Business Intelligence in Managerial Accounting 2/broj 2 tekst 8.pdf · Business Intelligence in...

Business Intelligence in Managerial Accounting

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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]


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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


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- 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


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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


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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


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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


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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.


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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.


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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.


September 2006 89

References

Inmon,William H., “Building the Data Warehouse”, Third edition, John Wiley & Sons, New York, 2002. (3rd ed.)

Inmon, William H. and Imhoff, Claudia and Sousa Ryan, “Corporate Information Factory,” John Wiley & Sons, New York, (1998)

Inmon, William H. and Welch, John D. and Glassey Kath-erine L., “Managing the Data Warehouse,” John Wiley & Sons, New York, (1997)

Kimball, Ralph and Reeves, Laura and Ross, Margy and Thornthwaite, Warren, “The data warehouse Lifecycle Toolkit,” John Wiley & Sons, New York, (1998);

MicroStrategy, “The Case for Relational OLAP,” www.strat-egy.com, July 1997.

Oracle, “Oracle Discoverer Desktop – User’s Guide 10g (9.0.4),” Oracle Corporation (2003)

Oracle, “Oracle Warehouse Builder – User’s Guide 10g Re-lease 2 (10.2),” Oracle Corporation (2006)

Panian, Zeljko and Klepac, Goran ”Poslovna inteligencija,” Masmedia Zagreb (2003)

Turban Efraim, Aronson Jay E., Liang, Ting-Peng “Decision Support Systems and Intelligent Systems,” Pearson Prentice Hall, New Jersey, 2005. (7th ed.)

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