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WHITE PAPER: DQ AUDIT
WHITE PAPER /
Uniserv Data Quality Audit:
Does the quality of company data meet the requirements of data
consumers?
In order to implement suitable measures for improving quality of
data, it should be ensured, that the actions (a) meet the require-
ments of data consumers and (b) increase the efficiency of the
company. The Data Quality Audit is used to determine the status
quo of the data quality.
In this respect, not only the data itself is at the focus, but, more
importantly, the requirements of data consumers with regard to the
data are considered and the data and information creation proc-
esses examined.
The Data Quality Audit has a modular structure, and each module
has its own area of focus. Possible concepts for optimization of
the data quality can be prepared in conjunction with the specialist
departments.
All company and product names and logos used in this document are trade names and/or registered trademarks of the
respective companies.
Page 2© Uniserv GmbH / +49 7231 936-1000 / All rights reserved.
WHITE PAPER: DQ AUDIT
Contents
The quality of data –
what lies behind it?
“Single View of Customer”
versus “Single View of Data”
The quality of
company master data
Status quo of the quality of the company data:
the Uniserv Data Quality Audit can help
Looking ahead
List of references
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Page 3© Uniserv GmbH / +49 7231 936-1000 / All rights reserved.
WHITE PAPER: DQ AUDIT
The quality of data –
what lies behind it?
Land, work and capital are spoken of as conven-
tional production factors in classic economics.
However, the term „knowledge“ or „information“
is increasingly referred to as one of the important
production factors. (Wikipedia, Bauer & Günzel
2009). In this context, the high importance of infor-
mation derived from data becomes apparent.
Another line of thought relates the production of
information directly to the manufacture of any
desired product (Ballou et al. 1998). These con-
siderations were preceded by the concept of Total
Quality Management (TQM). This concentrates on
the maximum satisfaction of the requirements for a
product and refers to all the processes and depart-
ments involved in the production and therefore the
entire company (Wikipedia).
The correlation between the quality of data and
products can therefore be expressed very simply
as follows: if „information“ is considered as a
product, certain requirements of the users of this
product can also be defined and provided as
specifications in production processes.
However, how can the requirements for information
and knowledge be defined? How can it be veri-
fied that the standards specified in the production
process are complied with?
If the production of a car is visualized, it is assumed
that the end product has four wheels and the doors
and windows actually open. The engine fits in the
body and the safety standards comply with the
requirements. All these and more specifications
were repeatedly checked during the actual pro-
duction process and therefore correspond to the
previously specified requirements of the subsequent
driver of the car. If defects occur during the produc-
tion, it is immediately stopped and fault tracing
and correction are started.
HOWEVER, HOW CAN THE
REQUIREMENTS FOR INFORMATION
AND KNOWLEDGE BE DEFINED?
HOW CAN IT BE VERIFIED THAT THE
STANDARDS SPECIFIED IN THE PRODUC-
TION PROCESS ARE COMPLIED WITH?
Page 4© Uniserv GmbH / +49 7231 936-1000 / All rights reserved.
WHITE PAPER: DQ AUDIT
The production of information should not be
any different.
THE FOLLOWING EXAMPLE HELPS TO PROVIDE A BETTER
UNDERSTANDING OF THE TERMS DATA QUALITY, INFOR-
MATION QUALITY AND KNOWLEDGE:
–– 0100010 + 01001101 + 01010111 -
-> DATA
–– char(66) + char(77) + char(87)
--> INFORMATION?
–– BMW --> Three letters! -
-> INFORMATION?
–– BMW --> Binary Moving Window
--> INFORMATION?
–– BMW --> Beer with Water
--> INFORMATION?
–– BMW --> Bayrische Motorenwerke
--> INFORMATION?
–– Bayrische Motorenwerke
--> KNOWLEDGE!
It becomes clear that data is at the beginning of
the chain. Information is generated from the data.
But the respective background information is
required to provide the meaning of this information
and to enable the information to be put in the right
context. In the end, the correct conclusions can
only be drawn and new knowledge therefore gen-
erated if the data and information at the beginning
of this chain are correct.
Several authors have taken a close look at this
subject, in order to give the term „data quality“
a more tangible form. To begin with, the term
„quality“ is concerned. Derived from the standard
EN ISO 9000:2005, quality states the degree
to which a product (goods or service) complies
with the existing requirements (Wikipedia). This
means that the quality can be good or bad if it
meets the requirements of the user or not.
In their studies, Wang and Strong (1996) asked
consumers of data to define the properties of
good quality data. The German Association for
Information and Data Quality (DGIQ) took up this
idea and described the categories and dimensions
of data quality in an easy to understand manner
(Rohweder et al. 2008).
IT BECOMES CLEAR THAT DATA IS AT THE
BEGINNING OF THE CHAIN. INFORMA-
TION IS GENERATED FROM THE DATA.
Page 5© Uniserv GmbH / +49 7231 936-1000 / All rights reserved.
WHITE PAPER: DQ AUDIT
According to this, the quality of data can be
divided into four categories and 15 dimensions.
Category	 Dimension
System		 Accessibility
		 Editability
Content	 Highly regarded
		 Freedom from errors
		 Objectivity
		 Credibility
Display	 Comprehensibility
		 Clarity
		 Standard display
		 Unambiguous interpretability
Use		 Up-to-dateness
		 Added value
		 Completeness
		 Reasonable extent
		 Relevance
Overview of the categories and dimensions of data quality
(after Rohweder et al. 2008)
If each of the dimensions stated here is con-
sidered to be „good“, the data quality can be
assumed to be optimum. Many of the stated
dimensions cannot be evaluated by the system
with simple performance indicators, instead the
consumers of the respective data always have to
decide whether the quality of the data is good.
Larry English (1999) has similar basic approach-
es but makes a fundamental distinction between
the quality of the data contents (correctness of
the data) and the pragmatic quality of the data
(data presentation).
It is inherent to both basic approaches that the
focus of attention is on the data consumer who
receives the data, so that can he carry out his
tasks in a satisfactory manner. Or to express it
in the words of Wang & Strong (1996), data
quality is defined as „data that are fit for use by
data consumers“.
IT IS INHERENT TO BOTH BASIC
APPROACHES THAT THE FOCUS OF
ATTENTION IS ON THE DATA CONSUMER
WHO RECEIVES THE DATA, SO THAT
CAN HE CARRY OUT HIS TASKS IN A
SATISFACTORY MANNER.
Page 6© Uniserv GmbH / +49 7231 936-1000 / All rights reserved.
WHITE PAPER: DQ AUDIT
„Single View of Customer“
versus „Single View of Data“
It is generally assumed that the „Single View of
Customer“ represents one of the highest levels of
data quality in the consideration of customer master
data. In the simplest case, the term „Single View of
Customer“ refers to a duplicate-free customer mas-
ter database. With regard to the above stated data
quality dimensions, the „unambiguous interpretabil-
ity“ has been considered here. Nevertheless, the
„Single View of Customer“ should not be equated
with the „Single View of Data“.
The illustration below makes the difference clear:
whereas the „Single View of Customer“ puts the
data itself at the focus, the „Single View of Data“
refers to the different requirements of the various
company departments for the data. If each individ-
ual user group about were asked the requirements
for the data, there would certainly be different
answers or lists of shortcomings.
There is therefore no „Single View of Data“ if
the requirements for the data vary. A „Differing
View of Data“ should be referred to instead.
  „Single View of Customer“ vs. „Single View of Data“ A
„Differing View of Data“ should be referred to in a company with
different departments which have different requirements for the
customer master data.
WHEREAS THE „SINGLE VIEW OF
CUSTOMER“ PUTS THE DATA ITSELF AT
THE FOCUS, THE „SINGLE VIEW OF
DATA“ REFERS TO THE DIFFERENT
REQUIREMENTS OF THE VARIOUS
COMPANY DEPARTMENTS FOR THE DATA.
MASTER DATA
Marketing
Sales
Finance
SupportDevelopment
Page 7© Uniserv GmbH / +49 7231 936-1000 / All rights reserved.
WHITE PAPER: DQ AUDIT
The quality of company
master data
There are many indications of possible data qual-
ity problems, particularly in the case of customer
master data.
THE FOLLOWING ARE MENTIONED HERE BY WAY OF
EXAMPLE:
–– There is a high proportion of returns in mailing
campaigns because of undeliverable address-
es
–– Customers complain because they receive
advertising material several times
–– Invoices are not paid, because they never
arrived on account of an incorrect address
–– Sales and marketing forecast analyses prove
to be unreliable, since the prospects of suc-
cess were booked to duplicates of different
prospective customers
–– Customers say that they are dissatisfied
with the support, because the employees
take too long to find all the relevant data in
the system
However, the phenomena described above only
concern the initial or most obvious symptoms of
poor data quality. Several fundamental require-
ments for customer master data can be derived
from this.
The postal address should be correct and every
customer should only be represented in the customer
master database once (points 1 to 4).
Furthermore, all the relevant data should be avail-
able to the personnel (point 5). The list of require-
ments for customer master data could probably be
extended indefinitely, but in the end, it would be
discovered that the requirements for the data are
different for each department.
BUT IN THE END, IT WOULD BE
DISCOVERED THAT THE REQUIREMENTS
FOR THE DATA ARE DIFFERENT FOR
EACH DEPARTMENT.
Page 8© Uniserv GmbH / +49 7231 936-1000 / All rights reserved.
WHITE PAPER: DQ AUDIT
For example, the marketing department attaches
high importance to a correct address for mailing
campaigns, whereas the staff in customer support
depend on the up-to-dateness and completeness
of the respective customer products, which are
displayed in an clearly arranged manner. The qual-
ity of the data can therefore only be assessed as
good or bad by comparing it with the requirements
of the respective data consumers.
Many of the requirements for data can be auto-
matically tested using appropriate analysis soft-
ware. Some of the deficiencies can also be cor-
rected in short-term one-off cleansing operations.
However, even if the above stated symptoms
are brought under control with cleansing opera-
tions, the reason why the quality of the data is
poor in the first place has neither been found nor
excluded. It has not yet been guaranteed that the
cleansing operation meets the requirements of all
the data consumers.
A Data Quality Audit should be carried out, in
order to be able to make a statement about the
current status quo of the quality of the company
data. The respective data is not only analyzed by
means of software in the audit, but, more impor-
tantly, the requirements of the data consumers are
considered.
Not until the results of the audit are known can
statements actually be made about which of the
company data meets the requirements of the
data users and which does not. The “perceived”
status of the quality of the data can be verified
(or refuted) with objective numbers. Furthermore,
appropriate activities for a long-term improve-
ment in the data quality can be considered.
A DATA QUALITY AUDIT SHOULD BE
CARRIED OUT, IN ORDER TO BE ABLE TO
MAKE A STATEMENT ABOUT THE CURRENT
STATUS QUO OF THE QUALITY OF THE
COMPANY DATA.
Page 9© Uniserv GmbH / +49 7231 936-1000 / All rights reserved.
WHITE PAPER: DQ AUDIT
Agreement must be reached about which criteria
the data quality should be measured against,
in order to carry out an appropriate assessment
of the quality of company data. Many of the
requirements can be checked by means of suit-
able analysis tools. The data consumers must be
asked about their requirements with regard to
other qualities. Finally, the creation process of
the „information“ product should also be carefully
considered. There should be clarity about who
requires which data for what purpose.
Uniserv GmbH offers a comprehensive Data
Quality Audit, in order to be able to answer
these questions and objectively assess the sta-
tus quo of the quality of the data. The Data
Quality Audit has a modular structure, the mod-
ules are based on each other.
EACH MODULE HAS ITS MAIN AREA OF FOCUS ON ONE
OF THE POINTS MENTIONED ABOVE:
–– Requirements for the data and their compliance
which can be verified by means of analysis
software. Data quality dimensions such as com-
pleteness or freedom from errors are a major
concern here.
–– Requirements for the data and their compli-
ance, about which the data consumers can
provide information. Data quality dimensions
such as comprehensibility or clarity are verified
here. The data consumers can also submit their
assessments of the credibility or the reputation
of the data. The data consumers can provide
valuable information about the editability or the
accessibility of the data.
–– Analysis of the data / information creation
processes, in order to be able to identify any
weak points. A fundamental understanding
of the creation history is important, since the
data creation processes frequently go through
many individual stages such as different soft-
ware applications and individual processes
which in turn concern different business areas.
Knowledge of the processes plays an important
role if long-term measures for optimization of
the data quality are to be specified.
Status quo of the quality of the
company data: the Uniserv Data
Quality Audit can help
UNISERV GMBH OFFERS A COMPREHEN-
SIVE DATA QUALITY AUDIT, IN ORDER TO
BE ABLE TO ANSWER THESE QUESTIONS
AND OBJECTIVELY ASSESS THE STATUS
QUO OF THE QUALITY OF THE DATA.
Page 10© Uniserv GmbH / +49 7231 936-1000 / All rights reserved.
WHITE PAPER: DQ AUDIT
MODULE 1: DATA QUALITY CHECK
The Data Quality check provides an initial view
of the customer master data of a company. In
this phase, a representative extract of the data
is analyzed by means of the Data Quality Batch
Suite. In this respect, particular importance is
attached to the completeness and the presence
of the name elements. The postal correctness of
the address elements is verified and a duplicate
check is carried out.
The following requirements for the data are
assumed in the Data Quality Check:
–– All the „must“ fields of every data record are filled
–– The address elements correspond to a valid
address and are therefore correct
–– The „Single View of Customer“ applies, i.e.
the data extract is duplicate-free, or so-called
„desired“ duplicates are marked
The results of the Data Quality Check are sub-
sequently presented and made available to the
customer.
THE UNISERV DATA QUALITY AUDIT IS THEREFORE
DIVIDED INTO THREE MODULES:
DATA QUALITY CHECK – TECHNICAL DETAILS
The following are required:
–– A data file, ideally in the delimiter format
–– Definition and meaning of the headers
–– Definition of any keys
–– Definition of any value ranges
–– Character coding: UTF-8 or ISO-Latin 1
–– All addresses come from one country
–– Maximum of 100,000 addresses
Page 11© Uniserv GmbH / +49 7231 936-1000 / All rights reserved.
WHITE PAPER: DQ AUDIT
MODULE 2: DATA QUALITY ANALYSIS
The Data Quality Check in Module 1 primarily
validates an extract from the customer master
data in a relatively simple process. Particular
importance is attached to the name elements and
the address elements.
The Data Quality Analysis goes a big step further.
The entirety of the company data can be consid-
ered here. Very specific data, such as telephone
numbers, customer sales, persistence, attached
data concerning other transactions, etc., can be
checked here as required. Compliance with spe-
cific business and plausibility rules can also be
verified. If required, the customer master data can
even be checked against sanction lists at this point.
(A check of the in-house customer master data
against the sanction lists is generally recommend-
ed, in order to prevent contravention of the relevant
anti-terrorism regulations. Details can be found in
the White Paper on Compliance.)
Specific requirements for the data to be validated
are identified in an opening workshop with the
heads of the specialist departments concerned.
A comparison of the technical analyses and the
evaluations of the workshop will indicate the extent
to which the requirements defined by the specialist
departments correspond with the actual and the
„perceived“ quality level.
After the analyses and evaluations have been
completed, the results are presented in a clos-
ing workshop. It is recommended that the heads
of all the specialist departments concerned are
invited, in order to take account of the „Differing
View of Data“.
If any measures for optimization of the data qual-
ity are to be adopted, it is indispensable that the
consumers of the data are included in the decision-
making process. Only in this way will the imple-
mentation of the measures be widely accepted.
Needless to say, the results of the Data Quality
Analysis are also provided in writing.
THE DATA QUALITY ANALYSIS
GOES A BIG STEP FURTHER.
THE ENTIRETY OF THE COMPANY
DATA CAN BE CONSIDERED HERE.
DATA QUALITY ANALYSIS – TECHNICAL AND
ORGANIZATIONAL DETAILS
The following are required:
–– Several files or databases
–– Description of the meta data:
––
––
––
–– Contact persons from the various departments
– Definition and meaning of the headers
– Definition of any keys
– Definition of any value ranges
– Character coding: UTF-8 or ISO-Latin 1
– Information about the business and/or plausibility
rules to be verified
Page 12© Uniserv GmbH / +49 7231 936-1000 / All rights reserved.
WHITE PAPER: DQ AUDIT
MODULE 3: DATA QUALITY PROCESS ANALYSIS
After the status quo of the data quality has
been determined in both the previous modules,
Module 3 deals with the creation of the data in
the company and its actual efficiency of use for
the data consumers. The following questions are
focused on here:
–– How are the processes for the creation, change
and deletion of the data described?
–– Are these processes up-to-date and are they put
into practice?
–– Does the data and information generated by
the processes enable the consumers to work as
efficiently as possible?
The processes are analyzed with regard to the
previously prepared requirements for the data.
Any weak points in respect of the data qual-
ity are identified. The data consumers are also
asked for their assessment of the quality of the
data. The emphasis in these interviews is on
whether the contents and form of the data are
presented in such a way that the daily work can
be carried out with a high degree of efficiency.
These requirements apply both to the operative
business and to analytical business areas.
Data quality dimensions which can only be
assessed with great difficulty by means of anal-
ysis software are examined in the interviews
with the data consumers. These concern e.g.
the dimensions, credibility, accessibility, editabil-
ity and objectivity. Since each of the consumer
groups concerned should be included in the inter-
views, the various views and requirements for the
data can be considered once more.
DATA QUALITY PROCESS ANALYSIS – TECHNICAL AND
ORGANIZATIONAL DETAILS
The following are required:
–– The relevant processes and workflow
descriptions
–– Contact persons (at least 2 to 3 data consum-
ers) from the departments concerned
Page 13© Uniserv GmbH / +49 7231 936-1000 / All rights reserved.
WHITE PAPER: DQ AUDIT
ALL THE ANALYSIS RESULTS ARE PUT INTO A CONTEXT AT
THE END OF THE DATA QUALITY AUDIT:
–– The evaluations made in the Data Quality
Analysis
–– The requirements of the specialist departments
for the data
–– Assessments of the data quality and individual
requirements of the data consumers
–– The status quo of the information-generating
processes with regard to the identified require-
ments for the data
The knowledge gained thereby is presented to
the specialist departments and data consumers
concerned in a workshop. Discussions on the pos-
sible causes of inadequate data quality are encour-
aged. Optimization measures and customization
of the processes to improve the quality of the data
can also be discussed.
The results of Module 3 and the findings of the
discussions conducted in the workshop are made
available in writing.
AFTER THE STATUS QUO OF THE
DATA QUALITY HAS BEEN DETERMINED
IN BOTH THE PREVIOUS MODULES,
MODULE 3 DEALS WITH THE CREATION
OF THE DATA IN THE COMPANY AND
ITS ACTUAL EFFICIENCY OF USE FOR THE
DATA CONSUMERS.
Page 14© Uniserv GmbH / +49 7231 936-1000 / All rights reserved.
WHITE PAPER: DQ AUDIT
List of references
–– Wikipedia: http://de.wikipedia.org/wiki/Produktionsfaktor
–– Wikipedia: http://de.wikipedia.org/wiki/Total-Quality-Management
–– Wikipedia: http://de.wikipedia.org/wiki/Qualität
–– Bauer, A., Günzel, H. 2009. Begriffliche Einordnung. In: Bauer, A. , Günzel, H. (Hrsg). Data Warehouse Systeme.
Architektur, Entwicklung, Anwendung. dpunkt.verlag. S. 6.
–– Ballou, D., Wang, R. Prazer, H. Tayi, G.K. 1998. Modeling Information Manufacturing Systems to Determine Information
Product Quality. Management Science, Vol. 44, p. 462-484.
–– Wang R. Y.  Strong, D. M. 1996. Beyond Accuracy. What Data Quality Means to Data Consumers. Journal of
Management Information Systems, Vol. 12, p. 5-34.
–– Rohweder, J.P., Kasten, G., Malzahn, D,. Piro, A., Schmid, J. 2008. Informationsqualität - Definitionen, Dimensionen
und Begriffe. In: Hildebrand, K., Gebauer, M. Hinrichs, H. Mielke, M. (Hrsg.) Daten- und Informationsqualität. Auf dem
Weg zur Information Excellence. Vieweg  Teubner. S. 25-45.
–– English, L. P. 1999. Improving Data Warehouse and Business Information Quality. Methods for reducing costs and incre-
asing profit. Wiley Computer Publishing. 518pp.
Looking ahead
The status of the quality of the company data has been determined and initial discus-
sions about measures for its improvement have been conducted. What is the next step?
Irrespective of the areas of the company in which
optimizations are to be implemented and the
measures which have been considered, you will
find the right contact partner at Uniserv GmbH.
As a solution-oriented provider covering all
aspects of data quality, Uniserv offers support in
the implementation and optimization of operati-
ve and analytical business applications. Uniserv
is also an expert partner in the areas of direct
marketing, compliance / block lists and data
migrations.
Individual solution concepts which improve the
quality of the data and information in the long
term are developed together with the customers.
As a result, the day-to-day business can be car-
ried out more efficiently, business numbers are
reliable and strategies for the future successful.
For further information
about the Uniserv Data Quality Audit please
visit our web page www.uniserv.com/Audit
or contact us directly:
We are looking forward for advising and sup-
porting you through your project.
Page 15© Uniserv GmbH / +49 7231 936-1000 / All rights reserved.
WHITE PAPER: DQ AUDIT
Uniserv
Uniserv is the largest specialised supplier of data quality solutions in Europe
with an internationally usable software portfolio and services for the quality as-
surance of data in business intelligence, CRM applications, data warehousing,
eBusiness and direct and database marketing.
With several thousand installations worldwide, Uniserv
supports hundreds of customers in their endeavours to
map the Single View of Customer in their customer data-
base. Uniserv employs more than 110 people at its head-
quarters in Pforzheim and its subsidiary in Paris, France,
and serves a large number of prestigious customers in all
sectors of industry and commerce, such as ADAC, Al-
lianz, BMW, Commerzbank, DBV Winterthur, Deutsche
Bank, Deutsche Börse Group, France Telecom, Green-
peace, GEZ, Heineken, Johnson  Johnson, Nestlé,
Payback, PSA Peugeot Citroën as well as Time Life and
Union Investment.
Further information is available
at www.uniserv.com
Experience:
OVER 40 YEARS
Market position:
LARGEST
EUROPEAN SUPPLIER
Employees:
MORE THAN 110 PEOPLE
DIRECT
MARKETING
BI/BDW
CPM
CRM
ERP
E-COMMERCE
DATA
MIGRATION
PROJECTS
SOA
ON-PREMISE/
ON-DEMAND
MDM/CDI
COMPLIANCE
Contact:
+49 7231 936-0
Uniserv GmbH
Rastatter Straße 13 • 75179 Pforzheim • Germany • T +49 7231 936-0 •
F +49 7231 936-3002 • E info@uniserv.com • www.uniserv.com
© Copyright Uniserv • Pforzheim/Germany • All rights reserved.

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Data Quality Audit

  • 1. Page 1 WHITE PAPER: DQ AUDIT WHITE PAPER / Uniserv Data Quality Audit: Does the quality of company data meet the requirements of data consumers? In order to implement suitable measures for improving quality of data, it should be ensured, that the actions (a) meet the require- ments of data consumers and (b) increase the efficiency of the company. The Data Quality Audit is used to determine the status quo of the data quality. In this respect, not only the data itself is at the focus, but, more importantly, the requirements of data consumers with regard to the data are considered and the data and information creation proc- esses examined. The Data Quality Audit has a modular structure, and each module has its own area of focus. Possible concepts for optimization of the data quality can be prepared in conjunction with the specialist departments. All company and product names and logos used in this document are trade names and/or registered trademarks of the respective companies.
  • 2. Page 2© Uniserv GmbH / +49 7231 936-1000 / All rights reserved. WHITE PAPER: DQ AUDIT Contents The quality of data – what lies behind it? “Single View of Customer” versus “Single View of Data” The quality of company master data Status quo of the quality of the company data: the Uniserv Data Quality Audit can help Looking ahead List of references PAGE 3 PAGE 6 PAGE 7 PAGE 9 PAGE 14 PAGE 14
  • 3. Page 3© Uniserv GmbH / +49 7231 936-1000 / All rights reserved. WHITE PAPER: DQ AUDIT The quality of data – what lies behind it? Land, work and capital are spoken of as conven- tional production factors in classic economics. However, the term „knowledge“ or „information“ is increasingly referred to as one of the important production factors. (Wikipedia, Bauer & Günzel 2009). In this context, the high importance of infor- mation derived from data becomes apparent. Another line of thought relates the production of information directly to the manufacture of any desired product (Ballou et al. 1998). These con- siderations were preceded by the concept of Total Quality Management (TQM). This concentrates on the maximum satisfaction of the requirements for a product and refers to all the processes and depart- ments involved in the production and therefore the entire company (Wikipedia). The correlation between the quality of data and products can therefore be expressed very simply as follows: if „information“ is considered as a product, certain requirements of the users of this product can also be defined and provided as specifications in production processes. However, how can the requirements for information and knowledge be defined? How can it be veri- fied that the standards specified in the production process are complied with? If the production of a car is visualized, it is assumed that the end product has four wheels and the doors and windows actually open. The engine fits in the body and the safety standards comply with the requirements. All these and more specifications were repeatedly checked during the actual pro- duction process and therefore correspond to the previously specified requirements of the subsequent driver of the car. If defects occur during the produc- tion, it is immediately stopped and fault tracing and correction are started. HOWEVER, HOW CAN THE REQUIREMENTS FOR INFORMATION AND KNOWLEDGE BE DEFINED? HOW CAN IT BE VERIFIED THAT THE STANDARDS SPECIFIED IN THE PRODUC- TION PROCESS ARE COMPLIED WITH?
  • 4. Page 4© Uniserv GmbH / +49 7231 936-1000 / All rights reserved. WHITE PAPER: DQ AUDIT The production of information should not be any different. THE FOLLOWING EXAMPLE HELPS TO PROVIDE A BETTER UNDERSTANDING OF THE TERMS DATA QUALITY, INFOR- MATION QUALITY AND KNOWLEDGE: –– 0100010 + 01001101 + 01010111 - -> DATA –– char(66) + char(77) + char(87) --> INFORMATION? –– BMW --> Three letters! - -> INFORMATION? –– BMW --> Binary Moving Window --> INFORMATION? –– BMW --> Beer with Water --> INFORMATION? –– BMW --> Bayrische Motorenwerke --> INFORMATION? –– Bayrische Motorenwerke --> KNOWLEDGE! It becomes clear that data is at the beginning of the chain. Information is generated from the data. But the respective background information is required to provide the meaning of this information and to enable the information to be put in the right context. In the end, the correct conclusions can only be drawn and new knowledge therefore gen- erated if the data and information at the beginning of this chain are correct. Several authors have taken a close look at this subject, in order to give the term „data quality“ a more tangible form. To begin with, the term „quality“ is concerned. Derived from the standard EN ISO 9000:2005, quality states the degree to which a product (goods or service) complies with the existing requirements (Wikipedia). This means that the quality can be good or bad if it meets the requirements of the user or not. In their studies, Wang and Strong (1996) asked consumers of data to define the properties of good quality data. The German Association for Information and Data Quality (DGIQ) took up this idea and described the categories and dimensions of data quality in an easy to understand manner (Rohweder et al. 2008). IT BECOMES CLEAR THAT DATA IS AT THE BEGINNING OF THE CHAIN. INFORMA- TION IS GENERATED FROM THE DATA.
  • 5. Page 5© Uniserv GmbH / +49 7231 936-1000 / All rights reserved. WHITE PAPER: DQ AUDIT According to this, the quality of data can be divided into four categories and 15 dimensions. Category Dimension System Accessibility Editability Content Highly regarded Freedom from errors Objectivity Credibility Display Comprehensibility Clarity Standard display Unambiguous interpretability Use Up-to-dateness Added value Completeness Reasonable extent Relevance Overview of the categories and dimensions of data quality (after Rohweder et al. 2008) If each of the dimensions stated here is con- sidered to be „good“, the data quality can be assumed to be optimum. Many of the stated dimensions cannot be evaluated by the system with simple performance indicators, instead the consumers of the respective data always have to decide whether the quality of the data is good. Larry English (1999) has similar basic approach- es but makes a fundamental distinction between the quality of the data contents (correctness of the data) and the pragmatic quality of the data (data presentation). It is inherent to both basic approaches that the focus of attention is on the data consumer who receives the data, so that can he carry out his tasks in a satisfactory manner. Or to express it in the words of Wang & Strong (1996), data quality is defined as „data that are fit for use by data consumers“. IT IS INHERENT TO BOTH BASIC APPROACHES THAT THE FOCUS OF ATTENTION IS ON THE DATA CONSUMER WHO RECEIVES THE DATA, SO THAT CAN HE CARRY OUT HIS TASKS IN A SATISFACTORY MANNER.
  • 6. Page 6© Uniserv GmbH / +49 7231 936-1000 / All rights reserved. WHITE PAPER: DQ AUDIT „Single View of Customer“ versus „Single View of Data“ It is generally assumed that the „Single View of Customer“ represents one of the highest levels of data quality in the consideration of customer master data. In the simplest case, the term „Single View of Customer“ refers to a duplicate-free customer mas- ter database. With regard to the above stated data quality dimensions, the „unambiguous interpretabil- ity“ has been considered here. Nevertheless, the „Single View of Customer“ should not be equated with the „Single View of Data“. The illustration below makes the difference clear: whereas the „Single View of Customer“ puts the data itself at the focus, the „Single View of Data“ refers to the different requirements of the various company departments for the data. If each individ- ual user group about were asked the requirements for the data, there would certainly be different answers or lists of shortcomings. There is therefore no „Single View of Data“ if the requirements for the data vary. A „Differing View of Data“ should be referred to instead.   „Single View of Customer“ vs. „Single View of Data“ A „Differing View of Data“ should be referred to in a company with different departments which have different requirements for the customer master data. WHEREAS THE „SINGLE VIEW OF CUSTOMER“ PUTS THE DATA ITSELF AT THE FOCUS, THE „SINGLE VIEW OF DATA“ REFERS TO THE DIFFERENT REQUIREMENTS OF THE VARIOUS COMPANY DEPARTMENTS FOR THE DATA. MASTER DATA Marketing Sales Finance SupportDevelopment
  • 7. Page 7© Uniserv GmbH / +49 7231 936-1000 / All rights reserved. WHITE PAPER: DQ AUDIT The quality of company master data There are many indications of possible data qual- ity problems, particularly in the case of customer master data. THE FOLLOWING ARE MENTIONED HERE BY WAY OF EXAMPLE: –– There is a high proportion of returns in mailing campaigns because of undeliverable address- es –– Customers complain because they receive advertising material several times –– Invoices are not paid, because they never arrived on account of an incorrect address –– Sales and marketing forecast analyses prove to be unreliable, since the prospects of suc- cess were booked to duplicates of different prospective customers –– Customers say that they are dissatisfied with the support, because the employees take too long to find all the relevant data in the system However, the phenomena described above only concern the initial or most obvious symptoms of poor data quality. Several fundamental require- ments for customer master data can be derived from this. The postal address should be correct and every customer should only be represented in the customer master database once (points 1 to 4). Furthermore, all the relevant data should be avail- able to the personnel (point 5). The list of require- ments for customer master data could probably be extended indefinitely, but in the end, it would be discovered that the requirements for the data are different for each department. BUT IN THE END, IT WOULD BE DISCOVERED THAT THE REQUIREMENTS FOR THE DATA ARE DIFFERENT FOR EACH DEPARTMENT.
  • 8. Page 8© Uniserv GmbH / +49 7231 936-1000 / All rights reserved. WHITE PAPER: DQ AUDIT For example, the marketing department attaches high importance to a correct address for mailing campaigns, whereas the staff in customer support depend on the up-to-dateness and completeness of the respective customer products, which are displayed in an clearly arranged manner. The qual- ity of the data can therefore only be assessed as good or bad by comparing it with the requirements of the respective data consumers. Many of the requirements for data can be auto- matically tested using appropriate analysis soft- ware. Some of the deficiencies can also be cor- rected in short-term one-off cleansing operations. However, even if the above stated symptoms are brought under control with cleansing opera- tions, the reason why the quality of the data is poor in the first place has neither been found nor excluded. It has not yet been guaranteed that the cleansing operation meets the requirements of all the data consumers. A Data Quality Audit should be carried out, in order to be able to make a statement about the current status quo of the quality of the company data. The respective data is not only analyzed by means of software in the audit, but, more impor- tantly, the requirements of the data consumers are considered. Not until the results of the audit are known can statements actually be made about which of the company data meets the requirements of the data users and which does not. The “perceived” status of the quality of the data can be verified (or refuted) with objective numbers. Furthermore, appropriate activities for a long-term improve- ment in the data quality can be considered. A DATA QUALITY AUDIT SHOULD BE CARRIED OUT, IN ORDER TO BE ABLE TO MAKE A STATEMENT ABOUT THE CURRENT STATUS QUO OF THE QUALITY OF THE COMPANY DATA.
  • 9. Page 9© Uniserv GmbH / +49 7231 936-1000 / All rights reserved. WHITE PAPER: DQ AUDIT Agreement must be reached about which criteria the data quality should be measured against, in order to carry out an appropriate assessment of the quality of company data. Many of the requirements can be checked by means of suit- able analysis tools. The data consumers must be asked about their requirements with regard to other qualities. Finally, the creation process of the „information“ product should also be carefully considered. There should be clarity about who requires which data for what purpose. Uniserv GmbH offers a comprehensive Data Quality Audit, in order to be able to answer these questions and objectively assess the sta- tus quo of the quality of the data. The Data Quality Audit has a modular structure, the mod- ules are based on each other. EACH MODULE HAS ITS MAIN AREA OF FOCUS ON ONE OF THE POINTS MENTIONED ABOVE: –– Requirements for the data and their compliance which can be verified by means of analysis software. Data quality dimensions such as com- pleteness or freedom from errors are a major concern here. –– Requirements for the data and their compli- ance, about which the data consumers can provide information. Data quality dimensions such as comprehensibility or clarity are verified here. The data consumers can also submit their assessments of the credibility or the reputation of the data. The data consumers can provide valuable information about the editability or the accessibility of the data. –– Analysis of the data / information creation processes, in order to be able to identify any weak points. A fundamental understanding of the creation history is important, since the data creation processes frequently go through many individual stages such as different soft- ware applications and individual processes which in turn concern different business areas. Knowledge of the processes plays an important role if long-term measures for optimization of the data quality are to be specified. Status quo of the quality of the company data: the Uniserv Data Quality Audit can help UNISERV GMBH OFFERS A COMPREHEN- SIVE DATA QUALITY AUDIT, IN ORDER TO BE ABLE TO ANSWER THESE QUESTIONS AND OBJECTIVELY ASSESS THE STATUS QUO OF THE QUALITY OF THE DATA.
  • 10. Page 10© Uniserv GmbH / +49 7231 936-1000 / All rights reserved. WHITE PAPER: DQ AUDIT MODULE 1: DATA QUALITY CHECK The Data Quality check provides an initial view of the customer master data of a company. In this phase, a representative extract of the data is analyzed by means of the Data Quality Batch Suite. In this respect, particular importance is attached to the completeness and the presence of the name elements. The postal correctness of the address elements is verified and a duplicate check is carried out. The following requirements for the data are assumed in the Data Quality Check: –– All the „must“ fields of every data record are filled –– The address elements correspond to a valid address and are therefore correct –– The „Single View of Customer“ applies, i.e. the data extract is duplicate-free, or so-called „desired“ duplicates are marked The results of the Data Quality Check are sub- sequently presented and made available to the customer. THE UNISERV DATA QUALITY AUDIT IS THEREFORE DIVIDED INTO THREE MODULES: DATA QUALITY CHECK – TECHNICAL DETAILS The following are required: –– A data file, ideally in the delimiter format –– Definition and meaning of the headers –– Definition of any keys –– Definition of any value ranges –– Character coding: UTF-8 or ISO-Latin 1 –– All addresses come from one country –– Maximum of 100,000 addresses
  • 11. Page 11© Uniserv GmbH / +49 7231 936-1000 / All rights reserved. WHITE PAPER: DQ AUDIT MODULE 2: DATA QUALITY ANALYSIS The Data Quality Check in Module 1 primarily validates an extract from the customer master data in a relatively simple process. Particular importance is attached to the name elements and the address elements. The Data Quality Analysis goes a big step further. The entirety of the company data can be consid- ered here. Very specific data, such as telephone numbers, customer sales, persistence, attached data concerning other transactions, etc., can be checked here as required. Compliance with spe- cific business and plausibility rules can also be verified. If required, the customer master data can even be checked against sanction lists at this point. (A check of the in-house customer master data against the sanction lists is generally recommend- ed, in order to prevent contravention of the relevant anti-terrorism regulations. Details can be found in the White Paper on Compliance.) Specific requirements for the data to be validated are identified in an opening workshop with the heads of the specialist departments concerned. A comparison of the technical analyses and the evaluations of the workshop will indicate the extent to which the requirements defined by the specialist departments correspond with the actual and the „perceived“ quality level. After the analyses and evaluations have been completed, the results are presented in a clos- ing workshop. It is recommended that the heads of all the specialist departments concerned are invited, in order to take account of the „Differing View of Data“. If any measures for optimization of the data qual- ity are to be adopted, it is indispensable that the consumers of the data are included in the decision- making process. Only in this way will the imple- mentation of the measures be widely accepted. Needless to say, the results of the Data Quality Analysis are also provided in writing. THE DATA QUALITY ANALYSIS GOES A BIG STEP FURTHER. THE ENTIRETY OF THE COMPANY DATA CAN BE CONSIDERED HERE. DATA QUALITY ANALYSIS – TECHNICAL AND ORGANIZATIONAL DETAILS The following are required: –– Several files or databases –– Description of the meta data: –– –– –– –– Contact persons from the various departments – Definition and meaning of the headers – Definition of any keys – Definition of any value ranges – Character coding: UTF-8 or ISO-Latin 1 – Information about the business and/or plausibility rules to be verified
  • 12. Page 12© Uniserv GmbH / +49 7231 936-1000 / All rights reserved. WHITE PAPER: DQ AUDIT MODULE 3: DATA QUALITY PROCESS ANALYSIS After the status quo of the data quality has been determined in both the previous modules, Module 3 deals with the creation of the data in the company and its actual efficiency of use for the data consumers. The following questions are focused on here: –– How are the processes for the creation, change and deletion of the data described? –– Are these processes up-to-date and are they put into practice? –– Does the data and information generated by the processes enable the consumers to work as efficiently as possible? The processes are analyzed with regard to the previously prepared requirements for the data. Any weak points in respect of the data qual- ity are identified. The data consumers are also asked for their assessment of the quality of the data. The emphasis in these interviews is on whether the contents and form of the data are presented in such a way that the daily work can be carried out with a high degree of efficiency. These requirements apply both to the operative business and to analytical business areas. Data quality dimensions which can only be assessed with great difficulty by means of anal- ysis software are examined in the interviews with the data consumers. These concern e.g. the dimensions, credibility, accessibility, editabil- ity and objectivity. Since each of the consumer groups concerned should be included in the inter- views, the various views and requirements for the data can be considered once more. DATA QUALITY PROCESS ANALYSIS – TECHNICAL AND ORGANIZATIONAL DETAILS The following are required: –– The relevant processes and workflow descriptions –– Contact persons (at least 2 to 3 data consum- ers) from the departments concerned
  • 13. Page 13© Uniserv GmbH / +49 7231 936-1000 / All rights reserved. WHITE PAPER: DQ AUDIT ALL THE ANALYSIS RESULTS ARE PUT INTO A CONTEXT AT THE END OF THE DATA QUALITY AUDIT: –– The evaluations made in the Data Quality Analysis –– The requirements of the specialist departments for the data –– Assessments of the data quality and individual requirements of the data consumers –– The status quo of the information-generating processes with regard to the identified require- ments for the data The knowledge gained thereby is presented to the specialist departments and data consumers concerned in a workshop. Discussions on the pos- sible causes of inadequate data quality are encour- aged. Optimization measures and customization of the processes to improve the quality of the data can also be discussed. The results of Module 3 and the findings of the discussions conducted in the workshop are made available in writing. AFTER THE STATUS QUO OF THE DATA QUALITY HAS BEEN DETERMINED IN BOTH THE PREVIOUS MODULES, MODULE 3 DEALS WITH THE CREATION OF THE DATA IN THE COMPANY AND ITS ACTUAL EFFICIENCY OF USE FOR THE DATA CONSUMERS.
  • 14. Page 14© Uniserv GmbH / +49 7231 936-1000 / All rights reserved. WHITE PAPER: DQ AUDIT List of references –– Wikipedia: http://de.wikipedia.org/wiki/Produktionsfaktor –– Wikipedia: http://de.wikipedia.org/wiki/Total-Quality-Management –– Wikipedia: http://de.wikipedia.org/wiki/Qualität –– Bauer, A., Günzel, H. 2009. Begriffliche Einordnung. In: Bauer, A. , Günzel, H. (Hrsg). Data Warehouse Systeme. Architektur, Entwicklung, Anwendung. dpunkt.verlag. S. 6. –– Ballou, D., Wang, R. Prazer, H. Tayi, G.K. 1998. Modeling Information Manufacturing Systems to Determine Information Product Quality. Management Science, Vol. 44, p. 462-484. –– Wang R. Y. Strong, D. M. 1996. Beyond Accuracy. What Data Quality Means to Data Consumers. Journal of Management Information Systems, Vol. 12, p. 5-34. –– Rohweder, J.P., Kasten, G., Malzahn, D,. Piro, A., Schmid, J. 2008. Informationsqualität - Definitionen, Dimensionen und Begriffe. In: Hildebrand, K., Gebauer, M. Hinrichs, H. Mielke, M. (Hrsg.) Daten- und Informationsqualität. Auf dem Weg zur Information Excellence. Vieweg Teubner. S. 25-45. –– English, L. P. 1999. Improving Data Warehouse and Business Information Quality. Methods for reducing costs and incre- asing profit. Wiley Computer Publishing. 518pp. Looking ahead The status of the quality of the company data has been determined and initial discus- sions about measures for its improvement have been conducted. What is the next step? Irrespective of the areas of the company in which optimizations are to be implemented and the measures which have been considered, you will find the right contact partner at Uniserv GmbH. As a solution-oriented provider covering all aspects of data quality, Uniserv offers support in the implementation and optimization of operati- ve and analytical business applications. Uniserv is also an expert partner in the areas of direct marketing, compliance / block lists and data migrations. Individual solution concepts which improve the quality of the data and information in the long term are developed together with the customers. As a result, the day-to-day business can be car- ried out more efficiently, business numbers are reliable and strategies for the future successful. For further information about the Uniserv Data Quality Audit please visit our web page www.uniserv.com/Audit or contact us directly: We are looking forward for advising and sup- porting you through your project.
  • 15. Page 15© Uniserv GmbH / +49 7231 936-1000 / All rights reserved. WHITE PAPER: DQ AUDIT Uniserv Uniserv is the largest specialised supplier of data quality solutions in Europe with an internationally usable software portfolio and services for the quality as- surance of data in business intelligence, CRM applications, data warehousing, eBusiness and direct and database marketing. With several thousand installations worldwide, Uniserv supports hundreds of customers in their endeavours to map the Single View of Customer in their customer data- base. Uniserv employs more than 110 people at its head- quarters in Pforzheim and its subsidiary in Paris, France, and serves a large number of prestigious customers in all sectors of industry and commerce, such as ADAC, Al- lianz, BMW, Commerzbank, DBV Winterthur, Deutsche Bank, Deutsche Börse Group, France Telecom, Green- peace, GEZ, Heineken, Johnson Johnson, Nestlé, Payback, PSA Peugeot Citroën as well as Time Life and Union Investment. Further information is available at www.uniserv.com Experience: OVER 40 YEARS Market position: LARGEST EUROPEAN SUPPLIER Employees: MORE THAN 110 PEOPLE DIRECT MARKETING BI/BDW CPM CRM ERP E-COMMERCE DATA MIGRATION PROJECTS SOA ON-PREMISE/ ON-DEMAND MDM/CDI COMPLIANCE Contact: +49 7231 936-0 Uniserv GmbH Rastatter Straße 13 • 75179 Pforzheim • Germany • T +49 7231 936-0 • F +49 7231 936-3002 • E info@uniserv.com • www.uniserv.com © Copyright Uniserv • Pforzheim/Germany • All rights reserved.