=Paper=
{{Paper
|id=Vol-1420/ilog-paper4
|storemode=property
|title=Self-Service BI does it Change the Rule of the Game for BI Systems Designers
|pdfUrl=https://ceur-ws.org/Vol-1420/ilog-paper4.pdf
|volume=Vol-1420
|dblpUrl=https://dblp.org/rec/conf/bis/JohanssonAC15
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==Self-Service BI does it Change the Rule of the Game for BI Systems Designers==
https://ceur-ws.org/Vol-1420/ilog-paper4.pdf
Self-Service BI does it Change the Rule of the Game for
BI Systems Designers
Björn Johansson, Dogan Alkan, Robin Carlsson
Department of Informatics, Lund University, Ole Römersväg 6,
SE-223 63 Lund, Sweden
bjorn.johansson@ics.lu.se,
{dogan.alkan, robin.carlsson}@outlook.com
Abstract. Users of Business Intelligence (BI) systems have started to demand
more flexible systems in which they could be empowered to serve themselves –
self-service BI. In this paper we aim at explaining how such development
influences designers of BI solutions and how it impacts the design situation. To
say something about this we adapted the PACT (People, Activity, Context,
Technology) framework on the BI design situation by conducting semi-
structured interviews with vendors and suppliers of BI systems. From the
research we found that self-service BI should be seen as a complement rather
than a substitute to traditional BI. The concluding remark on the design
situation is that designers of BI systems have to consider a more complex
design situation where designers need to have increased knowledge about users
mental models, decision focus and usage of BI systems in the analysis and
design phases for being able to design useful self-service BI systems. The main
conclusion from this is that designing for self-service BI is a more demanding
design situation for designers of BI solutions.
Keywords: Business Analytics, Business Intelligence, Design, Self-service.
1 Introduction
Data analysis started to be used already in the 1950s, but as technology and the focus
of decision making has changed over time, different terminologies (e.g. decision
support, executive support) has been suggested, with slightly different meanings [1].
One of the more recent terms is Business Intelligence (BI) evolving in the 1990s [1],
and later on “extended” into Business Analytics (BA) [2] and now often referred to as
BI&A. However, designers of BI&A solutions often struggles to understand what
users of BI wants and needs [3], or more specifically struggling to understand which
user needs what information; which information have been produced for a specific
user; and whether or not there is a demand or need for the delivered information [4].
Similarly, it can be discussed that users do not know their need meanwhile designers
do not understand users' need [5], and users cannot even anticipate what the needs
will be [6]. At the same time Big Data has been a fact, as the volume of information
in organizations becomes larger, with more variants and increased velocity, the
Copyright © 2015 by the authors. Copying permitted for private and academic purposes.
This volume is published and copyrighted by its editors.
48
challenge of distinguishing between ‘wants’ and ‘needs’ have become an even greater
challenge [7]. These problems have been exacerbated with the introduction of more
self-service BI, where users performing data analysis are expected to adapt the data
used for decision support to their needs [8], making decisions more independently and
self-determinant [9]. However, the larger amount of available data and technological
evolution increases the need for systems which enable flexible usage for decision
support and data analysis [8]. In line with this, BI designers must be capable of
understanding the organizations better and the needs among their users, and thereafter
frame appropriate solutions for the users [2]. From this short introduction the
following research questions are suggested: How does self-service BI development
influences designers of BI solutions and how does it impacts the design situation?
The rest of the paper is organized as follows. The next section introduces self-
service BI solutions by comparing it with traditional BI. Section 3 presents the PACT
framework and describes how it is used in this research. Section 4 presents how the
research was done, followed by Section 5 that presents and discusses empirical
findings. In Section 6 we then present our conclusions and finally in Section 7
contributions and some future research directions are presented.
2 Self-Service BI versus Traditional BI
Self-service technologies were initially created to enable customers to develop and
provide services to themselves without direct involvement of the IT department [10].
As the name suggests, the nature of these technologies inherently carry with it
openness and flexibility to enable users coming from different backgrounds, using
different technologies to create satisfactory services using an uniform technology
provided to them. Self-service BI is a derivative of that, which is primarily used by
organizational employees based on ad-hoc needs, often without much structure, to
make their own decisions. Self-service BI is described by Imhoff and White [9] as a
technological option that give users the possibility to modify the system or the
content. Baars and Zimmer [11] state that self-service BI highlights flexibility by
joining new data sources, increasing the speed of report development and providing
new data warehouse methods. It is claimed by for instance Pour [12] that self-service
BI is representing one of the most significant trends in the business intelligence field,
and as such are a quicker, more simplistic and operative, and much less expensive
solution than standard BI solutions. However, Pour [12] also state that self-service BI
is not able to serve the same complexity and integration as the case of standard BI
solutions, and therefore it could be questioned if it would be able to fully replace
standard BI solutions. Abelló et al., [13] gives the view that self-service BI enables
non-expert BI users to make well-informed decisions by adding situational data
giving a narrow focus on a specific business problem. The data as such are said not
being owned and controlled by the decision maker; their search, extraction,
integration, and storage for reuse or sharing should be accomplished by decision
makers without any intervention by designers or programmers. To sum up, self-
service BI are characterized by flexibility, fluidity, openness, and dynamism.
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3 Understanding the design situation by using PACT Framework
The basic idea behind the PACT framework, is to use it to collect requirements before
designing interactive products [14]. Benyon [14] explains design as a creative process
for creating new interactive products, where designers ought to produce various
layouts, color schemes, graphics, and a design for the overall structure. The PACT
framework can therefore be used to understand the current situation within
organizations, to scope potential problems, improvements and to provide the right
thinking for designers about design situation for interactive systems. PACT
framework is thus useful for both analysis and design phases of interactive systems
[14]. The elements (People, Activity, Context, Technology) in the PACT framework
can be used by designers to distinguish Personas, and to create both scenarios and
user stories in a design situation [14]. In the following sub-sections we discuss the
elements in the PACT framework from the perspective of how it can be used to
understand design situations when designing self-service BI systems.
3.1 People
By people, we mean decision makers who are users of a BI system. Benyon [14]
suggests that people using a system could be presented as Personas. The concept of
Persona has become a widely used method for designers to create user profiles [15]. It
is stated that Persona should have a name, a background, behavior, attitudes, abilities
and motivation [15]. Similarly, Benyon [14] claimed that people might have different
goals, needs and motivation when it comes to the usage of technologies. In this
context can motivation be related to goals and be explained and defined either to
experience goals, end goals or life goals [15]. Experience goals refer to the feeling
users want to experience during the interaction with a product; end goals refer to the
users' motivations to accomplish a task; and life goals refer to people's long-term
desires and motivations. End goals and thus the decision making differs depending on
people’s roles and at which organizational level they act. Goals at different levels
affect which data that has to be gathered [16]. Other categorizations of users are
however possible. For instance, elastic users are those who are first-time users or
power users, but rarely use a product. Meanwhile real users are those who use a
product more regularly. These user types should be differentiated as designers
primarily should meet the needs of real users [15]. Similarly, some researchers
differentiate the usage in BI systems by categorizing them as either information
consumers or information producers [9, 17], based on different end goals of using the
BI systems. Considering users as Personas might provide a coherent way of
categorizing users, as it has been proven to be an important part of the design process
for interactive systems [14, 15, 18].
3.2 Activity
Decision making can shortly be described as an activity consisting of several phases
[19-21], where users has to evaluate alternative choices among and then make a
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decision. The decision making becomes even more complex if there exists an
increased variety of information available, as this increases difficulties in terms of
information gathering and information use [22, 23]. In the decision making process,
the most important determinant leading to complexity lies with the amount of
decision alternatives available [24] and increase in the amount of alternatives which
causes a higher degree of complexity. However, decisions can be either rational,
irrational or non-rational [25].
Information can be either prepared in beforehand for expository, or for discovery
[26]. Similarly, the exploratory usage can lead to discoveries and decision makers can
use exploration without any purpose or goal, and still find valuable discoveries [27].
Discovery requires that decision makers are able to create hypothesis and to validate
them, but problem solving through discovery can be a better way of making decisions
[19, 27]. More recently, exploratory usage also towards a more continuous approach,
where discovery and experimentation with data is becoming more important [1].
Decision making is also depending on temporal aspects. Infrequent activities affect
decision makers' abilities to effectively use a BI solution. Time pressure during the
usage is also a significant factor when it comes to designing a product, as users may
not have a sufficient amount of time to explore the data [20, 28]. However, this is
related to the context of the usage, which is discussed next.
3.3 Context
Context is the general environment which surrounds the users during their activities
[14]. There are many opinions on where the BI function should be placed within an
organization, and how it should operate, but there appears to be ‘no single best way’
to build an organizational model to support an effective BI solution [4]. Instead,
organizations use a combination of different solutions, depending on: the industry
they are in, on the business, organizational size, leadership in the organization and
level of competence among employees [1, 4, 29]. It is also likely that organizations do
not establish new structures, but rather evolve and integrate new solutions into
existing ones [1].
The context refers to among things, the nature of control within an organization,
for example, whether it is centralized or decentralized. It can be argued that with too
much decentralization, users have better support but have less ability to use an
information system in a consistent way. Further, IT professionals usually value a
system’s technical elegance, while users would rather prefer a system which supports
their needs [30].
Further, the structure of the organization can also set the stage for the
organizational context, and three types of organizational structures are relevant:
special department model, top-down model, and bottom-up model [4]. Special
department model can be defined as intelligence function works in a special
department, often alone in isolation, without using competence existing in other
departments and without sharing competence with others [4]. Further, the top-down
model can be defined as the intelligence is communicated by people from the top
level management and therefore this model works best for companies where the
employees have low skills, e.g. in mass production based companies. In contrast to
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the top-down model, bottom-up model can be defined as employees from the bottom
level of the organization are allowed to access valuable information. It can be
suggested that the bottom-up model is common in sales- and marketing-driven
organizations indicating that the intelligence function could or should be distributed to
the bottom level employees [4]. The discussion on context implies that there exists a
need for different technologies in different contexts. However, there is also a need to
discuss the technology element in the PACT framework and this is done in the next
section.
3.4 Technology
Technology is described by Benyon [14] as hardware and software components in
interactive systems. These two components' needs to work together in order to
support users' activities, which in the BI solution case is decision making. Moreover,
Benyon [14] claims that designers need an understanding on how these components
work and how to design something in the best way for users. More specifically,
interactive systems should be designed according to various possibilities of inputs,
outputs, communications and contents [14].
Within BI, visualization is defined as a process of displaying data for the user [31],
and dashboards are often used to present reports as an interactive system. Designers
should be aware of screen size, as some information should not be on the dashboard if
the screen size is small [14]. In other words, this means that some functions cannot be
available on smaller screens. Display sizes in a desktop computer, tablet and
Smartphone differs and thus users ability to use certain functionalities differs [32].
The interactive visualization refers to analyzing large amount of data and
visualization information. From that follows that a good visualization results in a
better decision making [27]. In terms of interactive visualization, there are three
categories within visual reasoning: exploratory, supervisory, and routine visualization
[27]. However, exploratory is the most interesting among these tasks with the user
having no purpose or idea of what will be investigated. Once the discovery has been
found, the user can continue to explore the new perspectives. In other words, new
discoveries can be achieved when engaging with visualizations [27]. From the
discussion of the elements: People, Activity, Context, and Technology in the PACT
framework, we were interested in exploring how does self-service BI development
influences designers of BI solutions and how does it impacts the design situation,
which made us doing a research study, which is shortly presented below.
4 Method
When choosing an appropriate research strategy, initially we discussed the object of
analysis and purpose of our study. For our research questions, we found that a
explorative study would be appropriate, as we aim at exploring the social and
organizational context, as well as understanding how the usage of a new concept [33]
(in this case self-service BI) influences the designers and the design situation. Further,
we decided to conduct semi-structured interviews [34] enabling us to explore our
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research questions more in-depth by asking follow-up questions, and to make sure
that all our research items, within the PACT framework, were discussed during the
interviews.
Based on Benyon’s description of the PACT framework [14], we formulated
questions for our interview guide. The design of our interview guide was made in
regard to the research question [35] of how the designers and the design situation was
influenced.
We aimed at interviewing designers since the study aimed at exploring the context
of designing self-service BI solutions. The specific selection of informants was based
on two criteria; (1) that informants should have knowledge regarding self-service BI
and (2) to personally be in contact with users and other designers of BI systems. Our
selected informants were thereafter selected from two suppliers who’s main business
are to deliver BI systems, and one vendor which has been positioned as a leader in
user-driven Business Intelligence (i.e. self-service BI). In total, five individual
interviews were conducted with experts who possessed experience in design of BI
systems and could provide understanding of implications in designers work.
The interviews were recorded and then transcribed before the data were analyzed
from the questions in the interview guide and the four elements from the PACT
framework. Since we had data from two suppliers and one vendor of BI solutions we
had the possibility to compare between different statements. The next section presents
the analysis and discusses the findings.
5 Empirical findings and discussion
In this part we present our empirical findings followed by our discussion in relations
to previously presented literature.
5.1 People
Our empirical findings indicate that provision of information, when using a traditional
BI solution, often fell short in providing decision makers with the information they
need. In line with this, several researchers [e.g. 14, 15, 18, 36] have argued that each
individual has different needs, goals and motivations, it might thereby be difficult to
design a product for various users. From this it can be claimed that users' needs
depends on their business role, which is supported by several researchers [1, 4, 16,
19] as the decision focus changes dependent on the job they have. It is thus possible to
conclude that business strategy and user's roles in firms are reflected on the end goals
among decision makers. We thereby stress that goals within organizations have
significant influence on decision maker's requirements on self-service BI.
Decision makers at tactical and operational levels need current values as indicators
in a BI solution, while decision makers at a strategic level need target values as
indicators based on the business strategy [16]. This implies that there may exist a
problem for designers which they have to consider, namely that the diversity of needs
and skills differs and must be identified at every organizational level [19]. A further
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implication is that people’s roles will vary, implying that users have different styles in
decision making [19]. A similar description has also been pointed out by Davenport
[1], who has pointed out that designer should design systems which promote decision
makers to use their skills for data analysis. Davenport has further suggested that
decision maker’s skills can be categorized based on their role, emphasizing that they
can have roles such as; business experts, trusted advisors, quantitative analysts,
scientists or hackers. Among these roles, skills for data analysis, knowledge about the
business and ability to frame decisions can be found. The distinction of user types is
not new, but it is crucial and must be defined correctly when delivering BI systems
[4].
Importantly, the findings indicate that usage of self-service BI solutions implies a
challenge for designers, demanding increased knowledge about users’ end goal,
which decision support and thus which data that is needed. Also, there appears to be a
concern regarding decision makers’ skills. To handle diversity of skills among users,
we have found that designers at the vendor organization are using Persona and Mental
Models. Thereby, their designers are able to distinguish different types of users
dependent on their job roles, different skills and abilities. Meanwhile, the supplier
organizations uses other terms to categorize their users, i.e. information consumer and
power user [9, 17]. Whatever term used, we thus emphasize that user types should be
determined by how users are using BI systems. This might be of relevance, since we
have found that users need to have skills and ability to explore data themselves, which
implies that decision makers need to assign the appropriate level of self-service that
best fits a decision maker.
We have found out that end goals are to make more rational decisions as
articulated by Simon [25]. Interestingly, during the interviews we came across the
term ‘freedom’; as businessmen want to work without having to contact their IT
departments, and want to create new analysis based on new data. Our empirical
findings indicate that the need for self-service has resulted in decreasing reliance on
IT departments, i.e. which can be described as an experience goal. Our informants
have however described that decision makers do not necessarily want self-service
functionality per se, but more importantly to have support for rational decision
making.
Decision makers do however make unsupported decisions which might lead to
what could be called irrational decisions. Due to this, decision makers might want and
need BI systems which decrease irrational decision making. A conclusion drawn by
the informants is that exploration and discovery has become a functional requirement
as it is assumed to decrease the amount of irrational decisions. In other words,
decision makers end goals might have changed because of self-service BI solutions
now are available. It is likely that for instance; technical capabilities, coming with
self-service therefore influence the functional requirements in BI systems. It is
however questionable whether self-service fulfill such end goals. Users do however
use the term ‘Big Data’ to motivate their need of self-service functionality, which
means increased amount of information available and consequently implies increased
amount of decision alternatives. One finding is thus that technical capabilities in self-
service might influence decision makers to require BI systems, and that the
requirements are motivated by changed end goals and experience goals.
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We can conclude from our empirical findings that different types of users are
significant for designers at vendors and suppliers in order to make better and more
useful BI systems. This is also pointed out by many other researchers [e.g. 1, 6, 9, 14,
15, 17, 18]. However, we found a non-coherent use of terms for user types among
both scholars and our empirical findings. We thereby suggest that there should be a
common terminology among vendors and suppliers to avoid any ambiguities of who
the users are in BI systems.
5.2 Activity
In self-service BI, our informants stated that users are expected to use both static and
flexible dashboards. The newness in comparison to more traditional approaches is that
users are supposed to conduct data analysis themselves by creating assumptions about
their business, and verify that these are correct by elaborating with data. Decision
makers are then supposed to evaluate alternative choices in terms of their actions and
then make a choice [19-21, 25]. The usage of BI solutions can therefore be described
as either expository or exploratory usage [26, 27].
In the decision making process, complexity in decision making comes with the
amount of available decision alternatives [24]. However, as also found in the
literature, BI is only supposed to provide support in identifying the decisions to be
made [19]. Moreover, our empirical findings indicate that verification of assumptions
is too complex to be supported by static dashboards, and we can thereby state that
static dashboards enable some decision support, but with limited functionalities.
Further, we have found that designers have to open up the creativeness for users and
allow them to find data themselves by providing a personal visualization possibility.
Therefore, we can conclude that designers face new design situations in self-service
BI development. When evaluating alternative choices with self-service BI, it appears
to be more flexible for decision makers. In contrast to all the benefits of self-service,
our empirical findings identify a concern that self-service implies that users may
select unqualified data sources, which we deem resulting in users requiring more
business and technical skills. Self-service therefore implies that designers present
whether a data source is qualified, where a data source originates from and how its
dimensions are related to each other.
An even further explanation of decision support complexity in BI solutions, is the
activity whereby decision makers are 'on the lookout' for information and knowledge
needed to support their decisions. In the study we found that traditional BI solutions
builds on having so called static reports, while self-service BI enables the users to
make business discoveries. This corresponds to earlier research that decision activities
can be categorized as exploratory or discovery [26, 27]. We thus can conclude that
decision complexity (variety of variables) can be handled better in self-service BI.
We acknowledge that the combined usage of expository usage and discovery usage
will provide a more generalizable way of problem-solving and understanding problem
domains. It is however important to point out that the self-service approach will not
substitute static reports, but rather complement and increase the opportunity to
support decision making [26]. Further, as BI solutions nowadays involve increased
information gathering about competitors in the market, technical competences,
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possible partners, organizational or individual influencers that define and limit the
business activities in order to keep the organization business competitive, this
increases the amount of alternatives for decision makers [4], and thus the complexity
of supporting decisions [22, 23].
In the study we also found that there is a tension between business people and IT
people, due to the fact that decision makers have to go IT departments to request new
reports. As pointed out by Benyon [14], a significant factor in BI solutions is
cooperation, implying that decision activities are completed alone or in relation to
work with other people. Similarly, as pointed out by one interviewee, people are
likely to go to the colleague nearby rather than contacting IT. Our interpretation is
thus that self-service BI might enable the users to complete tasks (i.e. decision
making) themselves, but that self-service also might demand that support are provided
by other decision makers. This brings us to the social and organizational context.
5.3 Context
Initially, our empirical findings indicate that viability of self-service does not depend
on the organizational size, but rather on type of business and structure of the firm.
Hence, business type and structure also change how IT departments work. An IT
department should not be the primary support for decision makers in a BI solution, as
the IT department would be overwhelmed if they supported every single app and
dashboard.
The structure of an Intelligence function has been thus explained in our literature
review, as organizations might have structure their BI functions accordingly to one or
several organizational models, i.e. there is no single best structure for all firms [4]. In
line with this, the research indicates that firms, traditionally seen, have structured their
BI functions in a top-down approach, where IT departments are in charge of the IT
strategies, delivering static dashboards and also acting as gatekeepers for BI users
who requests changes. As our results indicate, the self-service approach thus implies,
that the structure of the BI function in firms change, and for IT departments, their role
evolve in relation to the BI function into management of infrastructure and enabling
flexible use of BI solutions, rather than only delivering static reports. The role of IT
department are in other words changing and we have found that the new role of IT
departments in the BI function is to manage and enable, rather than delivering, in
other words IT departments can support decision making by qualifying that decision
makers are more independent when using BI solutions.
We do however find it important to point out, that static dashboards will not
disappear and large organizations still need a traditional approach as it can fulfill
much need of information for decision makers without using self-service. Further,
self-service BI should be seen as a complement to the traditional static reports. We
thereby emphasize that there is still a need for static reports, especially in large
organizations.
Firms can structure the support for BI work in different ways [4]. What is
important for BI designer is however that they should consider the social context in
where activities (decision making) take place, as it may dictate the acceptability of a
design. Firms need a supportive function which can provide help for decision makers
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when using BI solutions. In line with the bottom-up model [4], users are more likely
to solve problems themselves, but need support and responsible for data security, i.e.
there is a need on deciding who has access to which data and when they can gain
access [14]. More importantly, despite the security challenge, our interpretation is that
firms who wish to use self-service should allow the so called bottom-up structure. All
this has some implications on the technology which is discussed next.
5.4 Technology
The research findings show that input and output differs depending on screen's size,
which also is supported by Tona and Carlsson [32], who have evaluated the usability
on smartphones, tablets and PC/laptops. As pointed out, the use of self-service BI as a
technology implies increased flexibility, in order to make explorations and discovery.
However, as shown, there is currently a lack of ability to use self-service on mobile
devices, but the ability to use self-service at different devices might evolve and thus
expand in the future, especially on tablets. The ability to use self-service will probably
always be higher on larger screens [27], as the ability for exploratory use increases at
larger screen sizes. In other words, it will be easier for a user to investigate new
discoveries and new perspectives on data if it is explored on larger screens.
In terms of input and output at one screen, we believe that designers should
facilitate the business discovery by giving more "freedom" to the users. Due to the
fact that users can have different type of devices, designers have to consider this, but
surprisingly the study shows that designers do not consider which device that users
will be using in the future. This was explained by the interviewee at the vendor as a
result from the fact that their product use responsive design in which the visualization
adapt based to a device's screen size.
We summarize the PACT characteristics of self-service BI, and its differences
from traditional BI in the table below.
Table 1 Comparison of Traditional and Self-Service BI through the lens of PACT Framework
Dimensions of Pact Traditional BI Self-Service BI
Framework
People “Real” users Elastic Users
Information consumers Information producers
Activity Expository and Explanatory Explorations and Discovery
Context Top Down organizational Bottom UP organizational Model
Model Decentralized decision-making
Centralized decision-making in allowing users at multiple levels to
the way that BI usage is make decisions
predefined
Technology Predefined datamodels Larger screens
Dynamic Dashboards
In the next section the findings are summarized and we provide some concluding
remarks on the research questions: How does self-service BI development influences
designers of BI solutions and how does it impacts the design situation?
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6 Conclusions
Decision makers’ experience goal is to use self-service BI with freedom, without
being forced contacting the IT department, while their end goal is to make better
decisions. Depending on the decision makers’ goals, designers of self-service BI
should allow users to choose variables, dimensions and visualizations themselves.
The variables and dimensions thus depend on which business role the decision maker
has, which can be categorized by their level in the organization. Designers must thus
consider that users’ requirements differ depending on their role within organizations.
Some decision makers have a need for making infrequent decisions, and therefore
might have a need for independent data analysis by exploration of new data to make
new discoveries, while others fulfill their need for decision support by using static
report. So, from the findings that users becomes more of information producers it can
be claimed that the designers has to fulfill the possibility to add more data points into
the BI solution.
Exploration and thus discovery enable decision makers to consider more decision
alternatives. The challenge for designers in such situation is to know which decision
makers have sufficient abilities and skills to use self-service, as decision makers are
required to have both skills about their business, the technology they use, and the
ability to create and validate hypothesis. As self-service implies that BI users' skills
are of increasingly varying nature, it demands that various skills and mental models
are taken into account by designers. Designers should then consider which level of
self-service BI should be used by different decision makers.
Also, in respect to organizational structures, designers need to know that the role
played by IT support might have evolved, towards e.g. a bottom-up or special
department structure. Designers should acknowledge this, especially as our findings
show that there are concerns regarding data quality and data responsibility,
particularly if users are supposed to include unverified external data sources. This
demonstrates a challenge for designers, as they have to recognize which decision
makers will access and use specific data.
Another point worth noting is that self-service BI should be seen as a complement
rather than a substitute to traditional BI, and that the varying design opportunities that
self-service BI creates, implies several implications as designers have to consider. All
in all this make that the design situation changes and it implies that designers needs to
have increased knowledge about users mental models, decision focus and usage of BI
systems in the analysis and design phases for being able to design useful self-service
BI systems
7 Contribution and future work
This paper contributes to existing literature by providing an adapted PACT
framework for BI, which aims at improving BI designers understanding and our
knowledge of the new design situation in BI systems. The adapted PACT framework
for BI systems might be used by designers as guidance in their work, to create
Personas, scenarios and user stories in their own design of BI systems. Interestingly,
58
we have found that designers at the vendor organization are using Persona, whilst
suppliers might use notions as e.g. information consumer or power user.
Further research is thus suggested to complement eventual perspectives which
designers and users can provide. A study of the like might also result in a better
adapted PACT framework for Business Intelligence. It would be worthwhile to study
users' perspectives as a longitudinal study, and how decision makers perceive the use
of self-service, in order to understand the suitability of activities in certain scenarios.
Moreover, a further study could explore whether self-service is required by decision
makers based on their life goals, and whether firms should consider incorporating
such requirements. From an organizational perspective, another study might be to
investigate how organizations support their decision makers by evolving the structure
of their Business Intelligence function. Perhaps it would even be useful to create an
assessment form, whereby the results would indicate whether the designers should
design the dashboards, or if the users themselves should be allowed to create the
design, and if so, what potential consequences that could result in.
References
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