to be knowledge workers, the principal
Enterprise : US$ 3 bn in
annual revenues, 9000
employees, 500 IT
Efficacy
difference among them being the frequency of decision
2
professionals, 4300
desktops, 470 servers
Problem
Resolution
making and the associated value-at-risk. Typically, we
spread over 40 sites 1
Rate
provide decision support systems for two main areas: first,
for users who have to make frequent and not too risky or
costly decisions (service-desk agents, for example); and
Figure 5. Generic IT Landscape: Service Desks And second, for users who have to make infrequent but risky or
Operational Centres costly decisions (a CIO, for example).
Strategic managers and senior managers are typically
supported with knowledge automatically extracted from
different repositories deployed within a decision support
system [9]. For these managers, the primary purpose of 4 An IT Support & Service Architecture
K. A. Delic, U. Dayal 7-4
At Hewlett-Packard, we have implemented an IT information from the user’s environment can enable the
architecture for support and services that is based on the expansion of another line of support services (e.g.,
principles of knowledge management outlined above predictive support).
[Del98]. This architecture is depicted in Figure 7. In their
daily work, our help desk analysts use HP WiseWare1, a Realizing that the integration of the whole support
knowledge based system that contains various types of landscape (self-healing, self-support, technical support
knowledge documents covering well over 70 standard and administrative support) is an obvious future necessity,
products, as well as customer specific problems, for a total we designed the architecture to cover all relevant problem
of about 150,000 solutions. Approximately 70 to 80 areas with competent user support aimed at the integration
percent of the analysts use the WiseWare knowledge base and correct management of data, information, and
regularly. To illustrate the problem-solving power of knowledge.
WiseWare, let us mention that it is equivalent to a library
containing 2000 books of 100 pages, which could be
packed onto 20 bookshelves, each containing 100 books.
From the word count perspective, WiseWare contains
circa 47 million words, which is more than any currently
available on-line encyclopedia. A search engine enables
pinpointing of the relevant content with 2,3 word long
query (long-term average length of the query), which is
equivalent to finding in the above library the appropriate
subchapter (10-15 pages) or even a single page in a few
seconds. On average, 8-15 percent of the users provide
feedback (annotation) about content usage, and this
feedback drives content management tasks.
HP RuleWare is an extension of WiseWare that provides
procedural knowledge about what a customer is entitled to
and procedures for handling customers. Currently, HP
RuleWare contains circa 6000 rules, which capture Figure 7. Layered Knowledge Management Architecture
knowledge about customers.
The upper layer of the architecture contains a front-end
These two knowledge repositories represent the middle system (which in our architecture is a Web-based portal
layer of our architecture. All user interaction traces are called Nimbus) that delivers support and simple advice
captured in the back-end system, Search & Access Mine. enabling end-users to self-solve their problems, while
which is a huge repository of interaction traces containing gathering information from the user's environment and
several million sessions that are collected from several capturing usage patterns. All user interactions with the
global servers, pre-processed, transformed and stored in knowledge-based system are captured. These include all
the appropriate format. Such transformations typically queries launched, documents retrieved and accessed with
create tables, charts and graphs, and drive the computation user ratings of the documents.
of various business indicators.
The back-end layer of the architecture contains traces (i.e.,
The design of HP WiseWare combines Web technology logs) of all user interactions with the knowledge-based
with an indexing engine to enable good coverage of system. This enables the profiling of individual users,
Wintel-related problems for help desk analysts. A well drives the adaptation (personalization) of the system’s
controlled and ISO 9001-certified knowledge responses to meet each user’s needs, and the reporting and
management process (Knowledge Refinery) enables a alerting functions that are provided to the business
constant feed of relevant source material for HP managers. Profiling knowledge is stored in database tables
WiseWare. As the user population has shifted from help and associated procedures.
desk analysts towards service desk and channel partners,
the nature and complexity of the service calls and support To summarize, this layered architecture contains problem
has changed as well. For instance, there is an increased solving knowledge in various document repositories;
emphasis on self-support. The productivity of end users knowledge about user behavior is captured in user models;
can be greatly increased by providing assistance for and interaction models provide the basis for strategic
simple, repetitive problems. Exploiting real-time decision making. Judicious management and exploitation
of knowledge enables an evolution from problem solving
1
HP WiseWare, HP RuleWare, HP Search & Access Mine are assistance toward a decision support system [Tur98] for
internal HP products
K. A. Delic, U. Dayal 7-5
users and managers. Consequently, IT support processes
will be governed by business metrics and measurements, We see this message-brokering paradigm evolve into a
instead of rough indicators and intuition. knowledge brokering paradigm, where components can
publish and subscribe to knowledge (IT knowledge,
5 Enterprise Service Architecture business knowledge, legal knowledge, financial
knowledge), not just to messages or events. Ultimately,
IT support services are just one of the touch points that this will lead to a dynamic marketplace for electronic
typical enterprises have with their customers and partners. services [Qim99] within an enterprise, and even across
In general, within an enterprise we find several different multiple enterprises, in which service providers advertise
repositories containing data, information, and knowledge their capabilities and the types of knowledge they can
about different aspects of the business. For instance, provide, and consumers can dynamically find service
document repositories contain project, product, process, providers that can satisfy their requirements.
and workflow information. Structured databases contain
financial data, accounting, sales and marketing figures, 6 Conclusions
operational and business tables. Messaging systems
contain communication and collaboration traces. Knowledge is a crucial ingredient for enterprise IT service
Interaction histories contain log files and web-access & support. Knowledge may appear explicitly in
interaction databases. Various data & knowledge feeds documents, it may be embedded in algorithms, tools and
come in and out of the enterprise. Ideally, we would like processes that assist users in automatic problem solving.
to create an integrated architecture that will augment the Knowledge Management is still more an art than an
support services we described earlier with many other established scientific theory or commonly accepted
types of decision support services for various corporate methodology. It combines several techniques and
users and for various applications such as customer technologies aiming to capture, articulate and disseminate
relationship management, marketing campaign design and knowledge [IDC99, Gar99]. In different areas, it takes
optimization, consulting, and so on. different forms, but its absence will always be very
palpable. Companies that are able to transform data and
Knowledge
information (costs) into knowledge assets (values) can
Document Database
Base
Systems
Systems Base
Systems
Workflow
Systems claim that they have successfully applied Knowledge
Management.
I - Engine
Integration Bus
References
Data
Information
Interaction
[Dav00] Thomas H. Davenport, Laurence Prusak,
Knowledge
Feeds
Message
Histories &
Systems
Working Knowledge, Harvard Business School Press,
Stores &
Systems
May 2000.
[Del00] Kemal A. Delic and Birgit Hoellmer,
Knowledge-Based Support in Help-Desk Environments,
Figure 8. Integrated Enterprise Service Architecture IEEE IT Professional, Vol. 2, No. 1, pp. 44-48,
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