Publicação
How to Data: A Data Strategy Framework for SMEs
| Resumo: | Small and medium-sized enterprises (SMEs) face increasing pressure to harness data for improved decision-making and long-term competitiveness, yet often lack the resources, technical infrastructure, and cultural readiness to effectively adopt Business Intelligence and Analytics (BI&A). While existing frameworks provide valuable guidance for large organizations, they fall short of addressing the specific challenges SMEs encounter in their data journeys. This study proposes a tailored Data Strategy Framework to guide sustainable BI&A adoption in SMEs. The framework is structured around five core dimensions: Business Objective Alignment, Data Architecture & Integration, Data Governance & Quality, Data Talent Development, and Data Culture & Decision-Making. Grounded in a Design Science Research methodology, the framework was developed through an extensive literature review and validated through semi-structured interviews with six data experts and seven SME decisionmakers across diverse industries and data maturity levels. Insights from these interviews informed refinements to the framework, ensuring it remains both theoretically sound and practically applicable. Key findings highlight the importance of aligning data use with core business goals, developing lightweight and pragmatic architectures, empowering internal talent through targeted upskilling, and fostering a data-driven culture led by strong leadership. Barriers such as unclear ownership, fragmented systems, and skill shortages are contrasted with enablers like leadership support, pragmatic architecture choices, and targeted upskilling efforts. The result is a holistic and adaptable roadmap that SMEs can use to incrementally build their data capabilities, while remaining agile and resource-conscious. By integrating strategic, operational, organizational, technical, and external adoption factors, the framework offers practical guidance for SMEs aiming to unlock value from data without overextending their capacities. This thesis contributes to bridging the gap between academic models and the operational realities of smaller firms, offering actionable insights for both practitioners and researchers working at the intersection of data strategy and SME transformation. |
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| Autores principais: | Witt, Anton |
| Assunto: | Data Strategy SMEs Business Analytics and Intelligence Analytics Adoption Data Maturity |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Small and medium-sized enterprises (SMEs) face increasing pressure to harness data for improved decision-making and long-term competitiveness, yet often lack the resources, technical infrastructure, and cultural readiness to effectively adopt Business Intelligence and Analytics (BI&A). While existing frameworks provide valuable guidance for large organizations, they fall short of addressing the specific challenges SMEs encounter in their data journeys. This study proposes a tailored Data Strategy Framework to guide sustainable BI&A adoption in SMEs. The framework is structured around five core dimensions: Business Objective Alignment, Data Architecture & Integration, Data Governance & Quality, Data Talent Development, and Data Culture & Decision-Making. Grounded in a Design Science Research methodology, the framework was developed through an extensive literature review and validated through semi-structured interviews with six data experts and seven SME decisionmakers across diverse industries and data maturity levels. Insights from these interviews informed refinements to the framework, ensuring it remains both theoretically sound and practically applicable. Key findings highlight the importance of aligning data use with core business goals, developing lightweight and pragmatic architectures, empowering internal talent through targeted upskilling, and fostering a data-driven culture led by strong leadership. Barriers such as unclear ownership, fragmented systems, and skill shortages are contrasted with enablers like leadership support, pragmatic architecture choices, and targeted upskilling efforts. The result is a holistic and adaptable roadmap that SMEs can use to incrementally build their data capabilities, while remaining agile and resource-conscious. By integrating strategic, operational, organizational, technical, and external adoption factors, the framework offers practical guidance for SMEs aiming to unlock value from data without overextending their capacities. This thesis contributes to bridging the gap between academic models and the operational realities of smaller firms, offering actionable insights for both practitioners and researchers working at the intersection of data strategy and SME transformation. |
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