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 ﬁve

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.

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

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

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

ICEIS 2022 - 24th International Conference on Enterprise Information Systems

(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

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

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

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-

ICEIS 2022 - 24th International Conference on Enterprise Information Systems

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

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.

ICEIS 2022 - 24th International Conference on Enterprise Information Systems

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

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

• 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/

ICEIS 2022 - 24th International Conference on Enterprise Information Systems