ISO 5116-2:2021

INTERNATIONAL ISO
STANDARD 5116-2
First edition
2021-07
Improving transparency in
financial and business reporting —
Harmonization topics —
Part 2:
Guidelines for data point modelling
Reference number
ISO 5116-2:2021(E)
©
ISO 2021

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ISO 5116-2:2021(E)

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ISO 5116-2:2021(E)

Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 What is a data model . 3
4.1 General . 3
4.2 The term “model” . 3
4.3 Data-oriented process of modelling . 3
4.4 The conceptual data model as a first step aiming for a database system . 4
4.5 Description of data modelling approaches for supervisory purposes . 4
4.5.1 General. 4
4.5.2 Using the “form centric” modelling approach . 5
4.5.3 Using the “data centric” modelling approach . 6
4.6 Description of dimensional modelling . 8
4.7 The concept of normalization . 9
5 Why use a multidimensional data model .13
5.1 General .13
5.2 Multidimensional data model .13
5.3 Operations that can be carried out on a multidimensional data model .14
6 Why data modelling is essential for collecting supervisory information .16
6.1 General .16
6.2 Objective of Data Point modelling .16
6.3 Main features .18
6.3.1 Increase of knowledge and understanding .18
6.3.2 Improvement of integration of changes.18
6.3.3 Reduction of risk of duplicate information .19
6.3.4 Higher harmonization .22
6.4 Classification of Data Point modelling in the data modelling concept .23
6.5 Area of application .24
6.6 What are the technical constraints .25
7 How do you proceed in creating a Data Point Model .26
7.1 General .26
7.2 Define dictionary elements .27
7.3 Specify hierarchies .28
7.4 Define Data Points .29
7.5 Define normalized tables and ensure quality of Data Point Model .29
7.6 Distribute Data Point Model .31
8 What the future holds for us .31
Bibliography .34
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ISO 5116-2:2021(E)

Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
bodies (ISO member bodies). The work of preparing International Standards is normally carried out
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electrotechnical standardization.
The procedures used to develop this document and those intended for its further maintenance are
described in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the
different types of ISO documents should be noted. This document was drafted in accordance with the
editorial rules of the ISO/IEC Directives, Part 2 (see www. iso. org/d irectives).
Attention is drawn to the possibility that some of the elements of this document may be the subject of
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any patent rights identified during the development of the document will be in the Introduction and/or
on the ISO list of patent declarations received (see www. iso. org/p atents).
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expressions related to conformity assessment, as well as information about ISO's adherence to the
World Trade Organization (WTO) principles in the Technical Barriers to Trade (TBT), see www. iso. org/
iso/f oreword. html.
This document was prepared by the European Committee for Standardization (CEN) (as CWA
XBRL 002) and was adopted with the following modifications by Technical Committee ISO/TC 68,
Financial services, Subcommittee SC 9, Information exchange for financial services.
— minor editorial change to Clause 1;
— Clause 2, Normative references, added;
— minor editorial changes.
A list of all parts in the ISO 5116 series can be found on the ISO website.
This document uses different verbal forms from those listed in the ISO/IEC Directives, Part 2.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www. iso. org/m embers. html.
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ISO 5116-2:2021(E)

Introduction
0.1  General
The purpose of this document is to support supervisory experts in the creation of a Data Point Model
(DPM). According to the definition of the European Banking Authority (EBA), a DPM “is a structured
formal representation of the data [.], identifying all the business concepts and its relations, as well as
1)
validation rules, oriented to all kinds of implementers.”
The underlying rules for the creation of such methods were initially introduced by the Eurofiling
Initiative and developed further by the European Insurance and Occupational Pensions Authority
(EIOPA). The main objective of data point modelling, the process of creating a DPM, is as follows: “[it]
should help to produce a better understanding of the legal background to the prudential reporting data
2)
and make data analysis much easier for both the institutions and regulators” .
Further goals are to prevent redundancies, lower maintenance efforts and, in general, to facilitate
working with national extensions on the European agreed-upon data set to facilitate the descriptions
of requirements that are sharable across national legislations. It is a requirement to have all the
information collected by the national supervisory agencies, particularly in Europe, transformed into the
same data structure with the same quality in order to be able to carry out standardized analysis of the
data across Europe. The current implementations are not able to meet these European requirements for
3)
supervision “to achieve higher quality and better comparability of data” . The main reasons for this are
the differences between the data definitions and the data formats of the various national supervisory
agencies, making comparison of reported data virtually impossible.
0.2  Objective
The aim to harmonize the European supervisory reporting is to be able to carry out more comprehensive
analysis and an increase of comparability of data. Since the supervisory agencies are already acquainted
with the representation of regulations specified in laws, this document is going to introduce the reader
to the concept of Data Point modelling methodology, as well as to its main terms and definitions that will
enable you to create Data Point Models that contain “all the relevant technical specifications necessary
for developing an IT reporting format” on your own.
0.3  Target audience
In general, as a banking supervisor you are responsible for communicating with Information
Technology (IT) experts in order to support the transfer of the essence of regulatory reporting to IT
systems. In 2009, the Eurofiling Initiative published the concept of Data Point modelling. Structures
of data represented in supervisory tables, as well as underlying laws and guidelines, were defined in
order to enable the interpretation of the reporting information by IT applications. IT specialists are
responsible for the development of software. However, most of the time they do not have the special
business knowledge needed to gather reporting requirements from various sources, such as legal texts
like Solvency Regulations and National Banking Acts, in order to build a flawless system. Therefore, the
task of creating a DPM is assigned to you.
This document introduces the basic principles deemed necessary in the modelling process. On the basis
of the explanations given in this document, you will be able to provide prerequisites for deriving data
formats on the basis of a DPM, as well as setting up a powerful data warehouse. This implies that the
model is published in a format that is understood by both parties involved in transforming legislation
into a model: business experts and IT specialists. The topics regarding supervisory reporting are kept
short and limited to the content relevant for this document. The idea is to convey the creation of the
Data Point Model to you, as you are a supervisor with analytical capabilities and personal interest in
this topic. No special IT knowledge is expected. The first sections will give you an overview on the
required IT knowledge.
1) EBA (2011a), p.22
2) EBA (2011a), p.30
3) EBA (2011a), p.29
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National banking supervisors have a mandate to evaluate the financial situation of financial
institutions in their country. To be able to perform the necessary analytics, financial data is required
from these institutions. The requirements are described in the form of texts and tables of data. To make
a comprehensive model from these texts and tables, a model is being created to enable IT support in
communicating and storing the necessary data. A common problem with the National Supervisory
Authorities (NSAs) is that IT staff has little financial background and financial specialists have little
IT background. This makes data modelling a problematic area, as both specialities are needed. This
document is aimed at providing the tools and knowledge of creating a DPM by the financial specialists.
The result, a model, can be perfected by IT staff later in the process.
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INTERNATIONAL STANDARD ISO 5116-2:2021(E)
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. IT specialists
originally introduced these terms. However, for an understanding and creation of Data Point Models, they are
now established in the language of business specialists as well.
3.1
data point
combination of quantitative and qualitative aspects to describe a reportable information
3.2
default member
specific element of a dimension which is applied when a dimension is not explicitly associated to a Data
Point
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3.3
dictionary element
abstract term for all qualitative and quantitative aspects of a multidimensional model
3.4
dimension
data set of one characteristic area which is composed of individual and non-overlapping data elements
used to define “by” conditions which represent the qualitative aspects of a Data Point
Note 1 to entry: Dimensions literally describe the dimensioned element in order to limit the range of interpretation
and thereby qualify the dimensioned element. One dimension either has a definite (i.e. countable) number of
members, which is called an explicit dimension, or an infinite number of members represented as values, that
follow a specific typing pattern, which is known as a typed dimension.
3.5
dimensioned element
element that shows the nature of the data by typing it and holds information about the underlying
structure of the cell that is specified
Note 1 to entry: In IT contexts, a dimensioned element is referred to as metadata.
3.6
domain
category with items that share a common semantic identity
Note 1 to entry: A Domain provides, therefore, an unambiguous collection of items in a value range. The items of
a Domain can have a definite, and therefore countable, number of items, or an infinite number of elements that
follow a specific (syntax) pattern.
3.7
domain member
element that is part of a domain
Note 1 to entry: It is also possible to have members that do not belong to a domain; they can refer to a dimension
directly.
Note 2 to entry: Domain members can either be explicitly named or defined by a type.
3.8
enumerable dimension
dimension that specifies a finite number of members
3.9
fact
quantitative aspect of data reported
EXAMPLE An amount, a number, a string of text, a date.
3.10
hierarchy
nesting (setting relationships in a parent–child-like architecture) of dictionary elements
3.11
non-enumerable dimension
dimension that specifies an undefined number of elements by defining syntactic constraints on the
values of the members, i.e. a data type or a specific pattern
3.12
sub-domain
subset of the members of a domain
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3.13
taxonomy
description of a valid Data Point Model
3.14
template
graphical representation of a set of supervisory data
4 What is a data model
4.1 General
4)
Data models outline the relationships between data. It is important that the person responsible for
modelling takes the time to capture all relations between data that can be shown in the model. It is
essential that the model is reviewed by third parties involved for errors to be identified in advance.
Furthermore, it helps to get a clearly structured model that can save time and costs later.
4.2 The term “model”
5)
The term model has its origin in the Middle French noun “modelle”. In IT context, a model pictures a
6)
target-oriented system instead of directly intervening in the complex system. Specifically, in terms
of data models, this means a real system, a system from the domain comprising real components that
7)
are tangible and dynamic, which is mapped to a model to reduce complexity. This may help to find a
suitable solution to an existing problem. The model needs to be created as close to reality as possible,
with attention to requirements regarding structure and behaviour. Nevertheless, in order to raise the
comprehensibility, aspects irrelevant for the purpose of modelling may be left out. The importance of
a single aspect, and whether it is worth being specified in the model, depends on the decision of the
domain experts. This strongly depends on the modeller’s understanding, creativity and capability to
associate the object system with the model.
The challenge of data modelling is that a data model “must be simple enough to communicate [it]
to the end user [.] [and] [.] detailed enough for the database design to use it to create the physical
8)
structure“. The same principle applies to message design and its physical representation.
In the following paragraph, the procedure of data-oriented modelling is presented.
4.3 Data-oriented process of modelling
The data-oriented process focuses on describing the static structure of the reporting system, in contrast
to the function-oriented process, which begins with modelling the functions of the reporting system
and adds the data in a later stage.
As data is the focus point of the banking supervisors, the data-oriented process is applied. Additionally,
in the course of time, data [objects] do not change as much as processes do. Functions are not being
taken into account here.
Applying the data-oriented process, data objects are specified first, as well as the attributes that belong
to each data object. The next step is to put the objects in relation to each other. Furthermore, the data
9)
model can imply integrity conditions and define operations that can be carried out on the data.
4) Cf. Gartner (2012)
5) Harper,D.(2013)
6) Cf. Ferstl, O./ Sinz,E. (2013), p. 22
7) Cf. ibidem, p. 20
8) ZaZa Network (2007)
9) Cf. Baeumle-Courth P./Nieland S./Schröder H. (2004), p.56
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4.4 The conceptual data model as a first step aiming for a database system
The data-oriented modelling takes place on 3 different levels that are built upon one another.
Figure 1 — Levels of data-oriented modelling
The conceptual data model reflects your reporting requirements. You are in the best position to know
what pieces of information are requested. The conceptual model helps you in the communication
with your IT specialists. This is an important step to avoid unpleasant surprises later when the
model is implemented in the IT department. The model is built regardless of the database system
10)
or data warehouse to be used. Relevant facts of the object system are to be specified without loss
of information. However, you, as the creators of the conceptual model do not need to be technically
skilled because the succeeding steps of data modelling are carried out by IT specialists. They should be
concerned about the technical requirements. It is very important that this first step of preparing the
conceptual data model is carefully elaborated before transferring the information to the IT. This can be
ensured by early reviews, which include all parties concerned.
The logical data model, as well as the physical data model, is prepared by the IT specialists. In essence,
the logical data model immediately follows the conceptual model (see Figure 1). When aimed at a
database approach, in contrast to the conceptual model, it also takes the requirements of the database
11)
or the data warehouse into account. The physical data model, as a final step, describes the actual
12)
implementation into an existing database system.
4.5 Description of data modelling approaches for supervisory purposes
4.5.1 General
This paragraph deals with the methods that are used to disseminate data and identify all of its
appropriate aspects. The two most appropriate methods of expressing regulatory data in a structure to
determine the context of the information will be discussed here.
10) Cf. 1keydata (2013a)
11) Cf. 1keydata (2013b)
12) Cf. 1keydata (2013c)
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Both modelling approaches refer to metadata.
Definitions for data and metadata are given below:
Data is “information processed or stored by a computer. This information may be in the form of text
documents, images, audio clips, software programs, or other types of data. Computer data may be
13)
processed by the computer's CPU and is stored in files and folders on the computer's hard disk.”
14)
Metadata “describes data. It provides information about a certain item's content.”
While data is a number like “50”, the metadata adds qualifying information to the number. The
explanation on the “form centric” and the “data centric” modelling approaches will clarify the difference.
4.5.2 Using the “form centric” modelling approach
The “form centric” approach is an ordinary table format with information held in a cell of a predefined
table called a template. Here a template is understood as a graphical representation of a set of
supervisory data. This approach identifies reporting data by their position in the templates. In this case,
each datum is defined by its coordinate in the table that is represented by the combination of columns
and rows of a template. Each coordinate has a code that is based on the row code and the column code.
This means that the data reported on the basis of coordinate codes is meaningless without the context
of the template. In the following example, each cell that represents a data requirement is described by a
code combination of its column and its row of the table Market Risk: Standardised form for position risk
in equities (MKR SA EQU) of the COREP framework. The form represents market risk equity positions
of the institutions that are subject to mandatory reporting. Throughout the whole document, this table
serves as an example to introduce terms and concepts of Data Point modelling to you. The table with
annotations can be found in the appendix in full size in order to deliver better clarity.
15)
Figure 2 — Table MKR SA EQU as an example of a form centric approach
13) TechTerms (2013a)
14) TechTerms (2013b)
15) EBA (2013)
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The “form centric” approach is oriented as the visualization of the data. Dependencies between the
codes of the data are only shown in the templates, i.e. by identifying the appropriate headlines or by
the indents of the label rows. A report based on the “form centric” approach, which uses codes for the
identification of data, is not able to incorporate the dependencies visibly.
Figure 3 — Close up of table MKR SA EQU for higher visibility on important aspects
On the basis of the section of sample table MKR SA EQU, shown in Figure 3, the “form centric” approach
is explained. The value reported by the monetary institution in each cell is called a fact. Facts are
classified as data. Let us say that the oval circled cell, defined by the row position r021 and the column
position c010, holds the monetary value “50”. The coordinate code r021c010 in the red circle is the
combination of the row position followed by the column position. Taking the template into account,
we realize the number “50” represents a value for derivatives as a gross position. When we include
additionally the headl
...