Description
One of the findings is that requirements engineering is critical, and even small issues have a tendency to cause big problems.
Business intelligence adoption: a case study in the retail chain
CECÍLIA OLEXOVÁ
Department of Mangement
University of Economics in Bratislava
Faculty of Business Economics in Košice, Tajovského 13, 041 30 Košice
SLOVAK REPUBLIC
[email protected]http://www.euke.sk
Abstract: Business Intelligence (BI) tools are adopted by more and more companies in the current environment
that requires companies to operate as efficiently as possible. The paper investigates a BI adoption in a retail
chain. Based on qualitative research methods, it analyses the Business Intelligence life cycle; it evaluates
factors impacting the adoption from the Diffusion of Innovations perspective. One of the findings is that
requirements engineering is critical, and even small issues have a tendency to cause big problems. This links to
the sentiment among managers, often worrying that IT projects will run over-budget and/or over-time. Finally,
the presented research identifies benefits that are considered to be the most important by the retail chain
managers. An important finding is that managers consider improved decision-making to be the most significant
benefit.
Key-Words: Business intelligence, adoption, diffusion of innovations theory, system life cycle, benefits, retail
chain, speed of adoption.
1 Introduction
The present need to increase the efficiency of
management in retail chains on an ongoing basis
and the growing pressure of cost efficiency in this
field require the use of different approaches,
methods and tools to meet these demands. One
opportunity is the use of sophisticated business
analytics, such as business intelligence (BI). BI is a
wide term that is commonly used for technologies,
applications, tools and processes to gather, store,
access and analyse data for better decision-making.
The literature review on BI has been published by,
for example, Foley and Guillemette [1].
According to the Gartner Group surveys [2], BI
is implemented in almost 80% of companies in the
U.S.A. and in 50% of companies in Europe. Slovak
companies have used these systems only in recent
years. The next growth of BI is evident, as
according to the Gartner group press release [3]
from the Gartner Business Intelligence Summit, the
BI, analytics and performance management software
market was the second-fastest growing sector in the
overall worldwide enterprise software market in
2011. As principal analyst at Gartner, Dan Sommer,
reported, “The strong growth was driven by two
major forces. The first is that IT continues to spend
and earmark money to BI, despite constrained
budgetary environments... and second, new buying
centers are opening and expanding outside of IT, in
line-of-business initiatives, and taking an
increasingly large stake of the spending pie. Key
drivers for this are self-service data discovery tools,
the race among vendors to provide business context
through packaged analytics, and CFOs taking a
renewed interest in BI and Performance
Management.”
BI can produce many benefits if it is
implemented well. Some literature argues that IT
projects, in general, are most often unsuccessful in
being on-time, being on-budget and/or delivering
the expected benefits [4, 5]. As the J ohansson’s and
Sudzina’s [6] research on actual versus planned
ERP system implementation costs in European
SMEs, including Slovakian companies, shows,
although not all companies manage to stay on
budget when it comes to ERP system
implementation, the situation in the investigated
European SMEs is not too critical, probably due to
managing to stay on budget and having a prevalent
fixed price policy for ERP implementation projects
in Europe. It is assumed that this will also apply to
BI implementation, and thus being on-time and
meeting clients’ requirements must be a
consideration for successful BI adoption. Chuah and
Wong [7] state that according to the EMC
Corporation, many BI initiatives have failed not
only because tools were not accessible to end users
but also because the end users’ needs were not met
effectively.
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This article is the result of a longitudinal study of
BI adoption based on in-depth analysis in a sports-
fashion multibrand chain of retail stores operating in
Slovakia. It presents the successful application of a
system life cycle [8] in BI adoption in which the
progression of a system is reached through several
stages. The results of the examination of benefits of
BI adoption are reported, and the factors impacting
the rate of adoption [9, 10] of BI in the retail chain
are analysed. The findings in the study contribute to
the real-life experience of successful BI adoptions.
2 Theoretical background
This section describes the most important basics of
BI, including possible BI benefits for the company.
The system life cycle and the diffusion of
innovations (DOI) theory focused on the factors that
impact the speed of adoption of innovations are also
presented.
2.1 Business intelligence (BI)
BI, developed primarily as a system to solve
analytical tasks, is generally considered to be a way
of better decision-making, reducing costs and
improving the quality of processes and performance.
However, there are different definitions of BI.
IBM researcher, Hans Peter Luhn [11], used the
term BI the first time in his article. He defined
intelligence as “the ability to apprehend the
interrelationships of presented facts in such a way as
to guide action towards a desired goal.” The term BI
has become popular thanks to Howard J . Dresner, a
Gartner Group analyst. He described the term BI as
“a set of concepts and methods to improve business
decision making by using fact-based support
systems” in 1989, and this usage has become
widespread [12]. This concept highlights the
importance of data analysis, reports and query tools
that provide users with data, and help them to
synthesise valuable and useful information.
Further, Golfareli and Rizzi [13] underline the
decision-making process, as BI provides corporate
decision-makers with software solutions that help
them identify and understand the key business
factors in making the best decisions for the situation
at the time. “In general, business intelligence
systems are data-driven DSS” [12]. According to
Wixom and Watson [14], BI is “a broad category of
technologies, applications, and processes for
gathering, storing, accessing, and analysing data to
help its users make better decisions.“ It includes
both getting data in (to a data mart or warehouse)
and getting data out (through technologies or
applications that meet some kind of business
purpose). Wixom and Watson [14] underline the
processes as an important part of BI – e.g.,
processes for extracting, loading and storing data,
maintaining metadata for IT and users, and
prioritizing BI projects. Some of these processes are
the responsibility of the BI staff, while others are the
joint responsibility of BI staff and business units.
Foley and Guillemette [1] define BI as “a
combination of processes, policies, culture, and
technologies for gathering, manipulating, storing,
and analyzing data collected from internal and
external sources, in order to communicate
information, create knowledge, and inform decision
making. BI helps report business performance,
uncover new business opportunities, and make
better business decisions regarding competitors,
suppliers, customers, financial issues, strategic
issues, products and services.”
The BI applications cover analytical and
planning functions of most management branches,
such as marketing, purchase and sale, financial
management, production management, marketing
management, controlling, human resource
management, etc. The BI is also used in other
business fields, such as corporate performance
management or customer relationship management.
The BI systems enable getting new information and
knowledge useful to achieve a competitive
advantage for any company with the use of efficient
analytical components (reporting, OLAP
technologies, and data mining).
There is a general concept of BI solution
architecture that contains several layers with
subsistent components. Two of the most significant
components of BI are data warehouse and data
marts. A data warehouse (DW) is a subject oriented,
integrated, non-volatile and time-variant collection
of data to support management decisions [15]. At
the present time, DW is a central component of data
storing in a company’s information system. “The
data warehouse supports the physical propagation of
data by handling the numerous enterprise records
for integration, cleansing, aggregation and query
tasks. It can also contain the operational data which
can be defined as an updateable set of integrated
data used for enterprise wide tactical decision-
making of a particular subject area” [16]. Data marts
can be defined in different ways. According to
Inmon [15], a data mart is “a collection of subject
areas organized for decision support based on the
needs of a given department.”
Analytical components of BI are: reporting, on-
line analytical processing (OLAP) and Data Mining.
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Reporting is a broad category, and there are many
options and modes of its generation, definition,
design, formatting and propagation. A successful
reporting platform implementation in a BI
environment requires great attention to be paid from
the point of view of both the business end users and
IT professionals. OLAP is an approach to swiftly
answer multi-dimensional analytical queries [17].
As part of the broader area of BI, OLAP embraces
both relational reporting and data mining [18].
“OLAP tools enable users to interactively analyse
multidimensional data from multiple perspectives.
OLAP consists of three analytical operations:
consolidation, drill-down, and slicing and dicing”
[19].
The third analytical component of BI is data
mining. This extraction of hidden predictive
information from large data sets is the newest
analytical component of BI. It helps companies to
centre the attention on the key information in their
data warehouses. “Data mining tools can answer
business questions that traditionally were too time-
consuming to resolve. They scour databases for
hidden patterns, finding predictive information that
experts may miss because it lies outside their
expectations” [20].
2.2 Benefits of business intelligence
A wide range of the benefits for an organization
emerges from the basic principles of BI. Hannula
and Pirttimäki [21] carried out a study among the
large Finnish companies to find out the benefits
gained from BI. The most significant benefits
provided by BI activities were:
? better quality information acquired for
decision-making (95%),
? improved ability to anticipate earlier the
possible threats and opportunities (83%),
? growth of knowledge base (76%),
? increase of sharing information (73%),
? improved efficiency (65%),
? easier information acquisition and analysis
(57%), and
? faster decision-making (52%).
Time-savings (30%) and cost-savings (14%) were
not considered particularly important. The
researchers also asked the interviewees to name one
factor to describe the most significant benefit of
their BI activities. The following benefits were
considered to be important:
? harmonizing the way of thinking of company
personnel,
? broadening understanding of business in
general,
? strengthening strategic planning,
? increasing professionalism in acquisition and
analysis of information, and
? understanding the meaning of information [21].
The major benefits of BI, as presented by
Thompson [22], on the basis of the results of the
survey, are:
? faster, more accurate reporting (81%),
? improved decision making (78%),
? improved customer service (56%),
? increased revenue (49%).
Many of the benefits of BI are intangible.
Wixom and Watson [14] present tangible benefits as
well as those that are difficult to measure. For
example, companies may eliminate software and
hardware licences and fees when they consolidate
and retire data marts, or companies may reduce
headcount when they replace manual reporting
processes. Other benefits, such as the enabling of
new ways of doing business, are much more
difficult to quantify, but may generate a competitive
advantage or open up new markets for the company.
A wide range of possible benefits resulting from BI
is presented in Figure 1.
Figure 1: Benefits of business intelligence [14]
The most tangible and easy-to-measure benefits
have more of a local impact, typically happening at
the departmental level. The more intangible benefits
– things such as process improvement and strategic
enablement – can have impacts across an
organization [14].
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2.3 System life cycle
The system life cycle [8] that divides the
development of a system into stages is an attempt to
establish a structured approach to analysing,
designing and building software systems. The stages
in a typical system life cycle are as follows:
? problem definition
? feasibility study
? requirements engineering
? design
? implementation
? maintenance
Each of the stages must be completed and agreed
upon by the client before progressing to the next
stage. These stages are described in greater detail in
the results section.
The problem definition provides an initial
description of the business problem in a form of a
written statement of the client´s current problems
and the objectives of the new system. In this stage,
the scope and size of the project can also be
specified, as well as preliminary ideas and
recommended action for the next stage. The
investigation whether there is a practical solution to
the problem defined, from technical, economic and
organizational points of view, is the content of the
next stage, the feasibility study. The most crucial
part of the life cycle is the requirements
engineering, consisting of the discovery and
agreement of what the problems are, what the new
system should do and how it will be performed.
Then, the design is specified and the system is
physically built during the implementation stage.
The last stage is maintenance, referring not only to
finding and correcting errors, but mostly to
modification of the system to meet evolving client
requirements.
2.4 Diffusion of innovations theory
Rogers’ [23, 9] diffusion of innovation (DOI)
theory, consistent with the theory of reasoned action
[24], defines five factors that impact the rate of
adoption of innovations: relative advantage,
compatibility, trialability, observability and
complexity. The factors are positively correlated
with rate of adoption, except complexity, which is
generally negatively correlated with rate of adoption
[9]. Moore and Benbasat [10] developed this DOI in
IT and generated eight factors with the effect on IT
adoption: relative advantage, compatibility,
trialability, image, voluntariness, ease of use,
visibility and result demonstrability.
Table 1: Comparison of factors in DOI [9] and
DOI in information technology [10]
1. relative
advantage
= 1. relative
advantage
2. compatibility
= 2. compatibility
3. trialability = 3. trialability
4. visibility
4. observability
5. result
demonstrability
5. complexity 6. ease of use
7. image
8. voluntariness
A comparison of Rogers’ [23, 9] and Moore and
Benbasat’s [10] DOI theories indicates that the first
three characteristics of both are similar in meaning.
Relative advantage is the degree to which an
innovation is better than current technology.
Compatibility is the degree of an innovation
matching the existing values, needs and experience
of potential adopter. Trialability is the degree to
which an innovation can be experimented with
before using it.
Roger’s observability, as the degree to which the
outcomes of an innovation are visible for others, is
substituted by Moore and Benbasat’s visibility and
result demonstrability. Visibility means that the
degree of the idea of the innovation itself can be
visible. Result demonstrability is the “tangibility of
using the innovation, including their observability
and communicability” [10].
Roger’s complexity, understood as the relative
difficulty to understand and use an innovation, is
replaced by Moore and Benbasat’s characteristic
ease of use. This refers to the degree to which one
perceives that adoption of an innovation would be
without physical and mental effort.
There are two new factors that Moore and
Benbasat introduce: image and voluntariness. Image
is “the degree to which the use of an innovation is
perceived to enhance one’s image or status in one’s
social system” [10]. Voluntariness concerns the
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degree to which the innovation adoption is
voluntary or is of free will.
Rogers [9] suggests using Moore and Benbasat
instruments and various settings for future research
in the diffusion of technology innovations. This is
the model that has been used in the paper.
3 Data and methodology
The subject of the case study is a sports-fashion
multibrand chain of retail stores of large size,
operating in Slovakia since 2003. In 2010, the top
managers of a retail chain of stores decided to
purchase and implement the software system SAP
BusinessObjects and to tailor it to their company
needs. According to the press release of the Gartner
group in 2012 [3], “SAP remained the No. 1 vendor
in combined worldwide BI, analytics and PM
software revenue in 2011, accounting for 24% on
the market, followed by Oracle, SAS Institute, IBM
and Microsoft.”
The study presented here was based on a 5-
month analysis conducted during and after the
implementation stage of BI. The study was based on
document analysis and in-depth interviews.
Relevant documents, such as the deliverables of
different stages of the life cycle, i.e., business
strategy, procedures, reports from requirements
engineering, project tasks and schedule, training
materials, retail management documents, as well as
software developers’ analysis reports, budget and
reports from the old system were obtained to
understand and analyse the process of BI adoption.
Two semi-structured interviews were conducted
after the adoption of BI.
The first included an interview of the president
and chief commercial officer in order to understand
the main objective and reasons for adopting the new
system. The second interview was with the chief
information officer to understand and analyse the
process and problems with the BI adoption. This
interview lasted more than 120 minutes.
A total of nine interviews were conducted with
the managers of the retail chain: chief commercial
officer, five senior category managers, supply chain
manager, marketing manager and e-commerce
manager. Each of the interviews lasted
approximately 60 – 90 minutes.
The main structure of the interviews was as
follows:
A. Stages of system life cycle (problem definition,
feasibility study, requirements engineering,
design, implementation, maintenance) -
questions were asked and answered in an open-
ended manner.
B. Problems and other important facts from the
experience with the BI adoption- questions were
asked and answered in an open-ended manner.
C. Benefits of BI - the interviewees were probed on
particular benefits of BI adoption to achieve
unambiguous interpretation of their answers.
There was also the possibility of mentioning
other benefits, since these were personal
interviews. The benefits were taken from the
other surveys [21, 22, 14] and from problems
definition and the objectives of BI adoption in
the retail chain as follows:
? acquiring up-to-date and better quality
information for decision-making,
? easier information acquisition and more
efficient information analysis,
? improved decision-making (faster, better,
based on better quality information),
? improved ability to anticipate earlier changes
on the market,
? better planning,
? better pricing,
? increase of shared information among
different functional areas,
? stock management – optimization, and
? other aspects specified by the respondent.
As was expected, since interviewees would
consider all benefits to be very important, they
were asked to name only one factor as the most
significant benefit of their BI activities.
D. Factors with the impact on the pace of BI
adoption, were structured according to Moore
and Benbasat’s [10] theory.
The written records were subsequently approved
by interviewees.
4 Results
4.1 The BI life cycle in the retail chain
The analysis of the adoption of BI in the retail chain
was based on a sequence of stages, as described in
the system life cycle mentioned earlier. Each stage
is described in greater detail here.
Problem definition
Analysis of problems preceding the decision on BI
adoption in the retail chain was conducted by
managers of the retail chain. Typically, managers
will look for opportunities to improve the
management of business processes in reaction to the
pressure on business processes efficiency (mostly
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due to increasing importance of e-commerce in
comparison to B&M business) and the importance
of the permanent need of innovation. The managers,
mostly from the commercial department, faced
several problems to be solved to achieve better
efficiency:
? Share of information and analysis: The
information system used in the company was not
efficient, the particular systems were not
connected (thus a problem in sharing information
among departments) and analysis of the data
from various aspects was lacking– production,
sales, marketing, finance, logistics, and stock.
Managers often used extensive Excel files to
acquire information and indicators in the
required format for defined business purposes, a
process that was inefficient and time-consuming.
? Decision-making: Managers’ decisions were
sometimes delayed or changed due to lack of
relevant data and information or other details.
? Planning and pricing: The planning and pricing
were not efficient due to imperfections in market
change forecasts.
? Stock optimization: There was the need to
decrease the costs of stock management.
The main objectives of the new system in the
retail chain are to ensure that data are on time,
accurate and appear in the required format, that the
links of data from different functional areas are
ensured and that shared information for better
cooperation of departments is available. This should
result in improved forecasts of customer demands,
planning, pricing, stock optimization, clear
operatives, as well as strategic decision-making.
Fulfilment of these objectives is to facilitate
business processes in order to ensure future
expansion of the business.
The project encompassed mostly areas of
commerce and retail: e-commerce being the main
processes, but also involving supporting areas of the
business, marketing, distribution centre, logistics,
finance and human resources. The project team in
the retail chain consisted of the following: president,
chief commercial officer, two senior category
managers and supply chain manager for the business
processes; chief information officer for the
coordination; and representatives from an external
company (SAP) and its cooperating company
providing the design and implementation of BI. The
final BI system was used by all top managers,
category and store managers, marketing manager,
supply chain manager and e-commerce manager - in
total 40 people.
Preliminary ideas included an adoption of BI to
improve decision-making procedures of top
managers and category managers, store managers
and supply chain manager, as well as to share
information in the company. Next, the project team
prepared recommended actions, not only
investigating and producing recommendations for
the introduction of the system for the highlighted
areas of the business, but also researching the costs
and benefits of each proposed system, in
comparison with old system (ERP and the system
based on Microsoft Excel platform). The stage of
problem definition lasted three months, as planned.
Feasibility study
The feasibility study examined the technical,
economic and organizational feasibility of the
project. It resulted in the decision that the company
would adopt a new system, and, after cost analysis,
decisions regarding the budget were made. The
feasibility study was prepared according to the time
plan: a month. The SAP BusinessObjects was
chosen which had been expected to fit in with
existing business procedures.
Requirements engineering
All interviewees concurred that the stage of
discovering and agreeing on exactly what the
problems were and what the new system would do
was the most demanding stage of BI adoption.
The managers worked out a detailed list of key
performance indicators (KPIs), including their
definitions, calculation, input data and the influence
of each KPI on company performance.
Comprehensive analysis of requirements was
finished by the list of reports demanded by
managers, containing the selected KPIs, periodicity
of reports and definition of end users of the reports.
The period of requirements engineering lasted five
months, while only four months had been
scheduled. Nevertheless, after the implementation of
the new system, new requirements evolved, and thus
more maintenance was needed.
Design
The technical solutions of BI adoption (OLAP cubes
and reports) and the data warehouse (DW)
administration were realised in this stage. The
subjects of BI development were: the analysis,
database design, ETL and reporting. After
implementation of BI, administration of BI was
needed: administration and maintenance of data
warehouse, administration of ETL, database servers,
reports and administration of complete BI solution.
The solution analysis focused mainly on the
definition of data sources, inputs and outputs of DW
and various functional areas, that are the basis for
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the functionality definitions included in a business
object universe. Presentation database of DW was a
direct source of the data for the implemented SAP
BO tools.
The data flow from determined data sources to
the implemented SAP BO tools is illustrated in
Figure 2.
Figure 2: Technical architecture of the BI solution.
Source: Internal company´s materials
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1. Data layer
Overview of the data sources: The following data
sources were identified for the DW needs:
? internal ERP system,
? POS (detail data from individual stores),
? ShopGuard system – data on customer turnover,
? planning data gathered from the planning of the
BI project, and
? manual CSV files.
Some of the data were acquired through third party
applications used in the company. All data from the
source systems were entered into the DW through
the defined input text files (CSV files). It was thus
necessary to specify the path to the specimen file,
destination directory and the frequency of data
import.
2. Integration layer
The most important parts of the integration layer
were:
? DW in the integration layer, consisting of three
databases:
? DW (presentation layer) – historical and
aggregated data in the form for reporting. The
data are imported by an incremental approach
in granularity and periodicity defined in the
analysis by managers.
? Stage (data transformation) – data are divided
into dimension and fact tables. The data are
changed and transformed for reporting. The
tables on this layer contain original ID from
source systems, as well as so-called
“surrogate” keys.
? Interface (source data) – between DW and
source data.
? Ad-hoc ETL processes, which run three main
tasks: importing the processing of the source data
that can be imported in irregular time periods,
plan actualisation and maintenance of the data
warehouse.
? Database jobs which divided into three groups:
1. Regular job – regular daily process triggered
in time set.
2. Ad-hoc jobs – are used for the purposes of
operative reporting.
a) Import of “irregular” or often changing
text files (e. g., plans, monthly data, etc.).
b) Actualisation of specific functional or data
areas. These jobs can be started at any
time, but it is not possible to run the same
job in parallel.
c) Actualisation of OLAP cubes.
3. Administrative jobs – are used for the needs
of the DW maintenance – creation (and
recovery) of DW.
Processing of DW installation was also defined. It
consisted of several steps, from server installation
through arrangement of other standard
administration precaution, e. g., maintenance plans
preparations. Regular production DW backup was
reserved by main ETL process. Errors and conflict
management was also the part of the BI design.
Technical suggestions were made, mostly
defining the servers for the project purposes:
? Two database servers – serving as data marts for
DW and at the same time as performance part for
ETL processes (with MS SQL server installed).
? Application server – used as the performance
part of the solution (with SAP business object
tools installed).
3. Reporting layer
The reporting layer was the most substantial part, as
seen from the managerial perspective. The data
areas had specific functions and also report values.
The functional areas were:
? sales,
? shopping cart,
? customer turnover and
? export.
The facts were defined for functional areas and
dimensions, including the hierarchy defined, for the
purposes of reporting.
The DW was built by using data sources. The
DW integrates and unifies the data from these
sources. The presentation layer of DW contained
related dimension and fact tables in the structure of
“star schema” that provided integrated and clean
data for a specific functional area.
Meta-data models for OLAP cubes - business object
universe
Each functional area was represented by an OLAP
cube and defined reports. OLAP cubes are basic
data sources for defined reports. The structure of
every OLAP cube in SAP BO tools is defined by
“universe.” The BO universe is a business
representation of a company´s data that helps end
users access data autonomously using common
business terms; it also isolates business users from
the technical details of the databases where source
data are stored. Universes are made up of objects
and classes that are mapped to the source data in the
database, and accessed through queries and reports
[25]. The SAP BO applications do not allow
creating OLAP cube with real data, and that is
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why each universe is identical with the OLAP cube.
In use, in any query of the OLAP cube, a join
between the OLAP cube and relevant universe is
created in order to transform the data from DW in
real time and in a defined way (defined in this
universe).
Reports were made in relation to OLAP cubes.
Granularity and periodicity were defined for each
report by managers of the project team. It was also
necessary to map the reports for all dimensions and
measures. The creation of a data dictionary was
essential in the preparation of BI adoption. It
contained the definition and description of all
business terms and measures used in the business
processes by end users.
Implementation
The adoption of BI, when the system was physically
built by external company, lasted six months, two
months longer than planned due to corrections after
testing the system by the managers of the retail
chain. A six-member team from the retail chain
(chief commercial officer, two senior category
managers, supply chain manager, chief information
officer and IT assistant) and developers from
external company were responsible for the system
implementation.
A parallel conversion strategy was used, where
the users had to operate both the old and the new
systems. Deliverables from this stage of the life
cycle included program listings, test plans and
supporting documentation, details of the hardware
on which the system would run, as well as manuals.
Insights from the interviews provided not only
the need of dealing with the new knowledge
requirements for the end-users of the BI (see also
[26]), but also the need of training and formal
system of coaching in the beginning of using the
new system. In addition, there was frustration until
the time of full exploitation of the BI system
occurred, and thus the top management support
helped end users. Finally, the new tasks for the
managerial positions were defined, those needed
during the implementation stage and after the BI
adoption [27].
Maintenance
After the BI adoption, maintenance of the system
was still needed to find and correct errors, taking
about two months. Also, new requirements evolved
into further maintenance in order to react to the
strategic decision of closing the stores in foreign
country, taking about one month. This was not as
demanding, since just a few parameters were
excluded from the system. Further, development of
dashboards was the object of the maintenance of the
BI system, something which could be dealt with
earlier in the requirements of managers. This
required additional maintenance time,
approximately three months from requirement
engineering until implementation, as well as
incurring additional costs.
4.2 Pace of the BI adoption in the retail chain
The impact of eight factors on the relative speed
with which the BI adoption was adopted by end-
users was dealt with in the interviews:
? Relative advantage: All the interviewees
answered that BI enabled them to accomplish
tasks more quickly, improved the quality of
their work and made it easier to do their job.
Using the BI improved the job performance,
was advantageous in the job, and increased the
productivity. The adoption of the BI was
impacted by good perception of BI if compared
to their previous systems.
? Compatibility: According to all six commercial
managers, BI was completely compatible with
their current situation. Other managers were not
sure about the compatibility with all aspects of
their work.
? Trialability: Only members of the project team
(chief commercial officer, one senior category
manager and supply chain manager) agreed that
they had a great deal of opportunity to try other
BI systems and test various applications. The
others could not experiment with other BI
systems, but did not think this was crucial for
their later use of the new system.
? Image: The interviewees did not perceive great
importance of BI for improvement of their
image or status in the company. Six of the
managers saw the advantage of the use of BI
and the experience for their image outside of
the company (suppliers – to present the
company as company using modern
technologies and systems, labour market – as
professional experience with the BI or the
involvement in the BI adoption).
? Voluntariness: The use of the BI was not
voluntary; its use was inevitable due to the
formal instruction in the company.
? Ease of use: The interviewees, from the
position of view as the end users of the BI
system, found it cumbersome to use BI at the
beginning. The members of the project team
became familiar with the BI much earlier than
the others did, and thus those who were feeling
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS Cecília Olexová
E-ISSN: 2224-2899 103 Volume 11, 2014
frustrated by using a new system at the
beginning needed coaching.
? Visibility: The use of BI was visible for all.
The interviewees could also see the use of BI
outside the company.
? Result demonstrability: The results of using the
BI were apparent to the managers.
4.3 Benefits of BI for the retail chain
Interviewees named only one factor from all offered
to them, as follows:
? chief commercial officer: acquiring up-to-date
and better quality information for decision-
making,
? senior category manager 1: improved decision-
making (faster, better, based on better quality
information),
? senior category manager 2: acquiring up-to-
date and better quality information for
decision-making,
? senior category manager 3: improved decision-
making (faster, better, based on better quality
information),
? senior category manager 4: improved decision-
making (faster, better, based on better quality
information),
? senior category manager 5: improved decision-
making (faster, better, based on better quality
information),
? supply chain manager: stock management –
optimization,
? marketing manager: improved ability to
anticipate earlier changes on the market and
? e-commerce manager: better pricing.
One category manager and chief commercial
officer named information for decision-making as
being the most important benefit. Four category
managers agreed that improved decision-making
was the most significant benefit of the new system.
The chief commercial officer ended the interview
with the final statement that all category managers,
senior as well as junior, got the right data on a daily
basis, with no need to filter and export the data from
the ERP. They also had the data in one system. The
managers of other functional areas indicated other
benefits that were related to their job position.
5 Discussion and conclusion
This section provides a short discussion and
conclusion to the system life cycle of the BI
adoption, as well as factors impacting the speed of
the BI as the innovation in the retail chain. The
customisation of the BI system, according to the
requirements of the managers, is the most important
factor of successful adoption of BI. From this point
of view, the importance of all the stages of the
system life cycle can be emphasized for the
successful BI adoption in general, with the focus on
precise requirement engineering as the most crucial
stage of the life cycle, and the use of more flexible
life cycle models suitable for project of large size
and budget, e. g., spiral model [28] or modified
waterfall with risk reduction [29] or the others.
The case study can be useful not only for
companies and BI vendors to improve the process of
BI implementation to be on-time, on-budget and to
meet requirements on the system, but also for
researchers to study the adoption process with the
focus on models and methods of requirements
engineering and their verification. The requirements
defined insufficiently can prolong the BI adoption
and cause additional costs for the maintenance of
the system. Also, the end-users who were not
involved in the process of requirement engineering
found the new system not easy to use, and thus there
is a question about their involvement and the extent
of their involvement in the process. The MUST
method [30] could be used to ensure end-users
participation in requirements gathering.
Unfortunately, the MUST method was not used
prior to the implementation in the analysed case. In
future research, the critical success factors of the BI
implementation could be examined as well [31].
The study confirmed that the generated factors
[10] with the effect on the speed of the diffusion of
the BI in the social-system were important in the
speed of the adoption of the new system: relative
advantage, visibility, result demonstrability and
trialability. Compatibility should be explained to the
end-users more in greater detail, and together with
ease of use, should be studied in relation to
involvement of the end-users in requirements
engineering. The voluntariness is difficult to discuss
because the use of the new system was mandatory.
A real difference from Moore and Benbasat [10]
occurred in understanding of the factor image. The
certitude of adoption of innovation was apparent
among all interviewees, may be thanks to the
innovation oriented company culture. People took it
for granted that all would use the new system and
have benefits from its use. Thus, the image outside
of the social-system was emphasized by managers
and could be studied by researchers.
The successful adoption of BI in the company
enhances the value of management in the company
and helps to improve business processes.
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS Cecília Olexová
E-ISSN: 2224-2899 104 Volume 11, 2014
Throughout the case study, the main benefit of BI in
the retail chain processes was determined as
improved decision-making (faster, better, based on
better quality information), along with further
advantages of using the new system.
This research was supported by the grant VEGA
No. 1/0328/13 „Innovation Causal Relation
Modelling for Small and Middle Enterprises“.
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doc_393216429.pdf
One of the findings is that requirements engineering is critical, and even small issues have a tendency to cause big problems.
Business intelligence adoption: a case study in the retail chain
CECÍLIA OLEXOVÁ
Department of Mangement
University of Economics in Bratislava
Faculty of Business Economics in Košice, Tajovského 13, 041 30 Košice
SLOVAK REPUBLIC
[email protected]http://www.euke.sk
Abstract: Business Intelligence (BI) tools are adopted by more and more companies in the current environment
that requires companies to operate as efficiently as possible. The paper investigates a BI adoption in a retail
chain. Based on qualitative research methods, it analyses the Business Intelligence life cycle; it evaluates
factors impacting the adoption from the Diffusion of Innovations perspective. One of the findings is that
requirements engineering is critical, and even small issues have a tendency to cause big problems. This links to
the sentiment among managers, often worrying that IT projects will run over-budget and/or over-time. Finally,
the presented research identifies benefits that are considered to be the most important by the retail chain
managers. An important finding is that managers consider improved decision-making to be the most significant
benefit.
Key-Words: Business intelligence, adoption, diffusion of innovations theory, system life cycle, benefits, retail
chain, speed of adoption.
1 Introduction
The present need to increase the efficiency of
management in retail chains on an ongoing basis
and the growing pressure of cost efficiency in this
field require the use of different approaches,
methods and tools to meet these demands. One
opportunity is the use of sophisticated business
analytics, such as business intelligence (BI). BI is a
wide term that is commonly used for technologies,
applications, tools and processes to gather, store,
access and analyse data for better decision-making.
The literature review on BI has been published by,
for example, Foley and Guillemette [1].
According to the Gartner Group surveys [2], BI
is implemented in almost 80% of companies in the
U.S.A. and in 50% of companies in Europe. Slovak
companies have used these systems only in recent
years. The next growth of BI is evident, as
according to the Gartner group press release [3]
from the Gartner Business Intelligence Summit, the
BI, analytics and performance management software
market was the second-fastest growing sector in the
overall worldwide enterprise software market in
2011. As principal analyst at Gartner, Dan Sommer,
reported, “The strong growth was driven by two
major forces. The first is that IT continues to spend
and earmark money to BI, despite constrained
budgetary environments... and second, new buying
centers are opening and expanding outside of IT, in
line-of-business initiatives, and taking an
increasingly large stake of the spending pie. Key
drivers for this are self-service data discovery tools,
the race among vendors to provide business context
through packaged analytics, and CFOs taking a
renewed interest in BI and Performance
Management.”
BI can produce many benefits if it is
implemented well. Some literature argues that IT
projects, in general, are most often unsuccessful in
being on-time, being on-budget and/or delivering
the expected benefits [4, 5]. As the J ohansson’s and
Sudzina’s [6] research on actual versus planned
ERP system implementation costs in European
SMEs, including Slovakian companies, shows,
although not all companies manage to stay on
budget when it comes to ERP system
implementation, the situation in the investigated
European SMEs is not too critical, probably due to
managing to stay on budget and having a prevalent
fixed price policy for ERP implementation projects
in Europe. It is assumed that this will also apply to
BI implementation, and thus being on-time and
meeting clients’ requirements must be a
consideration for successful BI adoption. Chuah and
Wong [7] state that according to the EMC
Corporation, many BI initiatives have failed not
only because tools were not accessible to end users
but also because the end users’ needs were not met
effectively.
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E-ISSN: 2224-2899 95 Volume 11, 2014
This article is the result of a longitudinal study of
BI adoption based on in-depth analysis in a sports-
fashion multibrand chain of retail stores operating in
Slovakia. It presents the successful application of a
system life cycle [8] in BI adoption in which the
progression of a system is reached through several
stages. The results of the examination of benefits of
BI adoption are reported, and the factors impacting
the rate of adoption [9, 10] of BI in the retail chain
are analysed. The findings in the study contribute to
the real-life experience of successful BI adoptions.
2 Theoretical background
This section describes the most important basics of
BI, including possible BI benefits for the company.
The system life cycle and the diffusion of
innovations (DOI) theory focused on the factors that
impact the speed of adoption of innovations are also
presented.
2.1 Business intelligence (BI)
BI, developed primarily as a system to solve
analytical tasks, is generally considered to be a way
of better decision-making, reducing costs and
improving the quality of processes and performance.
However, there are different definitions of BI.
IBM researcher, Hans Peter Luhn [11], used the
term BI the first time in his article. He defined
intelligence as “the ability to apprehend the
interrelationships of presented facts in such a way as
to guide action towards a desired goal.” The term BI
has become popular thanks to Howard J . Dresner, a
Gartner Group analyst. He described the term BI as
“a set of concepts and methods to improve business
decision making by using fact-based support
systems” in 1989, and this usage has become
widespread [12]. This concept highlights the
importance of data analysis, reports and query tools
that provide users with data, and help them to
synthesise valuable and useful information.
Further, Golfareli and Rizzi [13] underline the
decision-making process, as BI provides corporate
decision-makers with software solutions that help
them identify and understand the key business
factors in making the best decisions for the situation
at the time. “In general, business intelligence
systems are data-driven DSS” [12]. According to
Wixom and Watson [14], BI is “a broad category of
technologies, applications, and processes for
gathering, storing, accessing, and analysing data to
help its users make better decisions.“ It includes
both getting data in (to a data mart or warehouse)
and getting data out (through technologies or
applications that meet some kind of business
purpose). Wixom and Watson [14] underline the
processes as an important part of BI – e.g.,
processes for extracting, loading and storing data,
maintaining metadata for IT and users, and
prioritizing BI projects. Some of these processes are
the responsibility of the BI staff, while others are the
joint responsibility of BI staff and business units.
Foley and Guillemette [1] define BI as “a
combination of processes, policies, culture, and
technologies for gathering, manipulating, storing,
and analyzing data collected from internal and
external sources, in order to communicate
information, create knowledge, and inform decision
making. BI helps report business performance,
uncover new business opportunities, and make
better business decisions regarding competitors,
suppliers, customers, financial issues, strategic
issues, products and services.”
The BI applications cover analytical and
planning functions of most management branches,
such as marketing, purchase and sale, financial
management, production management, marketing
management, controlling, human resource
management, etc. The BI is also used in other
business fields, such as corporate performance
management or customer relationship management.
The BI systems enable getting new information and
knowledge useful to achieve a competitive
advantage for any company with the use of efficient
analytical components (reporting, OLAP
technologies, and data mining).
There is a general concept of BI solution
architecture that contains several layers with
subsistent components. Two of the most significant
components of BI are data warehouse and data
marts. A data warehouse (DW) is a subject oriented,
integrated, non-volatile and time-variant collection
of data to support management decisions [15]. At
the present time, DW is a central component of data
storing in a company’s information system. “The
data warehouse supports the physical propagation of
data by handling the numerous enterprise records
for integration, cleansing, aggregation and query
tasks. It can also contain the operational data which
can be defined as an updateable set of integrated
data used for enterprise wide tactical decision-
making of a particular subject area” [16]. Data marts
can be defined in different ways. According to
Inmon [15], a data mart is “a collection of subject
areas organized for decision support based on the
needs of a given department.”
Analytical components of BI are: reporting, on-
line analytical processing (OLAP) and Data Mining.
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Reporting is a broad category, and there are many
options and modes of its generation, definition,
design, formatting and propagation. A successful
reporting platform implementation in a BI
environment requires great attention to be paid from
the point of view of both the business end users and
IT professionals. OLAP is an approach to swiftly
answer multi-dimensional analytical queries [17].
As part of the broader area of BI, OLAP embraces
both relational reporting and data mining [18].
“OLAP tools enable users to interactively analyse
multidimensional data from multiple perspectives.
OLAP consists of three analytical operations:
consolidation, drill-down, and slicing and dicing”
[19].
The third analytical component of BI is data
mining. This extraction of hidden predictive
information from large data sets is the newest
analytical component of BI. It helps companies to
centre the attention on the key information in their
data warehouses. “Data mining tools can answer
business questions that traditionally were too time-
consuming to resolve. They scour databases for
hidden patterns, finding predictive information that
experts may miss because it lies outside their
expectations” [20].
2.2 Benefits of business intelligence
A wide range of the benefits for an organization
emerges from the basic principles of BI. Hannula
and Pirttimäki [21] carried out a study among the
large Finnish companies to find out the benefits
gained from BI. The most significant benefits
provided by BI activities were:
? better quality information acquired for
decision-making (95%),
? improved ability to anticipate earlier the
possible threats and opportunities (83%),
? growth of knowledge base (76%),
? increase of sharing information (73%),
? improved efficiency (65%),
? easier information acquisition and analysis
(57%), and
? faster decision-making (52%).
Time-savings (30%) and cost-savings (14%) were
not considered particularly important. The
researchers also asked the interviewees to name one
factor to describe the most significant benefit of
their BI activities. The following benefits were
considered to be important:
? harmonizing the way of thinking of company
personnel,
? broadening understanding of business in
general,
? strengthening strategic planning,
? increasing professionalism in acquisition and
analysis of information, and
? understanding the meaning of information [21].
The major benefits of BI, as presented by
Thompson [22], on the basis of the results of the
survey, are:
? faster, more accurate reporting (81%),
? improved decision making (78%),
? improved customer service (56%),
? increased revenue (49%).
Many of the benefits of BI are intangible.
Wixom and Watson [14] present tangible benefits as
well as those that are difficult to measure. For
example, companies may eliminate software and
hardware licences and fees when they consolidate
and retire data marts, or companies may reduce
headcount when they replace manual reporting
processes. Other benefits, such as the enabling of
new ways of doing business, are much more
difficult to quantify, but may generate a competitive
advantage or open up new markets for the company.
A wide range of possible benefits resulting from BI
is presented in Figure 1.
Figure 1: Benefits of business intelligence [14]
The most tangible and easy-to-measure benefits
have more of a local impact, typically happening at
the departmental level. The more intangible benefits
– things such as process improvement and strategic
enablement – can have impacts across an
organization [14].
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2.3 System life cycle
The system life cycle [8] that divides the
development of a system into stages is an attempt to
establish a structured approach to analysing,
designing and building software systems. The stages
in a typical system life cycle are as follows:
? problem definition
? feasibility study
? requirements engineering
? design
? implementation
? maintenance
Each of the stages must be completed and agreed
upon by the client before progressing to the next
stage. These stages are described in greater detail in
the results section.
The problem definition provides an initial
description of the business problem in a form of a
written statement of the client´s current problems
and the objectives of the new system. In this stage,
the scope and size of the project can also be
specified, as well as preliminary ideas and
recommended action for the next stage. The
investigation whether there is a practical solution to
the problem defined, from technical, economic and
organizational points of view, is the content of the
next stage, the feasibility study. The most crucial
part of the life cycle is the requirements
engineering, consisting of the discovery and
agreement of what the problems are, what the new
system should do and how it will be performed.
Then, the design is specified and the system is
physically built during the implementation stage.
The last stage is maintenance, referring not only to
finding and correcting errors, but mostly to
modification of the system to meet evolving client
requirements.
2.4 Diffusion of innovations theory
Rogers’ [23, 9] diffusion of innovation (DOI)
theory, consistent with the theory of reasoned action
[24], defines five factors that impact the rate of
adoption of innovations: relative advantage,
compatibility, trialability, observability and
complexity. The factors are positively correlated
with rate of adoption, except complexity, which is
generally negatively correlated with rate of adoption
[9]. Moore and Benbasat [10] developed this DOI in
IT and generated eight factors with the effect on IT
adoption: relative advantage, compatibility,
trialability, image, voluntariness, ease of use,
visibility and result demonstrability.
Table 1: Comparison of factors in DOI [9] and
DOI in information technology [10]
1. relative
advantage
= 1. relative
advantage
2. compatibility
= 2. compatibility
3. trialability = 3. trialability
4. visibility
4. observability
5. result
demonstrability
5. complexity 6. ease of use
7. image
8. voluntariness
A comparison of Rogers’ [23, 9] and Moore and
Benbasat’s [10] DOI theories indicates that the first
three characteristics of both are similar in meaning.
Relative advantage is the degree to which an
innovation is better than current technology.
Compatibility is the degree of an innovation
matching the existing values, needs and experience
of potential adopter. Trialability is the degree to
which an innovation can be experimented with
before using it.
Roger’s observability, as the degree to which the
outcomes of an innovation are visible for others, is
substituted by Moore and Benbasat’s visibility and
result demonstrability. Visibility means that the
degree of the idea of the innovation itself can be
visible. Result demonstrability is the “tangibility of
using the innovation, including their observability
and communicability” [10].
Roger’s complexity, understood as the relative
difficulty to understand and use an innovation, is
replaced by Moore and Benbasat’s characteristic
ease of use. This refers to the degree to which one
perceives that adoption of an innovation would be
without physical and mental effort.
There are two new factors that Moore and
Benbasat introduce: image and voluntariness. Image
is “the degree to which the use of an innovation is
perceived to enhance one’s image or status in one’s
social system” [10]. Voluntariness concerns the
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS Cecília Olexová
E-ISSN: 2224-2899 98 Volume 11, 2014
degree to which the innovation adoption is
voluntary or is of free will.
Rogers [9] suggests using Moore and Benbasat
instruments and various settings for future research
in the diffusion of technology innovations. This is
the model that has been used in the paper.
3 Data and methodology
The subject of the case study is a sports-fashion
multibrand chain of retail stores of large size,
operating in Slovakia since 2003. In 2010, the top
managers of a retail chain of stores decided to
purchase and implement the software system SAP
BusinessObjects and to tailor it to their company
needs. According to the press release of the Gartner
group in 2012 [3], “SAP remained the No. 1 vendor
in combined worldwide BI, analytics and PM
software revenue in 2011, accounting for 24% on
the market, followed by Oracle, SAS Institute, IBM
and Microsoft.”
The study presented here was based on a 5-
month analysis conducted during and after the
implementation stage of BI. The study was based on
document analysis and in-depth interviews.
Relevant documents, such as the deliverables of
different stages of the life cycle, i.e., business
strategy, procedures, reports from requirements
engineering, project tasks and schedule, training
materials, retail management documents, as well as
software developers’ analysis reports, budget and
reports from the old system were obtained to
understand and analyse the process of BI adoption.
Two semi-structured interviews were conducted
after the adoption of BI.
The first included an interview of the president
and chief commercial officer in order to understand
the main objective and reasons for adopting the new
system. The second interview was with the chief
information officer to understand and analyse the
process and problems with the BI adoption. This
interview lasted more than 120 minutes.
A total of nine interviews were conducted with
the managers of the retail chain: chief commercial
officer, five senior category managers, supply chain
manager, marketing manager and e-commerce
manager. Each of the interviews lasted
approximately 60 – 90 minutes.
The main structure of the interviews was as
follows:
A. Stages of system life cycle (problem definition,
feasibility study, requirements engineering,
design, implementation, maintenance) -
questions were asked and answered in an open-
ended manner.
B. Problems and other important facts from the
experience with the BI adoption- questions were
asked and answered in an open-ended manner.
C. Benefits of BI - the interviewees were probed on
particular benefits of BI adoption to achieve
unambiguous interpretation of their answers.
There was also the possibility of mentioning
other benefits, since these were personal
interviews. The benefits were taken from the
other surveys [21, 22, 14] and from problems
definition and the objectives of BI adoption in
the retail chain as follows:
? acquiring up-to-date and better quality
information for decision-making,
? easier information acquisition and more
efficient information analysis,
? improved decision-making (faster, better,
based on better quality information),
? improved ability to anticipate earlier changes
on the market,
? better planning,
? better pricing,
? increase of shared information among
different functional areas,
? stock management – optimization, and
? other aspects specified by the respondent.
As was expected, since interviewees would
consider all benefits to be very important, they
were asked to name only one factor as the most
significant benefit of their BI activities.
D. Factors with the impact on the pace of BI
adoption, were structured according to Moore
and Benbasat’s [10] theory.
The written records were subsequently approved
by interviewees.
4 Results
4.1 The BI life cycle in the retail chain
The analysis of the adoption of BI in the retail chain
was based on a sequence of stages, as described in
the system life cycle mentioned earlier. Each stage
is described in greater detail here.
Problem definition
Analysis of problems preceding the decision on BI
adoption in the retail chain was conducted by
managers of the retail chain. Typically, managers
will look for opportunities to improve the
management of business processes in reaction to the
pressure on business processes efficiency (mostly
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due to increasing importance of e-commerce in
comparison to B&M business) and the importance
of the permanent need of innovation. The managers,
mostly from the commercial department, faced
several problems to be solved to achieve better
efficiency:
? Share of information and analysis: The
information system used in the company was not
efficient, the particular systems were not
connected (thus a problem in sharing information
among departments) and analysis of the data
from various aspects was lacking– production,
sales, marketing, finance, logistics, and stock.
Managers often used extensive Excel files to
acquire information and indicators in the
required format for defined business purposes, a
process that was inefficient and time-consuming.
? Decision-making: Managers’ decisions were
sometimes delayed or changed due to lack of
relevant data and information or other details.
? Planning and pricing: The planning and pricing
were not efficient due to imperfections in market
change forecasts.
? Stock optimization: There was the need to
decrease the costs of stock management.
The main objectives of the new system in the
retail chain are to ensure that data are on time,
accurate and appear in the required format, that the
links of data from different functional areas are
ensured and that shared information for better
cooperation of departments is available. This should
result in improved forecasts of customer demands,
planning, pricing, stock optimization, clear
operatives, as well as strategic decision-making.
Fulfilment of these objectives is to facilitate
business processes in order to ensure future
expansion of the business.
The project encompassed mostly areas of
commerce and retail: e-commerce being the main
processes, but also involving supporting areas of the
business, marketing, distribution centre, logistics,
finance and human resources. The project team in
the retail chain consisted of the following: president,
chief commercial officer, two senior category
managers and supply chain manager for the business
processes; chief information officer for the
coordination; and representatives from an external
company (SAP) and its cooperating company
providing the design and implementation of BI. The
final BI system was used by all top managers,
category and store managers, marketing manager,
supply chain manager and e-commerce manager - in
total 40 people.
Preliminary ideas included an adoption of BI to
improve decision-making procedures of top
managers and category managers, store managers
and supply chain manager, as well as to share
information in the company. Next, the project team
prepared recommended actions, not only
investigating and producing recommendations for
the introduction of the system for the highlighted
areas of the business, but also researching the costs
and benefits of each proposed system, in
comparison with old system (ERP and the system
based on Microsoft Excel platform). The stage of
problem definition lasted three months, as planned.
Feasibility study
The feasibility study examined the technical,
economic and organizational feasibility of the
project. It resulted in the decision that the company
would adopt a new system, and, after cost analysis,
decisions regarding the budget were made. The
feasibility study was prepared according to the time
plan: a month. The SAP BusinessObjects was
chosen which had been expected to fit in with
existing business procedures.
Requirements engineering
All interviewees concurred that the stage of
discovering and agreeing on exactly what the
problems were and what the new system would do
was the most demanding stage of BI adoption.
The managers worked out a detailed list of key
performance indicators (KPIs), including their
definitions, calculation, input data and the influence
of each KPI on company performance.
Comprehensive analysis of requirements was
finished by the list of reports demanded by
managers, containing the selected KPIs, periodicity
of reports and definition of end users of the reports.
The period of requirements engineering lasted five
months, while only four months had been
scheduled. Nevertheless, after the implementation of
the new system, new requirements evolved, and thus
more maintenance was needed.
Design
The technical solutions of BI adoption (OLAP cubes
and reports) and the data warehouse (DW)
administration were realised in this stage. The
subjects of BI development were: the analysis,
database design, ETL and reporting. After
implementation of BI, administration of BI was
needed: administration and maintenance of data
warehouse, administration of ETL, database servers,
reports and administration of complete BI solution.
The solution analysis focused mainly on the
definition of data sources, inputs and outputs of DW
and various functional areas, that are the basis for
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the functionality definitions included in a business
object universe. Presentation database of DW was a
direct source of the data for the implemented SAP
BO tools.
The data flow from determined data sources to
the implemented SAP BO tools is illustrated in
Figure 2.
Figure 2: Technical architecture of the BI solution.
Source: Internal company´s materials
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1. Data layer
Overview of the data sources: The following data
sources were identified for the DW needs:
? internal ERP system,
? POS (detail data from individual stores),
? ShopGuard system – data on customer turnover,
? planning data gathered from the planning of the
BI project, and
? manual CSV files.
Some of the data were acquired through third party
applications used in the company. All data from the
source systems were entered into the DW through
the defined input text files (CSV files). It was thus
necessary to specify the path to the specimen file,
destination directory and the frequency of data
import.
2. Integration layer
The most important parts of the integration layer
were:
? DW in the integration layer, consisting of three
databases:
? DW (presentation layer) – historical and
aggregated data in the form for reporting. The
data are imported by an incremental approach
in granularity and periodicity defined in the
analysis by managers.
? Stage (data transformation) – data are divided
into dimension and fact tables. The data are
changed and transformed for reporting. The
tables on this layer contain original ID from
source systems, as well as so-called
“surrogate” keys.
? Interface (source data) – between DW and
source data.
? Ad-hoc ETL processes, which run three main
tasks: importing the processing of the source data
that can be imported in irregular time periods,
plan actualisation and maintenance of the data
warehouse.
? Database jobs which divided into three groups:
1. Regular job – regular daily process triggered
in time set.
2. Ad-hoc jobs – are used for the purposes of
operative reporting.
a) Import of “irregular” or often changing
text files (e. g., plans, monthly data, etc.).
b) Actualisation of specific functional or data
areas. These jobs can be started at any
time, but it is not possible to run the same
job in parallel.
c) Actualisation of OLAP cubes.
3. Administrative jobs – are used for the needs
of the DW maintenance – creation (and
recovery) of DW.
Processing of DW installation was also defined. It
consisted of several steps, from server installation
through arrangement of other standard
administration precaution, e. g., maintenance plans
preparations. Regular production DW backup was
reserved by main ETL process. Errors and conflict
management was also the part of the BI design.
Technical suggestions were made, mostly
defining the servers for the project purposes:
? Two database servers – serving as data marts for
DW and at the same time as performance part for
ETL processes (with MS SQL server installed).
? Application server – used as the performance
part of the solution (with SAP business object
tools installed).
3. Reporting layer
The reporting layer was the most substantial part, as
seen from the managerial perspective. The data
areas had specific functions and also report values.
The functional areas were:
? sales,
? shopping cart,
? customer turnover and
? export.
The facts were defined for functional areas and
dimensions, including the hierarchy defined, for the
purposes of reporting.
The DW was built by using data sources. The
DW integrates and unifies the data from these
sources. The presentation layer of DW contained
related dimension and fact tables in the structure of
“star schema” that provided integrated and clean
data for a specific functional area.
Meta-data models for OLAP cubes - business object
universe
Each functional area was represented by an OLAP
cube and defined reports. OLAP cubes are basic
data sources for defined reports. The structure of
every OLAP cube in SAP BO tools is defined by
“universe.” The BO universe is a business
representation of a company´s data that helps end
users access data autonomously using common
business terms; it also isolates business users from
the technical details of the databases where source
data are stored. Universes are made up of objects
and classes that are mapped to the source data in the
database, and accessed through queries and reports
[25]. The SAP BO applications do not allow
creating OLAP cube with real data, and that is
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why each universe is identical with the OLAP cube.
In use, in any query of the OLAP cube, a join
between the OLAP cube and relevant universe is
created in order to transform the data from DW in
real time and in a defined way (defined in this
universe).
Reports were made in relation to OLAP cubes.
Granularity and periodicity were defined for each
report by managers of the project team. It was also
necessary to map the reports for all dimensions and
measures. The creation of a data dictionary was
essential in the preparation of BI adoption. It
contained the definition and description of all
business terms and measures used in the business
processes by end users.
Implementation
The adoption of BI, when the system was physically
built by external company, lasted six months, two
months longer than planned due to corrections after
testing the system by the managers of the retail
chain. A six-member team from the retail chain
(chief commercial officer, two senior category
managers, supply chain manager, chief information
officer and IT assistant) and developers from
external company were responsible for the system
implementation.
A parallel conversion strategy was used, where
the users had to operate both the old and the new
systems. Deliverables from this stage of the life
cycle included program listings, test plans and
supporting documentation, details of the hardware
on which the system would run, as well as manuals.
Insights from the interviews provided not only
the need of dealing with the new knowledge
requirements for the end-users of the BI (see also
[26]), but also the need of training and formal
system of coaching in the beginning of using the
new system. In addition, there was frustration until
the time of full exploitation of the BI system
occurred, and thus the top management support
helped end users. Finally, the new tasks for the
managerial positions were defined, those needed
during the implementation stage and after the BI
adoption [27].
Maintenance
After the BI adoption, maintenance of the system
was still needed to find and correct errors, taking
about two months. Also, new requirements evolved
into further maintenance in order to react to the
strategic decision of closing the stores in foreign
country, taking about one month. This was not as
demanding, since just a few parameters were
excluded from the system. Further, development of
dashboards was the object of the maintenance of the
BI system, something which could be dealt with
earlier in the requirements of managers. This
required additional maintenance time,
approximately three months from requirement
engineering until implementation, as well as
incurring additional costs.
4.2 Pace of the BI adoption in the retail chain
The impact of eight factors on the relative speed
with which the BI adoption was adopted by end-
users was dealt with in the interviews:
? Relative advantage: All the interviewees
answered that BI enabled them to accomplish
tasks more quickly, improved the quality of
their work and made it easier to do their job.
Using the BI improved the job performance,
was advantageous in the job, and increased the
productivity. The adoption of the BI was
impacted by good perception of BI if compared
to their previous systems.
? Compatibility: According to all six commercial
managers, BI was completely compatible with
their current situation. Other managers were not
sure about the compatibility with all aspects of
their work.
? Trialability: Only members of the project team
(chief commercial officer, one senior category
manager and supply chain manager) agreed that
they had a great deal of opportunity to try other
BI systems and test various applications. The
others could not experiment with other BI
systems, but did not think this was crucial for
their later use of the new system.
? Image: The interviewees did not perceive great
importance of BI for improvement of their
image or status in the company. Six of the
managers saw the advantage of the use of BI
and the experience for their image outside of
the company (suppliers – to present the
company as company using modern
technologies and systems, labour market – as
professional experience with the BI or the
involvement in the BI adoption).
? Voluntariness: The use of the BI was not
voluntary; its use was inevitable due to the
formal instruction in the company.
? Ease of use: The interviewees, from the
position of view as the end users of the BI
system, found it cumbersome to use BI at the
beginning. The members of the project team
became familiar with the BI much earlier than
the others did, and thus those who were feeling
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frustrated by using a new system at the
beginning needed coaching.
? Visibility: The use of BI was visible for all.
The interviewees could also see the use of BI
outside the company.
? Result demonstrability: The results of using the
BI were apparent to the managers.
4.3 Benefits of BI for the retail chain
Interviewees named only one factor from all offered
to them, as follows:
? chief commercial officer: acquiring up-to-date
and better quality information for decision-
making,
? senior category manager 1: improved decision-
making (faster, better, based on better quality
information),
? senior category manager 2: acquiring up-to-
date and better quality information for
decision-making,
? senior category manager 3: improved decision-
making (faster, better, based on better quality
information),
? senior category manager 4: improved decision-
making (faster, better, based on better quality
information),
? senior category manager 5: improved decision-
making (faster, better, based on better quality
information),
? supply chain manager: stock management –
optimization,
? marketing manager: improved ability to
anticipate earlier changes on the market and
? e-commerce manager: better pricing.
One category manager and chief commercial
officer named information for decision-making as
being the most important benefit. Four category
managers agreed that improved decision-making
was the most significant benefit of the new system.
The chief commercial officer ended the interview
with the final statement that all category managers,
senior as well as junior, got the right data on a daily
basis, with no need to filter and export the data from
the ERP. They also had the data in one system. The
managers of other functional areas indicated other
benefits that were related to their job position.
5 Discussion and conclusion
This section provides a short discussion and
conclusion to the system life cycle of the BI
adoption, as well as factors impacting the speed of
the BI as the innovation in the retail chain. The
customisation of the BI system, according to the
requirements of the managers, is the most important
factor of successful adoption of BI. From this point
of view, the importance of all the stages of the
system life cycle can be emphasized for the
successful BI adoption in general, with the focus on
precise requirement engineering as the most crucial
stage of the life cycle, and the use of more flexible
life cycle models suitable for project of large size
and budget, e. g., spiral model [28] or modified
waterfall with risk reduction [29] or the others.
The case study can be useful not only for
companies and BI vendors to improve the process of
BI implementation to be on-time, on-budget and to
meet requirements on the system, but also for
researchers to study the adoption process with the
focus on models and methods of requirements
engineering and their verification. The requirements
defined insufficiently can prolong the BI adoption
and cause additional costs for the maintenance of
the system. Also, the end-users who were not
involved in the process of requirement engineering
found the new system not easy to use, and thus there
is a question about their involvement and the extent
of their involvement in the process. The MUST
method [30] could be used to ensure end-users
participation in requirements gathering.
Unfortunately, the MUST method was not used
prior to the implementation in the analysed case. In
future research, the critical success factors of the BI
implementation could be examined as well [31].
The study confirmed that the generated factors
[10] with the effect on the speed of the diffusion of
the BI in the social-system were important in the
speed of the adoption of the new system: relative
advantage, visibility, result demonstrability and
trialability. Compatibility should be explained to the
end-users more in greater detail, and together with
ease of use, should be studied in relation to
involvement of the end-users in requirements
engineering. The voluntariness is difficult to discuss
because the use of the new system was mandatory.
A real difference from Moore and Benbasat [10]
occurred in understanding of the factor image. The
certitude of adoption of innovation was apparent
among all interviewees, may be thanks to the
innovation oriented company culture. People took it
for granted that all would use the new system and
have benefits from its use. Thus, the image outside
of the social-system was emphasized by managers
and could be studied by researchers.
The successful adoption of BI in the company
enhances the value of management in the company
and helps to improve business processes.
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS Cecília Olexová
E-ISSN: 2224-2899 104 Volume 11, 2014
Throughout the case study, the main benefit of BI in
the retail chain processes was determined as
improved decision-making (faster, better, based on
better quality information), along with further
advantages of using the new system.
This research was supported by the grant VEGA
No. 1/0328/13 „Innovation Causal Relation
Modelling for Small and Middle Enterprises“.
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