Description
Despite the advances in IT, information systems intended for management informing did not uniformly fulfil the increased expectations of users; this can be said mostly about complex information needs.
International Journal of Artificial Intelligence and Interactive Multimedia, Vol. 2, Nº 3.
-31-
Abstract — Despite the advances in IT, information systems
intended for management informing did not uniformly fulfil the
increased expectations of users; this can be said mostly about
complex information needs. Although some of the technologies for
supporting complicated insights, like management decision
support systems and technologies, experienced reduction in
interest both from researchers and practitioners, this did not
reduce the importance of well-supported business informing and
decision making. Being attributed to the group of intelligent
systems and technologies, decision support (DS) technologies have
been largely supplemented by business intelligence (BI)
technologies. Both types of technologies are supported by
respective information technologies, which often appear to be
quite closely related. The objective of this paper is to define
relations between simple and complex informing intended to
satisfy different sets of needs and provided by different sets of
support tools. The paper attempts to put together decision
support and business intelligence technologies, based on common
goals of sense-making and use of advanced analytical tools. A
model of two interconnected cycles has been developed to relate
the activities of decision support and business intelligence.
Empirical data from earlier research is used to direct possible
further insights into this area.
Keywords — management decision support, business
intelligence, information needs
I. INTRODUCTION
HE job of informing business managers and other
people in charge of running organizations stays on the
agenda of many researchers and practitioners around the
information systems and information management community.
While the advances in technological foundations of
management information systems have been impressive, the
advances in efficient satisfaction of management information
needs have been less impressive. The development of systems
for managerial information needs, while having a rich history
of several decades, has been based on a heterogeneous set of
needs: some of these needs stay stable (developing,
implementing and adjusting strategy; keeping track of own
activities), and some evolve or have a turbulent life cycle:
monitoring close environment; looking out for threats and
opportunities. Information environment (support
infrastructure) is driven by the nature of business activities. On
one hand, this nature is recurrent and cyclical, supported
mostly by the function of a MIS. On the other hand, this nature
is turbulent and unpredictable, requiring intelligent and
insightful support; this is a function of a BI system and related
applications – decision support, competitive intelligence,
operational intelligence, early warning systems and other types
of systems to support monitoring, sense-making and problem
solving.
The recent research on complex information needs including
decision support and business intelligence has been diversified
into quite a few related areas; far from being an exhaustive set,
several examples follow. Lemieux and Dang [7] have
researched the issues of accountability for decision making,
and suggested tools for tracking the decision-making reasoning
of human agents, thus adding to the research on a problem of
experience management. Thorleuchter and Van den Poel [17]
have investigated the use of website content analysis in partner
search for improved research and technology collaboration
planning, adding to the body of research on information
integration. Saad et al [11] have researched a conceptual
framework for early warning information systems for crisis
situations, expanding the research on intelligence technologies
for monitoring and detection. Castano [1] has researched the
possibility of putting together business process management
(BPM) and data mining techniques to provide intelligent BPM
management functions. Redondo-Garcia et al [10] have
researched information integration tasks when using disparate
(heterogeneous) information sources.
The sample of research directions presented above for a
long time has been attributed to the area of decision support
systems and technologies, serving the complex or high-end
side of user information needs. In the field of technologies for
satisfying complex information needs, the once-prominent area
of management decision support systems (DSS) apparently has
settled to stable levels of both academic and practitioner
activities [9]. However, a somewhat faded interest in decision
support systems does not imply any reduction in importance of
well-supported decision making, as well as general awareness
of the state of internal and external business environment. On
From Management Information Systems to
Business Intelligence: The Development of
Management Information Needs
Rimvydas Skyrius, G?lyt? Kazakevi?ien?, and Vytautas Bujauskas
Economic Informatics Department, Vilnius University, Lithuania
T
DOI: 10.9781/ijimai.2013.234
Special Issue on Improvements in Information Systems and Technologies
-32-
the contrary, the current economic situation in most settings
demands an efficient and reliable, „military grade“
management environment to support decisions, insights,
recovery or mere survival.
Decision support alone, being reactive and activated only
when a problem is encountered, eventually proved to be
insufficient. The problem solving context received IT-based
support mostly from the resources of a regular information
system, therefore of a limited nature and in most cases
complicated by time pressures. An alternative use of decision
support, if coupled to a proactive monitoring of the
environment, ensured better understanding of the problem
context, leading to higher decision quality. A term “business
intelligence” came into use, serving as an umbrella term for
tools and technologies that let business information users stay
aware of changes in internal and external environments.
The research problem of this paper is centered around how
the current array of technologies and approaches provides
support for functions of insight building. Currently there is a
confusion in defining whether management information
systems overlap with intelligence systems, and whether
business intelligence is a part of decision support function, or
vice versa; eventually this confusion spreads to business
management community which at all times has expressed the
need for insight building and reliable decision support which
would justify substantial investments into support
technologies. In this paper, the authors have decided to use the
results of their earlier research to make an attempt in
developing a model positioning business intelligence and
decision support functions.
The paper is structured as follows. Section 1 defines the
dimensions of the problem and the goal of the paper. Section 2
clarifies the definition of business intelligence and its
information needs. Section 3 defines a relation between the
areas of decision support and business intelligence. Section 4
presents empiric data on user responses towards decision
support anad business inteligence functions. Finally, Section 5
presents conclusions and directions for further research.
II. BUSINESS INTELLIGENCE AND INFORMATION NEEDS
Although business intelligence is regarded as a relatively
new term, with authorship assigned to Howard Dressner of
Gartner Group in 1989, we can have a retrospective look at the
mission of management information systems (MIS), whose
role of keeping management aware of the state of business has
never been downplayed, and mission definitions for MIS
sound very much like the mission definitions for business
intelligence today. A few explanations of MIS role from earlier
sources are presented below:
? “Two types of information for strategy
implementation are in use. The first one is the
external information, used for strategy development.
The second type is internal information, used to
monitor strategy execution” [14].
? “A management information system refers to many
ways in which computers help managers to make
better decisions and increase efficiency of an
organization‘s operation” [7].
? “For information to be useful for managerial decision
making, the right information (not too much and not
too little) must be available at the right time, and it
must be presented in the right format to facilitate the
decision at hand” [4].
? “A management information system is a business
system that provides past, present, and projected
information about a company and its environment.
MIS may also use other sources of data, particularly
data about the environment outside of the company
itself.” [6].
? “The systems and procedures found in today’s
organizations are usually based upon a complex
collection of facts, opinions and ideas concerning the
organization’s objectives. … For an organization to
survive, it must learn to deal with a changing
environment effectively and efficiently. To
accomplish the making of decisions in an uncertain
environment, the firm’s framework of systems and
procedures must be remodeled, refined, or tailored on
an ongoing basis.” [3].
There are definitions of business intelligence that do not
differ much from the above definitions; e.g., Vuori [20] states
that “… business intelligence is considered to be a process by
which an organization systematically gathers, manages, and
analyzes information essential for its functions”. In order to
have a more precise definition of business intelligence, we
have to decide whether all informing functions are
„intelligence“ because they increase awareness, or does BI
have a clear separation from other (lower level) informing
functions. If so, the separation criteria between BI systems and
any other management information systems have to be defined.
For the purposes of this paper, we will use the division of
management information needs along two dimensions – their
simplicity or complexity, and common or specific focus, as
presented in the Table 1 and based on earlier work by one of
the authors [14]:
TABLE 1.
RELATION OF SIMPLE-COMPLEX AND COMMON-SPECIAL INFORMATION NEEDS
Simple needs Complex needs
Special needs
(problem-
specific)
Simple special
needs
Complex special
needs
Common needs
(available
permanently)
Simple common
needs
Complex common
needs
The mission of BI becomes clearer if weighted against the
types of served information needs. Regarding the positioning
of these needs against the axis of simple-complex information
needs, they usually fall into the more sophisticated part of the
information needs complexity spectrum. Same can be said
International Journal of Artificial Intelligence and Interactive Multimedia, Vol. 2, Nº 3.
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about the process of decision making, which often requires
sophisticated tools to support awareness, communication,
sense-making and evaluation of risks. The dimension of
common and special information needs separates decision
making from the rest of business intelligence in a sense that
while decision support activities are directed towards a certain
problem which has been recognized and has created a task of
its solving, business intelligence can be considered an activity
which, apart from encompassing decision support, has a
permanent nature and allows the discovery of problems and
general awareness about the state of activities.
III. DECISION SUPPORT AND BUSINESS INTELLGENCE
PROCESSES
A. Structure of Decision SupportProcess
A decision support process includes a number of stages, and
if accumulation and subsequent use of experience is included,
the process takes a cyclical nature (Fig. 1, from [13]):
Fig. 1. The decision support process
The structure of the decision support process can be related
to relevant information needs:
1. Monitoring (using previous experience): the
environment, both internal and external, is being
watched to notice things worth attention; simple and
common information needs prevail.
2. In the case of recognizing a situation of interest
(initial understanding of a problem or opportunity)
the situation is evaluated and given extra attention to
achieve desired understanding. At this stage special
information needs arise.
3. Additional analysis and decision development is
required if the situation is complex enough (semi-
structured or unstructured); simple needs are
complemented by complex needs; more information is
brought into decision making environment; specific
problem-solving tools such as formal approaches and
models are likely to be used to achieve an adequate
understanding of a problem.
4. The decision-making stage involves formerly
available as well as newly gained understanding of
the situation, and the decision maker or makers will
use all possessed knowledge to arrive at the best
possible decision, time or other circumstances
permitting. In this paper, the term “knowledge” is
deliberately avoided most of the time, but here it
serves to show that data or information alone are
insufficient for decision making; all that is known will
be used in its entirety, and new knowledge most likely
will be gained.
5. The experience accumulation stage records the newly
gained experience from both decision making and its
implementation, and keeps it for possible reuse.
Special needs become common, adding new material
to the already available body of experience, and the
need to capture the essential features of the recorded
case keeps this sort of information need in the
complex segment. This phase should also include the
practical experience in decision implementation,
which can sometimes reveal additional circumstances
of the problem.
6. The use of new experience, along with that formerly
accumulated, brings the process back to stage 1 –
monitoring.
Stage 1 of the above process is directly related to (or can be
considered a part of) business intelligence, because that’s
where the actual monitoring of the business environment is
being done. Stage 2 is a principal point of joining business
intelligence and decision support.
As we can see, during the decision making process the focus
of information needs moves around the quadrants of Table 1:
stage 1 concentrates in the simple/common sector; stage 2
moves on to simple/special sector, stages 3 and 4 concentrate
in the special/complex sector, stage 5 moves into complex
common sector, and finally stage 6 brings the focus back to
simple/common sector.
B. Structure of Business Intelligence Process
The business intelligence process, too, takes a cyclical
nature (Fig. 2., from [20]), and includes the stages of
information needs definition, information collection,
information processing, analysis, information dissemination,
information utilization and feedback. The cycle structure is
justified if the received feedback helps to reevaluate or
redefine information needs.
In business intelligence process, there‘s usually no clear
concentration on a specific topic or problem, and the resources
of a BI system are used for constant monitoring of internal and
external business environment. In other words, such systems
serve common information needs to keep users informed about
the state of business environment, often combining a
monitoring function with alerts, exception reports and other
tools to draw attention to changes or inconsistencies.
Therefore, an important feature of BI systems is their ability to
Special Issue on Improvements in Information Systems and Technologies
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produce a complete composite view that would help avoiding
surprises.
Fig. 2. A generic business intelligence process model [20]
The business intelligence cycle, as presented in Fig. 4.,
raises several questions. First of all, it does not disclose the
difference between regular management information systems
or their current incarnation, ERP systems, and business
intelligence systems. It is unclear, for example, whether
external information is used in the cycle, and if so, in what
ways. Secondly, the cyclical feedback should invoke the re-
evaluation of information needs, as business conditions
change, or some needs have been incorrectly assessed from
previous cycles (inclusion of irrelevant information or
omission of important information).
From the above descriptions of technologies and processes
for both decision support and business intelligence we can
define two different but interrelated cycles: cycle 1 for
business intelligence process, and cycle 2 for decision support
process (Fig. 3).
As cycles 1 and 2 unfold, the focus moves around different
types of information needs. In cycle 1, the steps of information
gathering and processing can be attributed to the common and
simple part of information needs. The analysis step uses
processed information and produces derivative results that
produce additional insight and move from simple to more
complex needs. If a problem situation is recognized, special
needs arise, and cycle 2 is activated. For a problem analysis,
special needs may be both of simple and complex nature,
depending upon the severity of a problem. A problem-specific
model is developed for better understanding of the problem
and evaluating the alternatives. Decision implementation
brings in valuable experience that is saved for later reuse and,
together with other experience, satisfies common information
needs important both for future business intelligence and
decision making.
Fig. 3. Relation of business intelligence (1) and decision support (2) cycles
IV. USER RESPONSES ON IT USE FOR DECISION SUPPORT AND
BUSINESS INTELLIGENCE
The opinions on IT role in supporting the sophisticated side
of information needs can be roughly split into deterministic
approaches and behavioural, human-centered approaches. The
former assign prime importance to IT performance and ability
to automate complex analytical procedures [2], while the latter
assign prime importance to human skills and creative powers
([16], [5], [19]), at the same time stating that the majority of
existing decision support and analytical tools are technology-
centric rather than user-centric. The conflicting attitudes have
initiated a survey, performed earlier by one of the authors [14],
where issues like monitoring of internal and external
environment, IT role in the monitoring process, and experience
management have been researched to gain insight on IT use to
support the compl;ex side of management information needs,
including DS and BI. The survey had yielded 250 responses
from a convenience sample of managers of small and medium
businesses in a Central-Eastern Europe country.
Regarding the monitoring of internal organization
environment, the users appeared to be quite comfortable using
IT for monitoring key data about their organization’s activities.
Such information is contained within their in-house
information system that has been created to monitor these
activities. The absolute majority of responders (161 or 64.4%)
have indicated that IT is used to monitor all issues relating to
an organization’s internal information needs; such needs are
International Journal of Artificial Intelligence and Interactive Multimedia, Vol. 2, Nº 3.
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attributed mostly to the simple common needs. The
information system-based information tasks are largely
routine, and satisfaction of this type of information needs does
not pose any significant problems.
For external monitoring the use of IT is significantly lower;
the number of responders having indicated that they use IT to
monitor all external issues has been 125, or 50%; 122
responders, or 48.8%, had stated that they use IT for some of
the external monitoring issues. The lower numbers of use do
not point to second-rate importance of external monitoring;
rather, they indicate that the sources of external information
are not under the control of a single own information system,
as it is in the case of internal information sources. The external
environment, being an important source of changes,
opportunities and risks, is much more turbulent, and there is a
greater variety of issues to be monitored, information sources,
formats, and access modes; this variety significantly
complicates the use of IT for external monitoring.
Supporting the detection of important changes, IT had been
considered a helpful aid in monitoring and detecting changes,
but rather limited in supporting information needs for sense-
making. The absolute majority of responses (105 out of 207
responders having indicated that IT has some role in detecting
important changes, or about 51%) stressed the role of IT as a
principal technical support tool. No responses stated that IT
had significantly supported the function of sense-making
(revealing important changes in the environment).
The reuse of experience and competence information is one
of the most important functions in the process chains of BI and
DS; this statement can be supported by a seemingly growing
number of published work on experience management
systems. The results of the survey have indicated that the reuse
of important problem-solving and decision making experience
is of mixed success; recorded practice is reused – in most
cases conditionally, as situations change and information needs
have to be constantly re-evaluated. The survey had also shown
that experience records are recorded in all convenient ways:
free text format in digital media, structured format (with some
standardized features and values) in digital media, and same
on paper. IT role can be seen mostly in arranging, managing
structures, imposing standards, and allowing easy filtering and
retrieval. Level of reuse is limited due to changing context,
although the reuse of templates, structures, models and other
procedural issues is commonplace.
Decision-making information needs are hard to plan because
of their variety and unstructuredness. Regarding this issue, the
respondees have been asked about:
? decision making infomation needs that are known
beforehand, and the principal types of such information;
? decision making information needs that are not known
beforehand and emerge in the process of developing a
decision, and the principal types of such information.
The known information needs relate to information whose
content and location are known and accessible because of
earlier experience, or this information is already available.
This information or tools for its access can be placed in close
proximity to the decision makers. The distribution of responses
between the different types of this information is given in
Table 2.
TABLE 2
KNOWN INFORMATION NEEDS FOR DECISION MAKING
Type of information No. of
cases
Percent
Market information (customers, sales,
needs, opportunities)
49 19,6%
Competition information (competitors’
status, strength, intentions, actions)
29 11,6%
Internal information (financials,
capacity, inventory)
27 10,8%
Legal information (laws, regulations,
standards)
26 10,4%
No such cases 26 10,4%
Technical information 2 0,8%
Did not specify 91 36,4%
Total: 250 100,0%
A separate important group of information needs is the
unexpected information needs, which emerge mostly because
of turbulent business nature, are hard to plan, and the use of
programmed solutions is rather limited. The distribution of
responses between the different types of this information is
given in Table 3.
TABLE 3
UNEXPECTED INFORMATION NEEDS FOR DECISION MAKING
Type of information No.
of
cases
Percent
No such cases 86 34,4%
Yes, there have (without specifying the
information)
46 18,4%
Market information 23 9,2%
Internal information 15 6,0%
Competition information 14 5,6%
Legal information 14 5,6%
Technical information 14 5,6%
Informal, “soft” information (e.g.,
opinions, foresights)
12 4,8%
Confidential information (e.g., customer
reliability checks)
5 2,0%
Did not specify 21 8,4%
Total: 250 100,0%
The distribution of both responses is not much different, and
suggests that often decision makers have to look deeper into
existing issues (“more of the same”). However, the significant
presence of unexpected information needs might require a set
of support tools that would allow tailored approaches using
assorted decision support techniques – e.g., modeling, data
mining, text mining, information integration and others.
The above separation of information needs into known and
unexpected roughly corresponds to the related cycles pictured
in Fig.5, where the business intelligence cycle is performed
mostly against known information needs. If a specific problem
Special Issue on Improvements in Information Systems and Technologies
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is detected, the known needs together with readily available
information move to the decision support cycle, where
additional information needs of unexpected nature are likely to
emerge. This approach can be useful in designing business
intelligence environments incorporating a sub-level for
decision support, with generic functionality contained mostly
in the 1
st
cycle, and the problem-specific tools and techniques
in the 2
nd
cycle.
V. DISCUSSION AND CONCLUSIONS
There’s no doubt that the need for well-informed business
decisions, as well as for general awareness of developments in
the business environment, will remain acute. The current state
of management decision support gets more complicated as
rapidly changing conditions often require swift reaction,
information overload is commonplace, and additional issues
arise regarding information quality [9]. Under these
conditions, a need for right information at the right time and in
the right place remains essential, and the well-aimed and
reasonable use of support technology can increase decision
making quality and efficiency, regardless of whatever name
this technology is bearing at the moment.
We suggest here to use here the arguments presented in this
paper, regarding the development of an efficient information
environment for decision makers. It has been proposed that
such environment should be split into two tiers:
? the first tier containing a simple set of support tools that
are close and easy to use;
? the second tier containing more distant and more
complicated information sources and processing
techniques that are required much less often;
? manageable support environment that allows easy
switching of items between tiers, similar to the form of
managerial dashboards with interchangeable items on
display.
The items contained in the first (“lite”) tier would be
required most of the time, simple to use and able to be
configured to the users’ needs:
? basic data on internal and external environment: sales,
market share, cash-at-hand, order or project portfolio,
comparative figures by time/place/product etc.;
? information access tools: simple search in own sources –
databases and data warehouses, simple search in public
sources, tools for arranging search results (e.g., by
relevance or size), easy classification and annotation;
? tools for simple calculations: templates, financial models,
other simple models.
The second (“heavy”) tier might include:
? access to more distant and complex information sources
with advanced search tools;
? modelling tools for forecasting, simulation, scenario
development;
? data analysis and presentation technologies – drill-down
tools, OLAP queries, data and text mining facilities,
graphing and visualization tools.
Such split of functionality would roughly reflect required
functions for generic business intelligence and decision
support cycles respectively. It would also allow for required
cross-functionality in the cases when simple decision support
needs would be well-served by first tier functions alone, or
when business intelligence needs would required more
advanced tools. The more defined set of features for both tiers
of the support environment could lead to a possible set of
requirements for the interface design of an information
environment for decision makers.
The further research is planned in several related and more
specific directions. Firstly, it is important to research what part
of business decisions are adequately supported by the first tier
of the support environment, thus possibly defining an efficient
and economical set of support tools. Secondly, the issues of
handling experience information and providing experience
support should be investigated in more specific terms of what
key information on decisions already made should be recorded
to create brief yet essential context, and what is the reusability
and relevance rate for different types of experience records.
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[17] Thorleuchter D., Van den Poel D. Analyzing Website Content for
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Rimvydas Skyrius is a professor at the Economic
Informatics department, Faculty of Economics, Vilnius
University, Lithuania. He has a PhD in Management
(Management Information Systems) from ASU-Moskva
research institute, Moscow, former Soviet Union, 1986.
From 2008 he is a head of the Economic informatics
department. His research interests include Management
Information Needs, Management Decision Support,
Business Intelligence, Information Systems Usability.
Vytautas Bujauskas is an Associate Professor (retired) at
the Economic Informatics department, Faculty of
Economics, Vilnius University, Lithuania. He has a PhD in
Management (Management Information Systems) from
Moscow Plekhanov institute of National Economy, 1978.
His research interests include Management Information
Needs, Management Decision Support, Business
Intelligence.
Gelyte Kazakeviciene is an Associate professor at the
Economic Informatics department, Faculty of Economics,
Vilnius University, Lithuania. She has a PhD in
Mathematics from Vilnius University, 1993. Her research
interests include Management Information Needs,
Management Decision Support, Financial applications.
doc_491392523.pdf
Despite the advances in IT, information systems intended for management informing did not uniformly fulfil the increased expectations of users; this can be said mostly about complex information needs.
International Journal of Artificial Intelligence and Interactive Multimedia, Vol. 2, Nº 3.
-31-
Abstract — Despite the advances in IT, information systems
intended for management informing did not uniformly fulfil the
increased expectations of users; this can be said mostly about
complex information needs. Although some of the technologies for
supporting complicated insights, like management decision
support systems and technologies, experienced reduction in
interest both from researchers and practitioners, this did not
reduce the importance of well-supported business informing and
decision making. Being attributed to the group of intelligent
systems and technologies, decision support (DS) technologies have
been largely supplemented by business intelligence (BI)
technologies. Both types of technologies are supported by
respective information technologies, which often appear to be
quite closely related. The objective of this paper is to define
relations between simple and complex informing intended to
satisfy different sets of needs and provided by different sets of
support tools. The paper attempts to put together decision
support and business intelligence technologies, based on common
goals of sense-making and use of advanced analytical tools. A
model of two interconnected cycles has been developed to relate
the activities of decision support and business intelligence.
Empirical data from earlier research is used to direct possible
further insights into this area.
Keywords — management decision support, business
intelligence, information needs
I. INTRODUCTION
HE job of informing business managers and other
people in charge of running organizations stays on the
agenda of many researchers and practitioners around the
information systems and information management community.
While the advances in technological foundations of
management information systems have been impressive, the
advances in efficient satisfaction of management information
needs have been less impressive. The development of systems
for managerial information needs, while having a rich history
of several decades, has been based on a heterogeneous set of
needs: some of these needs stay stable (developing,
implementing and adjusting strategy; keeping track of own
activities), and some evolve or have a turbulent life cycle:
monitoring close environment; looking out for threats and
opportunities. Information environment (support
infrastructure) is driven by the nature of business activities. On
one hand, this nature is recurrent and cyclical, supported
mostly by the function of a MIS. On the other hand, this nature
is turbulent and unpredictable, requiring intelligent and
insightful support; this is a function of a BI system and related
applications – decision support, competitive intelligence,
operational intelligence, early warning systems and other types
of systems to support monitoring, sense-making and problem
solving.
The recent research on complex information needs including
decision support and business intelligence has been diversified
into quite a few related areas; far from being an exhaustive set,
several examples follow. Lemieux and Dang [7] have
researched the issues of accountability for decision making,
and suggested tools for tracking the decision-making reasoning
of human agents, thus adding to the research on a problem of
experience management. Thorleuchter and Van den Poel [17]
have investigated the use of website content analysis in partner
search for improved research and technology collaboration
planning, adding to the body of research on information
integration. Saad et al [11] have researched a conceptual
framework for early warning information systems for crisis
situations, expanding the research on intelligence technologies
for monitoring and detection. Castano [1] has researched the
possibility of putting together business process management
(BPM) and data mining techniques to provide intelligent BPM
management functions. Redondo-Garcia et al [10] have
researched information integration tasks when using disparate
(heterogeneous) information sources.
The sample of research directions presented above for a
long time has been attributed to the area of decision support
systems and technologies, serving the complex or high-end
side of user information needs. In the field of technologies for
satisfying complex information needs, the once-prominent area
of management decision support systems (DSS) apparently has
settled to stable levels of both academic and practitioner
activities [9]. However, a somewhat faded interest in decision
support systems does not imply any reduction in importance of
well-supported decision making, as well as general awareness
of the state of internal and external business environment. On
From Management Information Systems to
Business Intelligence: The Development of
Management Information Needs
Rimvydas Skyrius, G?lyt? Kazakevi?ien?, and Vytautas Bujauskas
Economic Informatics Department, Vilnius University, Lithuania
T
DOI: 10.9781/ijimai.2013.234
Special Issue on Improvements in Information Systems and Technologies
-32-
the contrary, the current economic situation in most settings
demands an efficient and reliable, „military grade“
management environment to support decisions, insights,
recovery or mere survival.
Decision support alone, being reactive and activated only
when a problem is encountered, eventually proved to be
insufficient. The problem solving context received IT-based
support mostly from the resources of a regular information
system, therefore of a limited nature and in most cases
complicated by time pressures. An alternative use of decision
support, if coupled to a proactive monitoring of the
environment, ensured better understanding of the problem
context, leading to higher decision quality. A term “business
intelligence” came into use, serving as an umbrella term for
tools and technologies that let business information users stay
aware of changes in internal and external environments.
The research problem of this paper is centered around how
the current array of technologies and approaches provides
support for functions of insight building. Currently there is a
confusion in defining whether management information
systems overlap with intelligence systems, and whether
business intelligence is a part of decision support function, or
vice versa; eventually this confusion spreads to business
management community which at all times has expressed the
need for insight building and reliable decision support which
would justify substantial investments into support
technologies. In this paper, the authors have decided to use the
results of their earlier research to make an attempt in
developing a model positioning business intelligence and
decision support functions.
The paper is structured as follows. Section 1 defines the
dimensions of the problem and the goal of the paper. Section 2
clarifies the definition of business intelligence and its
information needs. Section 3 defines a relation between the
areas of decision support and business intelligence. Section 4
presents empiric data on user responses towards decision
support anad business inteligence functions. Finally, Section 5
presents conclusions and directions for further research.
II. BUSINESS INTELLIGENCE AND INFORMATION NEEDS
Although business intelligence is regarded as a relatively
new term, with authorship assigned to Howard Dressner of
Gartner Group in 1989, we can have a retrospective look at the
mission of management information systems (MIS), whose
role of keeping management aware of the state of business has
never been downplayed, and mission definitions for MIS
sound very much like the mission definitions for business
intelligence today. A few explanations of MIS role from earlier
sources are presented below:
? “Two types of information for strategy
implementation are in use. The first one is the
external information, used for strategy development.
The second type is internal information, used to
monitor strategy execution” [14].
? “A management information system refers to many
ways in which computers help managers to make
better decisions and increase efficiency of an
organization‘s operation” [7].
? “For information to be useful for managerial decision
making, the right information (not too much and not
too little) must be available at the right time, and it
must be presented in the right format to facilitate the
decision at hand” [4].
? “A management information system is a business
system that provides past, present, and projected
information about a company and its environment.
MIS may also use other sources of data, particularly
data about the environment outside of the company
itself.” [6].
? “The systems and procedures found in today’s
organizations are usually based upon a complex
collection of facts, opinions and ideas concerning the
organization’s objectives. … For an organization to
survive, it must learn to deal with a changing
environment effectively and efficiently. To
accomplish the making of decisions in an uncertain
environment, the firm’s framework of systems and
procedures must be remodeled, refined, or tailored on
an ongoing basis.” [3].
There are definitions of business intelligence that do not
differ much from the above definitions; e.g., Vuori [20] states
that “… business intelligence is considered to be a process by
which an organization systematically gathers, manages, and
analyzes information essential for its functions”. In order to
have a more precise definition of business intelligence, we
have to decide whether all informing functions are
„intelligence“ because they increase awareness, or does BI
have a clear separation from other (lower level) informing
functions. If so, the separation criteria between BI systems and
any other management information systems have to be defined.
For the purposes of this paper, we will use the division of
management information needs along two dimensions – their
simplicity or complexity, and common or specific focus, as
presented in the Table 1 and based on earlier work by one of
the authors [14]:
TABLE 1.
RELATION OF SIMPLE-COMPLEX AND COMMON-SPECIAL INFORMATION NEEDS
Simple needs Complex needs
Special needs
(problem-
specific)
Simple special
needs
Complex special
needs
Common needs
(available
permanently)
Simple common
needs
Complex common
needs
The mission of BI becomes clearer if weighted against the
types of served information needs. Regarding the positioning
of these needs against the axis of simple-complex information
needs, they usually fall into the more sophisticated part of the
information needs complexity spectrum. Same can be said
International Journal of Artificial Intelligence and Interactive Multimedia, Vol. 2, Nº 3.
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about the process of decision making, which often requires
sophisticated tools to support awareness, communication,
sense-making and evaluation of risks. The dimension of
common and special information needs separates decision
making from the rest of business intelligence in a sense that
while decision support activities are directed towards a certain
problem which has been recognized and has created a task of
its solving, business intelligence can be considered an activity
which, apart from encompassing decision support, has a
permanent nature and allows the discovery of problems and
general awareness about the state of activities.
III. DECISION SUPPORT AND BUSINESS INTELLGENCE
PROCESSES
A. Structure of Decision SupportProcess
A decision support process includes a number of stages, and
if accumulation and subsequent use of experience is included,
the process takes a cyclical nature (Fig. 1, from [13]):
Fig. 1. The decision support process
The structure of the decision support process can be related
to relevant information needs:
1. Monitoring (using previous experience): the
environment, both internal and external, is being
watched to notice things worth attention; simple and
common information needs prevail.
2. In the case of recognizing a situation of interest
(initial understanding of a problem or opportunity)
the situation is evaluated and given extra attention to
achieve desired understanding. At this stage special
information needs arise.
3. Additional analysis and decision development is
required if the situation is complex enough (semi-
structured or unstructured); simple needs are
complemented by complex needs; more information is
brought into decision making environment; specific
problem-solving tools such as formal approaches and
models are likely to be used to achieve an adequate
understanding of a problem.
4. The decision-making stage involves formerly
available as well as newly gained understanding of
the situation, and the decision maker or makers will
use all possessed knowledge to arrive at the best
possible decision, time or other circumstances
permitting. In this paper, the term “knowledge” is
deliberately avoided most of the time, but here it
serves to show that data or information alone are
insufficient for decision making; all that is known will
be used in its entirety, and new knowledge most likely
will be gained.
5. The experience accumulation stage records the newly
gained experience from both decision making and its
implementation, and keeps it for possible reuse.
Special needs become common, adding new material
to the already available body of experience, and the
need to capture the essential features of the recorded
case keeps this sort of information need in the
complex segment. This phase should also include the
practical experience in decision implementation,
which can sometimes reveal additional circumstances
of the problem.
6. The use of new experience, along with that formerly
accumulated, brings the process back to stage 1 –
monitoring.
Stage 1 of the above process is directly related to (or can be
considered a part of) business intelligence, because that’s
where the actual monitoring of the business environment is
being done. Stage 2 is a principal point of joining business
intelligence and decision support.
As we can see, during the decision making process the focus
of information needs moves around the quadrants of Table 1:
stage 1 concentrates in the simple/common sector; stage 2
moves on to simple/special sector, stages 3 and 4 concentrate
in the special/complex sector, stage 5 moves into complex
common sector, and finally stage 6 brings the focus back to
simple/common sector.
B. Structure of Business Intelligence Process
The business intelligence process, too, takes a cyclical
nature (Fig. 2., from [20]), and includes the stages of
information needs definition, information collection,
information processing, analysis, information dissemination,
information utilization and feedback. The cycle structure is
justified if the received feedback helps to reevaluate or
redefine information needs.
In business intelligence process, there‘s usually no clear
concentration on a specific topic or problem, and the resources
of a BI system are used for constant monitoring of internal and
external business environment. In other words, such systems
serve common information needs to keep users informed about
the state of business environment, often combining a
monitoring function with alerts, exception reports and other
tools to draw attention to changes or inconsistencies.
Therefore, an important feature of BI systems is their ability to
Special Issue on Improvements in Information Systems and Technologies
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produce a complete composite view that would help avoiding
surprises.
Fig. 2. A generic business intelligence process model [20]
The business intelligence cycle, as presented in Fig. 4.,
raises several questions. First of all, it does not disclose the
difference between regular management information systems
or their current incarnation, ERP systems, and business
intelligence systems. It is unclear, for example, whether
external information is used in the cycle, and if so, in what
ways. Secondly, the cyclical feedback should invoke the re-
evaluation of information needs, as business conditions
change, or some needs have been incorrectly assessed from
previous cycles (inclusion of irrelevant information or
omission of important information).
From the above descriptions of technologies and processes
for both decision support and business intelligence we can
define two different but interrelated cycles: cycle 1 for
business intelligence process, and cycle 2 for decision support
process (Fig. 3).
As cycles 1 and 2 unfold, the focus moves around different
types of information needs. In cycle 1, the steps of information
gathering and processing can be attributed to the common and
simple part of information needs. The analysis step uses
processed information and produces derivative results that
produce additional insight and move from simple to more
complex needs. If a problem situation is recognized, special
needs arise, and cycle 2 is activated. For a problem analysis,
special needs may be both of simple and complex nature,
depending upon the severity of a problem. A problem-specific
model is developed for better understanding of the problem
and evaluating the alternatives. Decision implementation
brings in valuable experience that is saved for later reuse and,
together with other experience, satisfies common information
needs important both for future business intelligence and
decision making.
Fig. 3. Relation of business intelligence (1) and decision support (2) cycles
IV. USER RESPONSES ON IT USE FOR DECISION SUPPORT AND
BUSINESS INTELLIGENCE
The opinions on IT role in supporting the sophisticated side
of information needs can be roughly split into deterministic
approaches and behavioural, human-centered approaches. The
former assign prime importance to IT performance and ability
to automate complex analytical procedures [2], while the latter
assign prime importance to human skills and creative powers
([16], [5], [19]), at the same time stating that the majority of
existing decision support and analytical tools are technology-
centric rather than user-centric. The conflicting attitudes have
initiated a survey, performed earlier by one of the authors [14],
where issues like monitoring of internal and external
environment, IT role in the monitoring process, and experience
management have been researched to gain insight on IT use to
support the compl;ex side of management information needs,
including DS and BI. The survey had yielded 250 responses
from a convenience sample of managers of small and medium
businesses in a Central-Eastern Europe country.
Regarding the monitoring of internal organization
environment, the users appeared to be quite comfortable using
IT for monitoring key data about their organization’s activities.
Such information is contained within their in-house
information system that has been created to monitor these
activities. The absolute majority of responders (161 or 64.4%)
have indicated that IT is used to monitor all issues relating to
an organization’s internal information needs; such needs are
International Journal of Artificial Intelligence and Interactive Multimedia, Vol. 2, Nº 3.
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attributed mostly to the simple common needs. The
information system-based information tasks are largely
routine, and satisfaction of this type of information needs does
not pose any significant problems.
For external monitoring the use of IT is significantly lower;
the number of responders having indicated that they use IT to
monitor all external issues has been 125, or 50%; 122
responders, or 48.8%, had stated that they use IT for some of
the external monitoring issues. The lower numbers of use do
not point to second-rate importance of external monitoring;
rather, they indicate that the sources of external information
are not under the control of a single own information system,
as it is in the case of internal information sources. The external
environment, being an important source of changes,
opportunities and risks, is much more turbulent, and there is a
greater variety of issues to be monitored, information sources,
formats, and access modes; this variety significantly
complicates the use of IT for external monitoring.
Supporting the detection of important changes, IT had been
considered a helpful aid in monitoring and detecting changes,
but rather limited in supporting information needs for sense-
making. The absolute majority of responses (105 out of 207
responders having indicated that IT has some role in detecting
important changes, or about 51%) stressed the role of IT as a
principal technical support tool. No responses stated that IT
had significantly supported the function of sense-making
(revealing important changes in the environment).
The reuse of experience and competence information is one
of the most important functions in the process chains of BI and
DS; this statement can be supported by a seemingly growing
number of published work on experience management
systems. The results of the survey have indicated that the reuse
of important problem-solving and decision making experience
is of mixed success; recorded practice is reused – in most
cases conditionally, as situations change and information needs
have to be constantly re-evaluated. The survey had also shown
that experience records are recorded in all convenient ways:
free text format in digital media, structured format (with some
standardized features and values) in digital media, and same
on paper. IT role can be seen mostly in arranging, managing
structures, imposing standards, and allowing easy filtering and
retrieval. Level of reuse is limited due to changing context,
although the reuse of templates, structures, models and other
procedural issues is commonplace.
Decision-making information needs are hard to plan because
of their variety and unstructuredness. Regarding this issue, the
respondees have been asked about:
? decision making infomation needs that are known
beforehand, and the principal types of such information;
? decision making information needs that are not known
beforehand and emerge in the process of developing a
decision, and the principal types of such information.
The known information needs relate to information whose
content and location are known and accessible because of
earlier experience, or this information is already available.
This information or tools for its access can be placed in close
proximity to the decision makers. The distribution of responses
between the different types of this information is given in
Table 2.
TABLE 2
KNOWN INFORMATION NEEDS FOR DECISION MAKING
Type of information No. of
cases
Percent
Market information (customers, sales,
needs, opportunities)
49 19,6%
Competition information (competitors’
status, strength, intentions, actions)
29 11,6%
Internal information (financials,
capacity, inventory)
27 10,8%
Legal information (laws, regulations,
standards)
26 10,4%
No such cases 26 10,4%
Technical information 2 0,8%
Did not specify 91 36,4%
Total: 250 100,0%
A separate important group of information needs is the
unexpected information needs, which emerge mostly because
of turbulent business nature, are hard to plan, and the use of
programmed solutions is rather limited. The distribution of
responses between the different types of this information is
given in Table 3.
TABLE 3
UNEXPECTED INFORMATION NEEDS FOR DECISION MAKING
Type of information No.
of
cases
Percent
No such cases 86 34,4%
Yes, there have (without specifying the
information)
46 18,4%
Market information 23 9,2%
Internal information 15 6,0%
Competition information 14 5,6%
Legal information 14 5,6%
Technical information 14 5,6%
Informal, “soft” information (e.g.,
opinions, foresights)
12 4,8%
Confidential information (e.g., customer
reliability checks)
5 2,0%
Did not specify 21 8,4%
Total: 250 100,0%
The distribution of both responses is not much different, and
suggests that often decision makers have to look deeper into
existing issues (“more of the same”). However, the significant
presence of unexpected information needs might require a set
of support tools that would allow tailored approaches using
assorted decision support techniques – e.g., modeling, data
mining, text mining, information integration and others.
The above separation of information needs into known and
unexpected roughly corresponds to the related cycles pictured
in Fig.5, where the business intelligence cycle is performed
mostly against known information needs. If a specific problem
Special Issue on Improvements in Information Systems and Technologies
-36-
is detected, the known needs together with readily available
information move to the decision support cycle, where
additional information needs of unexpected nature are likely to
emerge. This approach can be useful in designing business
intelligence environments incorporating a sub-level for
decision support, with generic functionality contained mostly
in the 1
st
cycle, and the problem-specific tools and techniques
in the 2
nd
cycle.
V. DISCUSSION AND CONCLUSIONS
There’s no doubt that the need for well-informed business
decisions, as well as for general awareness of developments in
the business environment, will remain acute. The current state
of management decision support gets more complicated as
rapidly changing conditions often require swift reaction,
information overload is commonplace, and additional issues
arise regarding information quality [9]. Under these
conditions, a need for right information at the right time and in
the right place remains essential, and the well-aimed and
reasonable use of support technology can increase decision
making quality and efficiency, regardless of whatever name
this technology is bearing at the moment.
We suggest here to use here the arguments presented in this
paper, regarding the development of an efficient information
environment for decision makers. It has been proposed that
such environment should be split into two tiers:
? the first tier containing a simple set of support tools that
are close and easy to use;
? the second tier containing more distant and more
complicated information sources and processing
techniques that are required much less often;
? manageable support environment that allows easy
switching of items between tiers, similar to the form of
managerial dashboards with interchangeable items on
display.
The items contained in the first (“lite”) tier would be
required most of the time, simple to use and able to be
configured to the users’ needs:
? basic data on internal and external environment: sales,
market share, cash-at-hand, order or project portfolio,
comparative figures by time/place/product etc.;
? information access tools: simple search in own sources –
databases and data warehouses, simple search in public
sources, tools for arranging search results (e.g., by
relevance or size), easy classification and annotation;
? tools for simple calculations: templates, financial models,
other simple models.
The second (“heavy”) tier might include:
? access to more distant and complex information sources
with advanced search tools;
? modelling tools for forecasting, simulation, scenario
development;
? data analysis and presentation technologies – drill-down
tools, OLAP queries, data and text mining facilities,
graphing and visualization tools.
Such split of functionality would roughly reflect required
functions for generic business intelligence and decision
support cycles respectively. It would also allow for required
cross-functionality in the cases when simple decision support
needs would be well-served by first tier functions alone, or
when business intelligence needs would required more
advanced tools. The more defined set of features for both tiers
of the support environment could lead to a possible set of
requirements for the interface design of an information
environment for decision makers.
The further research is planned in several related and more
specific directions. Firstly, it is important to research what part
of business decisions are adequately supported by the first tier
of the support environment, thus possibly defining an efficient
and economical set of support tools. Secondly, the issues of
handling experience information and providing experience
support should be investigated in more specific terms of what
key information on decisions already made should be recorded
to create brief yet essential context, and what is the reusability
and relevance rate for different types of experience records.
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Rimvydas Skyrius is a professor at the Economic
Informatics department, Faculty of Economics, Vilnius
University, Lithuania. He has a PhD in Management
(Management Information Systems) from ASU-Moskva
research institute, Moscow, former Soviet Union, 1986.
From 2008 he is a head of the Economic informatics
department. His research interests include Management
Information Needs, Management Decision Support,
Business Intelligence, Information Systems Usability.
Vytautas Bujauskas is an Associate Professor (retired) at
the Economic Informatics department, Faculty of
Economics, Vilnius University, Lithuania. He has a PhD in
Management (Management Information Systems) from
Moscow Plekhanov institute of National Economy, 1978.
His research interests include Management Information
Needs, Management Decision Support, Business
Intelligence.
Gelyte Kazakeviciene is an Associate professor at the
Economic Informatics department, Faculty of Economics,
Vilnius University, Lithuania. She has a PhD in
Mathematics from Vilnius University, 1993. Her research
interests include Management Information Needs,
Management Decision Support, Financial applications.
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