Data value creation in agriculture: A review

Traditional database management systems are inadequate for handling uncertain data due to design limitations. Various methods address database uncertainty, often oversimplifying and restricting representable uncertainties. Rapid data growth from technologies like IoT, multimedia, and social media has led to massive diverse data, known as big data, categorized into structured, semi-structured, and unstructured types. Coping with big data's challenges is encapsulated in the "Vs Model" with volume, velocity, and variety dimensions. It aims to gather variable information for causal understanding and informed decisions. Big data offers competitive advantages: smart decisions (69%), operational control (54%), customer insights (52%), and cost cuts (47%). This research studies big data management and cloud computing, where data and apps are accessed via the internet, saving resources globally. Cloud's simplicity and power facilitate tasks and storage. Risks include bankruptcy or breaches, yet cloud remains valuable for preserving big data. Vital factors in big data's value creation involve converting IT costs to assets and connecting big data outcomes to performance. This abstract summarizes exploration of traditional limitations, big data's essence, cloud's potential, and value creation nuances.

PurposeOrganisational learning drives tourism organisations towards more sustainable tourism. Digital transformation also provides opportunities for sustainable tourism development. This study aims to combine these perspectives and explore how digital transformation enables organisational learning to contribute to sustainable tourism, following organisational learning theory (OLT).Design/methodology/approachBased on a critical realist paradigm, this study focuses on developing an in-depth understanding of organisational learning in tourism organisations. Thirty qualitative interviews with tourism organisations participating in an executive development programme (EDP) show how tourism organisations create, retain and transfer knowledge.FindingsThis study demonstrates that the EDP initiates knowledge creation through content transmission and exchange, triggers knowledge retention through utilisation of digital technologies and reinforces digitalisation through data value creation. Furthermore, this study enables knowledge transformation as implementation, which contributes to the three pillars of sustainable tourism and facilitates the development of networks encouraging sustainable tourism.Originality/valueThis study identifies approaches that enable economic, social and environmentally sustainable tourism development by facilitating collaborations via digital transformation, digital technologies that guide guest streams, online mobility offers and online environmental awareness campaigns that reduce environmental impacts. Thus, this study strengthens OLT and has implications for organisational learning and tourism policymakers.

Despite a general awareness of the potential of big data in terms of public interest, several obstacles prevent their effective sharing. This study, linking the discourse on data to the concepts of data value and accountability, aims at emancipating the scientific debate from the emphasis on administrative transparency and the protection of privacy, tracing new perspectives for future research. The present research examines the main peer-reviewed articles published by journals that have dealt with data value and accountability across the public and private dimensions. The bibliometric analysis carried out indicates a propensity by current literature to consider the issue of data value creation either only in the private (data as input to improve business performance or customer relations) or in the public dimension (open data government models). This means that research on behavioral data for public governance has so far been underestimated. Evidence shows that big data value creation is closely associated with a collective process where multiple levels of interaction and data sharing develop among private and public actors in a multilayered accountability environment.

This paper identifies management capabilities for data value chains as a gap in current data value research. It specifies a data value management capability framework and a first data value monitoring capability maturity model (CMM). This framework and CMM will enable organisations to identify and measure the current state of their data value monitoring processes, and show how to take steps to enhance value monitoring in order to exploit the full data value potential in their organisation. This new approach to data value management is needed since, despite the success of Big Data and the appeal of the data-driven enterprise, there is little evidence-based guidance for maximising data value creation. To date, most data value optimisation has focused on technological gains such as data platforms or analytics, without bridging the gap to organisational knowledge or human factors research. The evidence of best practice gathered here from the state of the art shows that there is a hierarchy of data value dimensions for data value monitoring, starting with cost and peaking with utility (understanding value creation). The models are validated by a case study of three organisations that are managing data value and using it to support strategic decision- making.

In the context of the data economy, data governance (DG) has become a critical enabler for organizations aiming to transition into data-driven enterprises. This chapter explores the strategic relevance of DG as a bridge between corporate goals and data management practices. Beyond regulatory compliance and data quality control, DG must foster data value creation, interoperability, and responsible artificial intelligence (AI) practices. We also refer to some of the DG frameworks, such as DAMA-DMBOK and ISO standards, as well as maturity-based models used to evaluate the governance capabilities of organizations. A real-world case study from the logistics domain illustrates challenges and actions needed to evolve an small-to-medium enterprise toward a data-centric organization following the Trenti DG framework. The chapter offers methodological guidance and practical tools, and emphasizes the role of governance in ensuring quality, ethical, secure, and explainable use of data and AI models.

Connecting big data management capabilities with employee ambidexterity in Chinese multinational enterprises through the mediation of big data value creation at the employee level