The proliferation of data-intensive applications, such as artificial intelligence and smart manufacturing, has significantly increased the need for effective data management strategies. However, existing data management strategy frameworks are often too generic and lack practical implementation guidance. This leads organizations to develop ad hoc or custom-built solutions. We address this gap by developing a lightweight data readiness framework (DRF) specifically designed for data-intensive applications. Utilizing a tailored design science approach and a systematic literature review of 42 relevant studies, we identify five dimensions of DR: metadata management, data provenance and lineage, data quality, data interoperability, and data governance. The resulting DRF enables a structured classification and assessment of DR aspects in data-intensive projects. To evaluate the framework, we applied it to three digital twin demonstrators with varying levels of practical complexity. The evaluation provided early indications of the framework ‘s applicability, utility, and robustness.