# Analysis of Enterprise Internet of Things Maturity Models: A Review
## 5 Results and Analysis
### How Are Measurement Data Used to Support Maturity Improvement in Organizations? The data obtained from these mechanisms is one aspect of the post-establishment improvement processes of IoT technology maturity in organizations, as it provides information on the status and progress of the organization or process being assessed with the technology. However, based on the review conducted, this aspect is often not adequately addressed or discussed in most of the maturity models proposed in the literature. In general, most of the proposed maturity models do not explicitly discuss how measurement data are used to support improvement, where they mainly focus on assessment methods, model evaluation, and maturity level definitions, which highlights a gap in knowledge about leveraging measurement data for the explicit purpose of supporting improvement. Furthermore, it is essential to indicate that implicitly, one of the ways of taking data derived from maturity measurement is the self-assessment process that is recognized as constructive dialogues to define improvement actions [70]; therefore, no framework that directly employs measurement data for maturity improvement is detailed. Considering the above, some of the proposals in the domain explicitly state how they use data to improve the company's position concerning maturity measurement, such as the article by [PERSON] et al. [63], who describe a maturity model for data quality management, where the collected measurement information is used to analyze the causes of problems, correct nonconformities and transform processes as part of improving data quality at subsequent maturity levels. Another example is [51], which presents a maturity model for individual competence and curriculum guidelines, where individual competence levels help to identify areas for improvement through specific training. Finally, [PERSON] [46] proposes a maturity model for IoT data governance, where measurement data on current maturity levels for each control objective are used to define maturity states. Implementation teams then work to achieve those target states by applying controls. After implementation, a reassessment is performed to validate progress. The above are explicit examples of how data has been used in the domain to improve the position of IoT technology in organizations. Along these lines, the rest of the documents either do not mention how measurement data are utilized to support improvement or merely state that measurement data are used to assign a corresponding maturity level to strategic recommendations for improvement. However, they do not detail how these recommendations are implemented, monitored, or evaluated. Some research points to this aspect as part of future work, suggesting that the proposed models should be better defined and operationalized. In conclusion, this review highlights the need for measurement data to support improvement within the proposed maturity models. Although some articles allude to the potential application of measurement data for improvement, detailed frameworks describing the processes and methodologies involved are needed. This represents a research opportunity and underscores the need for future studies to address this critical aspect in developing and applying maturity models. 2.3 Q3. How Has the Proposed Maturity Models' Contribution, Utility, Value, or Effectiveness Been Validated in the Organizational IoT Domain? The metrics or parameters that validate the contribution, usefulness, value, or effectiveness of the proposed maturity models vary depending on the domain, scope, and purpose of the models as ways to demonstrate the applicability and potential value of the proposed mechanisms. The importance of correctly validating this type of mechanism is fundamental because it allows determining the degree of contribution to the scientific corpus of the domain, its applicability in other contexts, and its replicability [79].