Introduction

The path to integrated architecture across the organization begins with architecture d'entreprise and how it defines requirements for architecture des données.

A data architecture function establishes cohérence in definition and use of data throughout an organization. Adhering to a prescribed architecture des données forces business and technology resources to take the necessary steps to define and document data meaning, define the appropriate use of the data, and to ensure that proper governance is in place to manage data as meaning on a sustainable basis.

Définition

Le Entreprises et Architecture des données (DA) est un ensemble de capacités permettant d'assurer l'intégration entre le système d'information de l'entreprise et le système d'information de la société. processus et l'exécution de la DA fonction. L'entreprise processus est défini par le architecture d'entreprise fonction. L'AD définit des modèles de données tels que des taxonomies et des ontologies, ainsi que des domaines de données, métadonnées, Le DA est un système de contrôle des données qui permet de contrôler les données de l'entreprise et les données critiques pour l'entreprise afin d'exécuter les processus dans l'environnement de contrôle des données. Le DA fonction garantit le contrôle du contenu des données, que la signification des données est précise et non ambiguë et que l'utilisation des données est cohérente et transparente.

Champ d'application

• Établir une DA fonction au sein de l'Office de gestion des données (ODM).
• Travailler avec le bureau de gestion de projet du SM (PMO) pour concevoir et mettre en œuvre des processus et des outils durables pour l'AD, y compris l'intégration nécessaire avec les processus et les outils de l architecture d'entreprise.
• Identifier et établir des domaines de données, des sources faisant autorité et des points d'approvisionnement.
• Identifier et inventorier les données nécessaires pour répondre aux besoins de l'entreprise, y compris toutes les données nécessaires à la mise en œuvre de la stratégie de l'entreprise. métadonnées y compris glossaire, dictionnaire, classification, lignée, etc.
• Définir et attribuer des définitions d'entreprise, liées à l'inventaire des données.
• Veiller à ce que la gouvernance de l'AD soit intégrée dans la gouvernance des données (DG) et alignée sur les activités de gouvernance commerciale et technologique.

Proposition de valeur

Organizations that identify, record and make available information about the internal constituencies that define, produce, and use specific data demonstrate efficient and effective coordination, cooperation, and communications around this data.

Organizations that document information about highly valued data elements demonstrate improved understanding and business use of these data.

Organizations that effectively implement DA to understand their data and data ecosystem get a return on investment from several areas:

• Operational excellence in business processes creates efficiencies and lowers operating cost
• Creation of straight-through, fit-for-purpose data, reduced data debt and remediation costs and increased value derived from advanced analytics
• Greater understanding of your data leads to data simplification and reduces the cost of DM and maintenance
• Understanding your data also reduces operational, financial and reputational risks associated with using the wrong data for analytics, decision-making, and regulatory reporting

Vue d'ensemble

The DA component establishes the unambiguous definition and use of data throughout an organization. Adhering to a prescribed architecture des données forces business and technology to take the necessary steps to define and document data meaning, define the appropriate use of the data, and to ensure that proper governance is in place to manage data as meaning on a sustainable basis.

Data exists throughout an organization across all facets of business operations. The design of an organization's architecture des données is based on a comprehensive understanding of business requirements and their impact on what data is needed. Unraveling the business processus informs how data should be identified, defined, modeled and related. Architecture technologique then dictates how the architecture des données design is organized and placed into physical repositories in order to provide optimized access, security, efficient storage management and speed of processing.

To establish a successful DA fonction, the following sequence of activities is required.

First, the following two activities will allow an organization to understand what data is needed to satisfy the business requirements.

• Identification of Logical Data Domains

  Logical data domains are the logical groupings of data, not the databases themselves, that are needed to satisfy the business requirements.

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• Identification of Physical Repositories

  Underlying the logical data domains are multitudes of physical, often overlapping repositories of data that will map into the logical data domains. Identification of these underlying physical repositories is a critical step towards minimizing the complexity of legacy environments, reducing replication, better understanding lignée de données, assigning data ownership, and assessing la qualité des données (DQ).

Once the data domains and their underlying physical sources of data have been identified, precise business definitions using common semantic language for the identified data must be assigned and agreed upon by stakeholders. DA is about managing the meaning of data. The importance of assigning precise definitions in the context of business reality, the creation of a shared data dictionary and getting the agreement from both data producers and data consumers cannot be minimized. Without this common understanding of data attributes, aligned to business meaning, architecture des données will struggle to succeed. The risk of inappropriate use of data will increase and the ability to share data across an organization with confidence will be hindered.

The next step in addressing architecture des données is to define data taxonomies and business ontologies. Data taxonomies define how data entities are structurally aligned and related. For each officially designated domaine des données that is identified, inventoried and deemed critical, a taxonomie must be defined and maintained. The taxonomie is then mandated for all systems using this data as input into their business processes. With critical business fonction taxonomies defined and in place, the organization needs to modèle the relationships between taxonomies into a business ontologie. Ontologies are the relationships and knowledge of multiple related taxonomies across data domains.

A comprehensive architecture des données processus may include the following; however, this level of complexity is not always required.

• The business fonction defines the data in a business modèle based on the requirements for data as an input and output of the business processus.
• These business fonction data models are then consolidated into an organization-wide business modèle de données.
• For each domaine des données, a taxonomie is defined, maintained, and mandated for all systems using this data as input to the business processus. Data taxonomies define how data entities are structurally aligned and related.
• With business fonction taxonomies defined and in place, the organization models the relationships between taxonomies into a business ontologie. These ontologies represent the relationships and knowledge of multiple related taxonomies across data domains.

Taxonomies and ontologies define and relate the content of data to enable the organization to realize the maximum value of its data in a consistent and controlled manner. Once the content is defined, it needs to be precisely described using métadonnées. Some of the types of métadonnées may include, but not limited to, business, operational, technical, descriptive, structural and administrative.

Processes, Tools, & Constructs

• Business & Architecture des données Politique Mapping
• Business & Architecture des données Target-state
• Business & Architecture des données Execution Roadmaps
• Processus Optimization Framework
• Élément d'entreprise - Élément de données Construire
• Élément d'entreprise Criticality Criteria and Measurement Construire
• Actif de données Inventaire Construire
• Domaine Inventory – Authorized Provisioning Construire
• Modèle de données Le cadre
• Glossaire commercial – Data Dictionary Framework
• Métadonnées Le cadre
• Linéaire de données - Flux de données Construire
• Capability Optimization
• Matrice RACI
• Processus Designs and End-to-End Processus Integration
• Procedures Guide
• Processus Performance Measurement

Questions fondamentales

• Are business stakeholders driving requirements for data?
• Are policies in place to govern the creation and maintenance of data attributes and relationships?
• Are governance procedures in place to ensure adherence to established architecture des données standards?
• Are design reviews in place and required to ensure enhancements and new development are utilizing standard architecture des données définitions ?
• Is adherence to architecture des données standards auditable?

First Capability

The DA fonction strategy and approach must be defined and approved by stakeholders. Roles and responsibilities across the stakeholders must be established with operational processes in place.

First Sub-Capability

Description

The strategy and approach for the DA fonction must be defined and reflect the related vision and objectives of the Data Management Strategy (DMS). Once established, it must be formally empowered by senior management and its role communicated to all stakeholders.

Objectifs

• Formally establish the DA strategy and approach within the organization.
• Get approval of the DA strategy and approach from stakeholders.
• Assurer l'alignement des partie prenante plans and roadmaps with the DA strategy and approach.
• Obtain executive management support for the DA strategy.
• Communicate the role of the DA fonction across the organization through formal organizational channels.
• Operate the DA fonction collaboratively with DM initiative stakeholders.
• Secure authority to enforce DA compliance through politique et documenté procédure.

Conseil

The DA fonction is a critical bridge between the business and technology stakeholders in the DM initiative. Irrespective of whether the DA fonction aligns organizationally to the business or to the technology fonction, the pivotal role as the bridge between these two partie prenante groups must be recognized. Sometimes architecture des données is mistakenly viewed as a subset of architecture technologique. Successful architecture des données requires the integration of subject matter expertise from both architecture d'entreprise et technology Architecture.

Alignment of the DA strategy and roadmap to the DMS vision and objectives is achieved by agreement between the Operating Level Responsable des données and the individual responsible for delivering the fonction de gouvernance des données. The Operating Level Responsable des données est responsable de l'établissement des priorités pour chacune des exigences des composantes du cadre.

Questions

• Has the DA fonction a été formellement établie ?
• Is there a DA strategy and approach in place?
• Is the DA strategy and roadmap aligned to the DMS?
• Has the DA fonction been formally communicated to business, technology, operations, finance and risk stakeholders?
• Comment la direction générale a-t-elle manifesté son soutien ?
• Has authority been granted to the DA fonction mettre en œuvre et faire respecter les meilleures pratiques par le biais politique et des normes ?
• L'autorité a-t-elle été communiquée aux parties prenantes ?
• Existe-t-il un partenariat fonctionnel avec l'audit interne ?

Artéfacts

• The DA Plan
• Description of the roles and responsibilities of the DA fonction
• Communication d'un soutien spécifique de la part de la direction générale au moyen de listes de distribution
• Policies and procedures associated with executing and enforcing DA
• Bi-directional engagement with stakeholders on the DA fonction authority

Notation

Next Sub-Capability

Description

The DA fonction will require the coordination between the roles of the DA, architecture d'entreprise et architecture technologique. The integration activities between these disciplines must be defined by specific role descriptions within the different partie prenante teams. It is critical that each team be staffed at an optimal level for the scope and volume of work.

Objectifs

• Define and communicate the roles and responsibilities of the DA fonction.
• Fund and staff the DA fonction.
• Garantir et faire respecter l'alignement des activités et des projets sur les objectifs de l'UE. politique and standards through the authority of the DA fonction.
• Hold individuals accountable for the data control environment performance via annual reviews and compensation considerations.

Conseil

Effective DA is critical to the DM initiative. The data architect needs an understanding of the business processus and the technical environment to excel as the bridge between these two disciplines. The data architect will partner with the business data steward and technical data steward. The business data steward is accountable for the business element, which defines all the requirements for data. The technical data steward is accountable for the élément de données, which is the physical execution of the concept defined by the business element.

Questions

• Has the DA fonction a été établie ?
• Is the DA fonction des effectifs et un financement adéquats ?
• Does the DA fonction disposent-ils de l'autorité nécessaire pour être efficaces ?
• Have the roles and responsibilities of the DA fonction a été définie, documentée et socialisée ?
• Have the skills for data ethics review and execution of Machine Learning (ML) and Artificial Intelligence (AI) tools been added or developed within the stakeholders?
• Have milestones and metrics associated with DA execution been established?

Artéfacts

• Preuve de partie prenante identification
• Matrice RACI ou toute autre preuve de l'obligation de rendre des comptes
• Description of the roles and responsibilities of the DA fonction
• Qualifications et affectations du personnel
• Evidence of accountability linked to reviews and compensation
• Gap analysis of skills needed and those in place
• Liste des parties prenantes et preuves d'une communication bidirectionnelle

Notation

Next Sub-Capability

Description

Formal processes have been established for the activities of the DA fonction. These processes align with the DM politique et les normes de l'organisation et comprennent des procédures, des outils et des routines. Les routines sont nécessaires pour les opérations en régime permanent.

Objectifs

• Establish formal DA processes in alignment with the DM politique et des normes.
• Integrate the DA processes into the overall end-to-end processes of the DM initiative.
• Identify, schedule and maintain DA routines, meetings and working sessions required for operational support.

Conseil

The DA subject matter experts should work with the business processus design and optimization service within the Data Management Program (DMP) fonction. Together they will create and monitor the implementation of the DA processes in alignment to the end-to-end processus across the full DM initiative.

Questions

• Des processus formels ont-ils été définis et mis en œuvre ?
• Les procédures, outils et routines sont-ils en place pour la mise en œuvre des processus ?
• Have innovative technologies such as AI and ML been considered as part of the DCE processus et l'infrastructure ?
• Has the review of data ethics been included in the Qualité des données Management (DQM) strategy and approach?
• Are DA activities part of the normal operational routine of stakeholders?
• Are there standing meetings, planning sessions and regular communications about DA initiatives?

Artéfacts

• Processus des artefacts de conception, procédure guides et routines publiés
• Processus rapports sur les mesures de performance
• Meeting minutes, status reports and DA announcements

Notation

Next Capability

The DM initiative must be engaged with the architecture d'entreprise fonction for three activities: 1) the business processus must define data as an input and output; 2) manage restrictions on data access and use by the business processus; and 3) root-cause-fix for data issues that involve people or processus deficiency. This engagement must be supported by mutual governance and politique.

First Sub-Capability

Description

The DM initiative must be engaged with the architecture d'entreprise fonction of the organization to support the identification of requirements for data as input and output of the business processus design and optimization activity.

Objectifs

• Définir un processus optimization framework for the architecture d'entreprise fonction. Include the identification of requirements for data as input and output of the processus.
• Engage the DA fonction in business processus design and optimization. Integrate requirements for data into the standard data models of the organization.

Conseil

The BA fonction should engage with business subject matter experts to define requirements for data. The DA fonction then will engage with the business data stewards to interpret the requirements for data into a complete set of business element requirements. They should then record these requirements as métadonnées. The DA fonction can then engage with the technology fonction to translate business element requirements into the requirements for the physical data elements. In this way these physical data elements are brought into the real world.

Questions

• Are the BA fonction and the DA fonction integrated into the business processus design and optimization activities?
• Does the business processus design and optimization activity generate requirements for data as input and output of the processus steps?

Artéfacts

• Business & Architecture des données Politique Mapping
• Business & Architecture des données Target-state
• Business & Architecture des données Execution Roadmaps
• Processus Optimization Framework
• Élément d'entreprise - Élément de données Construire

Notation

Next Sub-Capability

Description

The business processus design and optimization effort must define requirements for data as an input and output of the business processus activities. To processus these requirements a review of any restrictions on the data usage is required. These restrictions may be due to data-subject owner consent, privacy, ethics of accessing the data and ethics of the data usage output of the business processus.

Objectifs

• Définir un processus to evaluate the data usage restrictions of the data.
• Define data restrictions that include the ethics of accessing the data and the ethics of the data usage output of the business processus.

Conseil

As the BA fonction delivers requirements for data to the DA fonction, a review of the data usage restrictions must be completed. This review should involve the business and technical data stewards as required. The data usage restriction review must be exhaustive across all regulatory, internal politique and ethical considerations.

Questions

• Have all the usage restrictions for the data been defined and reviewed?
• Is ethical data access and data usage output defined in the data usage restrictions?

Artéfacts

• Processus documentation for data usage restrictions
• Data usage restriction métadonnées

Notation

Next Sub-Capability

Description

When data fit-for-use issues are encountered, the BA fonction must be engaged if it is determined that the root cause fix required involves a people or processus deficiency.

Objectifs

• Définir un processus to engage the BA fonction to conduct root cause analysis and execute root cause fix solutions when the deficiency is related to people or business processus.

Conseil

When data fit-for-use issues are discovered the first step is to triage the potential cause. The triage review may determine it is a data issue, a technology issue, a business processus issue or a people issue. The type of issue will define the subject matter expertise required to determine the root cause fix. In the case of either business processus or people issues, the architecture d'entreprise fonction should be engaged in the root cause analysis. Direct business subject matter experts should be engaged as needed.

Questions

• Is there a triage processus in place to identify suspected root cause of data defects?
• Does the data defect issues log include categorizing issues that align to people or processus?
• Est-ce que le architecture d'entreprise fonction involved in the analysis and root cause fix of data defects aligned to people and processus?

Artéfacts

• Processus for engagement of architecture d'entreprise in root cause fix activities
• Issues log that includes defects aligned to people and processus

Notation

Next Sub-Capability

Description

Alignment of DA and BA governance includes: a definition of the business processus; data requirements as an input and output of the business processus; and integration of consommateur de données requirements including third party data contract specifications. DA governance is a part of the overall DM governance structure and routines.

Objectifs

• Align DA governance with BA governance to ensure semantic definitions, taxonomies and critical data elements (CDEs) are properly assigned and maintained.
• Utilize DA governance to monitor the capture of appropriate business metadata as defined by DM policies.

Conseil

The goal is to ensure that the management of data meaning is aligned with defined business processes. Business elements including their definitions and relationships to one another need to be properly assigned and maintained to capture and align with business reality. Data meaning needs to be aligned with operational processes and third-party data requirements. Collaboration is required to manage data vendor, producteur de données et consommateur de données relationships and entitlement control needed to maintain the flow of data.

Questions

• Have business processes been defined and verified?
• Are governance procedures in place to ensure unambiguous shared meaning across the organization?
• Have the business processes defined the priority data (CDEs) that is critical to the processus?
• Are there mechanisms to ensure collaboration between data producers and data consumers?
• Are third party data requirements and restrictions defined and accessible?

Artéfacts

• Entreprises processus flow diagrams with data as an input and output
• Bi-directional communication on data definitions and relationships
• CDEs defined by business processes
• Security and privacy classifications

Notation

Next Capability

Identifying the data includes: 1) defining the logical data domains; 2) mapping of physical data repositories to the logical data domains; and 3) cataloging the physical data in the repositories.

First Sub-Capability

Description

Identification of logical data domains must be driven by the business from the perspective of what data is needed to perform the required business functions. A logical domaine des données is the representation of a category of data that has been designated and named. Logical data domains represent the data, not the databases, which are needed to satisfy the business processus exigences.

Objectifs

• Impliquer les entreprises processus subject matter experts in the identification of the logical data domains.
• Identify and prioritize logical data domains.
• Structure logical data domains to contain the data of the domains irrespective of the various organizational structures where the data may be produced organization-wide.

Conseil

The overall goal is to ensure the proper use of data and to get stakeholders to think about DM in terms of data content concepts and not the physical database repositories. All this needs to be based on an understanding of how the business functions operate in reality. Once the logical data domains are defined, they must be mapped to their physical locations and associated with authorized provisioning points. The first step, however, is to define the domains.

Data domains include both internally generated data as well as externally acquired data. It is imperative that these strategic data assets are identified and inventoried to ensure their proper use in all consommateur de données les processus commerciaux critiques.

Questions

• Avoir domaine des données owners who are responsible for the quality and availability of the data been identified?
• L'entreprise domaine owner, as well as the DA fonction, been involved in the designation of the authoritative data domains?
• Avoir domaine des données taxonomies and conceptual/logical models been verified by business subject experts?
• Are all critical business functions represented in the discussion?
• Is the distinction between data domains and databases clear?

Artéfacts

• Politique indicating what authoritative data domains are and how they are to be used
• Criteria for determination of authoritative data domains
• Inventory of authoritative data domains
• Liste des parties prenantes et preuves d'une communication bidirectionnelle

Notation

Next Sub-Capability

Description

Underlying the logical data domains are physical repositories of data that will map to those logical data domains. The physical repositories may include application data, databases, spreadsheets, data warehouses, data lakes, streaming data or data stored in cloud services.

Objectifs

• Link underlying physical repositories to the logical data domains and record those links.
• Identify repositories that have been inventoried and document that the inventory is actively maintained.

Conseil

The data elements in any logical domaine des données need to be mapped to where the data physically resides. The first step is the creation of the inventory of data repositories. It doesn’t really matter where the content resides. This may be external, streaming, master/slave and cloud-based. What is important is that it is linked to the authoritative data domains and enforced. This is not about centralizing the data in a warehouse. It is adequate if the data has a unique repository identified as the known source of the data.

Questions

• Have the inventories of data repositories been compiled and verified?
• Have the authoritative data domains been mapped to their physical location?
• Are controls implemented to ensure namespace integrity and accessibility?
• A politique been drafted, verified and sanctioned on the use of authorized provisioning points?

Artéfacts

• Inventory of data repositories and authorized distribution points
• Mapping of authoritative data domains to the physical location
• Politique statements on the use of authorized provisioning points

Notation

Next Sub-Capability

Description

After the physical repositories of data aligned to the data domains are established, the next step is to catalog the physical data in the repositories.

Objectifs

• Établir un catalogue d'éléments de données alignés sur l'objectif de l'Union européenne. domaine des données.
• Capture basic métadonnées sur les éléments de données.
• Rendre le catalogue de données accessible aux parties prenantes.

Conseil

The data elements aligned to a domaine des données must have basic métadonnées captured including but not limited to source, terme name, terme definition, field name and field location. The basic métadonnées is required to make the data accessible. Any consommateur de données and particularly the data analytics consumers will need this métadonnées as part of their discovery processus in advance of defining data for production usage. As data is prioritized based on business needs and business processus criticality the data politique will require a more comprehensive set of business and technical metadata à capturer.

Questions

• Les éléments de données sont-ils alignés sur une domaine des données a été catalogué ?
• Has basic métadonnées been captured on the data elements?
• Est-ce que le métadonnées accessibles aux parties prenantes dans un catalogue de données ?

Artéfacts

• Données politique for basic métadonnées capture on data elements aligned to a domaine des données
• Data Catalog

Notation

Next Capability

Defining the data includes: 1) defining and documenting the conceptual and logical models; 2) establishing the business processus definition of the data; and 3) use of taxonomies to establish relationships between the data.

First Sub-Capability

Description

Conceptual and logical models for all entreprise data domains must be defined and documented. Alignment to the models must be required by politique and integrated into the entreprise change management policies.

Objectifs

• Define and document conceptual and logical data models.
• Verify conceptual and logical data models with key stakeholders.
• Capture data object relationships and document them into domaine ontologies.
• Verify authoritative data domains with business subject matter experts.
• Publish authoritative data domain taxonomies and demonstrate use by upstream/downstream systems.
• Align and cross-reference internal taxonomies to global standards.

Conseil

Conceptual models define how business processes work in the real world. Logical models organize the data in into manageable domains. Taxonomies define hierarchical relationships. Taxonomies are critical to establishing a common definition and language of data organization-wide and are required to ensure the data's proper use. Establishing these models should be done independent of the future physical instantiation of the data, however, issues related to relational data design vs. semantic (i.e.: graph or OWL design) should be considered strategically in designing the entreprise entity data.

Once the models are designated, they need to be managed and required by politique to ensure that they are implemented, maintained and used. Alignment to domaine des données taxonomies and conceptual models should be formally mandated by the organization’s change management policies including change approvals, impact analysis and controlled implementation.

Data repositories contain data that represents real concepts required in the business processes. The data have terms, define characteristics, express conditions, define triggers, specify requirements and translate activities in the business processus. The goal is the creation of a single conceptual view of data that defines how business concepts and processes work in the real world. The conceptual modèle is used to express the requirements for data in business terms, based on the business processus activities at the most granular level needed in the business processus. Les logical data model is used to define a logical set of data to align to a domaine des données. The scope of the logical ensemble de données can be validated by whether there is a natural subject matter expertise to support the ensemble de données. If the scope of the logical ensemble de données is too broad it will require desperate subject matter expertise and be inefficient to manage.

Granular data is often used to manufacture derived concepts to manage the objectives of the business processes. Derived concepts can sometimes be very complex and be manufactured from many data elements that may have variation if the data comes from multiple sources. Ensuring that the data is aligned with common meaning is an essential requirement for achieving automation, performing advanced analytics and generating trusted reports. This is one of the essential goals of the DM initiative and the building block of most business processes. Without shared meaning and transparency about how derived concepts are created, the organization will have challenges unraveling interconnections, managing complexity and most importantly using data to drive innovation.

Questions

• Have conceptual and logical data models defining the organization-wide data domains been verified by business subject experts?
• Are data taxonomies and models documented, made accessible and used in existing and new systems?
• Have policies and standards for managing data models and taxonomies been defined, verified, sanctioned and made accessible?
• Has governance over taxonomies been aligned with existing change management policies?
• Est-ce que le modèle of terms, definitions, and relationships been verified by business stakeholders and stored as métadonnées?
• Are there mechanisms for access such as glossaries that can be used as reference points for implementation?

Artéfacts

• Politique and standards on use and maintenance of data models and taxonomies
• Mapping and transformation to ensure implementation by upstream and downstream systems
• Business conceptual terms, definitions and relationships
• Agreement on business meaning verified by stakeholders
• Data models and taxonomies recorded in métadonnées dépôt
• Métadonnées repository content accessible to stakeholders
• Liste des parties prenantes et preuves d'une communication bidirectionnelle

Notation

Next Sub-Capability

Description

Business definitions must be developed as non-technical descriptions of data attributes that are based on contractual, legal and/or business facts.

Objectifs

• Document business definitions and verify them with stakeholders.
• Assign approved business definitions to defined taxonomies, which are fully attributed conceptual models.

Conseil

The precise meaning of data gets convoluted as data is moved around, copied and renamed. This is a problem because most organizations are run by business applications. Applications are driven by software– each with their own unique modèle de données. And all these models use glossaries as core factors of input. Meaning is often aligned with the specific software to make sure it works. It is not aligned across all applications and not harmonized across the organization. This creates circumstances where organizations use the same terms to mean different things and refer to identical things using different terms. These problems can be exacerbated when aligning front office to back-office processes because terms used in the front office don’t always communicate critical nuances that are needed to meet legal obligations in the back-office. These definitional differences create problems with integration and make it difficult to unravel complex business calculations or reuse data across new applications. The goal is the agreement on the meaning of data terms in the context of how they are used.

Questions

• Has the business meaning of atomic and derived terms been defined and verified?
• Have legal and compliance representatives been involved in the legal language used to define business concepts?

Artéfacts

• Business glossaries
• Complete front-to-back partie prenante engagement
• Liste des parties prenantes et preuves d'une communication bidirectionnelle

Notation

Next Sub-Capability

Description

Data identification, classification and taxonomie schemes and methodologies must be used to ensure precise organization of data. Establishing these schemes and methodologies are critical to defining how things relate, establishing standard treatment of data organization-wide and for aggregating data for analytical purposes. Unique and precise identifications, classifications and taxonomies are a foundational concept and is a required aspect for regulatory reporting, risk analysis and other internal analytics.

Objectifs

• Define identifiers for critical business elements.
• Assign and publish internal entity IDs and use them across business processes.
• Align and cross-reference internal IDs to industry standard identifiants.

Conseil

Data identification schemes are required for data factors of input such as Customer ID, Legal Entity ID and Product ID.

Data classification schemes are required for data factors such as privacy treatment, info-security treatment, masking, encryption and risk analysis.

Données taxonomie schemes define how things relate. Taxonomies define the relationship of Business Elements within a domaine des données. Taxonomies are critical to establishing a common definition and language of data organization-wide and are required to ensure the data's proper use.

Standard identification, classification and taxonomie schemes need to be mapped to any proprietary identifiers used in consuming applications. Unique identification is a core foundational tenet of DM that is governed by politique and enforced by standards.

Policies, procedures, and standards are needed to ensure the appropriate assignment, use, and maintenance of identification, classification and taxonomie schemes. The creation of these schemes requires participation from business, data, technology and legal and compliance stakeholders. In many cases, compliance policies may already exist, but they may not be integrated into the appropriate use or Software Development Lifecycle (SDLC) and/or change management processes within an organization.

Establishing these schemes and methodologies are critical to defining how things relate, establishing standard treatment of data organization-wide and for aggregating data for analytical purposes. Unique identifications, classifications and taxonomies are a foundational concept and is a required aspect for regulatory reporting, risk analysis and other internal analytics.

Identification schema for instruments, entities, clients, and products need to be unique and precise. Standard identifiers need to be mapped to any proprietary identifiers used in applications consuming data. Unique identification is a core foundational tenet of DM that must be governed by politique and enforced by standards. The scope and value of advanced analytics is very dependent on the standard identification schema.

Questions

• Have unique and precise identification schema been established for all instruments, entities, clients, and products?
• A politique been developed and approved to ensure these identifiers are used in business applications?
• Avoir standard identifiers been published and cross-referenced to any proprietary identifiers?

Artéfacts

• Politique à propos de standard identifiers
• Inventory of identification standards being used
• Documentation sur les références croisées et la transformation

Notation

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Description

An organization-wide standard métadonnées modèle is required. The métadonnées is the domaine des données that is owned and managed by the DM initiative. The same DM initiative requirements for managing any domaine des données are applied to the DM domaine des données.

Objectifs

• Define and implement the métadonnées modèle pour les domaine des données owned and managed by the DM initiative.
• Include all data required as input and output of the DM business processus dans le métadonnées modèle.
• Manage the DM domaine des données according to the data politique et des normes.

Conseil

Le DM domaine des données includes all métadonnées defined as requirements for data as input and output of the DM initiative business processes. To execute standard DM processes the métadonnées in the processes must align to a standard métadonnées modèle. An additional benefit of standard métadonnées is the interoperability across all the data domains as they interact. And finally, the standard métadonnées creates the ability to support, as appropriate, the implementation of DM processes centrally in the organization.

Another significant application of métadonnées is the classification des données schema that allows identification of data flagged for issues such as privacy, sensitive data, consent requirements, data-subject rights and the ethical use, access and outcome of data.

The types of métadonnées are many and may include business, operational, technical, descriptive, structural, administrative.

Questions

• Is the DM domaine des données defined with a métadonnées modèle?
• Est-ce que le métadonnées modèle include all data required by the DM business processes?
• Est-ce que le métadonnées modèle include a comprehensive classification des données schema?

Artéfacts

• DM domaine des données included in the organization domaine des données structure
• Métadonnées modèle defined for the DM domaine des données
• Data classification schema?

Notation