A Hybrid Infrastructure of Enterprise Architecture and Business Intelligence & Analytics to Empower Knowledge Management in Education

Abstract

The large volumes of data (Big Data) that are generated on a global scale and within organizations along with the knowledge that resides in people and in business processes makes organizational knowledge management (KM) very complex. A right KM can be a source of opportunities and competitive advantage for organizations that use their data intelligently and subsequently generate knowledge with them. Two of the fields that support KM and that have had accelerated growth in recent years are business intelligence (BI) and enterprise architecture (EA). On the one hand, BI allows taking advantage of the information stored in data warehouses using different operations such as slice, dice, roll-up, and drill-down. This information is obtained from the operational databases through an extraction, transformation, and loading (ETL) process. On the other hand, EA allows institutions to establish methods that support the creation, sharing and transfer of knowledge that resides in people and processes through the use of blueprints and models. One of the objectives of KM is to create a culture where tacit knowledge (knowledge that resides in a person) stays in an organization when qualified and expert personnel leave the institution or when changes are required in the organizational structure, in computer applications or in the technological infrastructure. In higher education institutions (HEIs) not having an adequate KM approach to handle data is even a greater problem due to the nature of this industry. Generally, HEIs have very little interdependence between departments and faculties. In other words, there is low standardization, redundancy of information, and constant duplicity of applications and functionalities in the different departments which causes inefficient organizations. That is why the research performed within this dissertation has focused on finding an adequate KM method and researching on the right technological infrastructure that supports the management of information of all the knowledge dimensions such as people, processes and technology. All of this with the objective to discover innovative mechanisms to improve education and the service that HEIs offer to their students and teachers by improving their processes. Despite the existence of some initiatives, and papers on KM frameworks, we were not able to find a standard framework that supports or guides KM initiatives. In addition, KM frameworks found in the literature do not present practical mechanisms to gather and analyze all the knowledge dimensions to facilitate the implementation of KM projects. The core contribution of this thesis is a hybrid infrastructure of KM based on EA and BI that was developed from research using an empirical approach and taking as reference the framework developed for KM. The proposed infrastructure will help HEIs to improve education in a general way by analyzing reliable and cleaned data and integrating analytics from the perspective of EA. EA analytics takes into account the interdependence between the objects that make up the organization: people, processes, applications, and technology. Through the presented infrastructure, the doors are opened for the realization of different research projects that increment the type of knowledge that is generated by integrating the information of the applications found in the data warehouses together with the information of the people and the organizational processes that are found in the EA repositories. In order to validate the proposal, a case study was carried out within a university with promising initial results. As future works, it is planned that different HEIs' activities can be automated through a software development methodology based on EA models. In addition, it is desired to develop a KM system that allows the generation of different and new types of analytics, which would be impossible to obtain with only transactional or multidimensional databases.