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Business Intelligence in Managerial Accounting
SEE Journal80
Business Intelligence in Managerial Accounting
Drazena Tomic Abstract
This paper researches the needs and possibilities of using technologies of business intelligence (especially data warehouse and OnLine Analytical Processing – OLAP tools) in managerial accounting. Namely, the most of data that is today used in manage-rial accounting is digitally stored in databases and/or data warehouse. Different technologies (data warehouse, OLAP, intelligent agent and etc.) comprised by the term business intelligence (BI) enable directly exploring, searching and analyzing of that data by end users e.g. managerial accountants. The paper on the concrete case study presented possibilities of using data warehouse and OLAP in managerial accounting.
JEL: M41, C88
1. Introduction Managerial accounting is the one of the most critical as-
pects in the successful operation of a business today, provid-
ing decision makers both inside and outside an organization
with relevant information for planning, decision making and
control. It is important to note that almost all data which mana-
gerial accountants need is stored in digital form in a database
or data warehouse. For high-quality and useful reports, the
modern managerial accountant has no option but to use the
technologies of business intelligence. In the last decade data
warehouses have become necessary for resolving data quality
problems and enabling quality data foundation, primarily for
decision-making purposes. As most managerial accountants’
analysis and reports are directly related to decision making, it
is obvious that they also have a need to use data from data
warehouses. Different technologies (data warehouse, OLAP -
On-Line Analytical Processing, intelligent agent and etc.) com-
prised by the term business intelligence (BI) enable directly
exploring, searching and analyzing data stored in data ware-
houses by end users, e.g. managerial accountants. This paper
presents some of these technologies (data warehouse and
OLAP) and theirs potential usefulness in managerial account-
ing (case study).
2. Business Intelligence The present meaning of the term business intelligence
(BI) was first used in 1989 by Howard Dresner, a Research Fel-
low at Gartner Group. He popularized BI as an umbrella term in
order to describe a set of concepts and methods for improving
business decision-making by using different information tech-
nologies.
Today, business intelligence (BI) is viewed as a broad cat-
egory of applications and technologies for gathering, storing,
analyzing, and providing access to data to help enterprise users
make better business decisions (Turban et al. 2005, p. 249). BI
applications include the activities of decision support systems,
query and reporting, online analytical processing (OLAP), sta-
tistical analysis, forecasting, and data mining.
Some BI technologies are back-end, infrastructure tools
that deal with extracting data, cleaning it up, transforming it,
re-organizing it, and optimizing it for use in decision making.
These backend tools include data warehouses, data marts, and
ETL tools.
*Tomic: University in Mostar
Faculty of Economics ,
Hrvatske bb, Mostar, Bosnia and Herzegovina
e-mail: [email protected]
Business Intelligence in Managerial Accounting
September 2006 81
Other BI tools are front-end, designed to extract knowledge
and insight from the data once it has been prepared. These in-
clude reporting, query, OLAP, visualization, decision modeling
and planning, and data mining tools
Emerging beyond these sets of back-end and front-end
tools is another breed of BI systems called analytic applications.
Typically, analytic applications are business process oriented,
often combining multiple BI technologies and domain exper-
tise about the business process. These applications can dra-
matically improve business processes while reducing the need
to learn complex technologies.
In the remainder of this paper, data warehouse (back-end
BI tool) and OLAP (front-end BI tool) are briefly presented with
regard to their possible use in managerial accounting (present-
ed in the case study).
2.1.Data Warehouses
The main purpose of data warehouses is to insure qual-
ity data for management analysis and decision making. Data
warehouses are attempts to integrate data from different and
discrete production systems inside as well as outside an orga-
nization. As data is entered into the data warehouse, many in-
consistencies in the application are resolved.
Figure 1: Data warehouse concept
At the heart of a data warehouse concept is the realization that
there are fundamentally two kinds of data (Inmon 2002, p. 18):
- Primitive or operational data
- Derived or decision support (DSS) data.
Primitive data is detailed data used to run the day-to-day
operations of the organization. Derived data is data that is sum-
marized or otherwise calculated to meet the needs of the man-
agement of the organization.
Because there are a host of differences between primitive
and derived data, the prevailing opinion is that both primitive
and derived data would not fit in a single database. As is obvi-
ous from figure 1, the foundation of the data warehouse con-
cept is the separation of day-to-day operations of production
applications from analysis and reports intended for analysts or
managers.
According to Inmon (Inmon et al. 1997, p. 123) there are
four key objectives that data warehouses should satisfy:
- Maintain appropriate and responsible care of corporate
data resources
- Provide better, faster ways for users to discover answers
to complex, unpredictable questions
Business Intelligence in Managerial Accounting
SEE Journal82
- Place end users directly in touch with the data they need
to make better decisions
- Enable users to become responsible for the specification,
creation and regeneration of the reports and analysis
they require.
As a central repository of business data, data warehouses
are very sensitive to data quality. The resulting integration of
data from multiple independent sources for effective informa-
tion sharing gives rise to certain incompatibilities. However,
without accurate data, users lose confidence in the database
and/or data warehouse and make improper decisions. Above
all, poor data quality results in loss of time, money and cus-
tomer confidence and causes embarrassment. Because of this,
the data staging process or ETL (Extraction, Transformation and
Loading) is a crucial step in data warehouse development. Its
purpose is to ensure the quality of data stored in the data ware-
house.
Kimball (Kimball et al., 1998, p. 16) states that ETL process
are the key part of the data warehouse project, including pro-
cesses that clean, transform, combine, de-duplicate, household,
archive and prepare source data for use in the data warehouse.
ETL describes the series of processes that, as is obvious from
figure 2, accomplish the following:
- Detect changes made to source data required for the
warehouse
- Move data from source systems
- Clean up and transform the data
- Restructure keys
- Index and summarize the data
- Maintain the metadata
- Load the data into the warehouse
These processes are absolutely fundamental in ensuring
that the data resident in the warehouse is required by and use-
ful to the business users, of good quality to ensure good infor-
mation, accurate enough to ensure accurate information and
easy to access so that the warehouse is used efficiently and
effectively by the business users.
ETL decisions and strategies can evolve over time through-
out the life of the warehouse. It may be prudent to track these
strategies and decisions to make it always possible to explain
algorithmic logic or business rules used at different points in
time with current, recent, or archived data. In this context,
metadata plays an invaluable role in the registration, access,
and control of both external and internal data. The metadata
should provide the warehouse manager with as much informa-
tion about the data as possible, averting the need to examine
the data closely.
Figure 2: Data staging (ETL) process
Business Intelligence in Managerial Accounting
September 2006 83
ETL processes allow the warehouse to become a single distri-
bution point of information for the enterprise and other levels
of the organization through feeds into data marts and desktop
applications.
he purpose of data warehouses is to deliver quality data
(information) to the end users. All of the other more frequent-
ly quoted objectives of data warehouse – increased profits,
reduced costs, better decisions – arise from the successful
achievement of the stated objective. When managers are able
to look at integrated, historical data with which they can per-
form trend analysis or make accurate projections, all these ben-
efits can be realized by the organization. The ultimate measure
of the success of a data warehouse is the degree to which the
end users receive benefits from the information it contains and
pass those benefits on to the organization.
The potential value of a data warehouse is realized only
if one can explore and use information from it. There are three
main categories of user tools that could help the end users to
“unlock” data warehouse potential (Kimball et al. 1998):
- Query and reporting tools
- On-Line Analytical Processing – OLAP tools
- Data mining tools
The promise of data warehousing can be considered as two-
fold:
- Easier access to consistent data,
- The ability to discover previously hidden information,
patterns and trends about business.
Query and reporting tools, such as OLAP (On-Line Ana-
lytical Processing) tools, address the first promise; they cannot
however, realize the second promise, for when one uses query
and reporting or OLAP tools, the answers one receives are only
as good as the questions one asks. One will only find interest-
ing patterns if one is actively looking for them.
Data mining, on the other hand, is designed to have the
computer dig though volumes of data to uncover interesting
connections. This approach speeds informed decisions by ex-
posing information that could not be discovered easily (or at
all) by manual exploration.
2.2.OLAP tools
On-Line Analytical Processing– OLAP tools are known as
a natural supplement of data warehousing. They allow users
to intuitively, quickly and flexibly manipulate data from data
warehouses using familiar business terms and in doing so pro-
vide analytical insight. These tools enable users to fully use all
the advantages of the multidimensional data model that is the
prevailing model for data warehouses.
At the center of the OLAP concept is multidimensional
data viewing. The item “dimension” is a data category and is
always connecting with a numerical value called “measure.” For
instance, products, time, market segment, salesman, kind of
costs, employee are dimensions and the amount of costs, price,
production volume, volume of sales, revenue etc. are measures.
In relational tables (relational data model) attributes correspond
to dimensions while the dimension values correspond to the
attributes domain. One has to be aware of a manager’s infor-
mation requirements to create multidimensional data tables
that will reflect a specific manager’s data views. The simplest
visualization of multidimensional data view is a spreadsheet.
Adding a new dimension one initiates a third dimensional data
view, the new data structure that is commonly known as a data
cube. The three-dimensional data view is usually visualized as
in Figure 3.
Figure 3: Data cube
A multidimensional array structure represents a higher
level of organization than relational tables. The structure itself
contains much valuable “intelligence” regarding the relation-
ships between the data elements because user “perspectives”
are imbedded directly in the structure as dimensions, as op-
posed to being placed into fields.
Data cube dimensions and cube-instances are not static
and can be viewed and manipulated using functions such as
slice, dice, nest, drill down and roll up (aggregate). The first
Business Intelligence in Managerial Accounting
SEE Journal84
three functions (slice, dice, nest) are viewing functions. Drill
down and roll-up are manipulating functions.
Slice means any two-dimensional data viewing and is often
called rotation.
Drill-down manipulation function is based on the hierarchical
nature of dimensions. Using the subsequent dimension values
it is possible to explore more detail along a dimension.
The inverse of drill-down is the roll-up function, which sum-
marizes at a higher level of the dimension hierarchy. Roll-up
means data aggregation according to the dimension value.
For example, OLAP users can “slice and dice” information
along a customer dimension and view business metrics by
product and over time. Reports can be defined from multiple
perspectives that provide a high-level or detailed view of the
performance of any aspects of the business. Users can navigate
throughout their data warehouse by drilling down on a report
to view elements at finer levels of detail, or by pivoting to view
Figure 4: MD-ROLAP and ROLAP
architecture of OLAP tools
reports from a different perspective. In this way, the end user
can follow his own way of thinking and analyzing by, for ex-
ample, first viewing data on a global – aggregate level – such
as sales region, then drilling down to detailed information for a
particular town or store.
Vendors of OLAP tools are classified as either multidimen-
sional OLAP or relational OLAP based on the underlying archi-
tecture of the system “Microstrategy (2003).” Figure 4 shows
both MD-OLAP and ROLAP architecture.
Multidimensional OLAP (MD-OLAP) utilizes a proprietary
multidimensional database (MDDB) to provide OLAP analy-
ses. The main premise of this architecture is that data must be
stored multidimensionally to be viewed multidimensionally.
MD-OLAP is a two-tier, client/server architecture where MDDB
serves as both the database layer and the application logic lay-
er. The presentation layer integrates with the application logic
layer and provides an interface through which the end users
view and request OLAP analyses.
Relational OLAP (ROLAP) accesses data stored in a rela-
tional data warehouse to provide OLAP analyses. The premise
of ROLAP is that OLAP capabilities are best provided directly to
the relational database, i.e. data warehouse. ROLAP is a three-
tier, client/server architecture where a database layer utilizes
relational databases for data storage, access and retrieval pro-
cesses. The application logic layer is the ROLAP engine, which
executes the multidimensional reports from multiple end us-
ers. The ROLAP engine integrates with a variety of presentation
layers, through which users perform OLAP analyses.
3. Importance of Business Intelligence in Managerial Accounting The use of business intelligence could in great measure
influence how managerial accountants have done their jobs.
An obvious example is financial statement analysis. No matter
how carefully prepared, all financial statements are essentially
historical documents. They tell what happened during a par-
ticular year or series of years. The most valuable information
to most users of financial statements, however, concerns what
probably will happen in the future. The purpose of financial
statement analysis is to assist statement users in predicting the
future by means of comparison, evaluation, and trend analy-
sis. It is easier for accountants to perform this analysis if there
is a data warehouse, because a data warehouse is a historical
repository of data, and by default includes long term series of
data (in this case financial statements) along with data from
Business Intelligence in Managerial Accounting
September 2006 85
different sources (from other companies, public databases etc.)
that are, through the data staging process, prepared for load-
ing into a data warehouse and for use in analysis. With such
prepared data in a data warehouse accountants could use dif-
ferent exploring tools (OLAP, data mining etc.).
The financial statement analysis is only one of the more
typical examples of how the existence of a data warehouse
could provide a good foundation for better qualitative analy-
sis.
BI technologies attempt to help people understand data
more quickly so that they can make better and faster decisions
and, ultimately, achieve business objectives. The main reason
behind BI objectives is to increase organizational efficiency and
effectiveness. All BI technology has common aims (Panian et al.
2003, p. 25), including:
• Providing access to good data; business analysis is diffi
cult without clean, organized data
• Enhance a user’s ability to understand the result. Simply
dumping numbers on people creates more problems
than it solves. Twenty years ago the problem may have
been acquiring the data; today it concerns more how to
deal with the data
• Increase a user’s business acumen. Knowing what the
data says is good, but ultimately one has to know what
to do with it. This knowledge is difficult to build into a
piece of software, but state-of-the-art analytic applica
tions use industry benchmarks and leverage expert’s
best practices to benefit casual and novice users.
• Help communicate the findings and take action. It is rare
that an individual can execute anything significant with
in an organization without involving others.
Today’s BI systems must address all four of these issues
and reach beyond simply dumping formatted data on users.
Technologies are available to help users understand, explore,
share, and collaborate.
BI is different from standard information systems because
the environment has been turned upside down. Users are more
than passive participants in the total data analysis process.
Through BI tools, the user gains an insight that may very well
lead to actions or further investigation. This is the essence of
ad hoc access interaction. Users need ad hoc access because
the data shows them what they did not already know and leads
them down paths they could not predict. It is just this support
of ad hoc interaction that differentiates BI from standard infor-
mation systems.
4. Case Study Here is a briefly presented pilot project of data mart (sub-
ject oriented part of data warehouse) development and use in
one private firm that deals with the production, distribution
and sales of beverages (wines and juices). The aim of this pilot
project was to present some of the BI possibilities in order to
persuade managers of that firm that BI technologies could con-
tribute to faster and better decision making.
Development of the data mart for financial analysis, in
this concrete example, was based on a business dimensional
lifecycle. This way of developing a data mart (and also a data
warehouse) was introduced by a group of authors concen-
trated around Ralph Kimball. They defined a business dimen-
sional lifecycle as a set of base directions whose implemen-
tation should ensure that different parts of a data warehouse
project assemble into a unity following the right sequence at
the right time (Kimball et al. 1998). The successful development
and implementation of a data warehouse, e.g. a data mart, de-
pends on the efficient and adequate integration of numerous
steps towards its development and implementation. It is not
enough to build a perfect data model or to buy the best tech-
nology and software – it is necessary to coordinate many differ-
ent parts that include different knowledge, preferences, styles,
thinking, technologies in order to get one unique picture that
is useful for end users (decision makers).
In developing and implementing a data mart for financial
analysis, the following steps are used:
- Business requirement definition – this means understand
ing the key factors driving the business in order to trans
late them into design considerations
- Dimensional modeling – the definition of the business
requirements determines the data needed to address
business users’ analytical requirements
- Technical architecture design – as a data mart environ
ment requires the integration of numerous technologies,
the aim of this step is establishing the overall architec-
ture of the framework and vision
- Physical database design – mapping of a dimensional
model into a database schema
- Data staging design and development – extracting, trans
forming and loading data from a transaction database
system
- End-user application specification – OLAP software was
chosen
Business Intelligence in Managerial Accounting
SEE Journal86
4.1.Business requirement defi nition
A business requirement definition related to data mart
development means the understanding of the business’ driv-
ing forces in order to efficiently define the dimensions of the
data mart. It is much more than the classical collection and
definition of user requirements. Business requirement defini-
tion means defining the main purpose of the data mart. In con-
crete terms this might mean:
- Understanding of revenue, profit and cost trends in the
last two years
Sizeof TMT
Sizeof TMT
Dimensions Description
o All customers (for example:
sum of revenues by all customers)
o Regions (for example: sum of
revenues by regions of customers)
o Single customer (for example:
revenue for single customer)
Shows how data vary by customers.
Expression of customer is related to
any value in Customer dimension,
regardless of its level.
Hierarchy of dimension
value
Table 1: Dimensions and theirs hierarchy
Customers
o Organization as a whole (for example:
profit for whole organization)
o Regions (for example: profit by
regions of organization units)
o Profit center (for example profit by
shops, or by distribution centers)
o Year (2004,2005,etc.)
o Quarter
o Month
Organization
structure
Time
Shows how data vary related to
organization units (structure).
Shows how data vary over time.
- Ensuring the quality analysis of revenues, profit and costs
related to the organization’s structure and customers
- Ensuring the simple integration of the data mart and
selected OLAP.
The final results of this step are presented in Table 1. This
table includes the main dimensions and their short descrip-
tions with examples. Defining business requirements and their
mapping into dimensions is a prerequisite for a building di-
mension model of business.
In a concrete case the dimensional model usually consists
of one main table with compound keys – called a fact table
(FIN_STATE table from figure 5) and a few tables called dimen-
sions (Customers, Organization structure and Time from figure
5) that form a so called star schema.
Tool Oracle Warehouse Builder 10gRelease 2 (version 10.2.) is
used for defining and presenting the dimensional data model.
This tool enables the relatively simple definition and creation of
data models and the formation, on that model, of a data base
schema for the data warehouse, such as the direct mapping of
a dimensional model onto data base tables “Oracle (2006).”
4.2.Technical architecture design
Technical architecture design includes the selection of
specific components of data mart architecture such as hard-
ware, DBMS and data staging (ETL) tools.
In this case, because the initial pi-
lot data mart is in question, the require-
ments for hardware were quite low. The
aim of developing this data mart was to
present to users the main possibilities
and advantages of this concept in order
for users to decide on the next iteration
in data warehouse building.
The concrete data mart hardware
environment consists of five computers
of which one is a Windows 2003 server
(Pentium IV with 2 GB RAM-a and 3x40
GB hard disk) and four are Windows XP
clients (Pentium IV with 256 MB RAM-a
and 1x40GB hard disk).
A DBMS was chosen, a Oracle 10g database because users
had had good experience with the Oracle 9i transaction data-
base, while as a ETL tool they used a Oracle Warehouse Builder
10g Release 2 because it provides a graphical user interface to
ensure import of source data, its transformation, cleaning and
standardization, and finally its loading into a data warehouse
for subsequent analysis.
4.3.Physical database and data staging design and development
The physical data mart data base design was built by mapping
a dimensional model onto relational schema. For this purpose
Oracle Warehouse Builder 10g Release 2 was used because it
enables the automatic mapping of a dimensional model onto
relational model and generates DDL scripts for table creating,
keys and indexes in the Oracle 10g database.
Business Intelligence in Managerial Accounting
September 2006 87
Figure 5: Dimensional model for financial analysis
Figure 6: Mapping transaction tables into dimensional model
The data staging process included the following steps:
- Extracting data from the transaction system (Oracle 9i)
into a data stage area (Oracle 10g)
- Data cleaning, meaning removing duplicate data, filling
missing data, joining data from different tables, filtering
data, etc. In this step procedures were used built into
Oracle Data Warehouse Builder 10g Release 2. Figure
6 shows the mapping from transaction tables (SIFA_
POSLOVNI_PARTNERI and SIFA_ADMINISTRATIVNE_
JEDINICE) onto a dimension table (SB_CUSTOM
ER) using the special operators “joiner” and “filter” built
into Oracle Data Warehouse Builder.
- Transformation and transportation of data from temporal
tables of the data staging area into data mart tables.
Business Intelligence in Managerial Accounting
SEE Journal88
Figure 7: Cross-tab report related to revenue
4.4.End-user Application Specifi cation
The real power of the data warehouse, e.g. data mart, be-
comes visible only through the use of different tools for data
access and analysis. In this case, the chosen tool was Oracle
Discoverer 10g (version 9.0.4) – an OLAP tool that ensures the
use of different methods for analyzing data warehouse data.
This tool includes all the standard options of an OLAP tool, such
as ad hoc reporting (including the drill down and drill up op-
tions), data slicing and data dicing with the graphics presenta-
tion “Oracle (2003).” Figure 7 shows an example of a cross-tab
report related to revenue examined by the dimensions of time,
organization structure and customers.
5.Conclusion
In modern businesses, increasing standards, automation,
and technologies have led to vast amounts of data becoming
available. Data warehouse technologies have set up reposito-
ries to store this data. Improved Extract, transform, load (ETL)
and even recently Enterprise Application Integration tools have
increased the rapid collection of data. OLAP reporting tech-
nologies have allowed for the faster generation of new reports
which analyze data. It is obvious that business intelligence has
now become the art of sifting through large amounts of data,
extracting pertinent information, and turning that information
into knowledge upon which decision can be based.
Business intelligence software incorporates the ability to mine
data, analyze, and report. Some modern BI software allows us-
ers to cross-analyze and perform deep data research rapidly for
better analysis of sales or the performance on an individual,
department, or company. In modern applications of business
intelligence software, managers are able to quickly compile re-
ports from data for forecasting, analysis, and business decision
making.
In the short case study presentation it was stressed that
modern tools for decision support (especially OLAP) based on a
well-designed data warehouse enable non-technical business
professionals (e.g. managerial accountants) to find answers
in data on their own. These systems give full control to those
users who master the new technology, enabling them to ac-
cess the data, ask and answer their own questions, share their
knowledge with others, and most important, compose better
and more reliable financial reports – the foundation for quality
management decisions.
Business Intelligence in Managerial Accounting
September 2006 89
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