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Publicação
How to Data: A Data Strategy Framework for SMEs
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 2.85 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
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.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Business Intelligence
Palavras-chave
Data Strategy SMEs Business Analytics and Intelligence Analytics Adoption Data Maturity