Le présent Modèle d'évaluation des capacités de gestion des données (DCAM®) (“Modèle”) is being provided to the Recipient, (“Recipient”) as a Member of the EDM Council, Inc. (“EDM Council”). The Modèle and all related materials are the sole property of EDM Council, and all rights, titles, and interests therein are vested in EDM Council. The Modèle, or any portion thereof, may not be copied by any Recipient and may not be distributed to, or made available for the use by any party other than Recipient, unless, in each case, Recipient has obtained the prior written authorization of EDM Council. Except as provided above, the Modèle, ou toute partie de celui-ci, ne peut être utilisé de quelque manière que ce soit par le destinataire ou ses agents, employés or agents or by any other party without the prior written consent of EDM Council. The Modèle may only be used by Recipient for external purposes or external assessments if it has entered into a separate licensing agreement with EDM Council governing the terms for such use. By reviewing or using the Modèle, ou toute partie de celui-ci, le destinataire (et chaque personne examinant ou utilisant les Modèle) accepte les conditions énoncées ci-dessus. Toute copie ou utilisation des Modèle à l'exception de ce qui est indiqué ci-dessus, est strictement interdite.

DCAM® is a registered trademark of the EDM Council, Inc., and may not be used or copied without the prior written authorization of the EDM Council, Inc.

Open Foreword

Le Modèle d'évaluation des capacités de gestion des données (DCAM®) is a structured resource that defines and describes the capabilities needed to establish and sustain a successful data management (DM) initiative in any organization. The modèle was created by the Entreprise Data Management Council based on the practical experiences and hard-won lessons of many of the world’s leading organizations. The result is the synthesis of a broad range of DM best practices from across the full spectrum of interconnected business processes. The DCAM addresses the strategies, organization-wide structures, technology and operational practices needed to successfully drive DM. It addresses the tenets of DM based on an understanding of business value combined with the hard reality of implementation.

To manage data in today’s organizations we must start by recognizing that proper DM is about managing data as meaning. This is a relatively new concept for many organizations and not very well understood. Managing data according to its meaning is a processus de définir chaque donnée par ce qu'elle représente ou décrit dans le monde réel. Il s'agit d'une processus permet d'obtenir une étiquette directe et facilement compréhensible pour ces données. En ajoutant métadonnées descriptives, Les données sont omniprésentes au sein d'une organisation et doivent être gérées de manière cohérente au sein d'un cadre de contrôle bien défini. Les données existent partout au sein d'une organisation et doivent être gérées de manière cohérente dans un cadre de contrôle bien défini. Les DCAM defines the framework and capabilities required to make DM a critical part of an organization's’ everyday operational fabric.

The challenges of properly managing data are significant. In most organizations there are numerous legacy data repositories and an overabundance of functions to unravel. There are social and political barriers to overcome. There are real technology challenges and execution gaps to address. Data ownership and accountability are hard to establish. Historically, funding often has been project based, making DM an intermittent priority. Data’s now critical place in the organization requires a commitment to robust, ongoing funding. An additional challenge is that many organizations may have to build the strong executive support needed to ensure that the organization stays the course in the face of short-term measurement criteria, operational disruption and conflicting partie prenante challenges to properly address the realities of the DM challenge.

We understand this reality because we’ve been there, and we have the scars to show for it. Data is foundational. It is the lifeblood of the organization. The bad data tax is a significant expenditure for many organizations though it may remain hidden in accepted inefficiencies and stunted results. Unraveling data silos through the creation of harmonized data is a prerequisite for eliminating redundancy, reducing reconciliation and automating business processes across the organization.

Managing this kind of fully interconnected data is essential if we are to gain insight from analytics, feed our models with confidence, enhance our service to clients and capitalize on new, but often fleeting, business opportunities. DCAM provides the guidance needed to assess the current-state of any organization’s DM and define the objectives and framework for the target-state of the DM initiative.

Le DCAM is organized into seven core components:

1. Stratégie de gestion des données et analyse de rentabilité
2. Programme de gestion des données et financement Modèle
3. Business & Architecture des données
4. Données & Architecture technologique
5. Qualité des données Gestion
6. Gouvernance des données
7. Environnement de contrôle des données

The components are organized into 31 capabilities and 106 sub-capabilities.

The EDM Council is indebted to the dozens of members who contributed to the development of the initial DCAM in 2014 and the subsequent updates. DCAM is quickly gaining adoption across the DM industry. DCAM training has now been delivered to more than 1,000 students around the world. Organizations ranging from banks to brokerages to consultancies to regulatory bodies have successfully adopted the DCAM Framework. Born in the financial service industry, increasingly organizations from other industries, manufacturing to government, are applying DCAM to their environment.

To keep the modèle relevant and on the leading edge, the EDM Council has committed to the product management discipline to review and update the Framework on a regular basis. For this current version, a small but global team of representatives from organizations and consultancies was assembled into a Work Groupe to recommend and approve the latest updates to the Framework.

To ensure DCAM addresses the needs of the DM industry in each unique organization, we have and always will accept input and contribution from members who have worked closely with the modèle.

Accusé de réception

The EDM Council is committed to leveraging the knowledge of the data management practitioners across our membership. For this current version, a small but global team of representatives from organizations and consultancies was assembled into a Work Groupe to recommend and approve the latest updates to the Framework. A thank you is extended to the groupe listed below for their role in advising the enhancements to DCAM.

EDMC President

• John Bottega - EDM Council

Co-responsables du groupe de travail

• Pete Youngs - Ortecha
• Mark McQueen - EDM Council

Chefs de sous-groupe

• Danny Saksenberg - (ML/AI Sub-group) Emerge
• Diana Ascher - (Data Ethics Sub-group) Information Ethics & Equity Institute
• John Paulson - – (Backward Compatibility Sub-group) Ortecha

Membres du groupe de travail

• Mike Vessey - Abu Dhabi Investment Authority (ADIA)
• Millie Townsend - Charles Schwab
• John Yelle & Marla Traub - The Depository Trust & Clearing Corporation (DTCC)
• Tracey Whitmore-Hall - Deutsche Bank
• Thomas Bodenski & Maryana Lazaridi - element22
• Jennifer Schultz & Lori Evans - Freddie Mac
• Dayana Yancheva & Umar Latif - ING
• Roshan Awatar & Kate Boyle - Lloyds Banking Groupe
• Eric Wallis - PNC Bank
• Irene Liu & Catherine Lee - PwC (Singapore)
• Fania Georgiades & Vamsi Sabbi - PwC (US)