Data Architecture Development Through Data Metrics Using Investment Banking Sector Insights

Abstract

The topic of data architecture, which is inherently linked to the broad concept of data, remains relevant and requires further analysis and development despite the evolving methods and models used among practitioners. This is most easily observed in the example of a specific area, namely the investment banking sector, which has been chosen for analysis. The analysis confirms, in addition to affirming the above statement, that creating an effective data architecture should somehow consider not only the business objectives of the chosen area or department, but also the strategy for managing the organizational structure. Thus, to combine business needs with other available information about these needs, the creation of data metrics is proposed. This model is based on principles of matching current data to the created structure in space. In the considered example, the structure assumes a cubic form and is modeled using matrix transformation. Matching data to this structure is an optimization task aimed to be performed with computer software. The proposed model has practical application in the selected department, and further case studies are being conducted to assess the scalability of the proposed approach.