Understanding data & analytics maturity: A systematic review of maturity model composition

Langer, Benedict Article Understanding data & analytics maturity: Asystematic review of maturity model composition Schmalenbach Journal of Business Research (SBUR) Provided in Cooperation with: Schmalenbach-Gesellschaft für Betriebswirtschaft e.V. Suggested Citation: Langer, Benedict (2025) : Understanding data & analytics maturity: Asystematic review of maturity model composition, Schmalenbach Journal of Business Research (SBUR), ISSN 2366-6153, Springer, Heidelberg, Vol. 77, Iss. 2, pp. 205-227, https://doi.org/10.1007/s41471-024-00205-2 This Version is available at: https://hdl.handle.net/10419/323726 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ REVIEW ARTICLE https://doi.org/10.1007/s41471-024-00205-2 Schmalenbach Journal of Business Research (2025) 77:205–227 Understanding Data & Analytics Maturity: A Systematic Review of Maturity Model Composition Benedict Langer Received: 21 August 2023 / Accepted: 20 December 2024 / Published online: 29 January 2025 © The Author(s) 2025 Abstract Leveraging data is becoming increasingly important for businesses. However, this transformation can be complex, as it requires a vast array of social and technical capabilities. To generate consensus in this domain, this study examines data & analytics maturity models by analyzing their architectures, maturity levels, and maturity domains. A systematic review based on the PRISMA framework identifies 38 maturity models and inductively derives insights into their composition. Three different content types are differentiated, namely organization-oriented, technologyoriented and data-oriented models. The initial findings provide a comprehensive overview of the status quo in data & analytics maturity models and provide a foundation for further research in this field. The study thus contributes towards enabling businesses to conduct more sophisticated data & analytics maturity assessments and support more effective use of data. Keywords Data · Analytics · Maturity · Maturity models · Literature review 1 Introduction Digital technologies are indispensable in today’s society having a significant impact on organizations and their businesses (Reis et al 2018; Thordsen et al 2020). The proliferation of digital technologies has led to a significant increase in the amount of data generated and collected by organizations (Bianchini and Michalkova 2019; Mcafee and Brynjolfsson 2012; Buhl et al 2013). Data is considered a valuable asset, with subsequent analytics providing insights that can inform strategic decisionConsent for publication This manuscript does not contain personal data. Benedict Langer TUM School of Management, Technical University of Munich, Munich, Germany E-Mail: [email protected] K 206 Schmalenbach Journal of Business Research (2025) 77:205–227 making and drive business growth (Pohl et al 2022; Grossman 2018;Wangetal 2015). Organizations are facing increasing pressure to acquire and effectively use data to remain efficient and competitive (Barton and Court 2012; Davenport 2006). Data & analytics maturity refer to an organization’s capability to effectively manage and utilize data (Grossman 2018; Comuzzi and Patel 2016). This includes, among others, the ability to collect, process, and interpret data to support decision making, achieve strategic goals, and support business objectives (Gartner 2022). However, data & analytics maturity is not a static concept and can vary widely among individuals and organizations (Król and Zdonek 2020). And, while data & analytics promise increased transparency and optimization at multiple levels of management and operations (Pohl et al 2022), the transition to realizing this potential requires extensive acquisition of new competencies and skills (Mcafee and Brynjolfsson 2012; Comuzzi and Patel 2016). In many cases, new standard processes must be implemented and new skills as well as high quality data must be acquired. Many organizations, especially SMEs, are wary of the challenges associated with advanced analytics. They ask for a defined structure, clear steps and a roadmap to master this transformation (Bianchini and Michalkova 2019; Comuzzi and Patel 2016). To guide transformative endeavors and structure capability development, maturity models are valuable artifacts offering guidance for research and practice (Mettler 2011; Becker et al 2009). From a research perspective, maturity models represent theories of how organizational capabilities develop along a maturation path (Pöppelbuß and Röglinger 2011). The frameworks typically define a series of maturity levels through which an organization can progress (Poeppelbuss et al 2011; Wendler 2012). In practice, maturity models are useful for assessing an organization’s status quo, determining a desired target state, and identifying fields of action (Pöppelbuß and Röglinger 2011). Maturity models can serve as a starting point and provide methods for assessing an organization’s capabilities in a particular area (Hüner et al 2009; De Bruin et al 2005). Indeed, existing maturity models offer valuable contributions to leverage data & analytics. These data & analytics maturity models cover a wide range of application-specific technical domains (Comuzzi and Patel 2016) and business capabilities (Woroch and Strobel 2021). The models typically provide a procedure for assessing an organization’s current level of competence in areas such as data governance, data quality, and data management, while also helping to identify areas for improvement (Król and Zdonek 2020; Comuzzi and Patel 2016; Helmy et al 2022). Despite the existence of various maturity models examining different areas of analytics, such as data warehousing (Spruit and Sacu 2015) data acquisition (Murphy and Chang 2009), or artificial intelligence (Alsheiabni et al 2019), as well as approaches to develop general maturity models for data & analytics capabilities of an organization or industry (e. g. Perales-Manrique et al 2019; Cosic et al 2012), there is no consensus on what such a model should look like. There is a lack of clarity about the content, structure and application of existing models (Reis et al 2018; Gökalp et al 2021b; Favoretto et al 2021). While there are literature reviews available (Proença and Borbinha 2018;Król and Zdonek 2020), most are limited to a descriptive analysis of data & analytics maturity models. There is a lack of depth, particularly in the analysis of maturity K Schmalenbach Journal of Business Research (2025) 77:205–227 207 levels and domains that need to be considered for a comprehensive analysis of the models. This gap is significant since it limits a holistic view of an organization’s data & analytics capabilities. To the best of current knowledge, there is currently no study that comprehensively compares and analyzes data & analytics maturity models. Without a deeper understanding of the composition of maturity models, organizations may not be able to identify the areas where they need to improve and realize the full potential of their data & analytics capabilities. The deficiencies in knowledge of data & analytics and its maturity models, and the general lack of structured approaches to maturity dimensions, may lead to under-exploited and under-demonstrated potential in the field (Kiron et al 2015; LaValle et al 2011;Kaneetal2017). Academics and practitioners alike can benefit from a better understanding of data & analytics maturity and the associated maturity models for better use of data. A systematic literature review is needed to synthesize existing knowledge, analyze and abstract the various extant data & analytics maturity models, and identify research gaps in the area of data & analytics maturity. The following overarching research question is derived: RQ: What are structures and contents of data & analytics maturity models? By reviewing the body of existing maturity models, the study aims to further the understanding of how data & analytics maturity is currently defined and measured, and what domains are considered important for organizations. In addition, by comparing and contrasting different maturity models, common compositions and areas of divergence can be identified that help inform the development of new models or the refinement of existing ones. The analysis thus focuses on the structure and content of the models, specifically in terms of their architecture, maturity levels, and maturity domains. The initial findings may provide a starting point for businesses looking to assess and improve their data & analytics maturity, as well as for researchers studying data & analytics maturity and the related transformation. To achieve the objective, address the research gap and answer the research question, the remainder of the paper is structured as follows: First, the theoretical background of data & analytics as well as maturity model development and data & analytics maturity models is given. Sect. 3presents the methodology of the literature review. The structures and contents of the identified maturity models are presented in Sect. 4. Model approaches found are broken down to their basic concepts through formalization in order to categorize and abstract them. Main findings, implications, further research directions as well as limitations are presented in Sect. 5. The paper concludes with a summary of the findings. 2 Background This section provides the theoretical background for the study by discussing the foundation underlying data & analytics maturity models. This includes defining the respective concepts and gaining an understanding of the motivation for and benefits of using these models. K 208 Schmalenbach Journal of Business Research (2025) 77:205–227 2.1 Data & Analytics The ability to effectively manage and leverage data has become a core competency for organizations (Pohl et al 2022;Wangetal2015). However, the increasing complexity and volume of data generated in many organizations today, can present challenges in understanding and deriving insights from it (Bianchini and Michalkova 2019). Data generated by ERP system transactions alone can be overwhelming for many businesses. Data analytics, business analytics, business intelligence, and other related disciplines have emerged as crucial areas of focus for organizations seeking to maximize the potential of their data. They aim to employ technologies from simpler descriptive analyses to more advanced machine learning or artificial intelligence. Different terms in the general field of technology have been used more or less synonymously, with differences in the time frame examined and how the results are used to generate value (Schniederjans et al 2014). For simplicity and for the purposes of the analyses in this study, all of these disciplines are referred to when talking about data & analytics. Data can be described as raw facts and figures that can be collected and processed (Parra et al 2017). In a business context, analytics refers to the methods used to analyze and interpret data to support decision making (Grossman 2018; O’Donovan et al 2016). The common goal of data & analytics is to derive insights from data to make decisions that drive business value (Paczkowski 2021; LaValle et al 2011). Organizations seek to improve their business processes, increase efficiency, and gain a competitive advantage through the effective use of data (Pohl et al 2022). Despite the growing importance of data & analytics, companies continue to struggle with building the necessary capabilities and competencies to effectively collect, manage, and use data (Kiron et al 2015; LaValle et al 2011; Davenport 2006). Extant research in data & analytics focuses more on technological and procedural advances in areas such as data governance, data quality, data integration, data security, and data visualization (Al-Sai et al 2022;Wangetal2022;Shi2022). Many organizations still lack the necessary knowledge about the skills and resources required to effectively leverage data to drive business performance (Hashem et al 2015; Pohl et al 2022). An overview and definition of the capabilities and competencies required to collect, manage, and use data is a critical prerequisite for organizations seeking to build the skills and infrastructure to remain competitive in the current data age. 2.2 Maturity Models Maturity models have become established managerial tools for organizations to assess and improve their performance in a particular area or with a particular technology (Mettler 2011;Hüneretal2009). The models provide a framework for identifying and addressing gaps in processes and capabilities (Poeppelbuss et al 2011). The concept of maturity models originated in the field of organizational development and was initially proposed as a tool for assessing the maturity of software development processes in the Capability Maturity Model (CMM) (Humphrey 1988; Paulk et al 1993). Since then, maturity models have been widely used in a variK Schmalenbach Journal of Business Research (2025) 77:205–227 209 ety of contexts, including manufacturing, supply chain, and IT (Becker et al 2009; Schumacher et al 2019; Hellweg et al 2021). Maturity models typically consist of a set of levels or stages, that represent different degrees of formalization and optimization (Wendler 2012). The models map maturity paths by describing the distinctive capabilities, practices, or outcomes that organizations should exhibit at each stage (Pöppelbuß and Röglinger 2011; De Bruin et al 2005). As organizations progress through the stages, they are expected to demonstrate increasing levels of maturity, resulting in improved performance. Maturity models can be used as a diagnostic tool to identify areas for improvement, as a prescriptive roadmap for the definition of target states and as a comparison instrument (Wendler 2012; Becker et al 2009). By providing a structured way to assess an organization’s maturity, maturity models can help organizations select improvement efforts and track progress over time. Several frameworks have been proposed for the development of maturity models. One of the most widely used frameworks is Capability Maturity Model Integration (CMMI), which provides a set of guidelines for developing and validating maturity models (Chrissis et al 2007,2011). The ISO/IEC 3100x family of standards – the successor to the ISO/IEC 15504 family of standards, also known as the Software Process Improvement and Capability Determination (SPICE) model – provides another commonly used framework for maturity model development (International Organization for Standardization 2015a,b). Approaches developed by Becker et al (2009) and De Bruin et al (2005) are well established in maturity model development. The key idea behind the development frameworks is to provide a structured way to build maturity models and subsequently assess an organization’s current level of maturity in a given area. Each framework provides a set of guidelines for developing and validating maturity models that can be adapted to meet the specific needs of different organizations and industries. Overall, while there are many different maturity models and several maturity model development frameworks in use today, they share the common goal of helping organizations improve their performance. The literature suggests that while maturity models can be a valuable tool for assessing and improving organizational processes and capabilities, their effectiveness depends on the quality of their development and validation (Becker et al 2009; Poeppelbuss et al 2011). Maturity model developers should ensure that their models are well defined, validated through empirical testing, and focused on continuous improvement (Becker et al 2009). 2.3 Data & Analytics Maturity Models As data has become increasingly valuable, more and more organizations have been seeking to assess and improve their capabilities in data-related areas (Buhl et al 2013;Wangetal2015). The growing importance of data-driven decision making has led to the development of data & analytics maturity models specifically designed to assess an organization’s data competency and its general ability to use data effectively (Ilin et al 2022; Helmy et al 2022; Comuzzi and Patel 2016; Hausladen and Schosser 2020). In the context of this study, the term data & analytics maturity is expanded to include commonly used terms such as analytics maturity, data analytics K 210 Schmalenbach Journal of Business Research (2025) 77:205–227 maturity, data science maturity, etc., as their meaning is similar, they are often used interchangeably and they pursue the same goal. There are many types of data & analytics maturity models. Some gray literature models, like those from IBM (2007) and Accenture (2018), are proposed by consulting firms, private research institutes, and even companies themselves. Other data & analytics maturity models follow a more academic and rigorous development, validation and review process to remove bias and increase generalizability. There are multiple approaches to assessing data & analytics maturity, leading many maturity models to focus on a specific area of organizational data competence. Some models are management-oriented, such as those proposed by Comuzzi and Patel (2016) and Parra et al (2017), focusing on organizational issues such as ‘culture’, ‘strategic alignment’, or ‘processes’. Other maturity models are technical in nature, like the models proposed by Spruit and Pietzka (2015) and Murphy and Chang (2009) covering ‘data acquisition’ and ‘data management’. The development approach, evaluation approach, and evaluation content of data & analytics maturity models can differ widely. This diversity can make it challenging for organizations to select an appropriate model for their needs, and can also hinder researchers and practitioners seeking to evaluate and compare different models (Gökalp et al 2021b; Król and Zdonek 2020). To address the heterogeneity of data & analytics maturity models and to analyze the existing evaluation approaches, literature reviews such as (Król and Zdonek 2020) and (Proença and Borbinha 2018) were conducted. However, despite the growing popularity of data & analytics maturity models, there is no consensus on how these models should be structured and what their content should be (Reis et al 2018; Gökalp et al 2021b). Constant change and progress in the field of data & analytics make static maturity models quickly outdated (Król and Zdonek 2020). An integrated perspective on required business and technology capabilities through a comprehensive review of these models is still lacking. As a result, it is desirable to create transparency for researchers and practitioners by evaluating existing models and reaching consensus on data & analytics maturity model composition. This can help businesses focus on appropriate capabilities, ensure that existing maturity models are rigorously refined and validated, and ultimately improve the effectiveness of future data & analytics maturity models as a tool for organizational improvement. 3Method To further the understanding of data & analytics maturity and address the identified deficits in research on the respective maturity models, a literature review was chosen as the underlying research method. The aim of the review was to systematically aggregate and analyze structures and contents of data & analytics maturity models, thus answering the research question. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was selected to assess the maturity models as it provides a rigorous and widely accepted foundation for literature based research. PRISMA sets out the process for finding and evaluating the literature for a research question (Page et al 2021). K Schmalenbach Journal of Business Research (2025) 77:205–227 211 3.1 Data Collection In this study, the databases Web of Science, Scopus, ScienceDirect, and EBSCOhost were selected as sources of records. Using multiple scientific databases of different publishers covering both information systems and business research ensured a more holistic picture of extant literature in different domains. The search was performed in each of these databases using a predefined and concatenated query consisting of three parts. The overarching goal of the search string was to identify distinct maturity models in the relevant data & analytics domains. The first part of the query covers these domains, addressed by several keywords such as data,artificial intelligence,ormachine learning as identified in Sect. 2. The second part describes the concept of maturity, represented by the word maturity itself. Since each record must relate to a specific maturity model, each title must contain the keywords model or framework. The complete search query can be written as: (data OR analytic*OR artificial intelligence OR ai OR machine learning OR ml OR digital OR business intelligence) AND maturity AND (model OR framework). The search was performed in August 2024. The titles of records were searched, as the search terms induced too generic abstract or full text searches, leading to too many unrelated results. Apart from this regulation, a broad search was performed, with no restrictions on publication year, publisher, category, or document type. A list of all records was generated and all available records were downloaded. A search of all databases using this procedure resulted in a total of 916 records, of which 547 remained after removing duplicates. In the first screening phase, records were assessed based on their title, abstract, and metadata. Records were screened to assess whether they fit within the broader domains of data & analytics maturity models. As this first screening removed clearly unsuitable records, this task was performed by a single researcher and removed records were cross-checked by a second researcher. The journal of publication of the records was also considered as part of the metadata information. Only peer-reviewed papers were considered, i.e. maturity models derived from news articles or consulting reports were excluded. Although gray literature provides some data & analytics maturity models, these studies often do not detail the development or validation processes. This hinders their wider adoption in practice and does not guarantee an unbiased academic view. Maturity models that cover the data & analytics activities of companies, as defined in the introduction, were filtered for. To limit the scope and reduce influence from regional regulatory and cultural differences the focus put was on standard profit-oriented companies, excluding NGOs, governmental institution, and the healthcare sector. The first screening phase excluded 258 records. This resulted in a total of 289 records sought for retrieval. With 12 records inaccessible, 277 records were assessed for eligibility. The second screening included a full-text eligibility assessment using the same guidelines as for the title/abstract screening. As this assessment is ambiguous, it was performed independently by two researchers for the full record set. Records that did not yield the same assessment were discussed in detail. The most common reasons K 212 Schmalenbach Journal of Business Research (2025) 77:205–227 Duplicates removed (n = 369) Records excluded (n = 258) Records not retrieved (n = 12) Reports ex cluded (n = 242) (Wrong subject: n = 121) (No peer review : n = 51) (No model: n = 30) Records identified (n = 916) Records screened (n = 547) Records sought for retrieval (n = 289) Reports assessed for eligibility (n = 277) Records included (n = 38) AssessmentIdentification Results Backward search (n=3) Fig. 1 Applied PRISMA search flow diagram (based on Page et al 2021) for exclusion were that the maturity models did not address data & analytics of forprofit companies (n= 121) and that the papers were not peer-reviewed (n= 51). The third most common reason for exclusion was that the publication did not develop its own maturity model (n= 30). A total of 242 records were excluded at this stage, leaving 35 records remaining. Last, a backward search was conducted by examining all primary sources of the identified records as well as the excluded literature reviews and meta studies. Three additional records were identified. This small number of records not identified by the systematic search strategy supports the broad literature search approach. With the addition of these two records, a total of 38 records were obtained using the PRISMA search strategy, as shown in the flow diagram in Fig. 1. 3.2 Data Analysis Subsequent to data collection and filtration of relevant maturity models, the identified records were analyzed. Their composition was inspected in detail. The most notable difference between the considered maturity models is the way they are structured. In this study this is referred to as the architecture of the maturity models. The architecture describes the level and domain dimensions that constitute the maturity model. A level dimension refers to the different levels used in the maturity model. Most often the maturity level is measured on a discrete scale, such as 1 to 5. In contrast, a domain dimension refers to the content of the maturity model and its assessment subjects, such as the maturity of the IT infrastructure. To get a better understanding of the employed domain dimensions of the identified models, a concept map was developed that graphically illustrates the domains of data & analytics maturity. A concept map is a visual tool used to organize and represent knowledge or information (Gurlitt 2012;Novak1990). Its purpose is to help learners or researchers understand complex relationships between concepts or ideas and to facilitate the development of new ideas or insights (Gurlitt 2012). The development of the data & analytics maturity domain concept map followed an iterative collaborative process adapted from Trochim (1989a,b). Starting point was an aggregation of all maturity dimensions and domains of the 38 identified K Schmalenbach Journal of Business Research (2025) 77:205–227 219 and complexity, as well as domains regarding different business functions such as supply chain management, research and development, production, marketing, and sales. The governance domains contain policies, ethics, compliance, and formalization, while the human resources subcluster consists of leadership, culture, and people. Leadership includes sponsorship, empowerment and evaluation, along with clear communication and well-defined objectives for the organization. The people domain includes a range of skills for entrepreneurship, HR, management, business, technology/analytics, and domain expertise. Sustainable learning and education are further maturity domains in this area. The products & services domain relates to product data, data-driven services, and product connectivity. Abstracted domains in the processes subcluster include process digitalization, horizontal and vertical process integration, process automation, and process quality assurance. Decision processes should be evidence-based and self-optimizing, and development should be agile with clear requirement definitions. Change management and service processes are additional domains in the concept map. These organization-oriented maturity domains help businesses understand their current organizational structure and culture, and identify optimization potential in terms of data & analytics processes, governance, alignment, and support. This includes ensuring that data informs strategic decision-making, enabling the organization to make more rational decisions (Grossman 2018). It also encompasses data literacy among employees, meaning that the organization is able to cover the skills and knowledge necessary to effectively use and interpret data (Cosic et al 2012). Creating a culture where data-driven decision-making is valued and encouraged and where data is seen as a strategic asset is a central driver of data & analytics (Comuzzi and Patel 2016; Hausladen and Schosser 2020). 5 Discussion By further examining the results and implications of this review, the aim is to concretize the effects of the study, and to identify opportunities for future research and practical implementation. 5.1 Theoretical Implications The paper contributes to the demanded clear and consistent understanding of data & analytics maturity models (Reis et al 2018; Gökalp et al 2021b) by defining and differentiating the various models’ architectures, levels and domains. By reviewing the existing literature and identifying key concepts and frameworks, the paper provides consensus and a common language for researchers. The findings can serve as an ‘analytical lens’ that allows an investigation of the width of the analytics transformation (Pöppelbuß and Röglinger 2011). With the gained insights, research can identify more detailed maturation paths and patterns. This may unravel success factors or impediments associated with distinct paths of maturation, as demonstrated in the study of Mugge et al (2020) for digital transformation endeavors. K 220 Schmalenbach Journal of Business Research (2025) 77:205–227 The consolidated compositions of data & analytics maturity models provide a new depth in the analysis of the maturity domains compared to previous reviews (Proença and Borbinha 2018; Król and Zdonek 2020). In particular, the results reveal a tension between standardization and individualization of data & analytics maturity models. This tension results in the lack of a generalizable approach for assessing organizational data & analytics capabilities. The findings support existing theories that transformations towards data-driven businesses are not accomplished in a uniform and aligned manner. For instance, Vanauer et al (2015) assume that these projects can, on the one hand, be driven from a business perspective (‘business first’), based on a business vision and requirements. On the other hand, these projects can be initiated by a resource perspective that builds on existing data and assets (‘data first’). This was supported by findings from Stahl et al (2023) and can be affirmed by the generated clusters of data & analytics maturity domains. While existing models focus on either organizational or technical capabilities, none of the models analyzed has a high degree of detail in the evaluation dimensions while still covering a predominant part of the domains of data & analytics maturity. To fill this identified research gap and to address the known challenge of the fast changing data & analytics domain (Król and Zdonek 2020), it is necessary to develop a better understanding of the dependencies between different data & analytics maturity concepts, allowing future maturity models to be more suited to address the complex real-world phenomenon of digital transformations (Baskerville et al 2018). Future models should be broader in scope and adaptable in both scope and depth. The maturity domains aggregated by this review can be the foundation for a more general maturity evaluation based on a dynamic framework. 5.2 Practical Implications Aside from its merits for research, the literature review provides practitioners a valuable overview of the domain of data & analytics maturity. By applying the insights from the concept map and its maturity domains, e.g. through adaptation in strategy development or maturity assessment, organizations can inform more mature data & analytics practices that support better decision making and improved outcomes. Practical benefits can be achieved in a more targeted way by addressing the areas driving maturity directly, consequently enabling value generation in the under-exploited field of data & analytics (Kiron et al 2015;Kaneetal2017; Langer and Pütterich 2024). The implication can be drawn from the divergence in focus of the analyzed maturity domains, that organizations should try to incorporate both a social and a technical perspective when assessing data & analytics maturity (Appelbaum 1997). On the one hand, management may leverage a social perspective - including organizational and human-oriented domains - to identify valuable, customer-oriented use cases (Müller and Buliga 2019; Baltuttis et al 2022). Leadership is called upon to develop a clear vision to mobilize the organization and create a data-driven culture (Davenport and Bean 2018). Hence, management must establish data-driven ’processes’ early on and define required ‘skills’ (Förster et al 2022; Malta and Sousa 2016). On the other hand, management may use the technical perspective - associated with technological K Schmalenbach Journal of Business Research (2025) 77:205–227 221 and data capabilities (Lehrer et al 2018) - to address the infrastructure requirements with dedicated investments in hardware and software (Pathak et al 2021). A dedicated strategy and roadmap will help identify levers that optimize data quality and availability in the long term (Kehrer et al 2016) as well as support the creation of scalable analytics practices (Grossman 2018). Further added value can be created through adaptation of the results of this study, e.g. by individualizing the maturity assessment procedures and deriving a dynamic maturity model from the identified maturity domains. 5.3 Limitations Some limitations of this research need to be highlighted to point toward the potential of future work in this area. First, this research is limited by its focus on generalization, which is inherently challenging due to the diverse configurations and associated capabilities of organizations. Specific attention is required for particular contexts, such as small and medium-sized enterprises (SMEs) or manufacturing sectors, as these may differ significantly in their technical capabilities and customer requirements. For instance, the focus areas for SMEs may diverge from those of larger organizations, as highlighted by prior research (Langer and Pütterich 2024; Bianchini and Michalkova 2019). Similarly, manufacturing companies may prioritize different aspects compared to other industries, as suggested by Hein-Pensel et al (2023). Additionally, this study exclusively considered models published in peer-reviewed journals, excluding gray literature. While this ensures a scientific basis and empirical validation, it also limits the scope, as gray literature and other data sources—such as interviews or surveys—could provide valuable insights into data and analytics approaches and related maturity models. Second, while efforts were made to provide a holistic representation of the identified models, the domains of data and analytics maturity presented are neither exhaustive nor mutually exclusive. The analysis prioritized the content of the models rather than the frameworks underpinning their development, leaving aspects such as structural interdependencies and hierarchies of the assessment dimensions underexplored. This focus aligns with the study’s objectives but restricts the depth of certain areas. Furthermore, as business, environmental, and technological conditions evolve (Becker et al 2009), the content must be periodically reviewed to maintain its relevance and fidelity. Future empirical research is necessary to validate the findings and ensure their comprehensiveness, as the reliance on existing models inherently limits the framework’s currency and applicability in rapidly changing contexts. Third, the practical application of the findings is constrained by the exploratory nature of the study. While the identified domains provide a foundational framework, further validation and refinement are needed to ensure a complete and actionable list of maturity domains. The insights representing an initial step rather than a comprehensive solution. Future work could adopt an inductive approach, deriving actionable practices directly from empirical data, as suggested by Stelzl et al (2020) and Becker et al (2009). This could lead to tools that move beyond diagnostics to operationalize transformations, offering actionable guidance and lessons learned for practitioners. K 222 Schmalenbach Journal of Business Research (2025) 77:205–227 The current study, therefore, represents a starting point for developing more practical and prescriptive tools that can deliver immediate value. 6 Summary and Conclusion This study investigates data & analytics maturity models and assesses their structure and content. The evaluation domains of data & analytics maturity are analyzed in detail to better understand the technologies requirements. A more accurate depiction of the organizational exigencies contributes towards enabling organizations for more efficient implementation and more effective use of data. Applying a twostep research approach, first, a systematic literature review is conducted using the PRISMA framework. The review identifies 38 maturity models in the context of data and digitalization as objects of analysis. From the literature, general architectures of data & analytics maturity models are discovered inductively. The architectures consist of levels and domains - with each considered maturity model using different evaluation dimensions. A detailed understanding of different types of data & analytics maturity models is created by distinguishing between organization-oriented, technology-oriented, and data-oriented models. By detailing the underlying evaluation domains, the research advances the understanding of data & analytics maturity. Managers may benefit from formalizing activities to develop effective strategies for implementing data as a technology and as a strategic asset. A standardizationindividualization tension in data & analytics maturity models is uncovered and the idea of a dynamic approach based on a general adaptable framework is brought forward. The initial findings describe the status quo of data & analytics maturity, deepen the understanding of the maturity models and underlying maturity domains, and form a basis for further research and application in the fields of data & analytics. Acknowledgements Recognition and thanks are extended to everyone involved in conducting this study as well as to the anonymous reviewers whose insightful and constructive feedback greatly contributed to the improvement of this manuscript. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Availability of data and material The datasets generated and analyzed during the study are available from the corresponding author upon request. Conflict of interest The author declares that they have no conflict of interests. Ethical standards For this article no studies with human participants or animals were performed. All studies mentioned were in accordance with the ethical standards indicated in each case. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly K Schmalenbach Journal of Business Research (2025) 77:205–227 223 from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4. 0/. References Accenture. 2018. Data industrialization: Becoming a data driven enterprise. https://de.slideshare.net/ slideshow/becoming-a-datadriven-enterprise/173539513. Al-Sai, Z.A., M.H. Husin, S.M. Syed-Mohamad, et al., 2022. Explore big data analytics applications and opportunities: A review. Big Data and Cognitive Computing 6(4):157. https://doi.org/10.3390/ bdcc6040157. Alsheiabni, S., Y. Cheung, and C. Messom. 2019. Towards an artifificial intelligence maturity model: From science fiction to business facts. In: PACIS 2019 Proceedings. https://aisel.aisnet.org/pacis2019/46. Appelbaum, S.H. 1997. Socio-technical systems theory: an intervention strategy for organizational development. Management Decision 35(6):452–463. https://doi.org/10.1108/00251749710173823. Aslanova I, Kulichkina A (2020) Digital maturity: Definition and model. In: Proceedings of the 2nd International Scientific and Practical Conference “Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth” (MTDE 2020). Atlantis Press, pp 443–449, https://doi.org/10.2991/aebmr.k.200502.073 Baltuttis, D., B. Häckel, C.M. Jonas, et al., 2022. Conceptualizing and assessing the value of internet of things solutions. Journal of Business Research 140:245–263. https://doi.org/10.1016/j.jbusres.2021. 10.063. Barton, D., and D. Court. 2012. Making advanced analytics work for you. Harvard Business Review 90(10):78–83. Baskerville, R., A. Baiyere, S. Gergor, et al., 2018. Design science research contributions: Finding a balance between artifact and theory. Journal of the Association for Information Systems 19(5):358–376. https://doi.org/10.17705/1jais.00495. Becker, J., R. Knackstedt, and J. Pöppelbuß. 2009. Developing maturity models for IT management. Business & Information Systems Engineering 1(3):213–222. https://doi.org/10.1007/s12599-009-00445. Bianchini, M., and V. Michalkova. 2019. Data analytics in SMEs. OECD SME and Entrepreneurship Papers https://www.oecd-ilibrary.org/content/paper/1de6c6a7-en.https://doi.org/10.1787/1de6c6a7en. Buhl, H., M. Röglinger, D. Moser, et al., 2013. Big data. Business & Information Systems Engineering 5(2):65–69. Chen, W., C. Liu, F. Xing, et al., 2022. Establishment of a maturity modelto assess the development ofindustrial AI in smart manufacturing. Journal of Enterprise Information Management 35(3):701–728. https://doi.org/10.1108/JEIM-10-2020-0397. Chrissis, M., M. Konrad, and S. Shrum. 2007. CMMI: Guidelines for Process Integration and Product Improvement. SEI series in software engineering. Addison-Wesley. Chrissis, M., M. Konrad, and S. Shrum. 2011. CMMI for Development: Guidelines for Process Integration and Product Improvement. SEI series in software engineering. Addison-Wesley. Christiansen V, Lasrado LA (2022) Towards managing analytics for incumbent banks: A maturity model. In: CEUR Workshop Proceedings Chuah, M.H. 2010. An enterprise business intelligence maturity model (EBIMM): Conceptual framework. In 2010 Fifth International Conference on Digital Information Management (ICDIM), 303–308. https://doi.org/10.1109/ICDIM.2010.5664244. Chuah, M.H., and K.L. Wong. 2012. Towards developing an integrated maturity model framwork for managing an enterprise business intelligence. In Knowledge Management International Conference (KMICe) 2012. Comuzzi, M., and A. Patel. 2016. How organisations leverage big data: a maturity model. Industrial Management & Data Systems 116(8):1468–1492. https://doi.org/10.1108/imds-12-2015-0495. Cosic, R., G. Shanks, and S. Maynard. 2012. Towards a business analytics capability maturity model. In ACIS 2012 Proceedings. Davenport, T. 2006. Competing on analytics. Harvard Business Review 84:98–107. Davenport, T., and R. Bean. 2018. Big companies are embracing analytics, but most still don’t have a datadriven culture. Harvard Business Review 6:1–4. K 224 Schmalenbach Journal of Business Research (2025) 77:205–227 De Bruin, T., R. Freeze, U. Kaulkarni, et al. 2005. Understanding the main phases of developing a maturity assessment model. In Proc. Australasian Conference on Information Systems (ACIS). Dinter, B. 2012. The maturing of a business intelligence maturity model. In AMCIS 2012 Proceedings. Farah, B. 2017. A value based big data maturity model. Journal of Management Policy and Practice 18(1):. Favoretto, C., and G. Hd S. Mendes, et al., 2021. Digital transformation of business model in manufacturing companies: challenges and research agenda. Journal of Business & Industrial Marketing 37(4):748–767. https://doi.org/10.1108/jbim-10-2020-0477. Fornasiero, R., L. Kiebler, M. Falsafi, et al., 2024. Proposing a maturity model for assessing artificial intelligence and big data in the process industry. International Journal of Production Research https:// doi.org/10.1080/00207543.2024.2372840. Förster, M., B. Bansemir, and A. Roth. 2022. Employee perspectives on value realization from data within data-driven business models. Electronic Markets 32(2):767–806. https://doi.org/10.1007/s12525021-00504-0. Fukas, P., J. Rebstadt, F. Remark, et al. 2021. Developing an artificial intelligence maturity model for auditing. In ECIS 2021 Proceedings. Gartner. 2022. Drive successful digital growth with data and analytics. https://www.gartner.com/en/ publications/the-it-roadmap-for-data-and-analytics. Gökalp, E., and V. Martinez. 2021. Digital transformation maturity assessment: development of the digital transformation capability maturity model. International Journal of Production Research 60(20):6282–6302. https://doi.org/10.1080/00207543.2021.1991020. Gökalp, M.O., E. Gökalp, S. Gökalp, et al., 2021a. The development of data analytics maturity assessment framework: DAMAF. Journal of Software: Evolution and Process https://doi.org/10.1002/smr.2415. Gökalp, M.O., E. Gökalp, K. Kayabay, et al., 2021b. Data-driven manufacturing: An assessment model for data science maturity. Journal of Manufacturing Systems 60:527–546. https://doi.org/10.1016/j.jmsy. 2021.07.011. Grossman, R.L. 2018. A framework for evaluating the analytic maturity of an organization. International Journal of Information Management 38(1):45–51. https://doi.org/10.1016/j.ijinfomgt.2017.08.005. Gurlitt, J. 2012. Concept maps. In Encyclopedia of the Sciences of Learning, 730–732. Springer US. https:// doi.org/10.1007/978-1-4419-1428-6158. Hashem, I.A.T., I. Yaqoob, N.B. Anuar, et al., 2015. The rise of big data on cloud computing: Review and open research issues. Information Systems 47:98–115. https://doi.org/10.1016/j.is.2014.07.006. Hausladen, I., and M. Schosser. 2020. Towards a maturity model for big data analytics in airline network planning. Journal of Air Transport Management https://doi.org/10.1016/j.jairtraman.2019.101721. Hein-Pensel, F., H. Winkler, A. Brückner, et al., 2023. Maturity assessment for industry 5.0: A review of existing maturity models. Journal of Manufacturing Systems 66:200–210. https://doi.org/10.1016/j. jmsy.2022.12.009. Hellweg, F., S. Lechtenberg, B. Hellingrath, et al., 2021. Literature review on maturity models for digital supply chains. Brazilian Journal of Operations & Production Management 18(3):1–12. https://doi. org/10.14488/bjopm.2021.022. Helmy, M., S. Mazen, I.M. Helal, et al., 2022. Analytical study on building a comprehensive big data management maturity framework. International Journal of Information Science and Management 12(1):225–255. https://ijism.ricest.ac.ir/article698366.html. Hortovanyi, L., R.E. Morgan, I.V. Herceg, et al., 2023. Assessment of digital maturity: the role of resources and capabilities in digital transformation in b2b firms. International Journal of Production Research 61(23):8043–8061. https://doi.org/10.1080/00207543.2022.2164087. Humphrey, W. 1988. Characterizing the software process: a maturity framework. IEEE Software 5(2):73–79. https://doi.org/10.1109/52.2014. Hüner, K.M., M. Ofner, and B. Otto. 2009. Towards a maturity model for corporate data quality management. In ACM Symposium on Applied Computing. IBM. 2007. Data governance council maturity model. http://www-306.ibm.com/software/data/information/ trust-governance.html. Ilin, I., A. Borremans, A. Levina, et al. 2022. Digital Transformation Maturity Model., 221–235. Cham: Springer. https://doi.org/10.1007/978-3-030-89832-812. International Organization for Standardization. 2015a. ISO/IEC 33001:2015, Information technology — Process assessment — Concepts and terminology. Standard, Geneva, Switzerland. https://www.iso. org/standard/54175.html. International Organization for Standardization. 2015b. ISO/IEC 33002:2015, Information technology — Process assessment — Concepts and terminology. Standard, Geneva, Switzerland. https://www.iso. org/standard/54176.html. K Schmalenbach Journal of Business Research (2025) 77:205–227 225 Kane, G., D. Palmer, A. Phillips, et al., 2017. Achieving digital maturity. MIT Sloan Management Review . Kehrer, S., D. Jugel, and A. Zimmermann. 2016. Categorizing requirements for enterprise architecture management in big data literature. In 2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW), 1–8. IEEE. https://doi.org/10.1109/edocw.2016.7584352. Khuen, C.W., and M. Rehman. 2018. A maturity model for implementation of enterprise business intelligence systems. In Mobile and Wireless Technologies 2017, ed. K.J. Kim, N. Joukov, 445–454. Singapore: Springer Singapore. Kiron, D., P. Prentice, and R. Ferguson. 2015. Innovating with analytics. MIT Sloan Management Review 1–6. Król, K., and D. Zdonek. 2020. Analytics maturity models: An overview. Information 11(3):142. https:// doi.org/10.3390/info11030142. Lahrmann, G., F. Marx, R. Winter, et al. 2011. Business intelligence maturity: Development and evaluation of a theoretical model. In 2011 44th Hawaii International Conference on System Sciences, 1–10. https://doi.org/10.1109/HICSS.2011.90. Langer, B., and F. Pütterich. 2024. Assessing Value Creation of Analytics Projects in SMEs. In: Wirtschaftsinformatik 2024 Proceedings, 12. https://aisel.aisnet.org/wi2024/12. LaValle, S., E. Lesser, R. Shockley, et al., 2011. Big data, analytics and the path from insights to value. MIT Sloan Management Review 52(2):21–32. Lehrer, C., A. Wieneke, J. vom Brocke, et al., 2018. How big data analytics enables service innovation: Materiality, affordance, and the individualization of service. Journal of Management Information Systems 35(2):424–460. https://doi.org/10.1080/07421222.2018.1451953. Lismont, J., J. Vanthienen, B. Baesens, et al., 2017. Defining analytics maturity indicators: A survey approach. International Journal of Information Management 37(3):114–124. https://doi.org/10.1016/j. ijinfomgt.2016.12.003. Machado, C.G., P. Almström, A.E. Öberg, et al. 2020. Maturity framework enabling organizational digital readiness. In Advances in Transdisciplinary Engineering, Vol. 13, ed. K. Säfsten, F. Elgh. In: IOS Press. https://doi.org/10.3233/atde200204. Malta, P., and R.D. Sousa. 2016. Process oriented approaches in enterprise architecture for business-it alignment. Procedia Computer Science 100:888–893. https://doi.org/10.1016/j.procs.2016.09.239. Mcafee, A., and E. Brynjolfsson. 2012. Big data: the management revolution. Harvard Business Review 90(10):60–68. Mettler, T. 2011. Maturity assessment models: a design science research approach. International Journal of Society Systems Science 3(1/2):81. https://doi.org/10.1504/ijsss.2011.038934. Mouhib, S., H. Anoun, M. Ridouani, et al., 2023. Global big data maturity model and its corresponding assessment framework results. IAENG International. Journal of Applied Mathematics 53(1):. Mugge, P., H. Abbu, T.L. Michaelis, et al., 2020. Patterns of digitization: A practical guide to digital transformation. Research-Technology Management 63(2):27–35. https://doi.org/10.1080/08956308. 2020.1707003. Müller, J., and O. Buliga. 2019. Archetypes for data-driven business models for manufacturing companies in industry 4.0. In ICIS 2019 Special Interest Group on Big Data Proceedings. Murphy, G.D., and A. Chang. 2009. A capability maturity model for data acquisition and utilisation. In ICOMS Asset Management Conference, ed. J. Hardwick Novak, J.D. 1990. Concept mapping: A useful tool for science education. Journal of Research in Science Teaching 27(10):937–949. https://doi.org/10.1002/tea.3660271003. O’Donovan, P., K. Bruton, and D.T. O’Sullivan. 2016. IAMM: A maturity model for measuring industrial analytics capabilities in large-scale manufacturing facilities. International Journal of Prognostics and Health Management 7.. Paczkowski, W.R. 2021. Introduction to business data analytics: Setting the stage. In Business Analytics, 3–30. Springer. https://doi.org/10.1007/978-3-030-87023-21. Page, M.J., J.E. McKenzie, P.M. Bossuyt, et al., 2021. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ https://doi.org/10.1136/bmj.n71. Parra, X., X. Tort-Martorell, C. Ruiz-Vials, et al., 2017. CHROMA: a maturity model for the information-driven decision-making process. International Journal of Management and Decision Making 16(3):224–242. https://ideas.repec.org/a/ids/ijmdma/v16y2017i3p224-242.html. Pathak, S., V. Krishnaswamy, and M. Sharma. 2021. Big data analytics capabilities: a novel integrated fitness framework based on a tool-based content analysis. Enterprise Information Systems https://doi. org/10.1080/17517575.2021.1939427. Paulk, M., B. Curtis, M. Chrissis, et al., 1993. Capability maturity model, version 1.1. IEEE Software 10(4):18–27. https://doi.org/10.1109/52.219617. K 226 Schmalenbach Journal of Business Research (2025) 77:205–227 Perales-Manrique, J., J. Molina-Chirinos, P. Shiguihara-Juárez, et al. 2019. A data analytics maturity model for financial sector companies. In 2019 IEEE Sciences and Humanities International Research Conference (SHIRCON). IEEE. https://doi.org/10.1109/SHIRCON48091.2019.9024885. Poeppelbuss, J., B. Niehaves, A. Simons, et al. 2011. Maturity models in information systems research: Literature search and analysis. Communications of the Association for Information Systems 29.https:// doi.org/10.17705/1cais.02927. Pohl, M., D.G. Staegemann, and K. Turowski. 2022. The performance benefit of data analytics applications. Procedia Computer Science 201:679–683. the 13th International Conference on Ambient Systems, Networks and Technologies (ANT) / The 5th International Conference on Emerging Data and Industry 4.0 (EDI40). https://doi.org/10.1016/j.procs.2022.03.090. Pöppelbuß, J., and M. Röglinger. 2011. What makes a useful maturity model? a framework of general design principles for maturity models and its demonstration in business process management. In ECIS 2011 Proceedings. Proença, D., and J. Borbinha. 2018. Maturity models for data and information management. In Digital Libraries for Open Knowledge, 81–93. Springer. https://doi.org/10.1007/978-3-030-00066-07. Raber, D., R. Winter, and F. Wortmann. 2012. Using quantitative analyses to construct a capability maturity model for business intelligence. In 2012 45th Hawaii International Conference on System Sciences https://doi.org/10.1109/hicss.2012.630. Reis, J., M. Amorim, N. Melão, et al. 2018. Digital transformation: A literature review and guidelines for future research. In Advances in Intelligent Systems and Computing, 411–421. Springer. https://doi. org/10.1007/978-3-319-77703-041. Schniederjans, M., D. Schniederjans, and C. Starkey. 2014. Business Analytics Principles, Concepts, and Applications with SAS: What, Why, and How. Pearson. Schumacher, A., T. Nemeth, and W. Sihn. 2019. Roadmapping towards industrial digitalization based on an industry 4.0 maturity model for manufacturing enterprises. Procedia CIRP 79:409–414. https:// doi.org/10.1016/j.procir.2019.02.110. Shaaban, E., Y. Helmy, A. Khedr, et al. 2012. Business intelligence maturity models: Toward new integrated model. In The International Arab Conference on Information Technology (ACIT ’11). Shi, Y. 2022. Advances in Big Data Analytics. Springer Nature Singapore. https://doi.org/10.1007/978981-16-3607-3. Soares, N., P. Monteiro, F.J. Duarte, et al. 2021. Extended maturity model for digital transformation. In Computational Science and Its Applications – ICCSA 2021, ed. O. Gervasi, B. Murgante, and S. Misra, et al., 183–200. https://doi.org: Springer Nature Switzerland. https://doi.org/10.1007/9783-030-86973-113. Spruit, M., and K. Pietzka. 2015. MD3m: The master data management maturity model. Computers in Human Behavior 51:1068–1076. https://doi.org/10.1016/j.chb.2014.09.030. Spruit, M.R., and C. Sacu. 2015. DWCMM: The data warehouse capability maturity model. Journal of Universal Computer Science 21:1508–1534. https://doi.org/10.3217/jucs-021-11-1508. Stahl, B., B. Häckel, D. Leuthe, et al., 2023. Data or business first?—manufacturers’ transformation toward data-driven business models. Schmalenbach Journal of Business Research 75(3):303–343. https://doi. org/10.1007/s41471-023-00154-2. Stelzl, K., M. Röglinger, and K. Wyrtki. 2020. Building an ambidextrous organization: a maturity model for organizational ambidexterity. Business Research 13(3):1203–1230. https://doi.org/10.1007/s40685020-00117-x. Tan, C.S., Y.W. Sim, and W. Yeoh. 2011. A maturity model of enterprise business intelligence. Communications of the IBIMA https://doi.org/10.5171/2011.417812. Tavallaei, R., S. Shokohyar, S. Moosavi, et al., 2015. Assessing the evaluation models of business intelligence maturity and presenting an optimized model. International Journal of Management, Accounting and Economics 2(9):1005–1019. Thordsen, T., M. Murawski, and M. Bick. 2020. How to measure digitalization? a critical evaluation of digital maturity models. In Lecture Notes in Computer Science, 358–369. Springer. https://doi.org/ 10.1007/978-3-030-44999-530. Trochim, W. 1989a. Concept mapping: Soft science or hard art? Evaluation and Program Planning 12:87–110. Trochim, W. 1989b. An introduction to concept mapping for planning and evaluation. Evaluation and Program Planning 12:1–16. Vanauer, M., C. Bohle, and B. Hellingrath. 2015. Guiding the introduction of big data in organizations: a methodology with businessand data-driven ideation and enterprise architecture management-based implementation. In 2015 48th Hawaii International Conference on System Sciences, 908–917. K Schmalenbach Journal of Business Research (2025) 77:205–227 227 Vásquez, D.M., R. Kukurelo, C. Raymundo, et al., 2018. Master data management maturity model for the successful of mdm initiatives in the microfinance sector in peru. International Journal of Engineering Research and Technology 24(4):621–636. Wang, J., C. Xu, J. Zhang, et al., 2022. Big data analytics for intelligent manufacturing systems: A review. Journal of Manufacturing Systems 62:738–752. https://doi.org/10.1016/j.jmsy.2021.03.005. Wang, X., L. White, and X. Chen. 2015. Big data research for the knowledge economy: past, present, and future. Industrial Management & Data Systems https://doi.org/10.1108/IMDS-09-2015-0388. Wendler, R. 2012. The maturity of maturity model research: a systematic mapping study. Information and Software Technology 54(12):1317–1339. Willetts, M., and S.A. Atkins. 2024. Big data analytics maturity model for smes. International Journal of Information Technology and Computer Science 16(2):1–15. https://doi.org/10.5815/ijitcs.2024.02. 01. Woroch, R., and G. Strobel. 2021. Understanding value creation in digital companies—a taxonomy of iotenabled business models. In ECIS 2021 Proceedings. Zitoun, C., O. Belghith, S. Ferjaoui, et al. 2021. DMMM: Data management maturity model. In 2021 International Conference on Advanced Enterprise Information System (AEIS). IEEE. 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