ISO 5116-2:2021 pdf download – Improving transparency in financial and business reporting — Harmonization topics — Part 2: Guidelines for data point modelling.
1 Scope This document provides guidelines for data point modelling for supervising experts. The main body consists of four sections. The interrogative form helps in choosing which section may best answer your question and lead you to a good understanding of the subject matter. After this first introductory section and the section containing terms and definitions, the main part starts to provide basic knowledge about different types of data models and data modelling approaches. The first and the second sections provide an overview of data models in general, in contrast to the third section that highlights the necessity of data modelling for supervisory data. This third section draws on the objectives and background information of the preceding sections. Furthermore, a paragraph classifies the Data Point Model introduced by the Eurofiling Initiative and elaborated by EIOPA and EBA, where many new terms related to DPM are introduced. Another paragraph explains the areas of application for the DPM. The third section concludes with a paragraph introducing a subset of the technical constrains that need to be considered in the creation process of the DPM. The fourth section gives step-by-step instructions on how to create a DPM. The paper concludes with remarks on the progress achieved so far, and provides an outlook on the software that is being developed at the moment to support you during the creation process. 2 Normative references There are no normative references in this document. 3 Terms and definitions For the purposes of this document, the following terms and definitions apply. ISO and IEC maintain terminological databases for use in standardization at the following addresses: — ISO Online browsing platform: available at https:// www .iso .org/ obp — IEC Electropedia: available at https:// www .electropedia .org/ NOTE The terms and definitions used in connection with Data Point modelling are inspired by vocabulary already known through their use in describing multidimensional databases and data warehouses.