Artificial Intelligence Based Business Transformation Projects-the Role of Data Sciences in Mixed-Methods Patterns (RDSMMP)

The Applied Polymathical/Holistic Mathematical Model for Integrating Data Sciences in Mixed-Methods (AHMM4MM) supports Business transformation projects (simply Project). The AHMM4MM uses various Polymathic Mathematical Models (PMM), that abstract, incorporate, and integrate Data Sciences (DS), AI-Subdomains, Intelligence based Information Communication System (IICS) components with Project’s transformed resources. Transformed resources can be services (and artefacts), success factors (or calibration variables), business processes (and scenarios), mixed-methods, AI-Models (AIM), and adequate Enterprise Architecture (EA) Models (EAM). PMMs, AI based services, resources/artefacts, and EAMs can be used to establish set of Mixed-Methods Patterns (MMP) that include DS technics/capabilities, data-platforms’ access (and management), algorithms-functions, mapping concepts, unbundled services; to model and implement Decision Making System’s (DMS) Processes (DMSP) related infrastructure, data-storage(s), components-models, and end-users’ integration. The integration of MMPs enforces and automated MMP based DMSPs, Project’s validity-checking, and Gap Analysis Processes (GAP); which all need adapted dynamic interfaces to manage and access Enterprise, Project, Datastorage(s), IICS, EAMs, pool(s) of Artificial Intelligence (AI) services, and other types of artefacts and resources. On the other hand, MMPs communicate with other, by using Project’s and AI components; and can use also various medias-types formats, like the eXtensible Markup Language (XML) format, and others. Implemented, imported (or exported) AI or Data-Sciences (DS) contents and structures are combined with other Project’s and IICS artefacts, services, and components, to deliver MMPs for various AI-Subdomains.