A model for turning knowledge into organizational value outcomes and vice-versa

Purpose - We observe that the pressure of regulatory practices, whether de facto, prescribed legally, imposed by means of standards or other, entails and increasingly so the adoption by organizations of Data Governance (DG) practices in the management of their data. What is interesting is that the prescriptive means employed to specify the said DG in the form of Controlled Language (CL), is not only itself a knowledge asset, but that furthermore interpretation of the said DG prescriptions in the context of organizational data can provide complementary knowledge assets in the form of organizational value outcomes. That this is possible is due to the integration of DG and operational access to organizational data, which has been possible for some time. To exploit this situation, we describe a model-theoretic model marrying the disciplines of `Knowledge Asset Dynamics', DG and formal linguistics enabling obtaining knowledge from organizational value outcomes and vice versa. Our model's data content is diachronic in nature, thus providing a sound basis for knowledge asset dynamics. Our approach enables building a knowledge base in the form of a validated CL corpus amenable to mechanical enquiry and inference. Design/methodology/approach - We propose an approach in which `organizational value outcomes' are the result of the global application of DG founded on a semantic meta model enabling modelling the semantic structure of an arbitrary database in which the traditional static attribute-value couple semantics is rendered dynamic by means of context polarization. DG which forms the model's knowledge is expressed in human and machine comprehensible form using semantic rules written in CL. Originality/value - This methodology puts in evidence the feasibility of a constructive approach for relating knowledge and organizational value outcomes, which is intensional and exhaustive in nature, whose development process is inherently agile due to its model-driven evaluation procedure, and which incorporates traceability with justifications (these being increasingly mandatory, imposed by regulatory authorities, legislation etc.), and which furthermore enables the mechanical generation of exhaustive case based benchmarks. Instantiations of the model with application domain specific data values provide the possibility of performing set theoretic operations (union, forward functional composition etc.). Practical implications - The outcomes of the application involve the practice of normalising inherent in our approach which is consistent with standardization for interoperability within and between digital organizations.