Originally Published: November 2018; Revised: March 2020

Best Practice Scribes
Gareth Isaac, Director and Principal Consultant, Ortecha, a DCAM Authorized Partner
Mark McQueen, EDM Council, Senior Advisor-DCAM

Vue d'ensemble

Process Design Framework

The EDM Council industry standard processus design utilizes a 6-level modèle as defined below. Practically, an industry standard can only design to Level 3. Beyond Level 3 a standard becomes organization and role specific.

• Level 0: Value Chain – Components
• Level 1: Processus Groupings – Processus Groups based on Component
• Level 2: Core Processes – Activities and Tasks based on Processus Groupe
• Level 3: Business Processus Flow – Processes and Sub-processes based on Functional Role

---

• Level 4: Operational Processus Flow – Processus Documentation based on Role
• Level 5: Detailed Processus Flow – Procedures (step-by-step documentation) based on Role

Stakeholders

Data Management Practitioners (Reference: Data Management Functional Construct)

• Responsable des données
• Data Governance Executive
• Executive Data Steward
• Data Architect
• Domaine des données Manager
• Business Data Steward
• Technical Data Steward
• Entreprises Processus Subject Matter Expert
• Data Custodian

Champ d'application

The driver for the design of the Level 2 Domaine des données Gestion processus was the development of a best practice tied to the identification of Critical Data Elements (CDEs). The processus of prioritizing data based on criticality was determined to be contained within the Domaine des données Gestion processus. (See: Donner la priorité aux données en fonction de leur criticité : Les éléments de données critiques (EDC) en contexte)

The following processus design presents a complete Level 2 Domaine des données Gestion processus. Only four of the Level 3 processes were developed as they were the processes required to represent the activities of the processus to prioritize data.

DCAM Component Alignment

The Level 2 processus is presented at the Component level with alignment back to DCAM Framework Capabilities and Sub-capabilities.

The table below represents the DCAM Components that are required to execute the Domaine des données Gestion processus.

Composant	Capability Requirement
7.0 Environnement de contrôle des données	● The processus of domaine des données management is in the Data Control Environment Component. ● The processus of prioritizing data based on criticality resides within the domaine des données management processus.
3.0 Business & Architecture des données	● The processus of prioritizing data based on criticality is dependent on defining requirements for data, identifying data, defining data, and profilage données.
5.0 Qualité des données Gestion	● The processus of prioritizing data based on criticality does not require the Qualité des données Management Component. Qualité des données Management is part of the overall Domaine des données Management and will be integral to the processus of managing the implications of criticality.
6.0 Gouvernance des données	● The processus of prioritizing data based on criticality leverages the Data Governance Component for approving the métadonnées and the criticality designation.

Level 2 1.0: Data Domain Management Process

Summary

Le Domaine des données Gestion Processus is where the DCAM Framework Components of Data Governance, Architecture des données, et Qualité des données are brought together to execute on a specific set of data in the Data Control Environment.

Process Flow

Process Details

The tasks outlined in orange below support the processus of prioritizing data based on criticality as defined in the best practice article. However, achieving the means of prioritizing data based on criticality and managing the criticality is dependent upon the complete Domaine des données Gestion Processus.

Task ID	Functional Role	Task Description
1.1	Environnement de contrôle des données	Define Requirements for Data ● The business processus consuming the data (Consommateur de données) develops requirements for data as an input to their processus. ● The requirements include proposed criticality with a description of material impact to business processus as a result of poor quality data.
1.2	Environnement de contrôle des données	Validate data in Scope ● The business processus producing the data (Producteur de données) determines whether the requested data is within the scope of their domaine.
D1	Environnement de contrôle des données	Data in scope? ● If yes, move to Task 1.3. ● If no, is there a referral for which domaine may be in scope. Communicate to Consommateur de données the scope outcome and recommendation if applicable. Processus stops.
1.3	Environnement de contrôle des données	Source Data ● Go through appropriate steps to analyze the requirements, identify the data, locate the data, source the data (access data for analysis and preparation for provisioning) and record basic métadonnées.
1.4	Environnement de contrôle des données	Negotiate Criticality ● Producteur de données reviews Consommateur de données’s proposed critical designation based on analysis of material impact to business processus of poor quality data. ● Agreement of Critical Elements is reached or disagreement is escalated.
D2	Environnement de contrôle des données	Agreement on Criticality? ● If yes, move to D3, is it critical ● If no, escalate criticality decision
D3	Environnement de contrôle des données	Is data critical? ● If no, move to D4, is standard data required. ● If yes, move to Task 1.5 to design métadonnées.
D4	Environnement de contrôle des données	Standard data required? ● Does the Consommateur de données require standardized data? ● If no, move to Task 1.14a to provision non-standard data with appropriate controls on use. ● If yes, move to Task 1.5 to design métadonnées.
1.10a	Environnement de contrôle des données	Define Accord de partage des données (DSA) & Accord de niveau de service (ALS) ● Establish restrictions on the use of non-standard data and parameters of provisioning.
Tool		Accord de partage des données Accord de niveau de service
1.15a	Environnement de contrôle des données	Provision Data ● Provision the non-standard data with appropriate controls on use (proportionate to the criticality of the data).
1.5	Architecture des données	Conception Métadonnées ● Design métadonnées (data about the data) that is required for the appropriate level of control on the data. ● This task works in conjunction with the parameters of the Entreprise Data & Data Management Politique and the proposed Accord de partage des données (DSA) which defines the required level of control on the data.
1.6	Architecture des données	Enregistrer Métadonnées ● Record métadonnées about the data in the appropriate repository(ies).
1.7	Architecture des données	Monitor Métadonnées Quality ● Monitor the métadonnées quality for précision, exhaustivité, et respect des délais. ● Once métadonnées quality is deemed adequate, submit to the appropriate data governance body for approval.
1.8a	Gouvernance des données	Make Data or Data Management Decisions ● Data governance body reviews métadonnées quality report gaps to validate alignment with Entreprise Data & Data Management Politique.
D5	Gouvernance des données	Métadonnées approved? ● If no, return to Task 1.5 to close identified gaps. ● If yes, move to Task 1.9 to standardize the data.
1.9	Environnement de contrôle des données	Standardize Data ● Apply logic to transform the data into the standardized form.
1.10b	Environnement de contrôle des données	Define Accord de partage des données (DSA) / Accord de niveau de service (ALS) ● Producteur de données et Consommateur de données agree to all parameters in the DSA. ● Producing Technology Manager and Consuming Technology Manager in accordance with the DSA parameters agree to all parameters in the ALS.
1.11	Qualité des données Gestion	DQ Rule Development ● Define the range of quality rules to run against each element to validate the fit-for-purpose of the data.
1.12	Qualité des données Gestion	Evaluate / Monitor Fit-for-Purpose ● Execute the defined rules to generate defect reporting. ● Evaluate exhaustivité of rule set.
D6	Qualité des données Gestion	Fit-for-purpose achieved? ● If no, move to Task 1.13 to remediate quality defects. ● If yes, move to Task 1.8b to make data or data management decision.
1.13	Qualité des données Gestion	Remediate Quality Defects ● Complete all processes to remediate quality defects.
1.8b	Gouvernance des données	Make Data or Data Management Decisions ● Data governance body reviews métadonnées et la qualité des données to approve data are fit-for-purpose and to validate alignment with Entreprise Data & Data Management Politique et DSA / ALS.
D7	Gouvernance des données	Fit-for-purpose approved? ● If yes, move to D6 to approve DSA / ALS. ● If no, return to 1.11 to close identified gaps.
D8	Gouvernance des données	DSA / ALS approved? ● If yes, move to 1.14 to processus data for provisioning. ● If no, return to 1.10 to close identified gaps.
1.14	Environnement de contrôle des données	Processus Data for Provisioning ● Run all period close activities to prepare data for provisioning.
1.15b	Environnement de contrôle des données	Provision Data ● Execute provisioning routine defined in the ALS.

Level 3 Stakeholder Functional Roles and Responsibilities

The Level 3 processes are presented at the Functional Role level with alignment back to DCAM Framework Capabilities and Sub-capabilities.

The chart below details Domaine des données Management functional roles and responsibilities aligned to the Data Management Functional Construct. These functional roles apply to all of the Level 3 processes detailed in the best practice article.

Functional Role	Detailed Description
Business Data Management – Producer	A processus, application or partie prenante that provisions data to one or more Data Consumers
Business Data Management – Consumer	A processus, application or partie prenante that receives or uses data from a Producteur de données.
Architecture des données	Le fonction that defines and implements the data content strategy for a given subset of data.
Technology Delivery – Producer	Le fonction that designs, builds, and runs the technical infrastructure supporting the Producteur de données.
Technology Delivery – Consumer	Le fonction that designs, builds, and runs the technical infrastructure supporting the Consommateur de données.

Level 3 1.1: Define Requirements for Data

Summary

Within the Domaine des données Gestion processus the activity of defining requirements for data are completed by the Consommateur de données.

Process Flow

Process Details

Task ID	Functional Role	Detailed Description
1.1.1	Business DM – Consommateur de données	Identify Target Élément d'entreprise (BE) ● Based on the needs of the business processus define the requirements for data
1.1.2	Business DM – Consommateur de données	Initiate Élément d'entreprise Request Form ● Record all known requirements in the form
Tool		Élément d'entreprise Request Form ● The form includes a standard set of required and optional (if known) attributes necessary to accurately communicate the request to the Producteur de données
1.1.3	Business DM – Consommateur de données	Review Entreprise Data Inventory ● Search the repository for a Élément d'entreprise record that aligns to the defined requirements for data
D1	Business DM – Consommateur de données	Est-ce que le Élément d'entreprise in Inventory? ● If yes, move to 1.1.5 to complete the BE request form ● If no, move to 1.1.4 to identify suspect domaine des données
1.1.4	Business DM – Consommateur de données	Identify Prospective Domaine des données ● Based on Data Consumers understanding of the data select the most likely domaine des données
1.1.5	Business DM – Consommateur de données	Complete Élément d'entreprise Request Form ● If found in inventory, cite the BE ID and identify any requirement discrepancies in the Entreprise Data Inventory ● If not found in inventory, complete all required items and those optional items that are known
Com1	Business DM – Producteur de données	Deliver Consommateur de données BE Request ● Consommateur de données delivers to the Producteur de données of the suspect domaine des données

Level 3 1.2 Validate Data in Scope

Summary

Within the Domaine des données Gestion processus the activity of validating that the Consommateur de données requested data are in scope is completed by the Data Producers.

Process Flow

Process Details

Task ID	Functional Role	Detailed Description
Com1	Business DM – Producteur de données	Receive Consommateur de données BE Request ● Consommateur de données delivers to the Producteur de données of the suspect domaine des données
1.1.2	Business DM – Producteur de données	Validate Data Owner ● Using the request form information investigate whether the data are owned by the receiving Producteur de données
D1	Business DM – Producteur de données	Est Data Owner correct? ● If no, move to D2 to decide if the non-owned data are in scope ● If yes, move to 1.2.2 to align the DE to the requested BE
D2	Business DM – Producteur de données	Is non-owned data in scope (Upstream Data Owner by DSA approves pass-through distribution non-owned data)? ● If yes, move to 1.2.2 to align the DE to the requested BE ● If no, move to Com2 to communicate data are out-of-scope
Com2	Business DM – Producteur de données	Communicate Out-of-Scope ● Producteur de données communicates to Consommateur de données that the requested BE is not in scope to the Producer’s Domaine (Producteur de données should share any suspected data domains if known)
1.2.2	Business DM – Producteur de données	Align DE to BE ● Based on the requirements for the BE identify all the DEs that align with the requirements
D3	Business DM – Producteur de données	Is data already sourced (available in Authoritative Provisioning Point)? ● If yes, move to Processus 1.4 Negotiate Criticality (non-owned data can be in scope if the upstream Data Owner allows the pass-through distribution of the data) ● If no, move to 1.2.3 to identify the DE System of Record(s) (SORs)
1.2.3	Business DM – Producteur de données	Identify SOR ● Based on the DE to BE alignment identify the SOR(s)
Com3	Business DM – Producteur de données	Data Sourcing Request ● Request the DE(s) to be sourced by the Technology Delivery Producer

Level 3 1.4 Negotiate Criticality

• Producteur de données reviews Consommateur de données’s proposed critical designation based on analysis of the material impact to the business processus of poor quality data.
• Critical or Non-critical agreement is reached or disagreement is escalated.

Summary

Within the Domaine des données Gestion processus the activity of negotiating criticality is completed by the Producteur de données with the Consommateur de données.

Process Flow

Process Details

Task ID	Functional Role	Detailed Description
Com1		Review Consommateur de données BE Request Form
1.4.1	Business DM – Producteur de données	Review Criticality Analysis ● Producteur de données reviews Consommateur de données’s proposed critical designation based on analysis of the material impact on the business processus of poor quality data.
1.4.2	Business DM – Producteur de données	Discuss Criticality ● Producteur de données discusses with Consommateur de données rationale for material impact on the Consumer’s business processus of poor quality data
D1	Business DM – Producteur de données	Agreement on Criticality? ● If no, move to 1.4.3 to escalate disagreement on criticality ● If yes, move to D2 for determination of whether data is, or, is not, critical
1.4.3	Business DM – Producteur de données	Escalate Disagreement ● Escalate disagreement on criticality to appropriate Governance body for resolution
1.4.5a	Gouvernance des données	Make Data or Data Management Decision ● Appropriate Governance body reviews escalated disagreement and resolves critical designation
D2		Is Data Critical? ● If yes, move to 1.4.4 to get approval of critical designation ● If no, move to D3 to determine if standardized data are required
1.4.4	Business DM – Producteur de données	Approval of Critical Designation ● The Producteur de données governance body must approve criticality designation
1.4.5b	Gouvernance des données	Make Data or Data Management Decision ● Appropriate governance body decisions approval of critical designation
D3		Critical data are Approved? ● If yes, move to 1.5 Design Métadonnées ● If no, move to D4 Standard Data Required
D4		Standard Data are Required (standard value is required across the ensemble de données)? ● If yes, move to 1.5 Design Métadonnées ● If no, move to 1.10 Define DSA/ALS

Level 3 1.10 Complete DSA/SLA

Summary

Within the Domaine des données Gestion processus the activity of completing the Accord de partage des données is conducted by the Business Data Management-Data Producer with the Business Data Management-Data Consumer. Similarly, completing the Accord de niveau de service is conducted by Technology Delivery Producer with the Technology Delivery-Data Consumer.

Process Flow

Process Details

Task ID	Functional Role	Detailed Description
Com1		Review Consommateur de données BE Request Form
1.10.1	Business DM – Producteur de données	Confirm Use & Define Use Constraint ● Review the consumer defined use in the BE Request Form ● Define the use constraints on the data
1.10.2	Business DM-Data Consumer	Propose Quality Measures for Data Elements ● Based on consumer business processus, define measurements for la qualité des données
1.10.3	Business DM – Producteur de données	Confirm Quality Measures ● Evaluate proposed measurements against current measurements and confirm agreed upon measures
1.10.4	Business DM – Producteur de données	Propose Quality Threshold ● Evaluating current la qualité des données and cost to enhance la qualité des données, propose the threshold for quality
1.10.5	Business DM-Data Consumer	Confirm Quality Threshold ● Based on the cost of poor quality to the producer business processus, confirm an acceptable threshold for quality
1.10.6	Business DM – Producteur de données	Create DSA Document ● Create or modify existing DSA document to include all data shared between the producer and consumer data domains
1.10.7	Business DM-Data Consumer	Confirm DSA Document ● Review and confirm the exhaustivité de la DSA document
1.10.8	Technology Delivery-Data Consumer	Propose ALS Requirements ● Based on the requirements of the consumer business processus and the constraints of the technical infrastructure, define the application-to-application parameters for data consumption
1.10.9	Technology Delivery-Data Producer	Confirm ALS Terms ● Review and confirm the ability to perform according to the defined parameters
1.10.10	Technology Delivery-Data Producer	Create ALS Document ● Create or modify existing ALS document to include data elements and the parameters for consumption
1.10.11	Technology Delivery-Data Producer	Review ALS Document ● Review and confirm the exhaustivité de la ALS document
D1	Technology Delivery-Data Producer	ALS Approved? ● If yes, move to D2 for Technology Delivery-Data Producer approval of the ALS ● If no, move to 1.10.9 to confirm the ALS terms
D2	Technology Delivery-Data Producer	ALS Approved? ● If yes, move to D3 for Business DM-Data Producer approval of the DSA et ALS ● If no, move to 1.10.9 to confirm the ALS terms
D3	Business DM-Data Producer	DSA /ALS Approved? ● If yes, move to D4 for Business DM-Data Consumer approval of the DSA et ALS ● If no, move to 1.10.1 to confirm use and define use constraints
D4	Business DM-Data Consumer	DSA /ALS Approved? ● If yes, move to 1.10.12 to obtain appropriate governance body approval of the DSA et ALS ● If no, move to 1.10.1 to confirm use and define use constraints
1.10.12	Gouvernance des données	Make Data or Data Management Decision ● Appropriate governance body decisions approval of DSA et ALS
D4	Gouvernance des données	DSA /ALS Approved? ● If yes, move to 1.10.13 to record documentation parameters in the métadonnées dépôt ● If no (DSA not approved), move to 1.10.1 to confirm use and define use constraints ● If no (ALS not approved), move to 1.10.9 to confirm ALS terms
1.10.13	Business DM-Data Producer	Record in Métadonnées Repository ● Record DSA et ALS documentation parameters in the métadonnées repository (critical designation, use constraints, consuming domaine, quality threshold, DSA et ALS agreement IDs, etc.)

Appendix

About the Work Group

The development of the Domaine des données Gestion processus was done within the Work Groupe to define the processus for prioritizing data based on criticality. The best practice developed by the Work Groupe was originally published in November 2018 and is titled: Donner la priorité aux données en fonction de leur criticité : Les éléments de données critiques (EDC) en contexte.

The project objective was to create an agreed upon understanding of the purpose and definition of a CDE. Then, based on that purpose and definition develop a best practice processus, procedures, and tools for the identification of CDEs and for managing the implications of criticality. The execution of the processus, procedures and tools will be aligned with the DCAM Framework and the Data Management capabilities it defines.

Work Group Members – organization affiliation as of November 2018

Arzaga, Raymund – Scotiabank
Atkin, Mike – EDMC
Bala, Sathya – Deutsche Bank
Bersie, Bret* – US Bank
Bland, Karen* – Moody’s Corporation
Brophy, Doris – Societe Generale
Deligiannis, Greg – S&P Global Ratings
Dewsbury, Jeff – DTCC
Dimitrion, Genevey – State Street
Doyle, Martin* – DQ Global
Farenci, Susan – MUFG Union Bank
Finnen, Michael – Mitsubishi UFJ Financial Groupe
Fruhstuck, Mary – BNY Mellon | Pershing
Giardin, Christopher – IBM Hybrid Cloud
Gordon, Andrew – Deutsche Bank
Hawkins, Matthew* – Goldman Sachs
Isaac, Gareth* – Ortecha
Jeffries, Denise          
Keslick, Rob – BMO
Klaentschi, Kathryn    
Lawson, Andrew – Brickendon
Liu, Irene – PWC
McAdams, Curtis       
McQueen, Mark* – EDMC / Ortecha
Nham, Annie – Macquarie Groupe Limited
Pandya, Hiten* – HSBC Bank
Robeen, Erica – Mastercard
Rolles, Daniel – EXL Service Holdings, Inc.

* Architecture des données Subgroup Member

About the Authors

Gareth Isaac is a Partner in Ortecha, an EDM Council DCAM Authorized Partner data consultancy. He is a professional Data practitioner who works with stakeholders – both leadership and subject matter experts – to understand the complex challenges involved with improving processes and data throughout the end to end information lifecycle. Gareth has worked with multiple GSIBs over the years to help improve their data management practices, specializing in lignée de données, control frameworks and governance functions.

gareth.isaac@ortecha.com
+44 20 3239 3823

Mark McQueen is the Senior Advisor – DCAM to the EDM Council. He joined the Council in 2016 and now leads the Best Practice Program to develop Data Management industry-standard processes for executing the DCAM Framework. Mark has over 20 years with a Fortune 25 GSIB where he was the business Data Management Executive for the Wholesale Bank. In addition to Best Practice Program facilitation, he provides training and EDMC Advisory Services related to the adoption and execution of the DCAM Framework in member organizations.

Mark is a DCAM Certified Trainer, Six Sigma Black Belt Certified, and Strategic Foresight accredited – University of Houston.

Mark is a Partner in Ortecha, an EDM Council DCAM Authorized Partner data consultancy.

mmcqueen@edmcouncil.org
+1 615.308.6465

---

Revision History

Date	Author	Description
November 2018	Garreth Issac; Mark McQueen	Initial Publication
March 2020	Mark McQueen	Knowledge Portal Release; Broken into a Separate Article from Prioritizing Data Based on Criticality: CDEs in Context