A Data Quality Management Framework to Support Delivery and
Consultancy of CRM Platforms
Renee Albrecht, Sietse Overbeek and Inge van de Weerd
Department of Information and Computing Sciences, Faculty of Science, Utrecht University,
Princetonplein 5, 3584 CC Utrecht, The Netherlands
Keywords:
Consultancy, CRM, Data Quality Management, Delivery, Design Science.
Abstract:
CRM platforms heavily depend on high-quality data, where poor-quality data can negatively influence its
adoption. Additionally, these platforms are increasingly interconnected and complex to meet growing needs
of customers. Hence, delivery and consultancy of CRM platforms becomes highly complex. In this study,
we propose a CRM data quality management framework that supports CRM delivery and consultancy firms
to improve data quality management practices within their projects. The framework should also improve data
quality within CRM solutions for their clients. We extract best practices for CRM data quality management by
means of a literature study on data quality definition and measurement, data quality challenges, and data qual-
ity management methods. In a case study at an IT consultancy company, we investigate how CRM delivery
and consultancy projects can benefit from the incorporation of data quality management practices. The design
of the framework is validated by means of confirmatory focus groups and a questionnaire. The results trans-
late into a framework that provides a high-level overview of data quality management practices incorporated
in CRM delivery and consultancy projects. It includes the following components: Client profiling, project
definition, preparation, migration/integration, data quality definition, assessment, and improvement.
1 INTRODUCTION
Contemporary enterprises heavily depend on data,
where data is seen as a strategic asset (Nagle et al.,
2020). The International Data Corporation (IDC)
states that during 2020 the amount of data that has
been created, captured, and replicated across the
world is more than 59 zettabytes, and over the next
five years the world will create more than three times
the amount of data compared to the previous ve
years (IDC, 2020). With an increasing volume, ve-
locity, and variety of data, the issues arising from er-
rors in data and the organizational impact of these is-
sues are amplified (Laney, 2018). Laney states that
“poor data quality can have grave consequences, from
strategic decisions that can lead to the death of a busi-
ness to operation decisions that can lead to the death
of individuals”, and: “[...] 40 percent of all failed
business initiatives are a result of poor data quality”
(Laney, 2018, pp. 246–247). Poor Data Quality (DQ)
is one of the greatest challenges facing contemporary
enterprises (Davenport and Harris, 2017). Simultane-
ously, enterprises struggle to address their data issues,
while high-quality data is rather the exception than the
rule (Nagle et al., 2020). By managing DQ, unwanted
consequences can be prevented, and valuable insights
can be discovered in regards to interactions with cus-
tomers.
An important operational aspect of enterprises that
relies upon its utilisation of high-quality data is Cus-
tomer Relationship Management (CRM). Poor DQ
and integration can negatively influence the adoption
of CRM (Cruz-Jesus et al., 2019). Additionally, sur-
vey data collected from about 300 organisations for
the State of CRM Data Management report shows that
44% of its respondents estimate a loss in revenue as a
result of poor quality CRM data, which ranges from
5%-20% of total revenue (Hanson, 2020). CRM is de-
fined as “the core business strategy that aims to create
and maintain profitable relationships with customers,
by designing and delivering superior value proposi-
tion” (Buttle and Maklan, 2019, p. 21). It is enabled
by information technology in the form of CRM plat-
forms, at present often provided by IT consultancy
firms as CRM (cloud) solutions. Those firms offer
Delivery & Consultancy (D&C) of the CRM platform
to a variety of customers. Those customers vary and
grow in their needs, business processes, and goals.
62
Albrecht, R., Overbeek, S. and van de Weerd, I.
A Data Quality Management Framework to Support Delivery and Consultancy of CRM Platforms.
DOI: 10.5220/0011092800003179
In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 1, pages 62-74
ISBN: 978-989-758-569-2; ISSN: 2184-4992
Copyright
c
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Consequently, the CRM platforms are increasingly in-
terconnected and complex, resulting in a continuous
need for more study in the area of the management
of CRM platform development, implementation, and
marketing (Stone et al., 2017).
The objective of this research is to provide a so-
lution for the need for data of adequate quality in in-
creasingly complex CRM platforms. The aim is to
improve data quality within CRM platforms by de-
signing a framework that supports data quality man-
agement in order to keep the quality of CRM data on
an adequate level. This translates to the following re-
search question: How can a data quality management
framework be designed to support CRM platform de-
livery and consultancy?
The following section introduces the research ap-
proach. Subsequently, we present a CRM DQ man-
agement framework in section 3, followed by its val-
idation in section 4. The results are discussed in sec-
tion 5 and, finally, we present our conclusions after
which possibilities for further research are elaborated
on in section 6.
2 RESEARCH APPROACH
In this study, we adopt a design science approach.
Our aim is to investigate and design a framework
for data quality management in CRM platform D&C.
In line with Wieringa’s Design Science Framework
(Wieringa, 2014), we follow an iterative set of
problem-solving tasks according to the structure of
the so-called design cycle.
The first task is Problem Investigation, where
the design of the CRM DQ Management Framework
(CRM-DQMF) is prepared by conducting exploratory
research to understand the problem. To increase the
robustness of the results of this research, methodolog-
ical triangulation is adopted (Kaplan and Maxwell,
2005). We carried out a literature review to iden-
tify, evaluate, and integrate findings of relevant high-
quality studies that address the research problem. We
determined the relevance of the literature by scope,
objectives, methods, and conclusions (Budgen and
Brereton, 2006). To ensure a complete review, ad-
ditional search methods that are used are forward and
backward searching (Levy and Ellis, 2006). We also
included grey literature in order to get insights on the
state of the art concerning DQ management in CRM
platforms in practice.
The second part of our problem investigation was
carried out in the form of a single embedded case
study at an IT consultancy firm (Yin, 2003). The goal
of this case study was to investigate the defined prob-
lem within its context. Its results are triangulated by
including exploratory expert interviews with a total
of 14 experts, as well as a documentation analysis
including 15 relevant documents mainly existing of
company documentation. The participating experts
and researched documentation can be found in table
1 and table 2 respectively.
Table 1: Participants Expert Interviews.
ID Role
Years of Experience
P1 Consultant 4
P2 Senior Analyst 2
P3 Senior Director 14
P4 Group Manager 15
P5 Senior Consultant 13
P6 Senior Consultant 5
P7 Senior Analyst 4
P8 Manager 9
P9 Consultant 4
P10 Consultant 6
P11 Analyst 2
P12 Senior Consultant 7
P13 Director 14
P14 Senior Consultant 7
Table 2: Researched documentation.
ID Purpose Year
D1 CRM platform insights 2020
D2 Data migration guidelines 2020
D3 CRM (on-premises) to cloud
migration guidelines
2020
D4 CRM platform adoption guide-
lines
2020
D5 Common Data Model 2020
D6 Report on the solution design
#1 for a utility client
2020
D7 Report on the solution design
#2 for a utility client
2020
D8 Data flows explanation within a
solution for a utility client
2020
D9 Functional design datamart for
a utility client
2020
D10 Education in CRM platform
functionalities
2021
D11 Global policy in data manage-
ment
2021
D12 Dedicated system for data qual-
ity checks in multiple systems
2011
D13 Data management roadmap
proposal
2021
D14 Data quality analysis strategy at
a product client
2019
D15 Design principles in a CRM
project of an NGO client
2021
The second task, Treatment Design, specifies re-
quirements for the CRM-DQMF, which are extracted
from insights of the triangulated collected data. Based
A Data Quality Management Framework to Support Delivery and Consultancy of CRM Platforms
63
on those requirements, the design of the CRM-DQMF
is established. The results are presented by means of
a Process-Deliverable Diagram (PDD) consisting of
two integrated diagrams, namely a process diagram
including all activities, and a deliverable diagram in-
cluding the deliverables that result from the activities
(van de Weerd and Brinkkemper, 2008; van de Weerd,
2009). We used an assembly-based method engineer-
ing approach that facilitates situational analysis and
design methods. Since situational factors play a key
role when managing DQ, e.g. the CRM platform, the
industry, and data processes, this is deemed as a suit-
able approach for the design of the CRM-DQMF.
In the last task, Treatment Validation, we vali-
date the initial design of the CRM-DQMF by means
of expert opinions extracted using confirmatory fo-
cus groups with a total of 6 experts (Tremblay et al.,
2010). Subsequently, we conducted an interactive
questionnaire among the same group of experts to ef-
fectively validate the first design of the CRM-DQMF
(Robson and McCartan, 2016).
This study has several contributions. First, the in-
vestigation of CRM and its D&C provides insights
in what CRM D&C projects entail and which prac-
tices have to be taken into consideration. Second,
the understanding of which types of data are utilised
in contemporary CRM platforms serves as input on
how a definition of CRM DQ can be established,
as well as how CRM DQ can be measured. Third,
the investigation of known challenges regarding DQ
within CRM platforms and the potential solutions
contributes to the development of criteria that DQ
management within CRM D&C should adhere to.
Lastly, the review of existing DQ management meth-
ods based on extracted requirements for their applica-
bility to CRM D&C projects results in a list of criteria
for DQ management in CRM D&C projects.
3 RESULTS
From the expert interviews as well as the documen-
tation analysis as part of the case study it can be
concluded that the level of DQ management in con-
temporary CRM D&C projects is lacking, as there is
no mutual awareness of the importance of DQ man-
agement in clients or CRM D&C employees, nor are
there best practices in place to perform DQ manage-
ment integrated in CRM D&C projects. Experts that
participated in interviews state that CRM D&C teams
face the challenge of the existing client’s context, with
its own constraints and levels of expertise regarding
DQ and DQ management. DQ management prac-
tices are not standardized within CRM D&C projects
at the IT company of the case study, and are ap-
plied and performed subjectively varying depending
on the experience(s) of the expert. Additionally, ex-
perts stated that there is need for DQ management, as
found challenges in CRM D&C projects are largely
related to poor DQ. Experts mutually experienced that
currently, assessment and improvement of DQ is not
done proactively, while they agree that this would
benefit the D&C project. Ideally, to provide a com-
plete and accurate as possible solution and advice as a
CRM D&C team to the client, DQ management needs
to be taken into account in every CRM D&C project,
from the start of the project.
The case study contributes to the findings of the
literature review in two different ways: (1) By con-
firming literature findings on DQ management and
CRM D&C projects, and (2) by providing new in-
sights on the current level of DQ management in
CRM D&C projects, the level of necessity for the
topic, and the manner on which DQ management
could be integrated in CRM D&C projects. From
the findings in literature and the case study, the re-
quirements for the CRM-DQMF as explained in the
following section are extracted. This is followed by
an elaboration on the individual components of the
CRM-DQMF in section 3.2.
3.1 Requirements Specification
For the CRM-DQMF to assist CRM D&C teams ef-
fectively in the management of DQ, it should ad-
here to certain criteria that ensure the management
of DQ in general, as well as the management of DQ
in the context of CRM D&C projects. It includes
Modularity, DQ Management Plan, DQ Management
Maturity Level, CRM D&C Client Context, Migra-
tion/Integration, Iteration, Business Impact Analysis,
DQ Assessment, and DQ Improvement.
3.1.1 Modularity
Customers expect a CRM platform to be tailored for
their organisation specifically, with limited effort, and
deployed promptly (Cricelli et al., 2019). Insights
as found in the case study indicate customer-centric
and agile approaches for CRM D&C projects. Hence,
in order to make DQ management in CRM D&C
projects succeed, the CRM-DQMF is required to be
designed in a modular fashion. Modularity refers to
the uniqueness of every client and CRM D&C project,
and therefore the need for uniqueness in DQ manage-
ment application. The CRM-DQMF should be ap-
plicable in varying situations serving different needs.
To serve this requirement, the CRM-DQMF exists
of different components that can be separated and/or
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(re)combined when required. This way, the CRM
D&C team can be either the executive or advising
force of a component, or take a more passive role and
omit the component of the CRM-DQMF, leaving the
responsibility entirely to the client. The modular vi-
sualization is facilitated by making use of situational
method engineering as proposed by van de Weerd and
Brinkkemper (van de Weerd, 2009). By the intro-
duction of an activity which produces a CRM D&C
project specific DQ management plan as explained in
section 3.1.2, the remainder of the utilisation of the
CRM-DQMF is decided upon.
3.1.2 DQ Management Plan
The CRM-DQMF facilitates the establishment of a
unique DQ management plan at the start of any
project. It describes the roles and responsibilities of
the client and the CRM D&C team with regards to
the required DQ management practices for the CRM
D&C project. The DQ management plan is estab-
lished based on the matters that make a client and
project unique. This includes a business case, as this
formats the required DQ management practices. The
business case comprises of the client’s budget, the
client’s business goals of a CRM D&C project, and
the scope of the project. The budget indicates to what
extent the client will be able to pay for DQ manage-
ment services. The business goals provide an indi-
cation of the need for DQ management. The scope
of the project indicates which functionalities are re-
quired for reaching the business goals, thus provides
for an indication of the extent to which DQ man-
agement is required. Additionally, this includes the
impact of DQ on the business goals, and the current
DQ management maturity level and goals of the client
(see section 3.1.3).
3.1.3 DQ Management Maturity Level
Results of the experts interviews indicate that DQ, and
therefore the need for its management within a CRM
D&C project along with the role of the CRM D&C
team, depends on the expertise of the client. P3 men-
tioned: “In the ideal case, organisations already have
an authority in place that takes care of data quality
matters. However, this varies per organisation and
industry. The interference of us depends on the ar-
rangements with the client”. Additionally, documen-
tation mentions various DQ Key Performance Indi-
cators, such as the number of data elements with a
definition. This indicates that there is need for the
determination of the client’s DQ management matu-
rity level, which determines the extent to which DQ
management will be applied and by whom. On the
one hand, the client might not have any knowledge on
their own data, nor its quality, which might indicate
that the data is not of sufficient quality for a CRM so-
lution and the client does not have sufficient in-house
DQ management expertise, meaning the expertise of
the CRM D&C team is required. On the other hand,
the client might already be in control of its data (and
quality) across the organisation, which means there is
no need for the CRM D&C team to conduct or advise
on any DQ management practices. Therefore, the cur-
rent DQ management maturity level of the client as
well as the goal DQ maturity level of the client play
relevant roles for the establishment of the DQ man-
agement plan. To determine the DQ management ma-
turity level of the client, the maturity matrix by Spruit
and Pietzka can be utilised (Spruit and Pietzka, 2015).
The capabilities of this maturity matrix are confirmed
by this research. Those capabilities read: Assessment
of DQ; Impact on Business; Root causes of poor DQ;
and DQ Improvement. For each capability, the client
can be at another maturity level, reading from low-
est to highest: Initial; Repeatable; Defined Process;
Managed & Measurable; and Optimized.
3.1.4 CRM D&C Client Context
Taking upfront considerations into account is consid-
ered to be important for DQ management in CRM
D&C projects (Batini et al., 2009). This is con-
firmed by the case study in terms of the definition of a
client context. Documentation showed the establish-
ment of a so-called blueprint of the project, which is
defined to create the framework for the CRM solu-
tion based on budget, goal, and scope. Experts men-
tioned this blueprint, as well as practices for the re-
construction of business processes of the client. The
client context comprises information for a reconstruc-
tion of the organisational environment in regards to
the CRM solution, which includes business processes,
data, data policies, and data standards. Convention-
ally, a client context is already established by the
CRM D&C team and the client as general part of the
CRM D&C project.
3.1.5 Migration/Integration
CRM D&C projects comprise the process towards a
CRM solution, which typically includes the migra-
tion and/or integration of data. P4 explained that
“Business requirements are defined in order to decide
which data is required for the solution. From this, a
data mapping is realized to load the data to the new
CRM solution correctly”. Migration and integration
practices comprise the mapping of data of two differ-
ent systems: a legacy system and the new CRM so-
A Data Quality Management Framework to Support Delivery and Consultancy of CRM Platforms
65
lution (Ali et al., 2017). Therefore, the CRM-DQMF
should contain guidance to integrate DQ management
into migration and integration practices, including
data mapping practices. The practices of the Trans-
form phase of the ETL process which is required for
data migration and integration practices as found in
literature (Thalheim and Wang, 2013) are confirmed
by experts and documentation of the case study. This
includes the mapping of the data, as well as the as-
sessment and improvement of DQ.
3.1.6 Iteration
In terms of the CRM D&C project, iteration needs
to take place to successfully establish migration and
integration, as the case study indicates that the mi-
gration and integration consist of continuous gather-
ing and refinement of business requirements, business
rules, and data mappings. In terms of DQ manage-
ment, this requires iterations of DQ assessment and
improvement practices (Batini et al., 2009; Cichy and
Rass, 2019). Experts mention phrases such as “I
think data quality should be measured frequently in
any case”, and in documentation as well as by ex-
perts the data lifecycle is mentioned, which consists
of the creation, management, and destruction of data.
This indicates that DQ should be managed up until its
destruction. Once the CRM solution is established,
the CRM-DQMF should still provide for iterations, as
the assessment and improvement phases of the CRM-
DQMF need to be ongoing processes, which solely
end in case the lifecycle of all concerned data within
the scope of the CRM D&C solution ceases to exist.
3.1.7 Business Impact Analysis
The business impact of poor DQ needs to be anal-
ysed, as this defines the data elements that are crit-
ical for the client’s business goals and thus require
DQ assessment and potentially improvement prac-
tices (Heinrich et al., 2018; Cichy and Rass, 2019;
Batini et al., 2009). This is also referred to as a top
down or demand driven approach. Due to the va-
riety in CRM D&C projects and clients, the eleven
business impacts of poor DQ as identified by Spruit
and van der Linden are recommended to be included
within the impact analysis, as they are found to be
applicable for a variety of industries (Spruit and Lin-
den, 2019). Furthermore, the case study indicates ag-
ile and customer-centric project approaches. By not
exclusively including monetary impact, the business
impacts support an agile project approach, as agile
values an emphasis on the quality, the flexibility and
the customer-centricity of services (Rosing and Gill,
2015) and cost efficiency is not at the centre of atten-
tion in an agile project approach (Gill and Henderson-
Sellers, 2006). The business impacts include lost
sales opportunities, customer service costs, customer
dissatisfaction, lost revenue, operational deficiencies,
delays in system/project deployment, regulatory com-
pliance, poor decision making, lost business opportu-
nities, employee moral, and system credibility. For
each significant business impact, a metric has to be
defined to calculate its value (Batini et al., 2007).
3.1.8 DQ Assessment
Within DQ management, DQ assessment is found to
be a critical part and should take place frequently (Ci-
chy and Rass, 2019; Batini et al., 2009). Therefore,
the CRM-DQMF should include guidance in assess-
ment practices. This includes the definition and mea-
surement of DQ and reporting on potential DQ issues.
DQ can have different definitions, which is highly
context dependent (Otto et al., 2007; Wang, 1998).
Each client and project is unique, meaning DQ is re-
quired to be defined for every project. A DQ defini-
tion is expressed in terms of DQ dimensions and DQ
thresholds (Otto et al., 2007; Wang, 1998; Cichy and
Rass, 2019). The unstructured, semi-structured, and
structured data used by CRM need to be taken into
account when defining the appropriate DQ definition
(Zahay et al., 2012; Missi et al., 2005). For each DQ
definition, DQ metrics need to be defined to be able
to quantify DQ (Pipino et al., 2002; Cichy and Rass,
2019). Additionally, for DQ assessment and improve-
ment to succeed, the assignment of data roles is re-
quired (Batini et al., 2009). From the case study can
be concluded that, without someone responsible for
the data, its quality will not be managed.
3.1.9 DQ Improvement
When data is found to be of insufficient quality, the
CRM-DQMF should offer guidance in establishing
an improvement strategy (Batini et al., 2009; Cichy
and Rass, 2019). For optimal DQ, an organisation
needs to be aware of different reasons for poor DQ
and where they are existent within the organisation,
hence the root causes of DQ issues need to be anal-
ysed (Spruit and Pietzka, 2015; Batini et al., 2009;
Cichy and Rass, 2019). The whereabouts of the weak
spots should be known, as well as the reason(s) for
the existence of weak spots, serving as input for the
establishment of an improvement strategy. Direct and
indirect costs of DQ are compared by making use of
a cost evaluation to support a decision making pro-
cess for its development of an improvement strategy
(Spruit and Pietzka, 2015). These costs include the
costs of the business impacts of the DQ issues, as well
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
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as the costs of potential improvement practices.
3.2 Activities & Deliverables
The entire CRM-DQMF comprises seven phases, as
can be seen in figure 1.
Figure 1: The CRM-DQMF.
Client Profiling is performed to reconstruct the
client’s organisational environment with regards to
the CRM solution. The output is a client profile which
can be utilised for the definition of data roles, busi-
ness impact analyses, and root cause detection. The
Project Definition is executed once at the beginning of
every project to indicate what the project will entail in
terms of DQ management. Its output is a unique DQ
management plan, which determines the utilisation of
the remainder of the CRM-DQMF. Preparation gath-
ers the required information for the data mapping and
assessment phases, which includes the definition of
data roles, business requirements, and business rules.
Migration/Integration is performed to migrate and/or
integrate the data with the new CRM solution. DQ
Definition and Assessment are performed to define
and measure the DQ, and Improvement is performed
to improve potential DQ issues. DQ is improved by
refining business requirements and business rules till
DQ is determined to be of sufficient quality. Then
a migration/integration plan is established and exe-
cuted. Once the migration or integration is estab-
lished, the data lifecycle does not come to an end,
hence DQ is still required to be managed. The phases
Project definition and Migration/Integration are no
longer part of the CRM-DQMF, and DQ monitoring
will take place through continuous assessment and
improvement of DQ. The framework is reconstructed
as can be seen in figure 2. The distinct phases along
with their activities are elaborated on below.
Figure 2: The CRM-DQMF after migration/integration is
established.
3.2.1 Client Profiling
To understand and reconstruct the client’s organisa-
tional environment with regards to the CRM solution,
a client profile is established containing information
on the data, business processes, resources, data poli-
A Data Quality Management Framework to Support Delivery and Consultancy of CRM Platforms
67
cies, and data standards. Below, the different activi-
ties that gather the required information are explained.
Identify Data: The concerned data is identified, so
it is known which data should be subject to DQ man-
agement practices. The volume of the data is required
in order to appropriately define a migration or inte-
gration strategy and to properly indicate the magni-
tude of potential DQ issues. The location of the data
is required to properly indicate where the data affects
the business, and hence determine the kind of DQ is-
sues. The type of the data is required to determine
the most appropriate DQ definition and measurement
techniques.
Identify Concerned Business Processes: Business
processes that are concerned with the CRM solution
are identified. The business processes create, use,
move, or modify the concerned data, and form a tech-
nical and business process landscape indicating the
whereabouts and purposes of the data.
Identify Resources: The resources of the data are
identified. This includes human resources, such as
employees that enter the data, data sources that pro-
duce the data, and applications that utilise, move, or
modify the data. The resources provide insights on
the places of potential business impact caused by DQ
issues. Subsequently, it can be used as input for the
development of an improvement strategy.
Identify Data Policies: Data policies at the client’s
side are identified, as well as regulatory policies.
They are directives that codify principles and man-
agement intent into rules that govern the data. Data
policies might include, for example, rules about data
classifications of criticality or GDPR. The data poli-
cies are input for the definition of DQ requirements.
Identify Data Standard: The existing data stan-
dard for all concerned data is identified. A data stan-
dard conditions the data to ensure that it meets rules
for content and format. Data standards contribute to
the definition of DQ, since they provide a means for
comparison. The data standard requires continuous
reviewing and refinement.
3.2.2 Project Definition
The project definition phase of the CRM-DQMF is
the only phase that is executed by default for every
project. As aforementioned, every client and project
of a CRM D&C team is unique. Therefore, every
project requires its own DQ management plan. In
the project definition phase of the CRM-DQMF, the
DQ management plan is defined. This definition takes
several actions, which are explained in the following
paragraphs.
Establish Business Case: A CRM business case is
established to define the CRM D&C project. This
business case includes the business goals of the client
and the CRM D&C project scope. The business goals
and scope indicate whether data is required to be of
high quality, and to what extent DQ management is
of relevance. The budget of the client is determined,
as this indicates the monetary boundaries of the CRM
D&C project and the possible inclusion of the DQ
management services of the CRM D&C team in the
project proposal. Often, concessions have to be made
either on DQ to deliver client experience within the
constraints of costs and technique, or on the budget
from the client’s side.
Perform Impact Analysis on Business Goals: By
defining the impact of poor DQ on the business goals,
as well as the way that high DQ will enable the busi-
ness goals the importance of DQ management is em-
phasized. This creates awareness on the topic for the
client and makes an indication of the need for DQ
management. When business impacts of poor DQ
are defined to be negligible for the specific client and
project, the remainder of the CRM-DQMF can be dis-
carded. This might result in less effort by the CRM
D&C team, as potential redundancy of the CRM-
DQMF can be detected at an earlier point in time mak-
ing the CRM-DQMF less expensive.
Identify Maturity Level: The DQ management
maturity level of the client is taken into account for
the design of a DQ management plan, as this indi-
cates to what extent the client requires the assistance
of a CRM D&C team in terms of DQ management.
The activity is extracted from the insights on the DQ
management expertise level of the client from the case
study, and its relevance is supported by Spruit and
Pietzka (Spruit and Pietzka, 2015).
Identify DQ Management Goal: Using the DQ
management maturity matrix the client’s goals of DQ
management are indicated as well. As every client
and project is unique, the goals of DQ management
depend on the scope of the CRM D&C project and
the business goals of the client, which determines the
importance of DQ management. The current matu-
rity level of DQ management next to the goal matu-
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rity level indicates at which capabilities of DQ man-
agement the client requires to grow with regard to the
CRM D&C project. This is used as input for the es-
tablishment of the DQ management plan.
Establish DQ Management Plan: Formatted by
the business case, and using the input of the impact
analysis, the current maturity level, and the goal ma-
turity level of the client, the CRM D&C team devel-
ops a DQ management plan together with the client.
The key activity is the definition of the key roles and
responsibilities for the realisation of the target DQ
management maturity level for each DQ management
capability. It describes the DQ management services
provided by the CRM D&C team, as well as the role
descriptions and responsibilities of the client. The
plan determines the remainder of the utilisation of the
CRM-DQMF.
3.2.3 Preparation
The preparation phase gathers the required informa-
tion for the data mapping and the assessment.
Define Usage & Ownership: For all data, the us-
age, ownership, and access are defined. Data usage
includes all that make use of the data. Data ownership
defines who is responsible for the data. Data access
defines all that have access to the data.
Define Business Requirements: Business require-
ments are extracted from the business goals with the
CRM D&C project. The business requirements de-
scribe what needs to be done to achieve the business
goals.
Define DQ Business Rules: Business rules are de-
fined and refined, describing expectations about the
concerned data. They should be created through
analysis of business processes, data policies, data
standards, business impact of data, assessment re-
ports, and common sense. Business rules are gen-
erally associated with the way data is collected or
created. For example, when a client wants to send
monthly newsletters to a specific sample of its cus-
tomers as part of its marketing strategy in CRM, a
business rule could be about the population of demo-
graphic fields such as birth date, or contact informa-
tion fields such as e-mail address. In this case, a va-
lidity rule might describe the format of the field birth
date in ‘dd/mm/yy’, and a completeness rule might
describe the population of the field e-mail address to
be mandatory.
3.2.4 Migration/Integration
To prepare for data migration or integration, the
source system of the specific data is identified, as this
determines the applicable data standards. Addition-
ally, a mapping model is developed by defining the
profile of the CRM platform and map this to the def-
inition of the legacy CRM or the integration. After
data mapping, the DQ is assessed. Once the DQ is
assessed and, when required, improved, a migration
and/or integration plan is created and executed. This
is done after (some iterations of) assessment and im-
provement practices, since ideally potential issues re-
garding DQ are resolved before migration or integra-
tion is established to prevent more significant prob-
lems. Most issues concerning DQ are discovered
when the migration is performed in a test environment
as part of a dry run. The rotating arrow is added as an
extension on the PDD notation as introduced by van
de Weerd and Brinkkemper (van de Weerd, 2009). It
indicates that migration practices can be performed
multiple times as dry runs. When the issues as dis-
covered by a dry run are eliminated, the migration will
be established either again as a dry run, or in produc-
tion. After the migration has been established, the DQ
definition and assessment are required to be ongoing
processes, hence the process loops back to DQ defini-
tion in case of no DQ issues till the end of the data life
cycle. When DQ issues occur, improvement practices
are implemented first.
3.2.5 DQ Definition & Assessment
The input for the DQ assessment phase is the gathered
knowledge of the previous phases. The output is a DQ
report.
Perform Impact Analysis: By performing an im-
pact analysis, critical data elements are identified.
Critical data elements represent data that is of utmost
importance for the achievement of the business goals.
Those elements are required to comply with their DQ
definitions. The result of the impact analysis is a pri-
oritised list of data elements which can be used by the
team to focus their work efforts.
Define DQ Requirements: DQ is defined by means
of DQ dimensions and DQ thresholds. DQ dimen-
sions enable the characterization of rules (e.g., e-mail
address must be populated) and findings (e.g., e-mail
address is 98% complete). They facilitate a mutual
understanding of what is being measured. The DQ di-
mensions provide the basis for the definition of mean-
ingful metrics. The DQ threshold defines the require-
ment belonging to the DQ dimension.
A Data Quality Management Framework to Support Delivery and Consultancy of CRM Platforms
69
Define DQ Metrics: Once the DQ dimensions are
defined, metrics can be defined in order to quantify
the findings of DQ. For example, a DQ business rule
can be for the field e-mail address to be mandatory,
which translates into the DQ dimension completeness.
The metric that can be used to measure the complete-
ness of the field e-mail address can be of type ratio,
dividing the number of records where the field is pop-
ulated by the total amount of records, and multiply
this by 100 to get the percentage of complete records.
Measure DQ: DQ is measured either subjectively
or objectively (Cichy and Rass, 2019). The metrics
are used to quantify the measurements. The output is
the quantified measurements of DQ.
Identify DQ Issues: Based on the measurements
and DQ business rules, DQ issues are identified. DQ
issues are identified by setting status indicators for
all data in terms of its dimension(s) and thresholds.
For example, the status indicator of the dimension of
completeness for the field e-mail address can be indi-
cated Unacceptable when the measurement results in
the threshold of below 80% complete.
Report on Findings: The final output is an assess-
ment report of the DQ and potential issues. The as-
sessment report might offer a new perspective on the
concerned data, from which new business rules could
be articulated. When DQ issues occur, improvement
practices will take place.
3.2.6 Improvement
The improvement phase of the CRM-DQMF is only
executed when DQ issues are reported on in the out-
put of the assessment phase. When improvement
activities have been applied, the CRM-DQMF loops
back to the Preparation phase to review business re-
quirements and business rules. In case of a strategy
correction as part of the improvement strategy, the
Client profiling phase should be revisited to review
the organisational environment.
Perform Impact Analysis: The identified DQ is-
sues are quantified and prioritized based on business
impact. Business impacts include monetary costs of
poor DQ, as well as non-monetary impacts. It also
takes into account the criticality of the data, the vol-
ume of the data, the number of business processes and
stakeholders impacted by the issue, and the risks asso-
ciated with the issue. This information is all extracted
during the Client profiling and Preparation phases of
the framework. The output is a ranked list of DQ is-
sues that should be taken into account within the im-
provement strategy.
Perform Root Cause Analysis: Ideally, the DQ is-
sues are remediated at their root cause (Batini et al.,
2009). This could also mean controls and process im-
provements to prevent further DQ issues from hap-
pening. Therefore, a root cause analysis is performed
to identify the root causes of DQ issues.
Develop Improvement Strategy: Based on the im-
pact analysis, an improvement strategy is developed,
evaluating the costs of the issue against the costs of
the required improvement actions. The improvement
strategy ranks the issues that can be addressed imme-
diately and at low costs, as well as more strategic im-
provements, such as root cause remediation and pre-
vention practices. It contains improvement goals that
are specific, achievable, and based on a quantification
of the business impacts.
Perform Improvement Actions: The improvement
strategy is put into practice. This might result in re-
visiting Client profiling or Preparation practices, or
direct improvements in the data. Either way, assess-
ment is performed again to assess the DQ.
4 VALIDATION
To validate the CRM-DQMF, a validation model of
the artifact is drafted. The validation model, or design
theory, consists of a description of the properties of
the artifact and the interaction with the problem con-
text (Wieringa, 2014). The discussions that form the
design theory are facilitated by the use of confirma-
tory focus groups (Tremblay et al., 2010) with a total
of 6 experts (see table 3). Additionally, to observe
and measure how well the CRM-DQMF supports DQ
management in CRM D&C projects, the evaluation
model of Moody (Moody, 2003) and evaluation crite-
ria by Prat et al. (Prat et al., 2015) are utilised. The
constructs of desired qualities (Perceived Alignment
with CRM D&C and Perceived Effectiveness), Per-
ceived Ease of Use, Perceived Usefulness, and Per-
ceived Completeness are measured by means of an
interactive questionnaire, of which the results on a 5-
point Likert scale can be found in table 4.
The main insights that are extracted are elaborated
on in the following paragraphs.
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Table 3: Participants Validation.
ID Role
Years of Experience
P1 Consultant 4
P2 Senior Analyst 2
P3 Manager 9
P4 Senior Consultant 4
P5 Senior Consultant 7
P6 Senior Analyst 2
Table 4: Participants Validation.
Construct P1 P2 P3 P4 P5 P6 Average
Alignment
with
CRM
D&C
Business
4,5 5 4,5 4,5 4 3,5 4,3
Perceived
Effec-
tiveness
5 5 4,5 4,5 3,5 5 4,6
Perceived
Com-
pleteness
4,5 3 4 2,5 3 3 3,8
Perceived
Ease of
Use
4,5 3,5 4 4 2,5 3 3,6
Perceived
Useful-
ness
5 4,5 4,5 4.5 3,5 5 4,5
Intention
to Use
5 5 4 4,5 3 4,5 4,3
Expensiveness: The establishment of a DQ man-
agement plan as it is presented in the first design of
the CRM-DQMF might be too expensive for smaller
projects where small amounts of data are involved.
To mitigate this problem, the establishment of the DQ
management plan should be more integrated in the
creation of the general CRM D&C project proposal,
rather than an activity on its own. However, this is not
part of the CRM culture at the case study environment
yet, and thus this might require change management
practices.
Awareness: Clients might not be aware of the im-
portance of DQ management, hence do not want to
spend their budget on DQ management services of
the CRM D&C team. Therefore, the impact of poor
quality data on the business goals needs to be defined
at the start of a project, as well as the way that high
quality data will enable the business goals (Interna-
tional DAMA, 2017). This emphasizes the impor-
tance of DQ management, creating awareness of the
topic and making an indication of the need for DQ
management. However, this needs to be done as in-
expensive as possible in order to be profitable for the
CRM D&C team, as this influences the development
of the DQ management plan and thus the project pro-
posal.
Agility: The agile project approach of CRM D&C
projects did not come through cogent enough accord-
ing to the validation sessions. The participants ar-
gued that they would like the CRM-DQMF to guide
the CRM D&C team in integrating DQ management
in the concept of so-called sprints in agile projects,
where specific work is selected for a set period of
time. First thoughts on this matter indicate that the
CRM-DQMF is supposed to support an ongoing pro-
cess, which could be translated into sprints, where the
assessment and potential improvement of DQ itera-
tively takes place in every new sprint in the project.
High-level: The contemporary design of the CRM-
DQMF is deemed too high level to put into practice
as it is. The CRM-DQMF creates awareness on the
importance of DQ management amongst CRM D&C
teams and provides relevant insights in what DQ man-
agement encompasses, rather than providing a step-
by-step guidance in implementing DQ management
in CRM D&C projects.
5 DISCUSSION
The factors that form a potential threat to the validity
of this research are elaborated on in following sec-
tions, along with the possible diminishing of those
threats. Subsequently, the limitations to this research
are discussed.
5.1 Threats to Validity
The five types of validity as described by Johnson
(Johnson, 1997) are used to examine the validity of
this research.
Descriptive Validity: For this research, only one
researcher interviewed participants and conducted
documentation analysis, which eliminates the possi-
bility to achieve this validity type through investiga-
tor triangulation. To improve the descriptive valid-
ity of this research nonetheless, all conducted inter-
views and validation sessions were recorded to facili-
tate more accurate recalls of the researcher.
Interpretive Validity: To ensure this type of valid-
ity, the researcher regularly incorporated participant
feedback within the case study (Johnson, 1997). This
is done through the utilisation of the question type
A Data Quality Management Framework to Support Delivery and Consultancy of CRM Platforms
71
interpreting questions as proposed by Kvale (Kvale,
1996) to inspect whether the interviewee’s answer is
interpreted correctly.
Theoretical Validity: To achieve theoretical valid-
ity, fieldwork is incorporated in this research. This
fieldwork consists of several elements: participation
in training session which were facilitated to CRM
D&C consultants; close observation of a collabora-
tion tool used by the CRM D&C community at the
case company; attending presentations on the execu-
tion of specific CRM D&C projects; joining a day
of scrum meetings between the case company and a
client of the financial industry; and gain certificates
which are required/recommended for CRM D&C em-
ployees.
Internal Validity: Method triangulation is utilised
to achieve internal validity (Kaplan and Maxwell,
2005; Johnson, 1997). This means that more than one
research method is used, namely literature review, ex-
pert interviews, and documentation analysis. Subse-
quently, data triangulation is applied by making use of
multiple data sources (Johnson, 1997). Multiple ex-
pert interviews are conducted with participants form
varying backgrounds. Subsequently, documentation
from a variety of sources is examined for the docu-
mentation analysis.
External Validity: The case study is performed at
one organisation. However, various literature sources
are used for the design of the CRM-DQMF, and doc-
umentation utilised for the documentation analysis
originated from two additional organisations. Fur-
thermore, the abstractness and high-level approach
of the CRM-DQMF increases its external validity, as
it facilitates generalisability to CRM D&C projects
varying in client, industry, and business goals.
5.2 Limitations
First of all, due to time and resource restrictions, this
research was not able to investigate the actual adop-
tion of the CRM-DQMF within a CRM D&C project.
Consequently, all conclusions are extracted from non-
empirical sources, and the experience and data of ex-
perts.
Second, most participants were not consciously
familiar with DQ management practices. Therefore,
much effort went to exploring the expertise level of
participants and being able to conduct the interviews
in such a way that participants understood the con-
cepts, while phrasing interview questions to extract
required information without creating researcher bias.
Additionally, the expert interviews with the partici-
pants evolved over time. This contributes to the for-
mulation of some question types, such as follow-up
and probing questions (Kvale, 1996), and the answers
to those questions weigh more when they are agreed
with by other interviewees. The disadvantage is that
it could create bias, as interviewees might have been
pushed towards a certain direction. To mitigate this
disadvantage, results from previous interviews were
only provided when the interviewee already provided
an answer on their own, and there existed sufficient
grounds for suspecting the previous results might be
applicable for the current interview as well.
Third, potential participants for the validation ses-
sions had busy and asynchronous agendas, which
made it difficult to schedule focus groups of sufficient
sizes. In the end, there was chosen to perform mini
focus groups consisting of two participants (Nyumba
et al., 2018).
Last, the research process has been impacted by
the need for all research efforts to be arranged on-
line due to the COVID-19 regulations set by the gov-
ernment, the university, and the case company. This
might have resulted in less sufficient sampling results.
Subsequently, it might have influenced the interpreta-
tion of the researcher, as online settings were some-
times lacking in terms of connection.
6 CONCLUSIONS AND FUTURE
RESEARCH
6.1 Conclusions
The designed CRM-DQMF combines scientific liter-
ature and practitioner’s insights on DQ management
and CRM D&C. It provides a high-level overview
of DQ management practices incorporated in CRM
D&C projects. With its current design, the CRM-
DQMF is a tool to plan on opportunities for the incor-
poration of DQ management in CRM D&C projects.
This incorporation involves the recognition of vari-
ety in clients and projects by the establishment of a
unique DQ management plan. This plan describes
to what extent DQ management services of the CRM
D&C team are required for the specific project. The
CRM-DQMF contains the following components:
Client profiling to gather required knowledge for
DQ management and the CRM solution;
Project definition to establish the data quality
management plan for the project;
Preparation to define data roles and business re-
quirements;
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
72
Migration/Integration to establish migration
and/or integration;
DQ definition to define quality and metrics;
Assessment to measure DQ and report on findings;
and
Improvement to define an improvement strategy
and improve DQ.
6.2 Future Research
This research leaves multiple opportunities for fur-
ther research. First, improvement opportunities can
be found in perceived ease of use and perceived com-
pleteness based on the validation. Thus, further re-
search could focus on the investigation of the usability
and exhaustiveness of the CRM-DQMF. The contem-
porary CRM-DQMF is a high-level overview of DQ
management practices incorporated in CRM D&C
projects. However, as found within this study, to be
of optimal use for CRM D&C teams, it requires more
step-by-step guidance on how to perform or consult
on the individual activities. Hence, more research can
be done on the incorporation of step-by-step guidance
on the execution of each activity that is included in the
CRM-DQMF as well as the utilisation of the CRM-
DQMF as a whole, to ensure its usability for CRM
D&C teams. This might include further research to
investigate on how CRM D&C teams can be directed
on which components of the CRM-DQMF suit their
client’s situation best.
Second, further empirical validation of this re-
search is required. This research solely evaluates the
design through validation sessions using a design the-
ory and a questionnaire. Possible evaluation can in-
clude, for example, expert interviews, technical ac-
tion research at actual CRM D&C projects, or sur-
veys. Furthermore, the sampling results of the case
study can be extended to experts from other fields to
include additional perspectives next to those that are
utilised within this study. The results can then be used
for the improvement of the CRM-DQMF.
Third, this research assumes that the participants
of the case study are able to decide which practices
would best fit the needs of the CRM D&C projects
of the organisation. During the case study and vali-
dation sessions, some experts argued that they would
require more guidance or persuasion for the applica-
tion of the CRM-DQMF or DQ management practices
at all. DQ management is not part of the CRM D&C
culture within this case study. Hence, the adoption of
the CRM-DQMF might involve change management
to ensure CRM D&C teams involve DQ management
practices within CRM projects more explicitly, which
could result in better CRM solutions. Fourth, the im-
plementation of the CRM-DQMF might be too ex-
pensive as it is. Therefore, an opportunity for fur-
ther study lies in how to integrate the establishment
of a DQ management plan into the development of a
CRM D&C project proposal in an as inexpensive as
possible manner, as this is only implicitly mentioned
within this research.
Lastly, potential further research can review the
incorporation of the CRM-DQMF into agile project
approach practices, such as sprints, as this is only im-
plicitly mentioned within this research.
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APPENDICES
Data Quality Management Methods, see:
https://osf.io/xkquj/
Interview Protocol, see: https://osf.io/3c9ys/
CRM-DQMF Design, see: https://osf.io/375up/
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