This Data Management Capability Assessment Model (DCAM®) (“Model”) is being provided to the Recipient, (“Recipient”) as a Member of the EDM Council, Inc. (“EDM Council”). The Model and all related materials are the sole property of EDM Council, and all rights, titles, and interests therein are vested in EDM Council. The Model, 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 Model, or any portion thereof, may not be used in any way by Recipient, its officers, employees or agents or by any other party without the prior written consent of EDM Council. The Model 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 Model, or any portion thereof, the Recipient (and each person reviewing or using the Model) agrees to the terms set forth above. Any copying or use of the Model except as set forth above is strictly prohibited.

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

The Data Management Capability Assessment Model (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 model was created by the Enterprise 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 process of defining each piece of data by what it represents or describes in the real world. This process results in a direct, readily comprehensible label for that data. By adding descriptive metadata, the precise nuanced connection between each piece of data and the real world is established. Data exists everywhere within an organization and must be managed consistently within a well-defined control framework. The 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 stakeholder 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.

The DCAM is organized into seven core components:

1. Data Management Strategy & Business Case
2. Data Management Program & Funding Model
3. Business & Data Architecture
4. Data & Technology Architecture
5. Data Quality Management
6. Data Governance
7. Data Control Environment

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

In this version, 2.2, an additional, optional component covers Analytics Management and is relevant where the scope of the DM program or the Chief Data Officer's responsibilities cover the Analytics functions of the organization. This component has seven capabilities and 30 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 model 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 Group 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 model.

Open Acknowledgement

The EDM Council is committed to leveraging the knowledge of the data management practitioners across our membership. For Version 2, a small but global team of representatives from organizations and consultancies was assembled into a Work Group to recommend and approve the latest updates to the Framework. A further group was formed to create the Analytics Management component. A thank you is extended to the groups listed below for their role in advising the enhancements to DCAM.

EDMC President

• John Bottega - EDM Council

Work Group Co-Leads

• Pete Youngs - Ortecha
• Mark McQueen - EDM Council

Sub-Group Leads

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

Work Group Members

• 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 Group
• Eric Wallis - PNC Bank
• Irene Liu & Catherine Lee - PwC (Singapore)
• Fania Georgiades & Vamsi Sabbi - PwC (US)

Analytics Management Work Group Co-Leads

• Colin Gibson - EDM Council

Analytics Management Work Group Members

• Hany Choueiri & Jonathan Wheeler - Aldermore Bank
• Danny Saksenberg - Emerge
• Jaap Ritzen - Federal Reserve Bank of New York
• Ankit Goel - Freddie Mac
• Rachel Harrison-Smith - HSBC
• Pauline Ozols & Robert Wentz - KPMG (Canada)
• Simon Lea, Nicolina Turcan, Dan Krumins & Tanguy Lienart - Lloyds Banking Group
• Alok Jain & Josie Steer - Mudano
• John Studley - PwC (Australia)
• Garance Legrand - PwC (Sweden)
• Jefferson Braswell - Tahoe Blue
• Malcolm Clarke & Michael Nolan - Wellington Consulting