It is essential that firms measure those activities that
contribute to its business model and corresponding strategy,
because whats get measured, gets done. The key notion that should
be conclude from this chapter is that an organisations strategy
should be based on the needs and preferences of the firm
stakeholders, and that a companys strategy drives the values,
objectives, goals and plans of company, which, in turn, determine
the (key) performance indicators. Therefore, a link between the BI
process and an organisations strategy is required. This link is
established by defining key-performance indicators that are based
on the firms business model. In turn, the business model should
reflect the organisations strategy. This perspective on business
intelligence is schematically presented in figure 2-6.
Decision making is a managerial process and function of choosing
a particular course of action out of several alternative courses
for the purpose of accomplishment of the organizational goals.
Decisions may relate to general day to day operations. They may be
major or minor. They may also be strategic in nature. Strategic
decisions are different in nature than all other decisions which
are taken at various levels of the organization during day-to-day
working of the organizations. The major dimensions of strategic
decisions are given below: Strategic issues require top-management
decisions: Strategic issues involve thinking in totality of the
organizations and also there is lot of risk involved. Hence,
problems calling for strategic decisions require to be considered
by top management. Strategic issues involve the allocation of large
amounts of company resources: It may require huge financial
investment to venture into a new area of business or the
organization may require huge number of manpower with new set of
skills in them. Strategic issues are likely to have a significant
impact on the long term prosperity of the firm: Generally the
results of strategic implementation are seen on a long term basis
and not immediately. Strategic issues are future oriented:
Strategic thinking involves predicting the future environmental
conditions and how to orient for the changed conditions.
Strategic issues usually have major multifunctional or
multi-business consequences: As they involve organization in
totality they affect different sections of the organization with
varying degree. Strategic issues necessitate consideration of
factors in the firms external environment: Strategic focus in
organization involves orienting its internal environment to the
changes of external environment.
Business Intelligence and Decisions All respondents argue that
they make better decisions with the Business Intelligence system
compared to before. The argument is that the decisions have become
more informed when you have more information, more data and more
correct data. One respondents states that there is varies ways and
possibilities to find information, both on high and detailed level,
which according to the respondent implies better decisions. Another
respondent expresses that the system is a foundation for strategic
decisions in his division; From the perspective of the business, I
will not say that it is crucial, like completely determinant, but
it is an important part of our price negotiations and the
respondents further on argues that the Business Intelligence
sys-tem and the reports it is providing are a business foundation.
In general the respondents state that they feel more secure and
safe with their decisions. I feel much safer with what I am doing.
Because you know that it is not just a feeling that I have, but
this is facts, this is how it is, this is correct So yes,
absolutely, this helps us a lot in our decision making. An-other
respondent compares the previous decision support with current
decision support and express; More feeling. Now you can confute
information, there is data actual showing how it is. It is not just
a feeling or conclusion. If this and this and that pointing in one
direction, it should be like that but now you actual can verify it
as wellThis data was not available before. The awareness of that
the competitors also work with Business Intelligence makes one
re-spondent to compare with the sport worlds; We have competitors,
we are competing with them in the same manner as any athlete. The
better tool we have the better chance to win, eh? If you are about
to being on track and fight towards the greatest, you will need to
have the greatest tools We might not achieve a better result
compare to before but we would have achieved a lower result if BI
would not have been a part of the picture. The respondents claim
that their organization has a better target achievement and that
the targets have become clearer. For example one respondent argues
that overall and generaltargets are easier to break down into
smaller and step-by-step goal and that it is easier to see what
your own division can do to reach the overall goals. The respondent
takes an ex-ample of the goal of 95 % timely deliveries; It can be
hard for us in operative level to work with. When it is so overall,
but then we can use BI to go to a lower level and see how much is
95 % of my sub-groups, my models, my storages? ...General goals are
not easy to transform into personal goals, but since you can go
down to details it is easier to get a concrete goal. This aspect
can be connected to the im-provement in the internal communication
as one of the IT-consultants mentioned in pre-vious section. It was
argued that it is easier to follow up internal goals when using
Business Intelligence. One respondent expresses that some decisions
are harder to take with the help from the system, for example if
going into a new market and setting price in new markets. The
or-ganization then applies previous experience or compares with
similar countries. That struc-tured decisions are easier to take
with help from IT-systems has been suggested by Turban et al.
(2007) and Davenport et al. (2001), and has also been indicated in
the interviews with IT-consultants. Yet, the respondent in the case
do not refer to structured and unstructured decisions, but the
described problem would match a unstructured problem described in
the literature. According to the respondents it happens now and
then that the core data turns out to be incorrect or misleading.
One respondent highlights that this may cause irritation and can
create distrust towards the numbers and the system. The same
respondent also expresses that it can be frustrating to be aware of
that some KPIs contain errors and in the same time be aware of that
the management evaluate their division based on these KPIs. The
opera-tive management knows that some measurements are built up by
false data but when the higher management only considering
aggregated and compiled numbers there is a risk that the decision
support also is false and misleading. The respondent stresses that
this can im-ply that the organization takes less appropriate
decisions if the higher management not be-come aware of which KPIs
that might be misleading. This aspect is of concern in Corpora-tion
A as well, since they fear that the decision base will be
misleading without anyone no-ticing and the danger with low data
quality is also mentioned by the IT-consultants. How Decision
Support Has Improved Several of the respondents argue that the
decision support has improved since it is much more extensive now,
the respondents state that they have access to more information
with the Business Intelligence system. The respondents claim they
take better decisions since they are more informed, more
information results in more informed decisions and there through
better decisions is the argument.
One respondent argues the Business Intelligence system makes it
easier to respond faster to certain events and takes the example of
monthly sales. When three weeks have passed the sales should be of
planned/budgeted sales for that month and if not, they can react
directly instead of following it up at the end of the month.
Another respondent argues that there is a value in the ability to
measure variables more often and build trends since he con-sidering
trends to be important, a Business Intelligence system can to a
larger extent allow
this kind of measuring compared to other system. The respondents
also mentioning that the Business Intelligence system is better in
producing reports and have improved the pos-sibilities for
analyses, especially when the data is compiled and aggregated, for
example on a world-wide level instead of on national level. There
are examples of situations when the system makes decision. In one
organization the suggestions by Eckerson (2003) is reality, and the
users have created certain rules to make the system to take
decisions. In this corporation the Business Intelligence system is
used for inter alia prognoses, the system calculates a production
need out of the current stock status together with the calculated
prognoses and transfer it to the production system.
To manage daily operations and to face the external competitive
pressure, business decisions need to be made on three levels:
strategic, tactical and operational. Strategic decisions look ahead
to longer time horizons, larger expenditures, greater uncertainty
and therefore greater deviations from the current business model
(Ramakrishnan et al. 2012), whereas operational decisions concern
short-term day-to-day activities. With the right information
available for the right person at the right time, an organization
can make conscious, fact-based decisions (Popovic et al. 2010). It
is able to compare its past performance against targets and set new
managerial objectives in strategic decision-making. It can protect
itself from business risk with tactical management and finally cut
costs with more efficient operations. Thus, BI serves the
information needs of all three levels of decision-making (Pirttimki
2007).
Van Roekel et al. (2009) recognize four levels of information
services within an organization that describe the information needs
of each organizational level. In their work, the operational
information directed to operational workers and operational
management is discussed separately.These four levels of information
services are as follows:Information services for operational
workers: the everyday administration of product, customer,
financial and process data that is usually in a structured form to
enable efficient, fast and reliable transactional
processing.Information services for operational management:
monitoring and managing the primary business processes and
up-to-date reporting of results. Data is structured and segmented
by product type, organizational function, or business
process.Information services for tactical management: indicating
trends and comparing the results across product groups, processes
and departments.Information services for strategic management: the
development of business models based on past and future market
development and internal capabilities. Different information is
needed during different phases of the strategic planning cycle and
it is structured according to various business objectives such as
customer value, financial analysis or risk management.On the
strategic level, BI makes potential to set goals accurately and to
pursue realization of the goals. BI facilitate carrying out a
variety of comparative reports, such as on chronological results,
profitability of a certain offers, effectiveness of distribution
channels together with doing modeling of development or
anticipating future trend on the base of some hypothesizes (Olszak,
& Ziemba, 2003). Strategic decisions are made or modified
rarely, but the scope of decisions is extensive and has influence
on other decisions (Karen, 2010).On the operational level, BI
Systems are utilized to carry out ad hoc analyses and respond
matters linked to departments current operations, the latest
financial status, collaboration with contractors and clients, sales
etc (Olszak, & Ziemba, 2003). Operational decisions represent a
policy hub, as the policies are applied to different decision
points where actions are taken (Karen, 2010).On the tactical level,
BI Systems can facilitate decision making in sales, marketing,
capital management, etc. BI is capable of optimizing future
achievements and changing organizational, financial or
technological aspect of business performance properly to support
organizations in achieving strategic goals successfully (Olszak,
& Ziemba, 2003). Decisions in this level can happen in high
volume and repeat regularly. Such decisions can also be made
automatically such as, approval of loans and assignment of credit
lines. These decisions are made by extremely programmed algorithmic
decision support processes (Karen, 2010).
It can be concluded that the information needs of strategic
management are the most extensive and varied. Nowadays the
information to support executive decision-making is gathered from
various sources both internal and external, in a structured and
unstructured form (Hovi et al 2009, Kaario and Peltola 2008).
Internal information is company specific information about the
companys performance and capabilities, such as sales data,
financial information and customer records (Pirttimki 2007). In its
most tacit form, it can also be firm-embedded know-how only shared
by the employees of the company. Internal information, in the form
of statistics and company internal reports, is often structured,
focused and closely aligned to operational information services
(Swash 1997), and therefore more easily processed and analyzed via
BI tools.External information, on the other hand, is gathered from
outside the company in publications such as reports, conference
proceedings, trade literature, external databases, and in legal and
technical documents, concerning the business environment,
technological advances and competitors (Swash 1997). More often
than not, it has diverse sources and it involves documents in an
unstructured form, which makes automated information systems
processing and the use of traditional BI tools challenging (Kaario
and Peltola 2008). External information is also more difficult to
process and requires interpretation to assess its value in terms of
relevance and usability (Swash 1997). Nonetheless, Uusi-Rauva
(1994) argues that the value of external information grows
remarkably in significance as decision-making moves from
operational to strategic. Most of the information presented in
operational enterprise applications is about the current state of
business (Popovic et al 2010). To transform this information from
operational to strategic a vigilant attitude towards historical
data is needed. Historical data is foremost an indication of past
performance with only some value in forecasting future scenarios
(Gilad and Gilad 1988), and as the pace of decision-making is ever
accelerating, the value of timely, forward-looking information
continues to increase (Hovi et al. 2009). Due to the greater impact
of strategic decisions on a company, the quality requirements of
strategic information and its sources are higher than in
operational decisions (Pirttimki 2007).As mentioned, identifying
information needs is the starting point of any information
management project, but unfortunately, it is also one of the most
problematic tasks. Decision makers and intelligence users who act
as data stewards by determining the specifications for the wanted
information can find articulating their needs demanding. Some of
the information needs are unconscious or change rapidly (Pirttimki
2007). In addition, decision-makers are not always aware of all the
possibilities information management can provide. Another issue is
the difference between subjective and objective information needs,
recognized by Hglund and Persson (1985). Subjective needs are those
an individual thinks he has, but that are not necessarily relevant
in objective terms. Subjective needs can thus also be called
information wants. Objective needs are more common, and they
involve information that is generally needed in the decision-making
process in question. Pirttil (1997) concludes that the most
significant information exists where the information needs and
wants or the objective and subjective needs overlap. When it comes
to recognizing both types, Hovi et al. (2009) suggest that
information needs could be collected, discussed and prioritized by
conducting surveys or workshops with the most essential
stakeholders of an organization. This is suggested to ensure
interaction between the decision-makers and information producers
and to reduce misinterpretations
The difference between the actual information needs and the
amount and content of information gathered is called the
information gap (Pirttimki 2007). It exists between the information
received, the information wanted and the information needed by the
decision maker. According to the author, during an information
management project, these gaps should be recognized and reduced by
successfully mapping the information needs, and by using
appropriate BI tools.They recognized the most common reasons behind
information gaps as 1) the lack of integration between enterprise
applications, 2) overly extensive reports that are not in line with
the current information needs or are too time-consuming to make use
of, 3) the amount of unexplored data in an organization, 4) the
time wasted on gathering the required information instead of its
analysis and 5) the lack of external and/or valuable competitive
information to support strategic decision-making. The authors
conclude that a higher level of information quality alone does not
generate business value, but it often leads to higher information
usage and therefore has an indirect impact on the profitability,
maturity and success of the information management process.
(Popovic et al. 2010)
2.2.2 The business value of BIRamakrishnan et al. (2012) discuss
the three general purposes for which BI is implemented. Firstly, an
organization wants to gain insight. The competitive pressure in the
market increases uncertainty, and the authors argue that BI systems
are fast becoming a necessity for an organization to be able to
deal with the more and more dynamic business environment
(Ramakrishnan et al. 2012). BI has become the key activity
assisting chief information officers (CIOs) in forecasting market
behavior, so that an organization can adapt to changing business
conditions (Smith and Lindsay 2012). BI provides the management
with a better understanding about the underlying trends and
dependencies that affect the environment they operate in. The other
two purposes of BI Ramakrishnan et al. (2012) offered are related
to the cohere nce of organizational information. The authors state
that BI provides an organization with a single version of truth and
it can also facilitate organizational transformation. Enterprise
data is under constant change especially as companies go through
mergers and acquisitions. Organizational changes bring in new
information consumers with possibly brand new information needs.
Obtaining a single version of truth facilitates the communication
between these individuals when all have access to the same
information. The clear business logic of figures, calculations and
terms also improves the quality of data and saves time for better
analysis. (Ramakrishnan et al. 2012)
The quality of information is important since mass quantities of
information are available from various sources and lack of
information is not a problem anymore; instead, the problem is
capability of gathering, related applicable and consistent
information from authentic sources. This means that, information is
not significant by itself; instead, the quality and excellence of
information as well as its accessibility at right time and quickly
for taking a correct decision is an important matter
However, it becomes crucial to consider human decision making
when studying Business Intelligence since this is an IT-solution
that to a large extent aims to support managerial decision
making
The need for Business Intelligence does according to Davenport
(2006) emerge from in-creasing competition. The business climate is
constantly changing and becoming more and more complex (Turban et
al. 2007). This development requires managers to react and re-spond
quickly, which in turn requires that the managers are able to
interpret their environ-ment. Davenport (2006) argues that in a
highly competitive environment where all indus-tries offer similar
products and possess equal technical equipment, the remaining
source of differentiation is the business processes.
Simons (2008) claims that the most significant reason for
investing in a Business Intelli-gence system, is the aim to improve
decision making. According to Davenport et al. (2001) the problem
is not that the decision-makers lack data, thanks to the ERP system
they are overwhelmed by data but they do not have the ability to
aggregate and analyse them and thereby create business value. In a
survey from Massachusetts Institute of Technology (re-ferred to in
Lindvall, 2013) it is indicated that more than 60 percent of the
managers expe-rience that they have more information than the can
incorporate in their operations.
Davenport et al. (2001) state that most companies are unable to
translate data into intelli-gence and thereby create business
value. This is confirmed by Nilsson & Sellns (2006) study on
Swedish companies and their usage of the Business Intelligence
system. Nilsson & Sellns (2006) conclude that Swedish
organizations do not exploit the full potential of their Business
Intelligence systems; in addition they are not as analytical as
they could be. This inability to fully exploit the system is not
unique for individual organizations, as one IT-consultant in this
study observes; There are few organizations that distinguish from
the rest; I think I would say individ-ual companies are quite
similar to other companies in the same situation. But they are in
no sense bad. However, if an organization would manage to exploit
the full potential of the system it is highly probable that that
organization would be far from the others pretty fastYou will be
able to beat your competitors pretty fast, due to the fact that the
rest are not brilliant either.
The data retrieved from systems, applications and the data
warehouse must somehow be translated to be useful in decision
making and analytics. Within the literature there are sev-eral
model and suggestions of how this can be done.Eckerson (2003)
illustrates Business Intelligence as a data refinery. When the data
have been extracted and loaded into the system, the user can
analyses the data through certain analytical tools. The aim is to
identify trends, patterns and exceptions and Eckerson (2003) argues
that this analytical phase allows the user to turn information into
knowledge. Out of this knowledge you can create decision rules, for
example order 50 more units whenever the inventory falls below 100
units, or forecasts and predications. The rules can be highly
complex and based on statistical algorithms and models. Examples of
statistical rules would be to automatically adjust prices in
response to changed prices on raw material, or to iden-tify
cross-selling opportunities by using data on customer response.
When these rules are implemented the user will gain experience and
can reevaluate the rules. The user might have launched a campaign
to a certain customer segment, based on a prediction of how
customers will respond to certain offers, or the result of previous
cam-paigns. Eckerson (2003) argues that this behavior becomes a
cycle which repeats itself and makes the organization into a
learning organization. When results constantly can be re-viewed and
evaluated, the organization will gain knowledge and insight of
their own busi-ness (Eckerson, 2003). To create knowledge and
intelligence out of data is referred to as intellective skills by
Zuboff (1985). These intellective skills consist of three
dimensions; the ability to think ab-stractly, inductive reasoning
and the ability to have a theoretical conception in mind. Zub-off
(1985) argues that the ability to think in abstract terms plays a
role since a computerizedenvironment implies more abstract elements
and that the physical actions are eliminated by the IT-system.
Tasks used to be performed through physical and concrete activities
but are now performed through pushing a button. The user must
understand what happens when the button is pushed and be able to
relate the data to the real activities and processes, and according
to Zuboff (1985) this requires an ability to think abstractly. The
second dimen-sion is inductive reasoning and Zuboff (1985) explains
it as the ability to determine poten-tial relationships between
variables and the use of data to build and test hypothesis. People
learn how to organize data in their minds. They build models in
their heads about what is really happening, and they build on the
model with data until they have a complete picture (Zuboff, 1985,
p. 11, quoting a system engineer) The inductive reasoning is
according to Zuboff (1985) related to the ability to keep a
theo-retical conception in mind. If you are about to generate
hypothesis on the data you must have some frame of reference. The
information system contains a huge amount of data and the user must
therefore know what is significant to be able to determine it.
Zuboff (1985) argues that the more of a theoretical conception the
user has in mind, the more infor-mation will be discovered in the
data.
4.3 The Process of Decision Making Conforme a los modelos
formulados por Herbert A. Simon1 , A.A.Rubenstein y C.J.
Haberstroh2 , con tres y cinco fases respectivamente. El proceso de
TD puede definirse de acuerdo a las etapas y resultados que se
ilustran en la Figura 2, denominada El proceso de toma de
decisiones, cuya representacin y explicacin se ofrece a
continuacin: La Investigacin es una tarea de discernimiento e
interpretacin compuesta por: Identificacin de problemas, el rol del
tomador de decisiones y la formulacin de problemas, de a cuerdo con
la siguiente descripcin.Identificacin de problemas. Busca alguna
diferencia entre la situacin existente y un estado deseado. Es
decir, compara el modelo del estado esperado con el existente,
precisa y evala las diferencias para determinar si existe un
problema. Por ejemplo, Pounds3 usa cuatro modelos para desarrollar
expectativas frente a las cuales se compara la realidad: Histricos.
Las expectativas se delinean como resultado de las experiencias
anteriores. De planeacin. La expectativa est definida por el plan.
Otras personas en la empresa. Son las expectativas de terceros.
Extra organizacionales. Se derivan de la competencia, clientes y
mercado entre otros.El rol del tomador de decisiones. Es la funcin
que est vela no solamente de la aparicin de las diferencias entre
la expectativa y la realidad, sino tambin a prevenir a que esto no
ocurra; asumiendo entonces un papel pro y reactivo de acuerdo con
las circunstancias 4 La formulacin de problemas. Para resolver un
problema es indispensable identificar su origen, desarrollo y
resultados que se han producido o estn por suceder. Esta definicin
debe ser clara, procurando reducir la complejidad conforme a las
siguientes estrategias: Precisin de los lmites. Identifica
claramente los elementos implicados en el problema. Examen de los
argumentos. Los cuales pueden haber precipitado el problema.
Descomposicin del problema. En varios problemas ms pequeos y
especficos. Concentracin. En los elementos controlables.
1 Simon, Herbert A. The New Science of Management Decision, p.
54 22 Rubenstein A. y C.J. Haberstroh. Some Theories of
Organization. p. 10. 33 Pounds, William F. The Process of Problem
Finding. p 1-19. 44 MacGrimmon, K.R. y R.N.Taylor, Decision Making
& Problem Solving. captulo 22.
El Diseo es la abstraccin, planteamiento de hiptesis, invencin,
anlisis y desarrollo de cursos de accin. Para ello, el responsable
debe comprender el problema, generar opciones, considerar su
repercusin y estimar la factibilidad de ejecucin con base a tres
elementos: Condiciones, Acciones y Consecuencias, los cuales se
presentan a continuacin.Condiciones. Describen la situacin conforme
a los valores que toman ciertos atributos, como los nmeros rojos en
las finanzas de una empresa, constituye un modelo del problema en
s.Acciones. Es la secuencia de actividades a realizar bajo un
programa y recursos determinados, que representa la respuesta de
solucin al problema.Consecuencias. Estiman la situacin que ocurrir
al cumplir las acciones establecidas, describiendo los valores de
los atributos que caracterizan al problema, como en el caso
anterior la obtencin de nmeros negros en los saldos financieros.La
Eleccin es la toma de decisin que el responsable realiza con el afn
de resolver el problema de acuerdo con los criterios considerados
en su definicin, adems de los recursos disponibles e intereses
organizacionales en vigor. El desarrollo de esta funcin clave, es
matizado por diversos factores como la magnitud del problema,
urgencia en resolverlo, consecuencias, los elementos de certidumbre
al alcance del tomador, lo extraordinario que resulta ser el
problema, as como los lineamientos establecidos por la propia
empresa para normar el proceso. Para efectos de estudio, resulta
conveniente describir los criterios de: Conocimiento de los
resultados, grado de programacin y el Grado de exigencia:
Conocimiento de los resultados. Se define la consecuencia de lo que
ocurrir al escoger una alternativa en funcin al grado de
conocimiento: Certeza. Representada por el conocimiento completo y
exacto del resultado de cada opcin, donde se establece una
consecuencia por alternativa.Riesgo. Aparece cuando existe la
posibilidad de que ocurran varios resultados para cada curso de
accin con una probabilidad asociada a ellos. Incertidumbre. Se
presenta cuando se aprecian mltiples consecuencias para cada
alternativa pero se ignora la probabilidad de que ocurran. Manejo
de certidumbre. Requiere del uso de conocimiento e informacin
especializada, modelos estadsticos y de la investigacin de
operaciones entre otros.Grado de programacin. Conforme a la
naturaleza del problema y a los requerimientos, se pueden emplear
dos modelos para estructurar el mtodo de TD: Decisiones
programadas. Son aquellas que resultan ser susceptibles de
expresarse de una manera clara, sencilla y completa, mediante un
conjunto de reglas, pudiendo documentarse a travs de manuales,
normas y polticas. Este modelo se aplica en condiciones de certeza.
Decisiones no programadas. Su definicin responde a situaciones
particulares y extraordinarias, resulta complicado establecer un
modelo que sirva como referencia tanto para la decisin en turno
como para las posteriores. Normalmente, ocurren en respuesta a una
crisis, cambios en las condiciones de la organizacin y de su
mercado de trabajo. Grado de exigencia. Hay dos modelos de toma de
decisiones, cuyo alcance se pretende lograr con la decisin, a
saber: prescriptivo o normativo, y el descriptivo. El modelo
prescriptivo o normativo de toma de decisiones. Es aquel modelo que
instruye en como tomar una clase de decisin, basado en el criterio
de la maximizacin u optimizacin de la utilidad o valor esperado que
se expresa cuantitativamente viene a ser la funcin objetivo para
una decisin procurando la utilidad mxima, rendimiento o menor
costo. Observa los supuestos de conocer todas las alternativas y
sus consecuencias, se busca maximizar el beneficio o utilidad y
existe un marco de referencia completo de conocimiento y
razonamiento. El modelo descriptivo de toma de decisiones. Precisa
la manera como se toman actualmente las decisiones, procurando la
satisfaccin, donde el decisor no est completamente informado sobre
las alternativas, ni aplica una racionalidad plena en su bsqueda
pues simplifica los factores considerados y limita la exploracin de
opciones, por lo que acepta la primera que satisfaga todas las
restricciones del problema, en lugar de proseguir hasta encontrar
el camino ptimo. El modelo est basado en la heurstica, asumiendo
que el decisor no conoce todas las alternativas ni todas los
resultados, hace una exploracin limitada para descubrir unas pocas
alternativas exploratorias y escoge una opcin que cumpla con el
nivel mnimo de satisfaccin.La Comunicacin e implementacin de la
decisin es una vez tomada la opcin es necesario proceder a
expresarla a los involucrados (personal, superiores, clientes,
etc.), adems de precisar el plan para su ejecucin, organizar los
recursos necesarios y proceder a la direccin de su puesta en marcha
para que se realice conforme a los tiempos y formas estipuladas.
Seguimiento y retroalimentacin insta a supervisar la ejecucin de
las actividades para detectar y corregir desviaciones del curso y
resultados planeados, ejerciendo la retroalimentacin constante que
inspire a modificar las acciones, los recursos y procedimientos
participantes, en aras de llevar alcanzar su objetivo en la solucin
del problema planteado.
Davenport et al. (2001) argue that the process of decision
making is highly influenced by the organizational and cultural
contexts, and to strive towards a culture that values decision
based on data is therefore important if you want your decision
making process to be influ-enced by data and facts. Within the
literature there is also a strong connection between Business
Intelligence and decision making, the Business Intelligence systems
are somehow aimed for the decision makers which also can be seen in
the presented definitions of Busi-ness Intelligence in section 4.1.
To dig deeper into how humans make decisions and how external
information is interpreted is therefore necessary. How Humans Make
Decisions The invisibility and irrationality in the process of
decision making makes it a diffuse area to address (Davenport et
al., 2001), and traditional theories of choices have been heavily
criti-cized for simplifying the human mind. Simon (1997) concludes
that humans act on intend-ed or bounded rationality instead of
perfect rationality, since the human mind is limited. Even if the
decision maker is intent on making a rational decision, he or she
is limited to bounded decisions which aim to satisfy rather than
optimize or maximize (Simon, 1997). March (1987) argues that
traditional theories are underestimating the ambiguity of choice.
Everything cannot be known and decisions are therefore likely to be
based on incomplete information concerning the alternatives and
consequences (March, 1987). Similarly, it is as-sumed that the
preferences of the decision makers are stable and consistent.
However, people do often have conflicting interests and preferences
are changing over time. March (1987) states that preferences are
expected to form actions and do affect actions but pref-erences are
at the same time affected by experience and consequences from a
certain be-havior. According to Kahneman (2003) ideological
theories of choice assume that the decision maker seeks utility and
select the option providing the highest utility. However, utility
can-not be separated from emotions and the feeling of loss; people
value losses differently. Kahneman (2003) suggests that
out-of-pocket losses are valued higher than opportunity costs which
imply that the decision maker can switch from risk averse to risk
seeking de-pending on which emotions the decision evokes. According
to Kahneman (2003) the change in wealth seems to be more important
for the decision maker than the actual state of wealth.Kahneman
(2003) states that humans are not accustomed to think hard and
twice, and are therefore likely to trust an automatically thought
when considering a problem. Lindvall (2013) describes that it is
hard for the individual to identify and determine human errors of
thinking since they are presented as truth. To create meaning
humans seek rational explana-tions for their own behavior and what
is going on around us and Lindvall (2013) claims that when these
conceptions of the world have been well formulated and defined, it
will suppress alternative explanations. In addition, it seems that
some thoughts are more acces-sible than others and that expectation
is a strong determinant of accessibility (Kahneman, 2003). The
human mind tends to suppress ambiguity and uncertainty and
therefore see what it wants to see. Kahneman (2003) states that an
observer will automatically put an event into a certain context,
and not automatically become aware of alternative interpreta-tions
since they will be repressed. Lindvall (2013) also claims that it
might be the case that humans seek information that confirms their
first conception. Kahneman (2003) differentiates between intuition
and reasoning and defines intuition as System 1 and reasoning as
System 2. System 1 implies fast and effortless response, often
emotional and automatic, while System 2 requires more effort and is
often more con-trolled and rule-governed. The ability to doubt and
revaluate options is connected to Sys-tem 2, Kahneman (2003)
expresses it as the: ability to think incompatible thought about
the same thing. (p.1454). System 2 does also have the ability to
correct errors. The Organizational Perspective When it comes to
decision making from an organizational perspective, March (1987)
de-scribes the real organization as a loosely coupled system with
weak connections between problem, solution and action;
Organizations seem to be loosely coupled systems in which the
connections between prob-lems and solutions are obscure, as the
connection between means and ends, between action today and action
yesterday, and between action in one part of the organization and
action in another part. People, problems, solutions and choice
opportunities seem to be combined in confusing ways... (p.157) This
perspective on organization leads March (1994) to conclude that
decisions are made to establish meaning and are always made in a
context of meaning (March, 1987). Decision making is considered as
a highly symbolic and ritual activity and March therefore argues
that decision making is much more than just choosing between
available alternatives. The interpretation of information and the
decision making do to a large extent contribute to the development
of meaning according to March (1987). In addition, March argues
that the search for information is not driven by the uncertainty of
alternatives or consequences but by a general lack of meaning.
Similarly, Simon (1997) concludes that not only the behavior of the
individual but the be-havior of organizations is boundedly
rational.Business Intelligence and Rational Decision Making
Lindvall (2013) conclude that unsuccessful attempts with decision
support systems are due to the ambition to implement traditional
and rational theories of decision making. The Business Intelligence
solutions are according to Lindvall (2013) implemented with the aim
of being rational as in the decision making theory. The systems
should be developed from a bounded rationality perspective instead,
since it is closer to how decisions are taken and acted upon in
reality (Lindvall 2013). March (1987) also claims that theories of
choice, game theory and statistical decision theory are in some
sense useful but are incomplete and even potentially misleading
when it comes to modifying the design of IT systems. Howev-er,
Eckersons (2003) study indicates that users who consulting data
more than intuition and use data to support intuition rather than
the other way around, is more likely to suc-ceed with a Business
Intelligence project.22 Since the decision making process is
characterized by ambiguity, Lindvall (2013) stresses the role of
the Business Intelligence system as sense maker. The need for
translation and identification of the organizations values,
expectations and conceptions do according to Lindvall (2013) become
more important than mathematical and statistical calculations. The
IT-system shall therefore be used to develop a meaning and context
which within the deci-sions can be taken. Lindvall (2013) argues
that poor decisions are mainly caused by the decisions process
itself and that it is a common human error to assume that there is
no need for a structured deci-sion making process. In addition,
Davenport et al. (2001) argue that managers will be more effective
if they become aware of what the decision making process looks
like. Lindvall (2013) even state that the use of a more
systematically defined decision process and statisti-cal model
would improve the decision making process. In System 2 the decision
making is more structured and less influenced by individuals
experiences and conceptions, and the decision making process and
analysis would be more structured if a model is developed. Lindvall
(2013) therefore concludes that statistical models to some extent
would neutralize the decision making process.
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