A Methodology for Constructing Patterns for the Management of Data
Science Projects
Christian Haertel
, Sarah Schramm, Matthias Pohl
, Sascha Bosse
, Daniel Staegemann
Christian Daase
and Klaus Turowski
Institute of Technical and Business Information Systems, Otto-von-Guericke University, Magdeburg, Germany
{christian.haertel, sarah.schramm, matthias.pohl, sascha.bosse, daniel.staegemann, christian.daase,
Data Science, Project Management, Pattern, Design Science Research.
In the era of Big Data, the successful completion of Data Science (DS) projects is crucial. However, DS
project management is quite challenging due to its interdisciplinary nature. Existing DS process models, such
as CRISP-DM, have limitations, resulting in low success rates for these undertakings. To address this issue,
a novel methodology for the construction of patterns in DS project management has been proposed, using
the Design Science Research methodology. The design draws inspiration from existing pattern concepts to
address common problems in DS project execution. The methodology is demonstrated through the creation of
patterns for best practices in DS project management, synthesized from scientific literature. The goal of this
approach is to provide a platform for exchanging and standardizing best practices in DS project management.
While initial demonstrations show the general applicability of the methodology, further evaluations and case
studies are necessary to assess its effectiveness and areas for improvement. The study identifies potential
ambiguities in certain activities within the process, suggesting opportunities for refinement. Overall, this
research contributes to the field of DS project management by offering a structured method to encapsulate and
disseminate effective practices, supporting the successful execution of data projects in organizations.
In a world where the amount of generated data is
steadily increasing, businesses of various domains
aim to derive potential advantages and enhance their
competitive positioning (de Medeiros et al., 2020).
Accordingly, Data Science (DS) as a discipline to
extract knowledge and insights from data using
various methods and techniques (Chang and Grady,
2019), has gained increasing significance (Cao,
2017). With its growing importance, the adequate
management of DS projects becomes crucial. How-
ever, organizations often encounter challenges in
implementing data-driven projects (Martinez et al.,
2021a). DS is considered an interdisciplinary field
that combines various areas such as statistics, com-
puter science, machine learning, and domain-specific
knowledge, which poses unique challenges to project
management (Martinez et al., 2021a). Various DS
process models have been developed to support the
implementation and management of DS projects
(e.g., CRISP-DM) (Saltz, 2015). However, research
indicated several weaknesses in these methodologies
(Martinez et al., 2021a). Hence, there is no widely ac-
cepted and applied approach, and custom methods are
derived instead (Saltz, 2015; Saltz et al., 2018). These
issues are reflected in the low DS project success rate
(VentureBeat, 2019), demanding improvements for
DS project management (Saltz and Krasteva, 2022).
As the literature identifies common problems in the
execution of DS projects (Martinez et al., 2021a),
the adaptation of the pattern concept to DS appears
promising. Patterns capture solutions to recurring
problems in a domain in a simple and straightfor-
ward form (Fehling et al., 2014). An overview and
structured presentation within patterns could ensure
alleviated and methodology-independent access to
common problems and solutions and, thus, contribute
to the improvement of DS project management
Haertel, C., Schramm, S., Pohl, M., Bosse, S., Staegemann, D., Daase, C. and Turowski, K.
A Methodology for Constructing Patterns for the Management of Data Science Projects.
DOI: 10.5220/0012705300003690
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 1, pages 354-365
ISBN: 978-989-758-692-7; ISSN: 2184-4992
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
activities. To the best of our knowledge, the pattern
concept has not been applied before to DS (project
management). Therefore, this research proposes a
methodology for pattern creation for the field of DS
project management, using the pattern identification,
authoring, and application process of (Fehling et al.,
2014) as a basis. Therefore, the following research
question (RQ) will be examined:
RQ: How can a methodology for the construction
of patterns for DS project management be designed
and applied?
For both, researchers and practitioners, this arti-
fact could form a platform for the exchange and stan-
dardization of best practices in the DS domain. Pat-
terns can be created, expanded, modified, and linked
to related areas. Overall, this study is intended to
contribute to supporting and conducting data projects
in organizations and, in turn, work toward improving
the success rates of these projects. The Design Sci-
ence Research (DSR) Methodology of (Peffers et al.,
2007) will be leveraged to develop the pattern con-
struction methodology. Therefore, in the next section,
the application of the DSR paradigm in the context of
this paper is described. Afterward, the relevant the-
oretical concepts for this work are discussed, includ-
ing DS and related terms, project management, and
fundamentals concerning the pattern concept. Con-
sequently, the pattern development process for DS
project management, an adaption of the process pro-
posed by (Fehling et al., 2014), is outlined. After
demonstrating the application of the artifact, this con-
tribution is concluded with a summary and an outlook
on future research endeavors.
Scientific research seeks new insights and relation-
ships in a specific field by applying scientific methods
(Eisend and Kuß, 2023). In the context of information
systems, DSR has emerged as a design-oriented ap-
proach to support the creation of artifacts to address
practically relevant problems (Hevner et al., 2004).
A pattern language construction methodology for DS
can also be categorized as an artifact (method), and
DSR can be considered suitable for its development.
The Design Science Research Methodology (DSRM),
according to (Peffers et al., 2007), is widely adopted
in the DSR context. According to the DSRM, an
artifact is designed, developed, and evaluated in six
phases. The individual steps for the work at hand are
explained in the following.
Problem Identification and Motivation: DS
projects suffer from high failure rates (VentureBeat,
2019). Hence, the development of new or revised
approaches for DS project management is necessary
(Saltz, 2022). Pattern languages are utilized to doc-
ument solutions to recurring problems in a given do-
main (Fehling et al., 2014) and have not yet been ap-
plied to DS. Accordingly, adapting this concept to this
field by proposing a specific methodology for the de-
velopment of patterns for DS can support addressing
common issues in the execution of these undertak-
Objectives of a Solution: The next step of the DSRM
involves deriving goals for a solution. Conducting a
DS project often involves encountering challenges re-
lated to team, project, and data and information man-
agement (Martinez et al., 2021a). The artifact of this
research, a methodology for the construction of DS
project management patterns, enables the capture of
solutions for these often-faced problems in DS from
the respective body of knowledge. Therefore, these
patterns can be utilized to overcome these obstacles
and, ultimately, lead to improved success rates in DS
Design and Development: Based on the definitions
by (Hevner et al., 2004), the developed artifact is
characterized as a method in the DSR context, as
the proposed pattern construction methodology pro-
vides a process on how to synthesize best practices
in the shape of patterns in the context of DS. For this
purpose, the general pattern identification, authoring,
and application procedure of (Fehling et al., 2014) is
adapted to DS project management. Therefore, foun-
dations and methodologies from the DS knowledge
base are used.
Demonstration: The application of the artifact to de-
velop patterns for best practices in the management of
DS projects is demonstrated. Because of page restric-
tions, only snippets from the created patterns can be
shown in this paper.
Evaluation: This step aims to observe and measure
the ability of the artifact with regards to providing a
solution to the initially stated problem (Peffers et al.,
2007). Thus, the Build-Evaluate pattern of (Sonnen-
berg and vom Brocke, 2012) is applied for this DSR
project, consisting of the four steps Eval 1 (justified
research gap), Eval 2 (validated design specification),
Eval 3 (proof of applicability), and Eval 4 (proof of
Communication: The final step within the DSRM is
achieved through writing, submitting, and presenting
this paper to the scientific community and interested
practitioners of the field.
A Methodology for Constructing Patterns for the Management of Data Science Projects
The development of a methodology for the construc-
tion of patterns for DS project management initially
requires establishing a sufficient knowledge base of
the underlying concepts. First, this implies discussing
the terminology around DS and its similarities as well
as delimitations to related disciplines. Additionally,
key aspects and existing research concerning (DS)
project management need to be covered. Finally, the
fundamentals of the pattern concept are introduced.
3.1 Data Science and Related Concepts
A widely accepted interpretation describes DS as the
methodology for synthesizing useful knowledge from
data through a process of discovery or formulation
and testing of hypotheses (Chang and Grady, 2019). It
is also characterized as an interdisciplinary field (Cao,
2018). Therefore, knowledge and methods from vari-
ous disciplines are brought together to utilize data ef-
fectively (Schulz et al., 2020). DS has emerged as
a unique discipline where a deep understanding of
the application-specific domain, mathematical knowl-
edge, and a solid technological background are es-
sential (Schulz et al., 2020). DS has been used since
the mid-2000s, focusing on gaining insights from data
(Chang and Grady, 2019). However, similar goals
were pursued earlier under different names. These re-
lated concepts are closely intertwined and cannot be
easily separated from one another (Chang and Grady,
2019). Examples are Data Mining and Big Data.
Data Mining is the exploration of patterns and
relationships in data using specialized algorithms
(Chang and Grady, 2019). Skills in statistics, math-
ematics, machine learning, algorithms, and domain
knowledge are applied in this process (Chang and
Grady, 2019). Data Mining is known as a part of
Knowledge Discovery in Databases (KDD), which
constitutes a comprehensive process for extracting
valuable knowledge from data (Fayyad et al., 1996;
Chang and Grady, 2019). Data Mining is a step in
this process (Fayyad et al., 1996). The proximity to
DS is evident, explaining the sometimes synonymous
use of the terms (Schulz et al., 2020). Generally, Data
Mining can be understood as a subfield of DS (Chang
and Grady, 2019).
Big Data can be defined by the four dimensions
(Vs) Volume (enormous size of datasets), Velocity
(speed of data generation and capturing), Variety (in
data sources and formats), and Variability (changes
in data flow, format, or volume) (Jeble et al., 2018;
Chang and Grady, 2019). Additionally, while other
characteristics such as Value (value of results), Ve-
racity (data quality), or Complexity (data complexity)
can be defined (Jeble et al., 2018), these dimensions
can be considered drivers for new scalable architec-
tures for data-intensive applications. Big Data posed
a challenge for data processing as well as analysis and
gave rise to the term DS to explore new techniques
for this matter (Chang and Grady, 2019). Thus, DS
encompasses Big Data and its analysis (Chang and
Grady, 2019). Based on the outlined overlaps be-
tween these disciplines, approaches for these domains
tend to be overarching.
3.2 Data Science Project Management
Project management can be defined as the ”applica-
tion of knowledge, skills, tools, and techniques to
project activities to meet the project requirements”
(PMI, 2017). Within project management, various
tasks such as project planning, risk management, and
many more are fulfilled (Wack, 2007). During project
planning, the scope, schedules, budget, and resource
allocation to achieve the project goals are defined
(Aichele and Sch
onberger, 2014). In IT projects,
the principles, procedures, methods, techniques, and
tools necessary for planning, controlling, and moni-
toring are summarized within IT project management
(Heinrich, 1997; Wieczorrek and Mertens, 2007). In
comparison, DS undertakings differ because of the
data focus and the resulting explorative character (Das
et al., 2015). Accordingly, both typical IT project
management methods and more specific process mod-
els are used in DS (Saltz and Hotz, 2020). Several
DS process models can be identified in the literature,
including KDD, CRISP-DM, TDSP, and SEMMA.
Typically, according to the DS lifecycle of (Haertel
et al., 2022), a DS project can be structured in the
broad phases Business Understanding, Data Collec-
tion, Exploration and Preparation, Analysis, Evalua-
tion, Deployment, and Utilization. However, research
suggests that the current DS methodologies are par-
tially unsuitable for tackling common challenges in
DS projects related to team, project, and data and
information management (Martinez et al., 2021a).
Hence, revised and new approaches are needed (Saltz
and Krasteva, 2022).
3.3 Pattern
For experts working on a problem, it is unusual to
develop a new solution that is entirely different from
existing ones (Buschmann, 1996). Often, there is a
tendency to rely on a similar and already solved prob-
lem, using the essential elements of the solution to
address the new problem (Buschmann, 1996). An
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
established concept for the structured documentation
of proven solutions is patterns (Fehling et al., 2014).
This principle originates from the architect Christo-
pher Alexander, who laid its foundation back in 1977
(Alexander, 1979; Coplien and Harrison, 2005). Pat-
terns describe a frequently occurring problem and the
core of its solution so that it can be used repeatedly
in different ways (Alexander et al., 1977). Patterns
are recorded in text form following a specific struc-
ture and generally consist of a particular context, a
problem, and a solution (Alexander, 1979). They are
hierarchically organized, documented, and intercon-
nected. Connections can be drawn within and to re-
lated subject areas, thus creating a complex and ab-
stract solution system that can be individually ac-
cessed (Fehling et al., 2014; Coplien, 2000). The
connections highlight relationships and capture hid-
den structures (Coplien, 2000). Accordingly, naviga-
tion is facilitated, and references to the application of
patterns are provided, resulting in the creation of a
pattern language (Fehling et al., 2014). In essence,
patterns can be understood as a concept for the struc-
tured documentation of proven solutions to recurring
problems in a specific domain (Fehling et al., 2014).
Although initially developed for architecture, the
concept was successfully transferred to other do-
mains such as the software field (Coplien and Har-
rison, 2005). Other authors have adopted, extended,
modified, and influenced the initial context-problem-
solution structure shaped by (Alexander et al., 1977).
Ultimately, the compilation, content, and sequence of
pattern sections are fundamentally left to the authors.
There are no predefined rules for the identification
and documentation of patterns, but there are guide-
lines for orientation like in (Wellhausen and Fiesser,
2011) and (Harrison, 2003). The creation of pat-
terns is often performed through expertise and expe-
rience or in collaboration with experts (Iba and Isaku,
2012). Another possibility is the extraction from lit-
erature (Fehling et al., 2014; G
unther and Knote,
2017). For instance, (Fehling et al., 2014) describe
a pattern-creation process that is used in this work.
Application of the pattern concept to (DS) project
management also appears sensible since several com-
mon challenges need to be addressed in the course
of a project. Accordingly, the synthesis of knowledge
from experts and the literature for summarizing corre-
sponding solutions in the form of patterns can provide
added value.
Figure 1: Pattern construction process, adopted from
(Fehling et al., 2014).
In the literature, there are different approaches to the
development of patterns. For the application in this
context, the process according to (Fehling et al., 2014)
was selected and adapted to the field of DS project
management since its applicability to various domains
has already been demonstrated. Despite the weak-
nesses outlined in DS methodologies, several publi-
cations discuss success factors and best practices for
DS project management. Therefore, the consolida-
tion of this knowledge is beneficial. (Fehling et al.,
2014) describe a detailed approach to the identifica-
tion and authoring of patterns and, thus, allows the
creation of patterns from the knowledge and exper-
tise conveyed through the literature. The following
subsections delve into the individual steps of this pro-
cess, based on which DS patterns shall be developed.
The method consists of three phases: pattern identi-
fication, pattern authoring, and pattern application, as
illustrated in Figure 1. Each stage involves several it-
eratively traversed activities to continuously improve
and adapt the developed results (Fehling et al., 2014).
In particular, the first two phases are repeated multi-
ple times to discover and form patterns. Finally, the
third phase involves refining the patterns for specific
use cases or application environments.
4.1 Pattern Identification
The first phase of the process, according to (Fehling
et al., 2014), is carried out through five iteratively tra-
versed activities as depicted in Figure 2. The exam-
A Methodology for Constructing Patterns for the Management of Data Science Projects
Figure 2: Pattern Identification phase, adopted from
(Fehling et al., 2014).
ined domain is initially structured in this phase, and
relevant information is gathered. In the first activ-
ity (Domain Definition), significant fundamentals of
the investigated field are elaborated and shared with
the group working on the patterns to build common
knowledge and understanding of the domain as es-
tablishing a foundation for the field is considered im-
portant (Fehling et al., 2014). Initially, this requires
creating a joint understanding of key terminology and
concepts of DS (e.g., process models, ML algorithms)
as seen in Section 3. Furthermore, since DS is a com-
plex and interdisciplinary field, the focus is purely on
DS project management.
The next activity, Coverage Consideration, fo-
cuses on assessing and narrowing down the scope of
the chosen domain (Fehling et al., 2014). The domain
can be extensive, making it impossible to consider the
entire scope. Accordingly, the scope is adjusted to the
size of the research group. Relevant topics are iden-
tified and aligned with characteristic problems of the
domain. Since DS project management is extensive
itself, it might be sensible to further limit the scope
to the issues for the group (e.g., best practices, certain
DS project stages) that mainly impact its DS project
success. Therefore, considered information sources
can be limited to a subset.
The subsequent task, Information Format Design,
aims explicitly at team collaboration and establish-
ing a unified structure for information capture and
processing. As patterns will be identified based on
information extracted from literature, uniform tools
and templates for the collection, filtering, and analy-
sis should be defined to achieve an efficient and trans-
parent process.
The following activity, Information Collection, in-
volves gathering information and coordinating its pro-
cessing within the research group (Fehling et al.,
2014). Since not all required information will be part
of human memory, other information sources are re-
quired. Therefore, we propose the use of a litera-
ture review to acquire relevant material in a structured
manner. The search process can involve both aca-
demic and non-academic databases. Depending on
the chosen focus within the Coverage Consideration,
the inclusion of grey/white literature might be help-
ful. It is recommended to use an established litera-
ture reviewing methodology (e.g., (vom Brocke et al.,
2009)) to ensure rigor in this process. The search
terms and inclusion/exclusion criteria should be de-
termined within the group based on the previously
selected DS scope. To identify patterns in the next
phase, common themes and solutions from the litera-
ture have to be synthesized based on the defined struc-
tures from the Information Format Design.
In the last activity of this phase, Information Re-
view, the information sources and the solutions to be
considered therein are reviewed concerning their fur-
ther processing by the research group (Fehling et al.,
2014). The obtained volume of literature may be too
large for the group to handle, refining the domain
structure to create smaller and achievable sets of so-
lutions. For example, limiting the scope to certain DS
project activities might be useful. The criteria for lit-
erature review, both in terms of content and the pos-
sibilities for pattern development, can also be sharp-
ened accordingly. The result set is further narrowed
down in different filtering stages until a feasible set re-
lated to the specific problems is identified. If needed,
forward and backward searches can be appended to
further strengthen the pool of information sources.
4.2 Pattern Authoring
In the second phase, five activities are similarly under-
taken (see Figure 3), and the patterns are formulated
based on the similarities of existing solutions from
the information basis (Fehling et al., 2014). There-
fore, in the Pattern Language Design, the structure
and sections of the patterns are defined (Fehling et al.,
2014). As there is no universally valid format, these
are individually determined by the pattern authors and
adapted to their specific needs. Here, we recommend
using a format using the sections name, problem, con-
text, challenges, solution, results, and links, mainly
built on the work of (Wellhausen and Fiesser, 2011).
The problem is documented to support users in identi-
fying and evaluating its suitability for their specific is-
sues. Context defines the setting in which the pattern
occurs. The difficulties encountered in addressing a
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
Figure 3: Pattern Authoring phase, adopted from (Fehling
et al., 2014).
problem are also captured under challenges (Well-
hausen and Fiesser, 2011). The application of the so-
lution leads to results. To facilitate the application of
patterns and illustrate their interconnections, relation-
ships to other patterns are captured in the links (Well-
hausen and Fiesser, 2011). Additionally, patterns are
given a name by which they are identified. Each pat-
tern in this context should be uniformly built upon the
mentioned elements.
With the next activity, Primitive Definition, the
definitions from Information Format Design can be
further elaborated if needed (Fehling et al., 2014),
depending on the insights gained from the literature.
This aims to ensure consistent usage and homogeniza-
tion of primitives within the research group.
In Composition Language Design, guidelines for
sketches and formal specifications of the composi-
tion language are determined. For DS project man-
agement, this could involve the use of a DS process
model (e.g., CRISP-DM) to clarify further the appli-
cability of a pattern solution within a DS project.
The next activity, Pattern Writing, involves the ac-
tual documentation of the patterns based on the previ-
ously defined structure. The identified patterns from
the literature are abstracted to a degree so that suf-
ficient information is provided while remaining ab-
stract enough to apply to various cases (Fehling et al.,
2014). The process is iteratively repeated, with the
patterns and individual sections revised and harmo-
nized repeatedly. Discussions with other pattern au-
thors or users are essential. Documentation is per-
formed based on the approach presented in (Well-
hausen and Fiesser, 2011) and begins with the so-
lution of the pattern. Notes for the identified pat-
terns and the information retrieved from the DS lit-
erature review are used for this purpose. Next, the
problem is formulated concerning the described so-
lution. The problem should not be trivial and is elu-
cidated by questioning the relevance of the solution
and the actual problem being addressed (Wellhausen
and Fiesser, 2011). The result is then formulated to
assess the impact of applying the solution. Subse-
quently, the challenges related to the results and the
solution are identified. This involves examining why
the described problem is more challenging to solve
than it might initially appear (Wellhausen and Fiesser,
2011). Only afterward is the context described, deter-
mining the circumstances under which the problem
arises. There is no optimal time to specify the name of
the pattern, and it can emerge during the development
process. In this case, the focus is on what supports the
recall of the solution (Wellhausen and Fiesser, 2011).
The links section is filled at the end once the interre-
lationships with other patterns have been clarified.
In the last activity of this phase, Pattern Lan-
guage Revision, the created pattern language is evalu-
ated and revised (Fehling et al., 2014). This can also
involve the incorporation of DS practitioners to as-
sess the usefulness of the individual patterns. Addi-
tionally, the structure and links between the patterns
should be investigated. Patterns written at the begin-
ning may have fewer connections and require revi-
4.3 Pattern Application
Figure 4: Pattern Application phase, adopted from (Fehling
et al., 2014).
The third phase, according to (Fehling et al., 2014),
consisting of four subactivities (see Figure 4), per-
tains to the application of the developed patterns and
can be considered and worked on independently of
A Methodology for Constructing Patterns for the Management of Data Science Projects
the other two phases. First, Pattern Search and Rec-
ommendation aims to facilitate users’ navigation and
identify suitable patterns for the specific use case at
hand (Fehling et al., 2014). Accordingly, the derived
DS patterns shall be accompanied by a summary sec-
tion containing a brief description of the problem and
solution of each of the patterns of the pattern language
within a sentence (e.g., in tabular form) (Meszaros
and Doble, 1997; Manns and Rising, 2012). There-
fore, relevant patterns can be selected and further ex-
amined. For improved navigation, the patterns con-
tain a links section, which indicates the relationship
to other patterns. Moreover, it is recommended that
this connection is graphically represented, too.
In Pattern-based Solution Design, support is pro-
vided for translating the abstract solution of a pattern
into a specific use case (Fehling et al., 2014). The
original literature, from which the solutions were ab-
stracted, can be revisited to include concrete, exist-
ing solutions in the patterns to facilitate their usability
(Fehling et al., 2014). Accordingly, the pattern nota-
tion is expanded by the section ”Examples”, which
provides a detailed reference solution of the DS prob-
lem described within the given pattern.
In the next activity, Refinement of the Solution De-
sign, patterns are constrained and adapted to a specific
environment where they should be applied (Fehling
et al., 2014). In the DS context, this could involve a
limitation to certain types of DS projects where the
patterns are used to combat the possible differences
in the manual pattern implementations.
Because of the determined pattern reference im-
plementations and limitation to viable use case
type(s), in the last activity, Instantiation of the Solu-
tion Design, the means to manage, configure, and de-
ploy the patterns are determined (Fehling et al., 2014).
Afterward, a specific refinement of the DS patterns
based on the previously selected focus is enabled.
In alignment with the adopted DSR methodology, the
application of the artifact to develop patterns for the
management of DS projects is demonstrated in the
following. This section describes how the authors ap-
plied the individual steps of the introduced method
for identifying and notating patterns in the DS project
management domain. Due to the page and time re-
strictions for this research, not all activities can be
outlined to the fullest extent in this paper.
5.1 Pattern Identification
Domain Definition: As the goal of this research is
centered around the development of patterns for DS
project management, a joint understanding of theo-
retical fundamentals in this domain had to be estab-
lished. Since some research group members already
acquired some experience in the field, reference lit-
erature on conceptualization (e.g., NIST definitions)
and common approaches (e.g., process models) were
exchanged and discussed. The common understand-
ing of the team was recorded. Section 3 constitutes
the result of this activity.
Coverage Consideration: Due to the extensive na-
ture of DS project management, it was necessary to
limit the scope. Therefore, the focus was set on gen-
eral successful approaches and the associated best
practices regarding DS project management to ad-
dress common problems and work toward mitigating
the high failure rates encountered in the execution of
data science projects (VentureBeat, 2019). The pat-
terns should guide common challenges that arise dur-
ing DS project phases and contribute to the develop-
ment of effective data science project management.
Information Format Design: To adequately pre-
pare for the Information Collection step, the research
group jointly agreed to the joint use of certain tools
and templates to facilitate collaboration for the fol-
lowing activities. For example, Citavi was used as
a reference management tool for the literature. Here,
the individual filtering stages were also depicted using
the category feature. Accordingly, grouping related
publications and mapping the inclusion/exclusion cri-
teria was possible. Furthermore, to further organize
the analysis of the obtained material, it was decided
to use the concept matrix of (Webster and Watson,
2002). A corresponding template was created in Mi-
crosoft Excel, which the group jointly used.
Information Collection: The Information Collection
for creating a DS project management pattern lan-
guage was performed through a structured literature
review according to the guidelines of (vom Brocke
et al., 2009), consisting of the five phases definition
of review scope, conceptualization of topic, litera-
ture search, literature analysis and synthesis, and re-
search agenda. After completing the first two steps of
this framework during Domain Definition and Cov-
erage Consideration, Scopus and SpringerLink were
queried with the search terms shown in Table 1, cor-
responding to the previously defined scope. The
search yielded 286 results for Scopus and 205 pub-
lications for SpringerLink, where merely articles and
conference papers were considered. In alignment
with the outcome of Coverage Consideration, the re-
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
Table 1: Applied search terms for Information Collection.
Database Search string
Scopus TITLE((”Data Science” OR ”Big Data” OR ”Data Mining” OR analytics) AND (project OR manage-
ment OR method* OR framework OR process OR model OR cycle)) AND TITLE-ABS-KEY ((”best
practice” OR success OR pattern) AND project)
SpringerLink 1) Title: ”data science”, All the words: project, Exact phrase: best practice
2) Title: ”data science”, All the words: project, One word: success
3) Title: ”data science”, Exact phrase: project management
search group agreed to include articles that describe
approaches or best practices for the execution and
management of DS projects. Publications with an un-
fitting thematic focus, such as particular DS applica-
tion scenarios and predominant coverage of techni-
cal questions (e.g., algorithms), were excluded. Each
group member was assigned a literature subset for re-
Information Review: The obtained material base
was filtered through multiple stages based on the in-
clusion and exclusion criteria. After the title assess-
ment and removal of duplicates, 96 papers remained.
Following the abstract evaluation, 38 articles were left
in the pool. The full examination led to the removal of
an additional 19 papers, resulting in an intermediate
result set of 19 contributions directly relevant to suc-
cessful approaches and best practices in DS project
management. To expand the literature base for pattern
writing, a backward and forward search (Webster and
Watson, 2002) was also performed. This step added
13 more papers (32 in total). Next, a concept matrix
was utilized to capture and analyze the common top-
ics covered in the works to facilitate the extraction of
the relevant information for the patterns.
5.2 Pattern Authoring
Pattern Language Design: Based on the introduced
methodology for pattern construction, the research
group employed the prescribed pattern structure, con-
sisting of the sections name, problem, context, chal-
lenges, solution, results, and links (see Table 3). In
the Pattern Writing step, these sections are completed
based on the literature base.
Primitive Definition: The design decisions made
during Information Format Design were reviewed and
largely confirmed. Next to the concept matrix used to
group the obtained information from the literature, the
authors further agreed to use the joint Citavi reposi-
tory for making more detailed annotations of impor-
tant information within the included material since
this can alleviate the pattern writing process.
Composition Language Definition: In this phase, it
was determined to position patterns in the context of
DS lifecycle stages proposed by (Haertel et al., 2022)
to better clarify the applicability of the created pat-
terns. Because of the focus on DS project manage-
ment best practices, it was expected that most patterns
would address the phase of Business Understanding.
Pattern Writing: Following the described approach,
the sections of the patterns are completed in a spe-
cific order and as briefly as possible. Because of the
page limitations, this step is described based on one
example pattern (”Alignment of Expectations”) that
was created during this process. The full pattern is
depicted below in Table 3 and is written based on
the inputs derived from the articles of (Cato et al.,
2015; G
okay et al., 2023; Martinez et al., 2021b;
Saltz and Shamshurin, 2016; Soukaina et al., 2019;
Sun et al., 2018; Varela and Domingues, 2021; Yeoh
and Koronios, 2010; Yeoh and Popovi
c, 2016; Schulz
et al., 2020). Documentation begins with the solu-
tion of the pattern. An alignment regarding the poten-
tial of the to-be-developed DS application is required
to raise awareness for realistic DS project expecta-
tions. This involves the project team, management,
domain users, and other stakeholders. A situation as-
sessment is needed to evaluate the feasibility of the
set objectives. Based on similar problems, relevant
resources (e.g., data, budget, skills) and their avail-
ability are discussed. Afterward, the problem section
is elaborated to outline the relevance of the solution.
Because of the data focus, DS projects have an ex-
plorative nature, which increases the difficulty of es-
tablishing goals and timelines. Additionally, manage-
ment and users tend to have high expectations in DS
applications. The results are written next to clarify
the impact of the solution. This leads to a joint un-
derstanding of suitable expectations and the roughly
required resources in the project. Based on the sit-
uation assessment, confidence is established regard-
ing the feasibility of the project and its added value
for the organization. Challenges in the context of this
pattern mainly relate to the resources, especially data.
A significant challenge in DS undertakings is data ac-
cess. Additionally, the data exploration might reveal
the unsuitability of the available data for achieving the
initially set goals. Hence, a modification of the expec-
tations could be necessary. This also applies to other
resources like computing infrastructure or personnel.
A Methodology for Constructing Patterns for the Management of Data Science Projects
Table 2: Abstracts for the created patterns.
Pattern name Summary
Alignment of Expectations Coordination of all stakeholders to sensitize regarding spe-
cific requirements, relevant resources, and challenges of the DS
Involvement of Senior Management Upper management support, encouragement, and guidance are
essential for the successful execution of data science projects,
considering their distinctive demands and uncertainties.
Strategic Alignment of the Project By aligning with the organizational strategy, the project enables
the generation of valuable outcomes for the organization.
Scope The project scope is carefully derived and maintained to facili-
tate the implementation of data science projects.
Process Organization Processes are defined and established to facilitate controlled and
targeted project execution, meeting project challenges.
Implementation of Change Management DS projects requires a corresponding willingness and accep-
tance of changes that need to be incorporated development pro-
Team Composition Forming a team with members from different areas is crucial for
managing DS projects, requiring comprehensive competencies
and skills.
Project Team Competencies Various technical and social competencies are required to man-
age aspects and requirements of DS projects.
Team Management Promoting high productivity and cooperation among team
members through effective leadership and coordination for suc-
cessful project completion.
Ensuring Data Security To ensure data security in data processing, security measures
are integrated into project infrastructure and processes.
IT Infrastructure To enhance productivity in the project, the IT infrastructure has
to be set up under consideration of the unique requirements of
the DS project.
Ensuring Data Quality To be able to achieve the business objectives, suitable data must
be integrated, prepared, and monitored to ensure high data qual-
Creation and Maintenance of Documentation Documentation provides access to project procedures and (DS)
Project Performance Monitoring Continuous monitoring of applications and processes enables
efficient project implementation and infrastructure, facilitating
project success.
Finally, the context of the pattern is described, which
constitutes a specification of the problem to detail the
circumstances under which it arises. Finally, the sec-
tion on the links to related patterns is filled out and
briefly elaborated (see Table 3). The Pattern Writ-
ing step is repeated multiple times to repeatedly re-
vise and harmonize the sections and patterns. Using
this procedure with the obtained material base, a total
of 14 patterns for DS project management best prac-
tices were created. These are summarized in Table
2. The repeated occurrence and observation of identi-
fied solutions in the literature were significant for the
formation of the patterns.
Pattern Language Revision: The components of the
derived patterns were subject to multiple joint revi-
sions within the research group. A further evaluation,
including external experts, is planned to obtain further
insights regarding the usefulness and completeness of
the DS patterns for actual application.
5.3 Pattern Application
As the application of the developed patterns in prac-
tice in a DS project is still pending, this phase has only
been partially completed so far, and thus, intermediate
results are reported.
Pattern Search and Recommendation: This first
activity focuses on improved search and navigation
through the patterns. Therefore, each pattern was
summarized in a sentence. The summary for the pat-
tern ”Alignment of Expectations” is shown in Table
2. Furthermore, the links between the patterns were
visually highlighted with the means of a cross matrix.
Pattern-based Solution Design: The pattern lan-
guage notation was expanded with an ”Example” sec-
tion to facilitate usability, using inputs from the ma-
terial base. The result for the example pattern can be
traced in the full notation in Table 3.
Refinement of the Solution Design: As the scope
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
Table 3: Pattern Alignment of Expectations.
Name Alignment of Expectations
Problem The explorative nature of DS projects increases the difficulty of establishing goals and timelines that
confirm with expectations of management and domain users.
Context DS project expectations are frequently not realized. Oftentimes, possibilities and results strongly de-
pend on available resources, data access, and quality.
Challenges The availability of resources impact the project outcome. A significant challenge in DS undertakings
is data access. Additionally, the data exploration might reveal the unsuitability of the available data for
achieving the business objectives. Hence, because of the inherent risks and uncertainties in DS projects,
flexibility regarding the modification of the expectations might be necessary. This also applies to other
resources like computing infrastructure or personnel.
Solution The project team, management, domain users, and other stakeholders perform an alignment regarding
the potential and limitations of the envisioned DS application. A situation assessment evaluates the
feasibility of the set objectives and their added value for the organization. Based on detected similar
problems and the corresponding solutions, the relevant resources (e.g., data, budget, competencies) and
their availability are discussed.
Result Development of a joint understanding of appropriate expectations and the approximately required re-
sources and timelines. Based on the situation assessment, confidence is established regarding the feasi-
bility of the DS project and its added value for the organization.
Links Project expectations result from the Strategic Alignment of the Project and Involvement of Senior Man-
agement. Objectives have to be aligned with requirements to the project execution, including the IT
Infrastructure and Team Composition to determine a realistic Scope. Moreover, expectations are de-
fined regarding Project Team Competencies to complete the project tasks. During project execution,
based on Project Performance Monitoring new or revised requirements and goals can arise.
Example This pattern can be assigned to Business Understanding, which is a common phase in various DS
process models. Here, the project circumstances are communicated with involved stakeholder groups
to elaborate opportunities, requirements, and functionalities of the DS application (Schulz et al., 2020).
A feasibility study can be used to evaluate the likelihood of fulfilling project requirements and objectives
(Schulz et al., 2020).
of the Coverage Consideration and resulting Informa-
tion Collection was set on general best practices and
successful approaches for DS project management, no
further limitations for application were defined in this
step. However, based on the feedback of the revision
with DS practitioners, this is subject to change.
Instantiation of the Solution Design: Due to the po-
tential for various changes to the patterns in the future,
the patterns are stored in a repository with shared ac-
cess for each member of the research group. Version
control is enabled to track changes that might be nec-
essary based on the evaluation results and possible ex-
tensions to the material base.
DS projects suffer from high failure rates (Ven-
tureBeat, 2019), which indicates the need for new
approaches for DS project management (Saltz and
Krasteva, 2022). Therefore, in this work, we applied
the pattern concept to DS since it allows for the struc-
tured summarization of solutions for common prob-
lems in a domain. Using the DSR methodology of
(Peffers et al., 2007), the pattern creation process of
(Fehling et al., 2014), consisting of Pattern Identifica-
tion, Authoring, and Application, was adapted to DS
project management. The functionality of the pro-
posed method was demonstrated by the creation of
patterns for DS project management best practices
from the synthesis of scientific literature. Accord-
ingly, researchers and practitioners can apply the in-
troduced pattern construction method to synthesize
solutions to frequently occurring issues in the exe-
cution and management of DS undertakings. Never-
theless, the study at hand is subject to certain limi-
tations. Because of page restrictions, the evaluation
within the DSR methodology following the guide-
lines of (Sonnenberg and vom Brocke, 2012) was not
covered in this paper. While the ex-ante evaluations
(Eval 1 and 2) were briefly touched upon in the back-
ground, further depth should be provided through lit-
erature reviews and expert interviews. The demon-
stration in Section 5 showed initial tendencies toward
the artifact’s general applicability, but a detailed as-
sessment is still pending. Additionally, further case
studies are needed to conclusively evaluate the use-
fulness of the proposed method and determine areas
for improvement. For instance, the delimitation be-
tween certain activities (e.g., Primitive Definition and
Composition Language Design) was not always clear,
indicating the potential for consolidating some of the
A Methodology for Constructing Patterns for the Management of Data Science Projects
Aichele, C. and Sch
onberger, M. (2014). IT-
Projektmanagement: Effiziente Einf
uhrung in
das Management von Projekten. SpringerLink
ucher. Springer Vieweg, Berlin.
Alexander, C. (1979). The timeless way of building. Oxford
Univ. Pr, New York.
Alexander, C., Ishikawa, S., and Silverstein, M. (1977). A
Pattern Language: Towns, Buildings, Construction.
Buschmann, F. (1996). Pattern-oriented Software Architec-
ture: A System of Patterns. Wiley, Chichester.
Cao, L. (2017). Data science: Challenges and directions.
Communications of the ACM, 60(8):59–68.
Cao, L. (2018). Data science: A comprehensive overview.
ACM Computing Surveys, 50(3):1–42.
Cato, P., Golzer, P., and Demmelhuber, W. (2015). An in-
vestigation into the implementation factors affecting
the success of big data systems. In 2015 11th Interna-
tional Conference on Innovations in Information Tech-
nology (IIT), pages 134–139. IEEE.
Chang, W. and Grady, N. (2019). Nist big data interoper-
ability framework: Volume 1, definitions.
Coplien, J. O. (2000). Software Patterns. SIGS Books &
multimedia, New York.
Coplien, J. O. and Harrison, N. B. (2005). Organiza-
tional Patterns of Agile Software Development. Pear-
son Prentice Hall, Upper Saddle River.
Das, M., Cui, R., Campbell, D. R., Agrawal, G., and Ram-
nath, R. (2015). Towards methods for systematic re-
search on big data. 2015 IEEE International Confer-
ence on Big Data, pages 2072–2081.
de Medeiros, M. M., Hoppen, N., and Mac¸ada, A. C. G.
(2020). Data science for business: benefits, challenges
and opportunities. The Bottom Line.
Eisend, M. and Kuß, A. (2023). Grundlagen em-
pirischer Forschung: Zur Methodologie in der Be-
triebswirtschaftslehre. Springer Fachmedien Wies-
baden and Imprint Springer Gabler, Wiesbaden, 3.,
uberarbeitete aufage edition.
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996).
The kdd process for extracting useful knowledge from
volumes of data. Communications of the ACM,
Fehling, C., Barzen, J., Breitenb
ucher, U., and Leymann, F.
(2014). A process for pattern identification, authoring,
and application. In Eloranta, V.-P. and van Heesch, U.,
editors, Proceedings of the 19th European Conference
on Pattern Languages of Programs, pages 1–9, New
York, NY, USA. ACM.
okay, G. T., Nazlıel, K., S¸ener, U., G
okalp, E., G
M. O., Genc¸al, N., Da
gdas¸, G., and Eren, P. E.
(2023). What drives success in data science projects:
A taxonomy of antecedents. In Computational Intel-
ligence, Data Analytics and Applications, pages 448–
462, Cham. Springer International Publishing.
unther, A. and Knote, R. (2017). How to design patterns in
is research a state-of-the-art analysis. Proceedings
der 13. Internationalen Tagung Wirtschaftsinformatik
(WI 2017), pages 1393–1404.
Haertel, C., Pohl, M., Nahhas, A., Staegemann, D., and Tur-
owski, K. (2022). Toward a lifecycle for data science:
A literature review of data science process models.
PACIS 2022 Proceedings.
Harrison, N. B. (2003). Advanced pattern writing: Patterns
for experienced pattern authors. EuroPLoP, pages
Heinrich, L. J. (1997). Management von Informatik-
Projekten. R. Oldenbourg Verlag M
unchen Wien.
Hevner, A. R., March, S. T., and Park, J. (2004). Design
science in information systems research. MIS Quar-
Iba, T. and Isaku, T. (2012). Holistic pattern-mining pat-
terns: A pattern language for pattern mining on a
holistic approach. 19th Pattern Languages of Pro-
grams conference.
Jeble, S., Kumari, S., and Patil, Y. (2018). Role of big data
in decision making. Operations and Supply Chain
Management, Vol. 11(No. 1):36–44.
Manns, M. L. and Rising, L. (2012). Fearless Change: Pat-
terns for Introducing New Ideas. Addison-Wesley.
Martinez, I., Viles, E., and Olaizola, I. G. (2021a). Data
science methodologies: Current challenges and future
approaches. Big Data Research 24.
Martinez, I., Viles, E., and Olaizola, I. G. (2021b). A survey
study of success factors in data science projects. In
2021 IEEE International Conference on Big Data (Big
Data), pages 2313–2318.
Meszaros, G. and Doble, J. (1997). A pattern language for
pattern writing. Pattern languages of program design,
pages 529–574.
Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chat-
terjee, S. (2007). A design science research method-
ology for information systems research. Journal of
Management Information Systems, 24(3):45–77.
PMI (2017). A guide to the project management body of
knowledge (PMBOK guide). Sixth edition edition.
Saltz, J. (2022). Nine questions to evaluate a data science
team’s process: Exploring a big data science team
process evaluation framework via a delphi study. In
2022 IEEE International Conference on Big Data (Big
Data), pages 2667–2672. IEEE.
Saltz, J., Hotz, N., Wild, D., and Stirling, K. (2018). Explor-
ing project management methodologies used within
data science teams. AMCIS 2018.
Saltz, J. S. (2015). The need for new processes, method-
ologies and tools to support big data teams and im-
prove big data project effectiveness. IEEE Interna-
tional Conference on Big Data 2015.
Saltz, J. S. and Hotz, N. (2020). Identifying the most com-
mon frameworks data science teams use to structure
and coordinate their projects. In 2020 IEEE Inter-
national Conference on Big Data (Big Data), pages
2038–2042. IEEE.
Saltz, J. S. and Krasteva, I. (2022). Current approaches for
executing big data science projects - a systematic lit-
erature review. PeerJ Computer Science, 8(e862).
Saltz, J. S. and Shamshurin, I. (2016). Big data team pro-
cess methodologies: A literature review and the iden-
tification of key factors for a project’s success. In
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
2016 IEEE International Conference on Big Data (Big
Data), pages 2872–2879.
Schulz, M., Neuhaus, U., Kaufmann, J., Badura, D.,
Kuehnel, S., Badewitz, W., Dann, D., Kloker, S.,
Alekozai, E. M., and Lanquillon, C. (2020). Intro-
ducing dasc-pm: A data science process model. ACIS
Sonnenberg, C. and vom Brocke, J. (2012). Evaluations in
the science of the artificial reconsidering the build-
evaluate pattern in design science research. In Pef-
fers, K., Rothenberger, M., and Kuechler, B., ed-
itors, Design science research in information sys-
tems, SpringerLink B
ucher, pages 381–397, Berlin.
Soukaina, M., Anoun, H., Ridouani, M., and Hassouni, L.,
editors (2019). A study of the factors and methodolo-
gies to drive successfully a big data project.
Sun, S., Cegielski, C. G., Jia, L., and Hall, D. J. (2018).
Understanding the factors affecting the organizational
adoption of big data. Journal of Computer Informa-
tion Systems, 58(3):193–203.
Varela, C. and Domingues, L. (2021). Risks of data science
projects - a delphi study. pages 982–989.
VentureBeat (2019). Why do 87% of data science projects
never make it into production?
vom Brocke, J., Simons, A., Niehaves, B., Reimer, K., Plat-
tfaut, R., and Cleven, A. (2009). Reconstructing the
giant: On the importance of rigour in documenting the
literature search process. ECIS 2009.
Wack, J. (2007). Risikomanagement f
ur IT-Projekte:
Zugl.: Hamburg, Univ., Diss., 2006, volume 54
of Betriebswirtschaftliche Forschung zur Un-
uhrung. Dt. Univ.-Verl., Wiesbaden, 1.
aufl. edition.
Webster, J. and Watson, R. T. (2002). Analyzing the past to
prepare for the future: Writing a literature review. MIS
Quarterly, Vol. 26, No. 2 (Jun. 2002), pages 13–23.
Wellhausen, T. and Fiesser, A. (2011). How to write a pat-
tern? a rough guide for first-time pattern authors. Pro-
ceedings of the 16th European Conference on Pattern
Languages of Programs.
Wieczorrek, H. W. and Mertens, P. (2007). Manage-
ment von IT-Projekten: Von der Planung zur Real-
isierung. Xpert.press. Springer-Verlag Berlin Heidel-
berg, Berlin, Heidelberg, 2.,
uberarbeitete und erweit-
erte auflage edition.
Yeoh, W. and Koronios, A. (2010). Critical success factors
for business intelligence systems. Journal of Com-
puter Information Systems, Vol. 50(No. 3):23–32.
Yeoh, W. and Popovi
c, A. (2016). Extending the under-
standing of critical success factors for implementing
business intelligence systems. Journal of the As-
sociation for Information Science and Technology,
A Methodology for Constructing Patterns for the Management of Data Science Projects