BUSINESS PROCESS MODELING TOWARDS

DATA QUALITY ASSURANCE

An Organizational Engineering Approach

Hugo Bringel

†

, Artur Caetano

*†

, José Tribolet

*†

†CEO - Organizational Engineering Center, INOV, INESC Inovação.

*Department of Information Systems and Computer Science, Instituto Superior Técnico, Technical University of Lisbon.

Surface Mail Address: INESC, Rua Alves Redol 9, 1000-029 Lisboa, Portugal.

Keywords: Data Quality, Business Process Modeling, Object-Oriented Modeling, UML

Abstract: Data is handled everyday by information systems and its inherent quality is a fundamental aspect to opera-

tional and suppo

rt business activities. However, inappropriate data quality may lead to economic and social

problems within the organizational context. This paper addresses how to syntactically and semantically en-

sure data quality at information entity level. To do so, we define a business process-modeling pattern for de-

scribing the features required to ensure and validate business object data using a conceptual data quality at-

tribute model. This pattern makes use of object-oriented concepts such as inheritance and traceability and is

described as an extension to the Unified Modeling Language. A case study is presented to exemplify the

proposed concepts.

1 INTRODUCTION

Assuring data quality is a complex process, in

which the tradeoff between cost and quality de-

pends on the application context and on the organi-

zation requirements.

Incorrect, incomplete or non-timely data may

cause econom

ic and social problems in organiza-

tions, which very often, only react to its conse-

quences, rather then having a proactive attitude.

Another common issue is that data quality is

not underst

ood in a process-centric, cross-

departmental perspective, but in a functional view,

as a duty or competence of the information sys-

tems department.

Problems with data quality occur widely on

nctional organizations, where specific databases

are created, forming information islands that con-

stitute one of the mainstream causes of lack of

consistency and of coherence of corporate data.

There are several approaches, but they are fo-

cuse

d at low-level data analysis, based on comput-

ing algorithms treating implicitly data quality at

the DBMS level, or they are mainly focused on

quality management systems, based on ISO stan-

dards. These approaches are however not sufficient

from the data consumer’s point of view, (Laudon,

1986).

Regarding this, we propose a proactive ap-

roach towards guarantying data quality, consider-

ing in our proposal two levels of granularity. First,

at high level when using a data quality business

processes pattern, focused on activities execution

upon information entities, consumed or produced

by business processes, as discussed in Section 2.1.

Second, at low level when assuming a data quality

attribute model, as discussed in Section 2.2.

The remaining of this paper is structured as fol-

ows: In Section 2, we define the problem and pro-

pose our approach to its resolution. In Section 3,

we illustrate an example of business process and

data quality modeling and finally in Section 4 we

present some conclusions.

2 PROPOSAL

The problem we intend to solve consists in how to

guarantee that attribute values of a business entity

are correct and correspond to the defined seman-

tics. To define data quality requirements, we need

multiple dimensions, depending on specific needs

from different organizational levels. For instance,

565

Bringel H., Caetano A. and Tribolet J. (2004).

BUSINESS PROCESS MODELING TOWARDS DATA QUALITY ASSURANCE - An Organizational Engineering Approach.

In Proceedings of the Sixth International Conference on Enterprise Information Systems, pages 565-568

DOI: 10.5220/0002649305650568

 SciTePress

the sales division may need inventory data to be

accurate and complete, while management may

need information that gathers other data quality

dimensions, like reputation or timeliness of data

for the decision making process.

2.1 Data Quality Modeling

In the past, data quality was often defined as non-

conformance to requirements, (Crosby, 1984).

However, just as there are several dimensions of

product quality, such as conformance, durability or

performance, in any industrial domain, data quality

embraces specific characteristics, denominated

quality dimensions.

In addition, data from several sources may have

common dimensions in which quality may be

measured. Even if data quality modeling does not

depend on business process modeling, the data

quality attributes should be tagged to business ob-

jects during entity/resource identification and defi-

nition.

Concluding, data quality is a multi-dimensional

and hierarchical concept, (Wang, 1995).

In our perspective, it is not possible to manage

different data quality perspectives of each data user

using an entity-relationship model. To accomplish

it, we need to model the several dimensions of data

quality requirements, proposing instead, the use of

role modeling, within the object-oriented para-

digm.

We shall use, in data quality modeling, two

concepts, namely (1) quality parameters and (2)

quality indicators, forming the quality attribute,

(Wang, 1995). Quality parameters relate with qual-

ity dimensions (qualitative aspects) while quality

indicators relate with measurable attributes (quan-

titative aspects, normally from physical goods).

For example, as quality parameters, we need to

attain confidence and timeliness, and as quality

indicators, we can decompose on data source and

creation date. The quality parameters and indica-

tors form the data quality attribute. This aggrega-

tion result in a hierarchical attribute tree that helps

defining the data quality requirements. This quality

attributes model provide a comprehension base to

understand the characteristics that defines data

quality.

2.2 Business Process Modeling Ap-

proach towards Data Quality

In this paper we use an object-oriented business

process modeling framework which is used to

model the interaction between process activities,

business goals, resources and information systems

(Vasconcelos, 2001).

To solve the problem stated above, we propose

the use of a business process pattern to ensure data

quality in an organization, making use of defining

data quality attributes upon information entities

considering different meanings on each business

perspective.

In this paper, we introduce the notion of Terti-

ary Organizational Processes. Tertiary processes

represent activities that cross over operational and

support processes planes, interacting within active

business entities, with the purpose of achieving

some special purpose objectives. The modeling of

tertiary processes is facilitated by the introduction

of the concept of an “orthogonal” plane, relative to

the operational and support business process plans.

We therefore consider three layers in which lies

the organization modeling of any business process.

Figure 1 – Process diagram on business (core and

support) processes and data quality processes interaction

Figure 1, depicts the main conceptual structure

and layer separation we use on our approach.

The first layer models the operational business

process activities, whereas the third layer models

the support process activities. The middle layer

deals with data modeling and data quality evalua-

tion (and assurance) activities, where designed

classes of objects “resources” are instantiated from

different perspectives, the quality (tertiary) proc-

esses versus operational or support processes.

2.2.1 The Resource Stereotype

In business process modeling, resources are objects

within the business that processes manipulate. The

resource types are represented as classes while

resource instances are represented as objects. The

business object in discussion is the resource stereo-

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type, which we focus in the informational entity as

specialization of a resource class. The informa-

tional entity is modeled at two different levels:

business process and class level.

The business process level is where the infor-

mation entity is modeled as a resource stereotype

and where it interacts with a process. The class

level is where data is modeled in object-oriented

classes, and where its attributes are specified.

There is an inherent complexity at this level, be-

cause, for the same data, we have overlapping and

crosscutting requirements (expressed by attributes

and methods) between core business processes,

support business processes and tertiary processes –

such as the data quality processes.

We shall also consider predefined quality

classes, with the data quality attributes of con-

ceived business objects. These classes compose the

quality attributes into the information entity.

We are thus extending the concept of informa-

tion entity by using new attributes devoted to qual-

ity, beyond the usual and basic pre-defined data

attributes.

2.2.2 Data Quality Process Pattern

We propose the use of a data quality pattern at

business process level, based on (English 1999),

which integrates best practices from the quality

management universe. This pattern consists on a

business process model that can be reused through

adaptation in specific organizational scenarios.

Figure 2 depicts the flow between pattern’s top

activities. It is important to note that this pattern is

composed of tertiary process and applies to an or-

thogonal organization plan, as previously discussed

on Section 2.1.4

Figure 2 – A data quality business processes pattern

The “Quality and Architecture Definition” and

“Evaluate Data Quality” activities focus on the

evaluation of data quality, describing how the or-

ganization deals with data quality and what proc-

esses and resources are involved. The interaction

between entities or resources, either consumed or

produced by these activities, is represented at

lower granularity level to make possible the activi-

ties autonomy and promote this pattern reuse.

Two processes may interact with shared infor-

mation entities; however, to capture the informa-

tion entity interaction in different contexts, we

propose using role-modeling concepts (v. Section

2.3).

2.3 Role-based Modeling

A role represents some unit of responsibility or

behavior. Actors play different roles while per-

forming business process activities. Roles can be

considered types in the sense they describe the

behavior that is carried out by an instance of that

role by a specific actor. Therefore, there may be

multiple instances of the same role when a process

is enacted. A single actor may also play multiple

roles. Role models can be instantiated, aggregated

and generalized.

By understanding the behavior of processes,

we are providing the means to reuse it and adapt its

organizational concepts.

2.3.1 Role modeling on data quality

Since role modeling allows the behavior of re-

sources to be clearly separated and identified, we

can have different contexts of data quality as at-

tributes of class that represents a business object.

Resources are specialized so that its attributes and

methods allow handling its quality features. Proc-

esses concerning quality attributes instantiate pre-

defined classes and set values to its quality attrib-

utes. The quality attributes, using predefined com-

binations of quality parameters and indicators, al-

low judging the data quality.

This approach leads to a better understanding

and confidence in data since quality information is

kept within a resource (informational entity type),

which facilitates the data quality evaluation proc-

ess.

3 CASE STUDY

This case study results from a research project on a

real organization. This scenario illustrates the

business processes of inbound logistics in a large

warehouse. The targeted company handles an aver-

age of 22.000 products and performs a few dozens

of daily inbound transactions.

The process starts when the materials arrive at

the warehouse. The process activities start with the

“Materials Checking”, “Materials Unloading”,

“Data Input” and ends with the “Material Storing”

in the warehouse facility.

BUSINESS PROCESS MODELING TOWARDS DATA QUALITY: A ORGANIZATIONAL ENGINEERING

APPROACH

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Figure 3 – Data quality evaluation using role modeling

Figure 3 depicts how to ensure data quality at

business process level making use of role model-

ing.

Regarding the “Data Quality Evaluation” (Fig-

ure 2) sub-process, it is here (Figure 3) represented

as “Data Quality Process”, while the “Core Proc-

ess” represents a logistics operational business

process.

An oval represents the role associated to a

class. It is a shorthand modeling for aggregating

the role class with the base class.

In this example, “Material” corresponds to the

“Material Set” used in the “Core Process”. “Data

Users” are the actors, which specify data quality

requirements as part of a “Data Quality Definition”

process. In this process, they define their respec-

tive data quality requirements, for instance: as Q1=

{Timeliness, Completeness}; Q2= {Accuracy, Cur-

rency} for the “Material Set”. The “Core Process”

produces updated “Material Data” informational

entity, later audited using of a sampling set of ma-

terials. A “Data Quality Team” acts in the data

quality requirements auditing. The “Data Quality

Process” result is a “Quality Report” for later

analysis. The overall process goal is to “Evaluate

Data Quality” generated by “Core Process”.

In this way, we have ensured data quality, using

role modeling to manage different context and se-

mantics of data quality, combined with the core or

support business processes, which have data qual-

ity support in their informational entities.

4 CONCLUSIONS

This paper proposes a data quality pattern to model

the data quality intrinsic to business processes.

This pattern can be used for data improvement on

any organization, and makes use of a set of busi-

ness processes to syntactically validate the data

according to a model, which depicts qualitative

and quantitative data quality attributes. Role mod-

eling is used to manage different quality contexts

quality from different data users, assuring data

quality at semantic level.

This contribution addresses data quality from a

organizational engineering perspective using busi-

ness process modeling, leveraging data confidence

and promoting continuous data quality improve-

ment.

REFERENCES

Crosby, P.B., 1984. Data Quality Without Tears.

McGraw-Hill.

English, L.P., 1999. Improving Data Warehouse and

Business Information Quality, methods for reducing

costs and increasing profits. Wiley.

Eriksson, H.E., Penker, M., 2000. Business Modeling

with UML: Business Patterns at Work. OMG Press.

Laudon, K.C., 1986. Data Quality and Due Process in

Large Interorganizational Record Systems.

Communications of the ACM, 4-11.

Vasconcelos,A, Caetano,A., Neves,J., Sinogas, P.,

Mendes R., Tribolet, J., 2001. A Framework for

Modeling Strategy, Business Processes and Informa-

tion Systems. 5

IEEE International Conference on

Enterprise Distributed Object Computing. IEEE

Press. Seattle, USA.

Wang, R.Y., Reddy, M.P., Kon, H.B., 1995. Toward

quality data: An attribute-based approach. Decision

Support Systems 13, pp.349-372.

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