End-to-End Data Quality: Insights from Two Case Studies
M. Redwan Hasan and Christine Legner
University of Lausanne, Switzerland
Keywords: Data Quality, DMAIC Cycle, Master Data, Information Supply Chain, Data Management, End-to-End.
Abstract: Maintaining high data quality in organizations have become indispensable. In the past, companies largely
concentrated their data quality efforts on a single point in the information supply chain – focusing either on
master data quality or on information products. As they start repurposing data and leveraging it for more
advanced and complex use-cases, they need to proactively manage data quality in an end-to-end approach.
Leveraging insights from two case studies, this paper analyses two different, yet complementary approaches
to end-to-end data quality management, namely first-time-right approach and use-case driven approach. The
findings highlight that end-to-end data quality management relies on common principles but can start from
either side of the information supply chain – either through a use-case or data entry point at the source.
The amount of data has been increasing at an
exponential rate. A survey of more than 2000
business and IT managers found that data is expected
grow nearly five times by 2025 with 57% fearing their
businesses would not be able to keep up with such
massive volumes (BusinessWire, 2020). Data flows
through an information supply chain which processes
and transforms it into an information product for the
use of data consumers (Wang, 1998). When more
users and systems interact with the data in the
process, this leads to a higher possibility of dilution
in the quality of data (Taleb, Serhani, & Dssouli,
2018). Thus, data quality needs to be embedded
throughout this journey of becoming an informational
output, rather than just focusing on the quality at a
single point in the chain. This calls for an end-to-end
view, that connects the different users, systems and
processes interacting with data in the information
supply chain facilitating a proactive and ongoing
exchange of details on identification and correction of
poor data quality when it manifests. Such view allows
for a better awareness and stronger control which is
vital for data quality (Jones-Farmer, Ezell, & Hazen,
2014). It closes the loop in two ways: First, by
connecting relevant entities that constantly
communicate and proactively ensure data quality
(Krishnan, Haas, Franklin, & Wu, 2016). Second, by
establishing continuous improvement cycles, as
suggested by data quality management methods, such
as the seminal Total Data Quality Management
(TDQM) approach (Wang, 1998) and the Define,
Measure, Analyse, Improve, Control (DMAIC) cycle
from Six Sigma (de Mast & Lokkerbol, 2012).
Although few recent papers (Byabazaire, O’Hare,
& Delaney, 2020; Taleb et al., 2018) stressed the need
for end-to-end data quality throughout the data
pipeline, they are mainly centred around big data.
Existing data quality research, on the other hand, has
mainly looked into barriers for master data quality
(Haug & Arlbjørn, 2011; Loshin, 2010), measuring
master data quality using a cockpit (Otto, Ebner, &
Hüner, 2010), improving data quality using master
data management (Hikmawati, Santosa, & Hidayah,
2021) and controlling data quality at source (Singh &
Singh, 2010). Other authors studied information
product or data product quality (Machado, Costa, &
Santos, 2021; Parssian, Sarkar, & Jacob, 2004), but
were limited to only relational databases using certain
data quality dimensions. We conclude that extant
literature has considered data quality at different
points in the information supply chain, but that we
lack empirical studies to better understand end-to-end
data quality management within the realities of data
flows in enterprises. To address these gaps, we
propose the following research question:
How do firms implement end-to-end approaches to
manage the quality of their data?
To analyse data quality management in a real-life
context, we opted for multiple case studies (Yin,
Hasan, M. and Legner, C.
End-to-End Data Quality: Insights from Two Case Studies.
DOI: 10.5220/0011276300003269
In Proceedings of the 11th International Conference on Data Science, Technology and Applications (DATA 2022), pages 392-399
ISBN: 978-989-758-583-8; ISSN: 2184-285X
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
2003). We selected two multinational companies that
we consider critical cases (Paré, 2004). Both have
implemented data quality from an end-to-end
perspective but pursue different approaches. Both
companies have matured data management teams
with strong data governance and put specific
emphasis on establishing closed loops that enable
continuous data quality improvements. In this paper,
we analyse both approaches with their commonalities
and differences through the lens of DMAIC cycle.
The latter outlines the main phases for managing
quality improvements in organizations (Montgomery
& Woodall, 2008).
From our within- and cross-case analysis, we find
that both companies address all phases of the DMAIC
cycle, but their approaches highlight different
initiation points, root-causes & improvement
methods. While one company implements master
data quality at the source to support an increasing
number of business processes at global scale, the
other leverages a use-case driven approach that
improves data quality for a small set of relevant data
attributes for high-priority analytics and operational
use-cases. By identifying patterns towards end-to-end
data quality, our findings contribute to existing data
quality literature (Otto & Österle, 2015; Zhu,
Madnick, Lee, & Wang, 2014) and provide a starting
point for future research regarding how data quality
can be ensured at each stage of the information supply
chain especially when organizations are increasingly
collecting and utilizing different forms of big data.
In the next section, we review the data quality
literature. Then, we define the research gap and
discuss the research methodology. Next, we introduce
the case studies and perform the within- and cross-
case analysis. Finally, we present our conclusions,
limitations and outlook on future research.
2.1 Defining Data Quality
Data quality is most often defined in terms of data’s
“fitness for use” (Tayi & Ballou, 1998). Thus, data
quality is likely to vary among people and functions
based on the tasks they seek to address. For instance,
Wang & Strong, (1996) produced an extensive initial
list of 179 data quality dimensions, 15 of which were
identified for practical use and were categorized into
four data quality hierarchies intrinsic, contextual,
representational and accessibility. Various data
quality dimensions, such as accuracy, volume,
completeness, timeliness and trustworthiness are
highlighted in various papers (Klein & Lehner, 2009;
Metzger, Chi, Engel, & Marconi, 2012). To assess
data sources, accuracy, validity and credibility were
underscored as required dimensions (Barnaghi &
Sheth, 2016). However, data quality dimensions
required to measure data quality varies for different
data types (Batini & Scannapieco, 2016) and also for
various application domains and data sources (Batini,
Rula, Scannapieco, & Viscusi, 2015). While existing
studies mostly elaborate on different data types and
the relevant data quality dimensions, they do not
consider the data flows in enterprises and where data
quality should be measured.
2.2 Data Quality Management as
Continuous Improvement
Another stream of the data quality literature focuses
on developing and applying various frameworks to
manage the quality of data (Batini, Cappiello,
Francalanci, & Maurino, 2009). For instance, the
seminal work by Wang (1998) on the TDQM
framework encourages a product perspective on data
and provides four stages to ensure end-to-end quality
improvement process. The Total Information Quality
Management (English, 1999) approach focuses on the
management implications of consolidating
operational data into data warehouses. To evaluate
web-based information using tools, the Information
Quality Measurement approach (Eppler &
Muenzenmayer, 2002) outlines assessment planning,
configuration, measurement and follow-up activities
as steps. The Activity-based Measuring and
Evaluating of Product Information Quality (Su & Jin,
2007) assesses data quality in manufacturing
companies that produce physical products. Most of
the frameworks above are designed to meet data
quality in a specific context and are not general-
purpose in nature. Seminal work like TDQM which is
argued to be general-purpose (Batini et al., 2009)
lacks the control step which is crucial in ensuring high
data quality (Jones-Farmer et al., 2014).
The DMAIC cycle from Six Sigma is widely used
for process and quality improvement. It provides a
structured and general problem-solving guideline
(Montgomery & Woodall, 2008), allowing
organizations to better understand the complexities
behind initiatives such as data quality. The DMAIC
cycle comprises five phases (Smętkowska &
Mrugalska, 2018):
a) Define The define phase starts with the
identification of the data quality problem, its
business impact and resource needs.
End-to-End Data Quality: Insights from Two Case Studies
b) Measure The measurement phase defines
the metrics that are scored in order to
quantify the existing data quality issues.
c) Analyse The analysis phase interprets the
metrics results and identifies the root causes
to the data quality problem.
d) Improve The improvement phase puts
actions, techniques or solutions in place to
fix the data values or change processes.
e) Control The control phase checks whether
the improvements are sufficient and
monitors deviations from the objectives.
The primary principle of DMAIC is to establish a
continuous cycle of identification and improvement
of data quality-related challenges that feeds into the
next iteration. By doing so, it closes the loop. As the
phases take place sequentially, it leads to a continuous
evaluation of the data quality initiatives within the
loop leading to a sustainable perpetuation of the
data quality tasks (Montgomery & Woodall, 2008).
2.3 Research Gap: End-to-End View
Despite the ongoing debate on data quality, we
observe a void of literature that captures data quality
from an end-to-end perspective. The need for end-to-
end view is exacerbated by emerging analytical use-
cases that are increasingly playing a key role in
creating business value. Such use-cases require a lot
of data from multiple sources within the organization.
These data have been collected, stored and
transformed in numerus ways by various teams.
Hence, while running the analytics use-cases, data
consumers could lack the insight whether the right
data with appropriate quality is being used implying
a lack of overview into the journey of data in the
information supply chain. Hence, an end-to-end view
will provide clarity regarding when, where and how
data quality was hampered and how to effectively fix
and sustain it. To address these gaps, we call for
empirical studies investigating how end-to-end data
quality has been put in practice enriching our
insights about the different and similar ways in which
organizations conduct the end-to-end implementation
with the singular objective of improving data quality.
Owing to the huge surge of data and advanced
analytics use-cases, this practical understanding is
crucial to manage the ever-evolving data quality
requirements and challenges because organizations
are becoming more data-driven. Therefore, this study
will also lay groundwork to guide organizations to
adapt and scale their data quality initiatives based on
changing data needs in their respective business
To address our research question, we opted for a case
study research design (Yin, 2003). Case studies
provide the opportunity to study the phenomenon of
interest in a naturalistic setting and understand it
within real-world context (Benbasat, Goldstein, &
Mead, 1987). Evidences garnered from several case
studies are often more compelling, regarded as more
robust and helps derive analytical generalizations
(Yin, 2003). We opted for two case studies, because
this significantly improves the analytical benefit and
the conclusions arising would be much stronger than
compared to a single case study. Hence, “… having
at least two cases should be your goal” (Yin, 2003, p.
54). We selected two companies as critical cases
(Paré, 2004), that have implemented end-to-end data
quality but use different strategies to attain this goal.
We used the following criteria to guide the selection
of the companies: First, the two companies are major
players within their respective industries and often
feature in the Fortune 500 list. They have significant
global presence and operate across multiple
continents. Secondly, both are large organizations
with strong experience in data management practices
and emphasis on end-to-end implementation. Thirdly,
the two companies received significant recognition
due to their innovative data quality management
approaches. They had been shortlisted as finalists for
good data quality practice award, after being assessed
by jury of international data management experts
comprising of academics and practitioners.
Therefore, being data-driven allows them to leverage
existing data and processes in order to create key
insights which allows them to efficiently run
operations globally. Due to their global presence, it is
particularly challenging to improve the quality of data
in an end-to-end manner – providing a setting to
empirically study different data quality management
approaches with the same goal. The overview of the
case companies is given in Table 1.
We collected data through the following primary
and secondary sources: The application documents of
the two companies submitted for the award were
initially analyzed. From this analysis we got a first
DATA 2022 - 11th International Conference on Data Science, Technology and Applications
Table 1: Overview of the case companies.
of employees
Data Quality
Goals of the Data Quality
Improvement Approach
FashionCo /
(Fashion and
$1-50B/60,000 Use-Case
Driven Data
Improve the efficient application
of vital use-cases (e-commerce,
sustainability, etc.) by fixing the
quality of data in a reduced set of
relevant data attributes
Improvement of 5 use-
cases with 3 feedback
loops. Up to 40000 data
defects were fixed in these
$1-50B/40,000 First-time-
right data
Supply high-speed and first-time
correct business partner data
through harmonization of various
data lifec
Process lead time
improved by 66%. First-
time-right rate of the data
reached 80% from 40%.
overview of their approaches. The companies also
included video demos to show certain aspects of their
data quality approach and provide details. Moreover,
we participated in their final presentations during the
award ceremony and in the subsequent discussion of
the cases with data management experts.
As part of the within-case analysis, we mapped an
initial breakdown of the case data against the DMAIC
cycle. This allows “the unique patterns of each case
to emerge” (Eisenhardt, 1989, p. 540) and eventually
helps lay foundation to gain deeper insights and rich
familiarity in the selected cases. Subsequently, to
grasp the patters across the cases, we perform a cross-
case analysis “to go beyond initial impressions,
especially through the use of structured and diverse
lenses on the data” (Eisenhardt, 1989, p. 541). This
better elucidates the commonalities and differences
between the two end-to-end data quality approaches,
enhancing the reliability and accuracy of the analysis.
Also, new insights could be found that might not have
been possible through a simple within-case analysis
because cross-case analysis deepens the explanation
and understanding of the identified patterns (Miles &
Huberman, 1994).
4.1 FashionC
As global fashion and retail company, FashionC faces
the challenge of a fast-changing seasonal product
portfolio with around 100,000 active products and
several 10,000 new products per season. FashionC
traditionally sold via retail channels, but e-commerce
and direct-to-consumer channels are playing an
increasingly important role leading to an increase in
the amount of data FashionC is producing.
The requests for resolving data quality issues for
both analytical and operational use cases were high.
Based on learnings from 13 high-priority use-cases,
FashionC developed a Use-Case Driven Data Quality
Management approach, which aimed at establishing
sustainable links between data consumers and data
creators. The key elements of the approach can be
summarized as follows: The trigger is a data quality
improvement request from the data consumers for
business-critical use-cases. The data quality team
identifies relevant data attributes with quality issues.
Depending on the use case, these are typically very
few attributes (up to 4), for which a definition and
shared rules need to be defined in a first step. Only
with these definition and rules, data quality can be
measured and the issues can be made transparent to
all stakeholders. This allows data change requests to
flow faster and be implemented within a short time
due to a direct connection of data consumers and data
producers. The data quality requests are sent to the
data producers through existing platforms such as MS
Teams and MicroStrategy data quality dashboards.
The data producers correct the data issues and provide
confirmation back to the data consumers.
For instance, a data consumer identifies problems
in ‘sustainability and ethics compliance validation’.
They look at the business rules that are behind the
use-case such as ‘material data should be compliant
to SEC’ and ‘SEC, product hangtag, F&B must
comply’. These rules then define the relevant data
attributes needed to run the use-case, such as
‘hangtag’, ‘features and benefits’, ‘technology
concept’ or ‘material composition’. These data
attributes fall under the jurisdiction of the material
team and they are informed to change the data values
creating a closed loop by connecting directly with
the responsible person at source and fixing data
quality issues quickly.
Within 3 months, FashionC aims to solve the data
quality issues existing in a selected use-case. Until
now, FashionC’s data quality team worked on 5 use-
cases and established 3 such feedback loops. For one
specific use case, this resulted into fixing defects
End-to-End Data Quality: Insights from Two Case Studies
worth 1 million Euros and 80% less effort in
correction and escalations. The overall success is
communicated via internal channels to garner interest
around data quality. Furthermore, it builds credibility
of the data quality team and encourages further
identification of use-cases to fix.
4.2 ChemicalO
Being a global specialty chemicals company,
ChemicalO’s adaptation of a new corporate vision of
‘profitable growth’ through innovative processes
have put the usage of data analytics and new
technologies into focus. The motivation driving this
vision is high innovative capability, consistent end-
to-end view of data and cost efficiency & reliability.
The data quality issues manifested due to the
decentralized data entry into several isolated systems.
This led to the creation of business partner data that
did not pass any formal quality checks but is manually
entered. Such isolated entries led to inconsistencies
and quality checks on the data were required before it
could be used in important processes. To do so, an
assessment of the criticality of the data is performed.
If the data was highly critical, a manual workflow was
separately installed to fix the data. On the contrary,
less critical data were fixed by the data consumer on
their end. These segregated steps made the whole
process slow and inefficient, ending up reducing the
quality of the data.
To address this issue, ChemicalO introduced the
CuVenSa – ChemicalO’s journey to a touchless first-
time-right data life cycle process. More precisely, the
company developed a user-friendly data self-service
to consolidate all the sub-processes, namely creation,
extension, change and deletion, that were needed to
manage the business partner data. The prior manual
checks were automatized by connecting with external
data sources and internal databases. This allowed
ChemicalO to utilize trustworthy external data to run
checks on the business partner data quality internally
and fix them through few clicks rather than waiting
for the internal process to learn about it first.
For instance, for payment fraud detection, prior
confirmation of vendor bank information was done
through manually communicating with a reliable
contact at the vendor company. With CuVenSa,
ChemicalO could approve bank information thanks to
external data from an inter-enterprise shared data
pool. Simultaneously, ChemicalO built up an internal
database with bank details in order to further
complement this pool. Together, ChemicalO
achieved 60% automatic bank validation from 10%
without contacting the vendor at all.
Following a time span of 12 months, ChemicalO’s
data quality team was able to deploy operational tasks
66% faster. In addition, over 500 Person Days per
year were saved for over 3000 data requesters and
maintainers. Better data also led to saving of efforts
worth 1.7 million Euros and 17000 hours for tax data
audits. The overall success was communicated using
short videos through organizational channels in order
to build credibility of the data quality team. More
importantly, it led to building trust in the usage of
external data to improve the quality of data.
5.1 Define Phase
In the case studies, companies have taken a
contrasting position in order to define the data quality
problems. More specifically, FashionC decided to
focus on data quality problems for high-priority
operational and analytical use-cases such as e-
commerce and sustainability. Therefore, they adopted
the strategy to drive data quality problem
identification from the side of data consumers. These
use-cases are driven by new data requirements, often
a combination of existing sources, and concern
specific data attributes. On the contrary, ChemicalOs
approach embraced the view that data quality issues
must be identified and fixed at source and
concentrated on one of their most fundamental master
data entities, i.e. business partners data. Business
partner data flows into downstream systems used by
many data consumers for master data or other
processes. If the data quality issues linger at source, it
will cascade down into global business processes and
create performance issues. This highlights that the
end-to-end approaches to improve data quality can
start from either side of the information supply chain.
5.2 Measure Phase
In this phase, the data quality issue is quantified in
order to better understand the nature of the problem.
ChemicalO’s data quality problems appear at the
source system for the entire dataset pertaining to
business partner data. As the dataset would be used in
global processes, the strategy was to ensure data
quality for all the data instances present in all the data
attributes. Since business partner is an established
master data object, existing data validation rules were
applied to measure whether the data values exist and
comply with the rules. These rules helped check
whether the data were inaccurate, missing or
DATA 2022 - 11th International Conference on Data Science, Technology and Applications
Table 2: Two approaches to end-to-end data quality.
Use-case driven data quality
First-time-right data quality
Trigger Increasing number of data use-cases in operations and
analytics area need combination of different data sources
Inconsistencies in business partner data that
impact global business processes
Define New data quality requirements in (few) relevant attributes
for business-critical use-cases
Data quality problems for established master
data objects (“single source of truth”)
Measure The data quality is measured in the specific datasets based
on shared definition and business rules
The data quality is measured in the source
system for the entire dataset based on data
validation rules
Analyse The root cause of poor data quality is either wrong data
capture, logic issues in the source systems, system
integration or wrong data usage
The root cause of poor data quality is
primarily due to defects in data creation
Improve Automated feedback loops between data consumer and
data producer to create transparency about the data quality
roblem and allocate res
onsibilities to fix the
Harmonize and automate the data life-cycle
processes through self-service application
and usin
external data connections
Control Stakeholder-oriented data quality dashboards to
constantly provide overview of the data relevant to the
Process-related KPIs dashboard to create
transparency across the whole organization
inconsistent. For instance, P.O. Box and location data
of suppliers must be consistent as it has vital tax
implication for a global company like ChemicalO.
On the other hand, FashionC had to start measuring
quality for datasets that combined different data
sources. They concentrated only on the data attributes
that are relevant for specific use-cases. The
problematic use-cases are re-engineered back towards
the data source and the data quality team had to work
with data consumers and producers to create a shared
definition of the business rules and attributes that
power it. Once identified, the business rules help
measure the data quality in only those attributes that
matter and the data values that do not meet the rules
are checked. For instance, for ‘customer inactivity
monitoring’, three relevant business rules were
identified and inactive customers were defined using
financial transaction data attributes. This allowed to
highlight around 20000 customers that were inactive.
5.3 Analyse Phase
The analysis phase concerns the understanding of the
root cause of the data quality problem. ChemicalO
had initially implemented multiple data lifecycle
processes in a way to respond to data consumers’
need of accurate and high-speed data. When it came
to entering business partner data into the system, only
certain data attributes that were deemed to be critical
went through extra steps of manual quality checks
whereas other non-vital data were readily entered
without much control. For the latter, the onus was on
the user to identify data problems and fix them on the
go. This fragmented approach made it difficult to
manage quality of incoming data at source in a
harmonized manner. Hence, the root cause of poor
data quality was identified at the data creation point
where data was wrongly entered through different
processes. On the other hand, FashionC’s root cause
for poor data quality was difficult to locate. As a use-
case typically requires multiple data items, analysing
data quality requires a re-engineering effort by going
back in the information supply chain to see what
happened to the relevant data attributes. The root
cause appears not only during data capture by data
producers but also due to system integrations and
usage challenges in the supply chain. As a result, data
quality gets diluted in the journey.
5.4 Improve Phase
In order to deal with the root cause and improve the
data quality problems, ChemicalO focused on data
creation and on being first-time-right. It developed an
in-house self-service application CuVenSa that
brought the various processes related to the creation,
extension, change and deletion of business partner
data into one platform. This application harmonized
the previously segregated processes allowing data to
have only a single point of entry and become a single
source of truth. Moreover, the manual data check
workflow was replaced by external data connections
that automatically checked whether right data entered
the system. This made data high-speed and correct.
Subsequently, FashionC addressed its root cause by
identifying exactly what went wrong with which data
and where in the information supply chain. For this,
it established an automated feedback loop starting
End-to-End Data Quality: Insights from Two Case Studies
from the data consumers towards the data producers
connecting all relevant stakeholders under one
chain of quality checks that quickly identifies data
quality issues and immediately feeds back that
information for the appropriate parties to fix.
Improvement approaches depended on the type of
root cause. For instance, data capture issues were
resolved through data instance correction in the data
attributes and usage error were treated by making data
consumers aware regarding right information usage.
5.5 Control Phase
In this phase, ChemicalO aimed to communicate the
success of the high-quality business partner data
through dashboards exhibiting process-related KPIs
such as ‘process lead time’. The improvements were
communicated to the wider organization because the
data lifecycle processes supported many global
functions. This built transparency and the opportunity
for the data consumers to provide feedback to the data
quality team in terms of upcoming use-cases that may
need extra attention. FashionC adopted a more
focused approach by developing stakeholder-specific
data quality dashboards that communicate to only
those relevant people to whom a particular data
quality issue matter. This achieves transparency with
only them who are concerned with the use-case in
question. Such focused approach can make
controlling more efficient and foster reduced lead
time between issue identification and resolution.
The cases demonstrate that data quality initiatives can
commence from either the input or output end in the
information supply chain. These findings extend the
pre-dominant approach of measuring mainly master
data quality (Otto et al., 2010) and managing master
data lifecycle (Ofner, Otto, Oesterle, & Straub, 2013)
towards focusing on new and upcoming analytics use-
cases driven by the increased usage and repurposing
of data. Moreover, prioritization of data quality issues
appeared to be a key action within the case studies.
ChemicalO prioritized only on business partner data
whereas FashionC concentrated on high-priority use-
cases, showing that successful data quality
improvements must be purposeful and cost-effective
(Kleindienst, 2017) and a smart way to do so is to fix
only what matters. We further observe that both firms
placed high importance on creating visibility on data
quality to create transparency and gain support. This
supports literature that have argued for creating data
quality awareness among stakeholders (McGilvray,
2021) and facilitating active participation in data
quality activities using new methods (Zhang,
Indulska, & Sadiq, 2019).We contribute to the extant
literature which looked into data quality in only
master data (Hikmawati et al., 2021), in information
or data products (Machado, Costa, & Santos, 2021) or
in enterprise systems (Glowalla & Sunyaev, 2014)
towards an end-to-end view. We argue that a
continuous monitoring and improvement cycle
connects the relevant entities that play a key role in
impacting data quality in the information supply
chain. This paper also contributes to the practitioner
knowledge by outlining an implementation blueprint
regarding end-to-end data quality approaches. Our
study comes with certain limitation. We studied only
two organizations, missing other interesting data
quality approaches with an end-to-end perspective.
For future research, the concept and definition of data
quality and relevant activities within the information
supply chain should be further refined. This could
provide basis for conceptualizing end-to-end data
quality not only from the source and information
product side, but also within the different data
processing steps. Upcoming studies can also
investigate end-to-end data quality approaches in
tech-savvy companies versus in traditional ones.
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End-to-End Data Quality: Insights from Two Case Studies