This dissertation details the development and application of a prescriptive Data Analytics Capability Maturity Model (DACMM), specifically designed for Small and Medium Enterprises (SMEs). Despite their need for effective data analytics for growth and competitiveness, SMEs often face challenges such as limited resources and lack of technical expertise. Traditional Capability Maturity Models (CMMs), mainly tailored for larger organizations, often fall short in addressing the specific needs and constraints of SMEs. To bridge this gap, the DACMM provides a comprehensive guide to help SMEs advance their analytics capabilities from nascent stages to more sophisticated, data-driven operations, tailored to their unique operational contexts.
Methodologically, this research adopts Action Design Research (ADR), a collaborative approach where researchers and practitioners work together to address real-world problems through the creation and application of design science artifacts. Employing teleological organizational change theory as a kernel theory, the DACMM was developed over five Build, Intervention, and Evaluation cycles with two SMEs. This iterative process allowed the DACMM to be informed and enriched by both the practical knowledge gained through organizational interactions and its strong theoretical underpinnings.
The DACMM stands out for its prescriptive nature, offering SMEs clear, actionable steps to enhance their data analytics maturity across various levels. The model encompasses five key dimensions: organizational, analytics operations, infrastructure, data management, and data governance. It delves deeper into subdimensions and elements, tailoring them specifically for SMEs and incorporating detailed best practices into the prescriptive process. Notably, the DACMM introduces a new scoring method for self-assessment by SMEs, a resource-based self-ranking tool for roadmap creation, and a systematic approach to tracking progress. Collectively, these components of DACMM provide a practical, structured pathway for SMEs to improve their data analytics maturity in line with their strategic and operational goals.
This research makes several significant contributions. It introduces the first fully prescriptive CMM tailored to data analytics for SMEs, offering a practical tool for these enterprises to enhance their data analytics capabilities. It also expands the theoretical understanding of CMM development, particularly in the application of teleological organizational change theory. Furthermore, the use of ADR in this research enriches the design process knowledge, demonstrating the effective application of principles such as reciprocal shaping, mutually influential roles, and concurrent evaluation in creating a responsive artifact that meets the needs of the involved organization.
","abstract_html":"<p>This dissertation details the development and application of <em>a</em> prescriptive <em>Data</em> <em>Analytics</em> <em>Capability</em> <em>Maturity</em> <em>Model</em> (<em>DACMM</em>), specifically designed <em>for</em> <em>Small</em> and <em>Medium</em> <em>Enterprises</em> (SMEs). Despite their need <em>for</em> effective <em>data</em> <em>analytics</em> <em>for</em> growth and competitiveness, SMEs often face challenges such as limited resources and lack of technical expertise. Traditional <em>Capability</em> <em>Maturity</em> <em>Models</em> (CMMs), mainly tailored <em>for</em> larger organizations, often fall short in addressing the specific needs and constraints of SMEs. To bridge this gap, the <em>DACMM</em> provides <em>a</em> comprehensive guide to help SMEs advance their <em>analytics</em> <em>capabilities</em> from nascent stages to more sophisticated, <em>data</em>-driven operations, tailored to their unique operational contexts.</p> <p>Methodologically, this research adopts Action Design Research (ADR), <em>a</em> collaborative approach where researchers and practitioners work together to address real-world problems through the creation and application of design science artifacts. Employing teleological organizational change theory as <em>a</em> kernel theory, the <em>DACMM</em> was developed over five Build, Intervention, and Evaluation cycles with two SMEs. This iterative process allowed the <em>DACMM</em> to be informed and enriched by both the practical knowledge gained through organizational interactions and its strong theoretical underpinnings.</p> <p>The <em>DACMM</em> stands out <em>for</em> its prescriptive nature, offering SMEs clear, actionable steps to enhance their <em>data</em> <em>analytics</em> <em>maturity</em> across various levels. The <em>model</em> encompasses five key dimensions: organizational, <em>analytics</em> operations, infrastructure, <em>data</em> management, and <em>data</em> governance. It delves deeper into subdimensions and elements, tailoring them specifically <em>for</em> SMEs and incorporating detailed best practices into the prescriptive process. Notably, the <em>DACMM</em> introduces <em>a</em> new scoring method <em>for</em> self-assessment by SMEs, <em>a</em> resource-based self-ranking tool <em>for</em> roadmap creation, and <em>a</em> systematic approach to tracking progress. Collectively, these components of <em>DACMM</em> provide <em>a</em> practical, structured pathway <em>for</em> SMEs to improve their <em>data</em> <em>analytics</em> <em>maturity</em> in line with their strategic and operational goals.</p> <p>This research makes several significant contributions. It introduces the first fully prescriptive CMM tailored to <em>data</em> <em>analytics</em> <em>for</em> SMEs, offering <em>a</em> practical tool <em>for</em> these <em>enterprises</em> to enhance their <em>data</em> <em>analytics</em> <em>capabilities</em>. It also expands the theoretical understanding of CMM development, particularly in the application of teleological organizational change theory. Furthermore, the use of ADR in this research enriches the design process knowledge, demonstrating the effective application of principles such as reciprocal shaping, mutually influential roles, and concurrent evaluation in creating <em>a</em> responsive artifact that meets the needs of the involved organization.</p>","abstract_has_math":false,"creators":["Marohn, Robert Karl"],"institution":null,"degree_name":"Information Systems and Technology, PhD","degree_level":"Open Access Dissertation","degree_discipline":"Center for Information Systems and Technology","degree_department":null,"school":null,"contributors":["Samir Chatterjee","Shan Pan","Ace Vo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-01-01T08:00:00Z","date_published":"2024-01-01T08:00:00Z","updated_at":"2026-07-24T01:40:36Z","subjects":["Action Design Research","Big Data","Business Intelligence","Capability Maturity Model","Data Analytics","Small and Medium Enterprises","Business Administration, Management, and Operations"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarship.claremont.edu/cgu_etd/817","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Samir Chatterjee","Shan Pan","Ace Vo"]},{"key":"dc:creator","label":"Author","values":["Marohn, Robert Karl"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2026-07-17T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Center for Information Systems and Technology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Information Systems and Technology, PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Action Design Research","Big Data","Business Intelligence","Capability Maturity Model","Data Analytics","Small and Medium Enterprises","Business Administration, Management, and Operations"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarship.claremont.edu/cgu_etd/817"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This dissertation details the development and application of a prescriptive Data Analytics Capability Maturity Model (DACMM), specifically designed for Small and Medium Enterprises (SMEs). Despite their need for effective data analytics for growth and competitiveness, SMEs often face challenges such as limited resources and lack of technical expertise. Traditional Capability Maturity Models (CMMs), mainly tailored for larger organizations, often fall short in addressing the specific needs and constraints of SMEs. To bridge this gap, the DACMM provides a comprehensive guide to help SMEs advance their analytics capabilities from nascent stages to more sophisticated, data-driven operations, tailored to their unique operational contexts.
Methodologically, this research adopts Action Design Research (ADR), a collaborative approach where researchers and practitioners work together to address real-world problems through the creation and application of design science artifacts. Employing teleological organizational change theory as a kernel theory, the DACMM was developed over five Build, Intervention, and Evaluation cycles with two SMEs. This iterative process allowed the DACMM to be informed and enriched by both the practical knowledge gained through organizational interactions and its strong theoretical underpinnings.
The DACMM stands out for its prescriptive nature, offering SMEs clear, actionable steps to enhance their data analytics maturity across various levels. The model encompasses five key dimensions: organizational, analytics operations, infrastructure, data management, and data governance. It delves deeper into subdimensions and elements, tailoring them specifically for SMEs and incorporating detailed best practices into the prescriptive process. Notably, the DACMM introduces a new scoring method for self-assessment by SMEs, a resource-based self-ranking tool for roadmap creation, and a systematic approach to tracking progress. Collectively, these components of DACMM provide a practical, structured pathway for SMEs to improve their data analytics maturity in line with their strategic and operational goals.
This research makes several significant contributions. It introduces the first fully prescriptive CMM tailored to data analytics for SMEs, offering a practical tool for these enterprises to enhance their data analytics capabilities. It also expands the theoretical understanding of CMM development, particularly in the application of teleological organizational change theory. Furthermore, the use of ADR in this research enriches the design process knowledge, demonstrating the effective application of principles such as reciprocal shaping, mutually influential roles, and concurrent evaluation in creating a responsive artifact that meets the needs of the involved organization.
"]},{"key":"dc:title","label":"Title","values":["DACMM - A Data Analytics Capability Maturity Model for Small and Medium Enterprises"]}]}],"canonical_facts":{"dc:contributor":["Samir Chatterjee","Shan Pan","Ace Vo"],"dc:creator":["Marohn, Robert Karl"],"dc:date.available":["2026-07-17T07:00:00Z"],"dc:description.abstract":["This dissertation details the development and application of a prescriptive Data Analytics Capability Maturity Model (DACMM), specifically designed for Small and Medium Enterprises (SMEs). Despite their need for effective data analytics for growth and competitiveness, SMEs often face challenges such as limited resources and lack of technical expertise. Traditional Capability Maturity Models (CMMs), mainly tailored for larger organizations, often fall short in addressing the specific needs and constraints of SMEs. To bridge this gap, the DACMM provides a comprehensive guide to help SMEs advance their analytics capabilities from nascent stages to more sophisticated, data-driven operations, tailored to their unique operational contexts.
Methodologically, this research adopts Action Design Research (ADR), a collaborative approach where researchers and practitioners work together to address real-world problems through the creation and application of design science artifacts. Employing teleological organizational change theory as a kernel theory, the DACMM was developed over five Build, Intervention, and Evaluation cycles with two SMEs. This iterative process allowed the DACMM to be informed and enriched by both the practical knowledge gained through organizational interactions and its strong theoretical underpinnings.
The DACMM stands out for its prescriptive nature, offering SMEs clear, actionable steps to enhance their data analytics maturity across various levels. The model encompasses five key dimensions: organizational, analytics operations, infrastructure, data management, and data governance. It delves deeper into subdimensions and elements, tailoring them specifically for SMEs and incorporating detailed best practices into the prescriptive process. Notably, the DACMM introduces a new scoring method for self-assessment by SMEs, a resource-based self-ranking tool for roadmap creation, and a systematic approach to tracking progress. Collectively, these components of DACMM provide a practical, structured pathway for SMEs to improve their data analytics maturity in line with their strategic and operational goals.
This research makes several significant contributions. It introduces the first fully prescriptive CMM tailored to data analytics for SMEs, offering a practical tool for these enterprises to enhance their data analytics capabilities. It also expands the theoretical understanding of CMM development, particularly in the application of teleological organizational change theory. Furthermore, the use of ADR in this research enriches the design process knowledge, demonstrating the effective application of principles such as reciprocal shaping, mutually influential roles, and concurrent evaluation in creating a responsive artifact that meets the needs of the involved organization.
"],"dc:identifier":["https://scholarship.claremont.edu/cgu_etd/817"],"dc:subject":["Action Design Research","Big Data","Business Intelligence","Capability Maturity Model","Data Analytics","Small and Medium Enterprises","Business Administration, Management, and Operations"],"dc:title":["DACMM - A Data Analytics Capability Maturity Model for Small and Medium Enterprises"],"thesis:degree_discipline":["Center for Information Systems and Technology"],"thesis:degree_level":["Open Access Dissertation"],"thesis:degree_name":["Information Systems and Technology, PhD"]},"updated_at":"2026-07-24T01:40:36Z"}