Data Quality Management

Ion Ivan
Academy of Economic Studies, Bucharest, Romania

1.	Introduction

Data can be regarded as a  final product. They have the same characteristics as ordinary final products (such as foods, automobiles, computers etc.). There are, of course, certain differences. The presence or lack of data is not visible, as for products characterised by volume, weight or function in execution.
The purpose of this article is to reveal certain aspects of data quality and data management.
The effects of data quality are not directly perceived by data users. Data quality is important for the producers of data as long as there are rules and restrictions for obtaining and transmiting the data. The effects of poor data quality are essential.
This article shows aspects which modify the behaviour of the communication process parteners.
Data quality changes its place in today’s world due to the very high expenses involved in the communication process. Even more, the price of information has been growing exponentially.
The development of data based processes, a society bound by computers require data to be diverse, complete and operational. Data is useful if it has quality characteristics. Data quality is created in the processes preceding communication. In today’s conditions, getting non-quality data can have serious consequences which require the development of process oriented data quality management

2.	Data volume

The data volume represents a quantitative type characteristic. Data is written in documents, is comunicated or directly perceived. The data volume measures the quantity of transferred data.
Let’s consider an alphabet consisting in symbols. Using the alphabet we construct words. Using the words we construct texts, which can be structured. The generality of the alphabet determines the generality of the text.
If the alphabet contains ciphres, the words are numbers, and the files with numeric data or tables with accountancy data are examples of texts developed using this alphabet.
If the alphabet contains basic movements as symbols, some dancers can develop a language. In this case, a dance can be considered a text.
Linking the symbols in the alphabet has the maximum degree of flexibility.
The data volume is a way to evaluate the length of the text. This is given by the number of symbols used in the text.
In all situations there are symbols defined as separators (a blank for the alphabet of letters and ciphres, black for the alphabet of colors, silence for the alphabet of sounds).
The data volume is mesured as length using the communication time.
For books, the data volume is given by the number of pages.
As for accountancy applications, the data volume is given by the number of invoices or bills.
The data volume for computer applications depends on the number of transactions. Operating systems measure the data volume as file lengths, in number of bytes.

3.	Data quality characteristics

As all products, data are endowed with quality characteristics.
The Completitude is a quality characteristic that shows the existence of all structural components.
The Fiability is a characteristic showing that the data proves to be useful and it helps to achieve the 
purpose for which they are generated.
The Corectitude shows the correspondence between the text of the real world and the text generated 
through the data language.
The Maintenance shows the ability to include all modifications that appear in time, both on a structural 
level as on the levels of data.
The Usability shows the existence of a possibility to use the data in order to achieve the defined 
purpose.
The Accessability is the quality characteristic that diferentiates data generators and the distance 
between these generators and the users.
The Homogeneity involves references to elements of the same type, expressed in the same measuring
units. Data homogeneity involves a dimensional analysis. Homogeneous data is the subject of direct aggregation.
Non-homogeneous data can become homogeneous by applying certain transformations or by using
homogeneity coefficients.

4.	Data metrics

We define indicators to evaluate each data quality characteristic.
The most frequent form for an indicator is:

where:

A is the number of occurences of the quality characteristic;
B is the total number of considered records..

We use models to evaluate quality characteristics,as follows:

I = f(a, b, …)

where:

f ( ) is an analitic function which aggregates independent variables;
a, b, … are independent variables, corresponding to the influence factors of quality characteristics.

In [   ] there are developed models to evaluate quality characteristics.

5.	Building data quality

Data quality depends in the first place on the people who generate data and in the second place it 
depends on the channels used to send the data. The processes of producing and using the data also influence the data quality.
A special place in the design and development of a computer system is taken by the involved 
personnel. Managing the data quality involves building this quality. Goals must be defined in the first place, then effective ways of achieving them.
Data involves primary documents. As processes take place data is recorded as a way to reflect them. The primary documents have their own course. Recording the document and using the data to take decisions are steps that define the requirements of data quality management.
We establish the required personnel qualifications, we define minimal lines of work, ways of using the words from the vocabulary.
The problems of filling in a form or using a form require the definition of a procedure. First we identify the situations when the data is characterised by non-quality and we specify the factors that generate errors.
Training the personnel should take into consideration those aspects leading to diminish data errors.
Process quality regards all the steps taken by the data to reach the recipient from the generator. In a more general context, process quality involves the recipe used to produce data. 
The management of data quality requires the definition of exact procedures to fill in the documents, to verify the data, first as a quantitative and then from a qualitative point of view. The management of data quality is characterised by continuity and it has an active side. The quality is regarded as something developed in time and not just noticed.
The evolution of data quality is done in halfway steps and the activities specific to quality management have the role of making corrections leading to a quality increase.

6.	Conclusions

The data quality management is a new domain with a wide applicability in publicly used databases and
computer applications with a global character. The aggregation of a balance for a national economy involves the use of data quality from the balance of each individual economic agent. Such applications as “The tax on the global income” require designing forms and creating the database with at least five milion records with complete, correct and homogeneous data. Public databases (to inform or allocate resources) are operational only if they are the result of a data quality management.
	The subsequent periods of time will require more data quality management in the computer sciences.

Bibliography:

[IVAN99] I. Ivan, P. Sinioros, F. Simion, M. Popescu – Software Metrics, Inforec Publishing House, Bucharest 1997
[IVAN98] I. Ivan, O. Parlog, P. Oprea, Gh. Nosca – Data Metrics, Proceeding of the 1998 Conference on Information Quality, Massachusetts, 1998, pp. 215-231
[NOSCA98] Gh. Nosca, I. Ivan, A. Parlog – Data Quality Assurance, Quality Assurance Review, vol. 2, no. 8, 1998, pp. 8-14
[ORR98] K. Orr – Data Quality ans System Theory, CACM, vol. 41, no. 2, 1998, pp 66-71
[VERM98] Bas H. P. J. Vermeer – Measuring the Impact on the Data Quality on an EDI Enabled Interorganizational Business Process, Proceeding of the 1998 Conference on Information Quality, Massachusetts, 1998, pp. 202-214