COMPARISON OF APPROACHES IN DATA WAREHOUSE

DEVELOPMENT IN FINANCIAL SERVICES AND HIGHER

EDUCATION

Janis Benefelds, Laila Niedrite

Department of computer science, University of Latvia, Rainja blvd. 19, Riga,Latvia

Keywords: Data warehouse development methodologies, banking, education

Abstract: When a decision to develop a Data Warehouse is made, some sensitive factors should be evaluated to

understand th

e tasks and prioritize them. In this paper we assume that there are common characteristics for

companies of similar business activities and different for those with opposite activities. This article looks at

the interpretation of the same criteria of two Data Warehouse projects in for-profit (banking) and not-for-

profit (higher education ) areas. We have used the criteria from (List et al. 2002) to compare the results of

the two projects. Each section of the paper describes this set of criteria and development methodology for

each of the two areas. An evaluation matrix is provided in Conclusion.

1 INTRODUCTION

More and more Data Warehouse solutions appear in

the world all the time. Still, there is a very high

percentage of Data Warehouse projects which fail

(Frolick & Lindsay 2003). There are a lot of articles

providing top ten mistake lists, success factors etc.

The existence of material on many ‘mistakes to

avoid’ and lack of comparative research that would

help make the best choice between different data

warehouse development methodologies served as a

starting point for this research. According to Frolick

and Lindsay (2003), project failure or success is part

of interpretation. The aim of this paper is to compare

the meaning of the same criteria and describe

methodologies used in Data Warehouse

development projects in profit and non-profit

companies.

List et al. (2002) give a comparison of three

app

roaches to data warehouse development, based

on the methodologies chosen by the authors.. In our

two data warehouse projects we have used the user-

driven approach for the first project and the process-

driven approach for the second one.

To compare Data Warehouse design in different

busi

ness areas, we used our experience in managing

such projects, made an analysis of projects in the

same industry and available publications, and

interviewed a number of key-people from similar

organizations.

The three approaches to the data warehouse

devel

opment are explained in the section „Related

work”. The section „Financial Services” presents the

main characteristics of Data Warehouse

development in “a for-profit company”, the section

„Higher Education” describes the methodology used

in our project and the main characteristics of “a not-

for-profit company”. The final section

“Conclusions” contains a comparison of two case

studies and different approaches.

2 RELATED WORK

The data warehouse development methods can be

divided into three groups – user-driven, data-driven

and process-driven. All three approaches to the data

Warehouse development are described and

compared by List et al. (2002). Some methods exist

in all three approaches, about how the approach can

be used in data warehouse development.

For example, the user-driven approach

appl

ication methodologies are described by

Westerman (2001) and (Poe 1996). This approach is

also used by Kimball and Ross (2002) in gathering

user requirements. The data–driven approach

methodologies are described by Kimball and Ross

(2002) as their basic method, Golfarelli, Maio and

Rizzi (1998), Boehnlein and Ulbrich-vom-Ende

(1999), Phipps and Davis (2002) and other authors.

552

Benefelds J. and Niedrite L. (2004).

COMPARISON OF APPROACHES IN DATA WAREHOUSE DEVELOPMENT IN FINANCIAL SERVICES AND HIGHER EDUCATION.

In Proceedings of the Sixth International Conference on Enterprise Information Systems, pages 552-557

DOI: 10.5220/0002618305520557

 SciTePress

As far as process-driven approach is concerned,

Kimball and Ross (2002) state its importance,

Boehnlein and Ulbrich-vom-Ende (2000) describe a

method based on the Semantic Object Model

technique for process modelling, List et al. (2002)

explain their method, which is derived from the

„stakeholder driven approach” (Kueng, Wettstein &

List 2001).

All approaches have their strengths and

weaknesses. The strengths could be described as

follows: the user-driven approach is the best for

finding out the needs of potential users, the data-

driven approach is the most precisely and formal

described and the fastest in getting a data warehouse

data model, the process (or goal)-driven approach

helps to define the key business processes and their

characterizing measurements and leads to

developing a data model oriented at analyzing these

measurements, and thus can be the most suitable for

decision support in a particular business area.

Among the weaknesses of the approaches we could

mention the following: in the case of the user-driven

approach the users often do not have a clear

understanding of the Data Warehouse and also of the

business strategy and the organization’s goals. In the

data-driven approach the data models, which in

some cases are developed in a semi-automated way

from the source system data structures, may not

contain the necessary data for the analysis (e.g.

derived and aggregated data). If the process-driven

approach is used, the difficulties arise when the

business process measurements are to be defined.

In conclusion, we would like to say that more

than one approach has to be used in most cases, to

get a data model which would match the analytical

needs of an organization. Data warehouse

developers have to decide which approach to use as

the primary one in a particular project.

3 FINANCIAL SERVICES

A real Data Warehouse project in one of the

commercial banks of Latvia is taken as a basis for

this paper. This is a universal bank with about half a

million of clients, its own branch network and

relevant subsidiaries. The financial group deals with

all the standard financial products.

The main goal regarding for-profit company is

profit. Strategic company’s profitability depends on

tactical and operational goals (List et al. 2002, Jones

& George 2002).

3.1 Development Method and Basic

Approach

Our personal experience and related research have

proved that goal-driven (or process-driven) method

is the right one in for-profit organizations. In

comparison with data-driven or user-driven

methods, goal-driven method is provided to use in

an environment where business processes are

designed throughout the company and are combined

with business goals (List et al. 2002). Successful for-

profit companies usually correspond to that.

For Data Warehouse development the

incremental approach (Todman 2001) was evaluated

as the most appropriate, because of particular

advantages:

– pilot implementation;

– quick wins;

– prioritization;

3.2 Project Sponsor and Organisation

Usage of goal-driven development method together

with incremental approach usually leads to a

particular type of the project organizational

structure, which is very similar to the organization’s

one and that is nothing new for staff.

Therefore the Data Warehouse project should

have a supervisor and adviser, i.e. a project sponsor,

from the company’s top management. Not having a

real project sponsor in the top management is

mentioned as one of the ‘top mistakes to avoid’ in

many researches and publications (Kimball & Ross

2002).

As Data Warehouse projects usually cover

almost all of the company, it’s impossible for one

person to keep track of everything. Therefore Data

Warehouse project is organized in smaller sub-

projects (usually per Data Mart, business process or

similar to that). Every sequential incremental sub-

project supervises an expert from the business area

(owner of tactical goals).

3.3 Application Area

The main Data Warehouse application areas in

financial industry are data mining and decision

support functionality. Data mining is very common

for business directions, which are interesting in

analysis of history, forecasting and data correlation

issues. In our case the lending and overall clients’

history was the goal.

COMPARISON OF APPROACHES IN DATA WAREHOUSE DEVELOPMENT IN FINANCIAL SERVICES AND

HIGHER EDUCATION

553

3.4 Timeliness or Maturity

There is no point in having a list of clients or

products, if that information is one week old already.

Commercial data, especially in finance services, are

changing very fast and sometimes very significantly.

Some authors describe and some vendors say that

they provide ‘real-time’ Data Warehousing

solutions. We have to be very careful with definition

of ‘real-time’ and we would like to use a term ‘right-

time’ instead (Connor 2003).

3.5 End-User Involvement

Any company has top experts where key knowledge,

competence and experience can be found. The

company’s Data Warehouse and its content should

be reviewed very carefully under the guidance of

these people. The representative of the Data

Warehouse project (usually an IT person with good

business understanding) taking part in those

discussions should be an expert of the same level –

otherwise it could lead to the situation, when

business experts influence the process, outside their

expertise (even, if they believe they are IT experts

too). If it’s not the same persons, real end-users

obligatory should be involved in that project stages,

where maturity, end-user application features and

related issues are discussed.

3.6 Source and Type of Measures

Especially specific for financial institutions is a very

high number of measurements to analyze, majority

of them are derivative (like client profitability,

average turnovers)So, instead of putting attention on

data mapping attention to data transformation,

aggregation and interpretation should be paid.

Financial industry’s company may have a

comparatively high number of source systems

because of the wide range of their business activities

and because of the interest in the key measurements

of any other industry or common knowledge bases.

It’s all related to business opportunities and threats.

It’s very common, when one particular or set of

some business areas build separate Data Marts

Although there may be a lot of source systems,

they usually are similar. Those, which are related to

the business processes, are based on the granularity

of business object (client, product) and many

different dimensions. Those, which are related to the

external statistic information, usually are provided

by some state institutions or similar for-profit

institutions and are pretty similar by the content.

4 HIGHER EDUCATION

The user-driven approach is used in the development

of the data warehouse at the University of Latvia.

Education belongs to the non-profit sector. The

University of Latvia is a higher education

establishment with 30,000 students. Its business

processes include education, research, finance and

the university management - all areas are equally

important for the university’s successful functioning.

The top management is deeply interested in

gaining objective criteria to estimate these and to

support making new decisions. The administrative

director of the university is the main sponsor for the

data warehouse project, also other IT projects in the

university are initiated by top management. It is

difficult to choose the right priorities for data

warehouse development at the university. This is not

a profit-oriented business. Therefore, the usual goal

for data analysis - the profit is not the case.

However, money matters like the study fees and

other payments have to be analyzed.

The data sources for the data warehouse are the

Student Information System and the Finance

System, two Oracle databases, but they are not

integrated. The first one is developed in the

university; the second is a commercial product. This

is typical solution for many universities

In the data warehouse project of the University

of Latvia, we applied the user-driven approach. The

approach is based on the interviews.

In our methodology we used some ideas from

Kimball and Ross (2002), namely, how to organize

interviews. These ideas we supplemented with our

ideas, how to manage and use the gathered

information.

After discussions with the main project sponsor,

we defined the groups of interviewees. The

potential user groups are the following – the top

management, the department leaders and the deans

of faculties. The last group is the users whose

responsibility is data analysis, the employees from

the departments and the administrative staff in the

faculties.

The interview content was modified for each

group of users.

In the selection of the interview questions we

followed the principle – the priority is given to the

questions that find out the business objectives and

measurements. The questions in the interviews were

divided into two groups: „Business goals and

influence factors” and „Data analysis demands”. The

following questions from the interviews could serve

as examples:

– What are the goals of your department? What do

you want to achieve?

ICEIS 2004 - DATABASES AND INFORMATION SYSTEMS INTEGRATION

554

– What information is the most important in your

work?

In each particular interview the questions were

chosen from the question list, evaluated, if they are

appropriate for the business process analyzed and for

the responsible employee.

The answers are summarized in the following

matrix: one matrix dimension is „Interest scope”; the

second dimension is „Interviewees”. The cells of the

matrix contain the answers to the questions. The

interest scope is the group of similar answers. The

developer’s responsibility is to define these groups,

based on similarity of the answers.

The Table1 represents a fragment of the above

mentioned matrix. In the project we defined 19

scopes of interest. This number is too large for

making decisions on the Data Warehouse

components – Data Mart development priorities.

Therefore, we merged the interest scopes into

interest groups. For example, we merged „Students”

and „PhD students”. A new matrix with 11 interest

groups was made. The cells of the matrix contain 1,

if the answers in the first matrix exist. The Table2

illustrates the new matrix.

This matrix served as a basis for analyzing the

number of potential users in each interest group, we

also applied coefficients, to emphasize the

importance of the needs of a user or user group.

We used following system for the result analysis:

1 - for faculties, 2 - for top management and

departments, 1.5 - if the interviewee emphasized the

particular issue as of the major priority for them.

As showed the results of information processing

after interviews the most popular application areas

for potential data warehousing were statistical

analysis of different business measurements, the top

management also expressed the needs concerning

decision support.

The above mentioned interest groups were

modelled, using dimensional modelling techniques.

Some interest groups from the matrix were modelled

as data warehouse dimensions (the light grey

columns); some of the others did not have source

data (dark grey columns in the matrix). Five of the

Table 2: The matrix “Interest groups”–“Interviewees”

coefficients

Students and

PhDs

Employees

The budget

planning

The finance

resources

Equipments

Study programs

Projects

Administrative

documents

Foreign

communication

Audi

Other

2 Rector 1 1 1 1 1 1 1 1 1

2 Rector’s assistant 1 1 1 1 1 1 1 1

2 Chancellor 1 1 1.5 1 1

…

2 The planning department

manager

1 1 1 1 1

…

1 Dean of Faculty of Phys. and

Math.

1 1 1 1 1 1

1 Dean of Faculty of Pedagogy 1 1 1 1.5 1

…

38 34 9 29.5 24 16 14 8 8 4 13

Table1: The fragment of the matrix “Interest scope”–“Interviewees”.

Students Study programs Employees Projects

Rector’s

assistant

The expected and real

number of students

by faculties. The

number of graduates.

Study programs,

their number and

development

trends

The number of

professors, the list of

employees by

faculties, salaries

The number of

projects,

financing the

projects

Chancellor

The number of

students financed by

the state and full-

paying students

The study fee by

study programs

The salaries of the

employees, the

workload of the staff.

COMPARISON OF APPROACHES IN DATA WAREHOUSE DEVELOPMENT IN FINANCIAL SERVICES AND

HIGHER EDUCATION

555

groups were used to model the data warehouse

conceptual model. In this point the information from

the first matrix was used.

Based on the previous categorized information,

the potential Data Marts were modelled.

We started with one interest group and analyzed

in detail all answers in the first matrix, concerning

this group. The models include all necessary

attributes and facts, to answer the known users’

questions. Usually one interest group was

implemented with more than one dimensional

model.

For the conceptual modelling we used derived

ME/R notation (Sapia et al. 1998). The modification

concerns the attributes – the attribute group is

introduced, to improve the presentation quality of

the model.

The data warehouse was planned as a

combination of individual data marts with confirmed

dimensions. The implementation of the data

warehouse was planned as a small subprojects chain,

one data mart after other, because the sponsors

wanted to gain usable results as fast as possible, so

the financing for the next subprojects depends on the

subproject’s results. Therefore, the next step is the

development of the data warehouse architecture bus

matrix according to Kimball and Ross (2002).

For the detailed description of the dimensions we

used a table, which contains the descriptions of the

dimension attributes, the evaluation of

corresponding data source attribute data quality.

The table and the bus - matrix are additional

information sources for the decision about the

development priorities, because they help to estimate

the development time. But they are not important for

finding out the organization’s goals and

measurements.

The results of the system analysis were presented

to the project sponsors. The suggestions about

priorities were made based on number of potential

users for particular interest group (not for data mart),

the existence of data and their quality, the

complexity of data marts and number of common

dimensions for the 1

development stage.

The user-driven methodology was applied in the

project, and the first data mart developed. Our

experience proved the right decision in the early

stage of project, which approach to chose.

The weakness of the method, common for all

user-driven methods, is the unclear needs. The

information necessary for a particular user can

dominate the analysis of the organization’s needs.

But in the case of the universities the

involvement of the users in IT projects and

particularly in the data warehouse project is

essential, because the potential users are informed

and their needs are considered, when the models are

developed.

5 SUMMARY AND CONCLUSION

Before starting the comparison we expected very

different results because of the different business

areas and different approaches.

As we can see from the summary table, our

assumption regarding “common characteristics for

organizations of similar behavior and different for

those with opposite behavior” doesn’t come true.

Only few qualifications are different for the finance

company and the state financed higher education

institution

The differences are in the data sources and

business measurements, not the data warehouse

project organization, e.g. end-user involvement,

basic approach, project duration, project sponsor or

application area.

We can conclude then, there are no big

differences in Data Warehouse project development

in organizations with respectively different behavior.

Table 3: The Summary evaluation matrix.

Criteria Financial services Higher Education

Development Method Goal-driven User driven

Basic Approach Incremental Incremental

Project sponsor Top Management Top Management

Project organisation Short sub-projects Short sub-projects

Application Area Data mining, decision support Statistic, decision support

Maturity Near to Real-time (right-time) Some data seasonal (study

semester), some real-time

End-User involvement Key experts and users Key experts and users

Type of Measures Mostly financial

Qualitative (scoring, etc.)

Rating, statistics, financial

Number of Source systems Many Few

Type of Source systems Similar Different

ICEIS 2004 - DATABASES AND INFORMATION SYSTEMS INTEGRATION

556

We are satisfied with the results of our

comparison. But still, from our experience, we can

say that there are some differences, too. In our next

research we will look at how the project external

environment (like organization itself, for instance) or

similar criteria (Boehm & Turner 2003) affects

characteristic of Data Warehouse projects.

REFERENCES

Boehm, B. & Turner, R., 2003. Balancing Agility and

Discipline, A Guide for the Perplexed, SERA 2003

Keynote Address Research Review San Francisco,

CA. Retrieved October, 2003, from http://acis.lsfk.org/

Support, Prentice Hall, New Jersey.

Boehnlein, M. & Ulbrich-vom-Ende, A., 1999. Deriving

Initial Data Warehouse Structures from the

Conceptual Data Models of the Underlying

Operational Information Systems, in Proceedings of

the ACM Second International Workshop on Data

Warehousing and OLAP, DOLAP'1999, ACM, pp. 15-

21.

Boehnlein, M. & Ulbrich-vom-Ende, A., 2000. Business

Process Oriented Development of Data Warehouse

Structures. In Proceedings of Data Warehousing 2000,

Physica Verlag.

Connor, M., 2003. A Practical View of Real-Time

Warehousing. Business Intelligence Journal, Spring

2003, vol. 8, No2. Retrieved September, 2003, from

http://www.dw-

institute.com/research/display.asp?id=6718

Frolick, M. N. & Lindsay, K., 2003. Critical Factors for

Data Warehouse Failure. Business Intelligence

Journal, Winter 2003, vol. 8, No 1. Retrieved

September, 2003, from http://www.dw-

institute.com/research/display.asp?id=6592

Golfarelli, M., Maio, D. & Rizzi, S., 1998. Conceptual

Design of Data Warehouses from E/R Schemes, in

Proceedings of the 31st Hawaii International

Conference on System Sciences HICSS'1998, IEEE,

pp. 334-343.

Jones, G. R. & George, J. M., 2002. Contemporary

Management, McGraw-Hill/Irwin, 3

edition.

Kimball, R. & Ross, M., 2002. The Data Warehouse

Toolkit, John Wiley & Sons, 2

edition.

Kueng, P., Wettstein, Th. & List, B., 2001. A Holistic

Process Performance Analysis through a Process Data

Warehouse. In Proceedings of the American

Conference on Information Systems.

List, B., Bruckner, R. M., Machaczek, K. & Schiefer, J.,

2002. A Comparison of Data Warehouse Development

Methodologies Case Study of the Process Warehouse.

In DEXA 2002, LNCS 2453, Springer-Verlag

Heidelberg, pp. 203–215.

Phipps, C. & Davis, K. C., 2002. Automating data

warehouse conceptual schema design and evaluation,

in Design and Management of Data Warehouses 2002,

Proceedings of the 4th Intl. Workshop DMDW'2002.

CEUR Workshop Proceedings, vol. 58, Technical

University of Aachen (RWTH), pp. 23-32.

Poe, V., 1996. Building a Data Warehouse for Decision

Support, Prentice Hall, New Jersey.

Sapia, C., Blaschka, M., Höfling, G. & Dinter, B., 1998.

Extending the E/R Model for the Multidimensional

Paradigm, in Proceedings of Advances in Database

Technologies, ER '98 Workshops on Data

Warehousing and Data Mining, Mobile Data Access,

and Collaborative Work Support and Spatio-Temporal

Data Management, LNCS vol. 1552, Springer.

Todman, C., 2001. Designing a Data Warehouse:

Supporting Customer Relationship Management,

Prentice Hall PTR.

Westerman, P., 2001. Data Warehousing using the Wal-

Mart Model, Morgan Kaufmann.

COMPARISON OF APPROACHES IN DATA WAREHOUSE DEVELOPMENT IN FINANCIAL SERVICES AND

HIGHER EDUCATION

557