Introduction

The Architecture – Business, Data & Technology component focuses on establishing an integrated architecture as a foundation for best-practice data management. Ensuring collaboration among business, data, and technology architectures is essential for achieving business objectives. The architecture disciplines provide the structure and blueprints that are leveraged in other DM capabilities. Business Architecture is the strategy, design, and execution of business capabilities needed to support the organization’s business processes and activities with a focus on deliverables used as inputs for data architecture. Data Architecture is the strategy and execution of design processes for data structures, definitions of data meaning, and metadata to support the organizational objectives. Technology Architecture is the strategy and execution of the design of the physical infrastructure based on business and data architecture to support the data needs of the organization. Business architecture is the blueprint for organizations to define and organize their business operations. Simply put, business architecture involves analyzing, designing, and communicating an organization’s core business capabilities, processes and model. The diagram below provides a simple representation of this concept.

Diagram 3.1: Business Architecture and Data Requirements

The DCAM framework uses ‘business architecture’ as a broad term for the function, group(s) or individual(s) that generate the business strategy, objectives, requirements, and needs for data. Data and data management requirements typically can be defined by business capabilities or business processes. Some organizations may not have a formal business architecture team, so it is important for Data Management to identify and collaborate with the appropriate business team to drive the identification of data requirements.

Definition

Business architecture defines the business activities that may be recorded as the business capabilities or as the business process requirements that are needed to meet the business objectives. Of specific interest to DCAM is the definition of the data requirements that will drive how data will be managed to support the defined business activities. These requirements are used as input to data architecture. Data architecture is a bridge between the business process requirements for data and the physical execution of that data in the technology infrastructure. It converts the data needs of the business into detailed plans for how data is defined, structured, managed, and controlled. It also outlines the technical requirements for how data is created, used, and shared across business processes. Data architecture assigns data to data domains, models data entities, establishes glossaries, taxonomies and ontologies, develops a metadata model, and articulates needs for data management technology tools. Technology architecture takes data and Data Management requirements from Data Architecture and applies them to the strategy, design and implementation of the technology infrastructure. Technology Architecture defines the platforms and tools needed for optimal support of the business processes, data, and Data Management requirements, including how data is physically acquired, moved, revised and distributed in an efficient manner. Designing for physical data proximity, bandwidth, processing time, privacy protection, backup, recovery and archiving are some important elements of mature technology architecture. Technology Architecture and Data Architecture may develop and share architecture patterns to help achieve this.

Scope

• Establish architecture policy and standards to guide business, data, and technology architecture as a foundation for data management across the organization
• Establish the roadmap for structured and integrated architecture processes (business, data and technology) to support data management execution across the organization
• Secure the alignment of Data Management with business, data and technology architecture, and strategy.
• Design and implement sustainable data architecture processes with due collaboration to address the data and data management requirements as defined as outputs from the business architecture process.
• Define the data architecture approach to define and design data architecture to achieve the business objectives and needs for data.
• Identify and inventory the organization’s data needed to support the business requirements.
• Develop and validate data domains, data models, authoritative sources, and provisioning points.
• Ensure that Business Architecture, Data Architecture and Technology Architecture governance is integrated in the Data Governance structures and aligned with business and technology governance activities.

Value Proposition

Organizations that implement an integrated architecture capability as a foundation for Data Management are more effectively positioned to support strategic and tactical business objectives more powerfully for business gain. Business architecture articulates the nature, purpose, criticality, and business benefits of curated data assets for the identified business processes to facilitate revenue generation, risk mitigation and cost containment from leveraging those assets. The formalized data and data management requirements clarify the business rules around data provision and use for each process, including regulatory obligations and modern analytics needs. The integration of business architecture with functional and adaptable data architecture and technology architecture completes the picture, setting foundational blueprints to optimize manual and machine-driven data management. This underpins a high-performing data ecosystem.

Overview

Business architecture plays a crucial role in defining and documenting the organization’s business capability and processing requirements for data. These outputs serve as critical inputs for data architecture, ensuring its alignment with technology architecture and the organization’s strategic data needs and business objectives. The effective integration of business, data, and technology architecture—through clearly defined inputs and outputs, role responsibilities, collaboration, and governance controls—is essential for architecture to effectively support data management. Data architecture oversees consistency in defining, managing, and utilizing data across an organization by aligning business and technology teams in data governance and sustainable data management. It bridges business data requirements with technology infrastructure, enabling structured data production, provisioning, and consumption. By providing blueprints for secure, scalable, and efficient data management, it supports adaptable platforms, applications, and infrastructure while ensuring accessibility of data catalogs and metadata. Strong governance frameworks and flexible toolsets maintain data integrity and responsible data use, while proper alignment with technology architecture enhances performance, cost efficiency, and long-term scalability. Technology architecture translates data architecture into physical data repositories, platforms, and tools that optimize security, storage, distribution, and processing speed. It must remain flexible to accommodate evolving business needs, including regulatory requirements and organizational stress conditions. As businesses increasingly adopt AI-driven data management solutions, architecture plays a critical role in structuring metadata and enabling efficient data discovery. Well-designed architecture supports emerging technologies, ensuring that data remains an asset for innovation and decision-making. Data Architecture offers a design framework for providing well-curated descriptive information and metadata to facilitate efficient discovery and access to data. Additionally, AI technologies can directly manage data, and architecture should be structured to support this capability.

Core Questions

• Are business stakeholders driving data requirements for their business process(es)?
• Are governance procedures in place to ensure adherence to established architecture standards?
• Is adherence to architecture standards enforceable?
• Is technology architecture driven by business process and data architecture requirements?
• Are policies or standards in place that require integration of the business, data and technology architecture?

Core Artifacts

The following are the core artifacts required to execute effective Business, Data & Technology Architecture capabilities. Items with an ‘*’ link to published best practice guidelines.

• Business Architecture Policy (data related)
• Data Architecture Policy
• Technology Architecture Policy (data related)
• Business Architecture, Data Architecture, Technology Architecture Policy Alignment Matrix
• Business Data Requirements
• Business Element – Data Element Model*
• Data Architecture Approach
• Data Domain Inventory Registry
• Data Asset Inventory
• Data Model Inventory
• Data Storage Criteria, Strategy, & Roadmap
• Data Management Tool Strategy & Roadmap

The Data Management initiative must be engaged with the Business Architecture Team for two primary activities:

1. Each business capability or process must define data requirements (i.e. inputs and outputs) and
2. Business Architecture must collaborate with Data Management and Data Architecture to support mutual governance and policy.

The Business Architecture Team supports the design and implementation of the capabilities and processes needed for business functions. While not all organizations have a team called "Business Architecture", most have groups responsible for these activities, even if they are called something else. It is critical that the Data Management Organization identify and collaborate with the appropriate team(s) to define data requirements based on business needs and objectives. Gathering comprehensive data requirements is essential as data management collaborates with the business. The following areas should be considered when defining complete data requirements:

Diagram 3.2: Architecture Table

Description

The Data Management initiative must be engaged with the business architecture of the organization to support the identification of data requirements based on business needs.

Objectives

• Define data requirements as (e.g., inputs and outputs) of each business capability or process.
• Collaborate with Data Architecture and Business Architecture functions to collect business capability or process data requirements.

Advice

The Data Management initiative along with Data Architecture must collaborate with Business Architecture and business subject matter experts to define requirements for data. A foundational activity is to identify the types of data required to execute the identified business capability or processes. Data Architecture will then translate the business’s functional data requirements into a complete set of operational data requirements. This process must support:

• Alignment of the data to approved data models
• Assignment of the data to business or operational domains with related requirements, rules and constraints
• Development and cataloguing of related metadata to communicate meaning, context, and any applicable business rule constraints
• Identification of any use of data restrictions which may be due to data-licensing constraints, privacy regulations, data sovereignty, regulatory considerations, or adherence to the organization’s ethical policies

Questions

• Are data requirements documented and managed?
• Are the Data Management initiative, Data Architecture and Business Architecture collaborating to define data requirements?

Artifacts

• Business Data Requirements, including rules and restrictions for data use
• Requirements to Models Matrix

Scoring

Description

Data Management must be engaged with the business and data architecture teams to support the identification and prioritization of data elements based on the requirements, needs, and objectives of the business.

Objectives

• Identify and prioritize data elements in support of business needs.
• Establish standard for characteristics and criteria used to determine prioritization of data elements.

Advice

Organizations rely on vast amounts of data, but not all data is of equal importance. The goal for an organization is to establish the standard characteristics and criteria that are used to prioritize data elements that are impactful to business operations. The data elements should be identified, communicated and managed to organization standards. Often, the most important data elements are referred to as critical data elements, where criticality of data to a business process is used to drive the degree of rigor (and resources) applied in managing the data. Requiring data quality checks, data lineage support, data governance, or more security on use and access can be resource-intensive. Prioritization of the data is an important determinant of what constitutes the most efficient deployment of those resources. Most organizations develop a classification approach for data elements based on a binary “is-critical/is not critical” determination. However, complex ecosystems may require multiple tiers of criticality. T-shirt sizing (high, medium, low) or numbered tiers of criticality may be more appropriate in such environments. Regardless of the number of criticality tiers, most approaches consider criteria such as impact on business functions, legal, compliance, security and privacy requirements to drive classification. A well-defined data element classification approach ensures that an organization’s data is maintained with standards for ownership, quality, consistency, security, and governance to enhance data reliability and operational efficiency.

Questions

• Does the organization recognize that not all data is of equal importance?
• Is data classified against a standard set of characteristics and criteria to determine levels of “business criticality” or “data standards applied”?
• Are the levels of “business criticality” supported by standard of treatment expectations?
• Is there an inventory of data elements and their associated “business criticality”?
• Are the business, data and technology teams collaborating in applying the data element prioritization approach to daily practices (quality, data governance, data management operations)?

Artifacts

• Inventory of “business critical” data elements
• Standard classification for key data elements

Scoring

Description

The alignment of Data Architecture and Business Architecture governance involves a common understanding of holistic data requirements to design a flexible and scalable data architecture, as well as appropriately managing changes in business needs within the data architecture and monitoring it in support of business requirements and objectives.

Objectives

• Align Data Architecture governance with Business Architecture governance to ensure proper design, management, and monitoring of the data architecture against business needs.

Advice

The goal of this governance alignment is to ensure that the architecture teams work collaboratively in defining, capturing, and supporting the data needs of the business. Both data architects and business analysts should drive the design of data architecture. Business analysts will provide insights into essential data for business operations and strategic initiatives while data architects will provide knowledge of how the data is best structured to support the business requirements. Working collaboratively, they can drive data architecture that will meet current needs while simultaneously anticipating future demands. While effective design can minimize the need to modify the data architecture when the business environment changes, some business factors cannot be anticipated. Again, a close collaboration between business and data architects will help to minimize disruption and to enhance the effectiveness of the updated data architecture. Once the data architecture has been aligned with the business goals, it is important to continually monitor the efficacy of the architecture. Business analysts can assist in establishing measures to track whether the data architecture continues to meet business objectives. Such ongoing evaluation helps ensure the data architecture remains optimally effective over time.

Questions

• Are Business Architecture and Data Architecture governance collaborating in support of meeting business data needs?
• Are Business Architecture and Data Architecture governance participating in Data Management governance activities and aligned with policies and standards?
• Have Business Architecture and Data Architecture governance established a regular cadence and communication plan?
• Are Business Architecture and Data Architecture governance aligned with Data Management initiative standards for data requirements identification?

Artifacts

• Data Requirements aligned to business capability or process
• Business Architecture and Data Architecture governance meeting schedules, minutes and communications
• Business Architecture and Data Architecture governance structure

Scoring

The Data Architecture Approach and Plan must be created to support the unique characteristics and structures of the organization. Roles and responsibilities across the stakeholders must be established with consideration for the operational processes in place.

Description

The Data Architecture Approach and Plan must align with the objectives of the Data Management Strategy in support of the business needs and objectives. Once established, Data Architecture must be formally empowered by senior management and its role communicated to all stakeholders.

Objectives

• Establish the formal Data Architecture Approach and Plan in support of the Data Management Strategy.
• Ensure alignment of stakeholder plans and roadmaps with the Data Architecture Approach and Plan.
• Ensure collaboration with Business Architecture, Data Management, and Technology Architecture.

Advice

Data Architecture serves as a crucial link between business and technology stakeholders within the Data Management initiative. Regardless of whether the Data Architecture function is organizationally aligned with the business or the technology sector, its essential role as an intermediary between these two stakeholder groups must be acknowledged. There is sometimes a misconception that data architecture is merely a subset of technology architecture. However, successful data architecture necessitates the integration of both business architecture and technology architecture expertise.

Questions

• Is there a Data Architecture Approach and plan in place?
• Is the Data Architecture Approach aligned to the Data Management Strategy?
• Has the Data Architecture Approach been formally communicated to Data Management stakeholders?
• Has executive management demonstrated its support for Data Architecture?
• Has this authority been communicated to stakeholders?

Artifacts

• Data Architecture Approach and Plan
• Communications of Data Architecture responsibilities and support

Scoring

Description

Effective Data Architecture requires clear roles for Data Architecture, Business Architecture, and Technology Architecture. Activities involving these disciplines should be integrated into the roles and responsibilities agreed upon by stakeholders.

Objectives

• Define the roles and responsibilities required for effective Data Architecture and collaboration with Business Architecture & Technology Architecture.
• Identify the organizational alignment of Data Architecture roles with the other architecture roles that impact Data Management.

Advice

Well-defined Data Architecture and associated architecture roles are essential to the Data Management initiative. They establish the connection between the data requirements identified by the business architecture function and the capabilities offered by technology and tooling, as determined by the Technology Architecture function. Effective Data Architecture requires collaboration and coordination with individuals and teams throughout the organization, and possibly with external parties, as well. The relationships between Data Architecture and the Business Architecture and Technology Architecture functions must be clearly defined and formalized, focusing on activities, roles, and accountability. Documenting accountability using a RACI matrix or a similar method is advisable for capturing roles and responsibilities. Staffing for all the roles should align with expectations regarding the scope and volume of work.

Questions

• Have the roles and responsibilities for Data Architecture activities been established?
• Is Data Architecture appropriately staffed and funded?

Artifacts

• Data Architecture roles and responsibilities
• Data Architecture stakeholder identification
• RACI matrix or other evidence of accountability assignment across the scope of the Business Architecture, Data Architecture and Technology Architecture activities

Scoring

Description

Formal processes have been established for Data Architecture activities. These processes must align with Data Management policy and standards of the organization. Data Architecture should be supported with standard tools and associated procedures to perform Data Architecture activities.

Objectives

• Establish formal Data Architecture processes in alignment with the Data Management policy and standards.
• Integrate the Data Architecture processes into the overall end-to-end processes of the Data Management initiative.
• Identify, schedule and maintain Data Architecture meetings and working sessions required for operational support.

Advice

The Data Architecture subject matter experts should work with the Data Management Program to design and optimize Data Architecture processes. Together they will create and monitor the implementation of the Data Architecture processes in alignment to the end-to-end process across the full Data Management initiative.

Questions

• Have formal Data Architecture processes been defined and implemented?
• Are those processes supported by standards to ensure a common approach?
• Do the processes account for the necessary collaboration with Data Management & Business Architecture?
• Are Data Architecture activities part of the normal operational routine of stakeholders?
• Are there standing meetings, planning sessions and regular communications about Data Architecture initiatives?

Artifacts

• Process design artifacts, procedure guides or playbooks, and published routines
• Process performance metrics reports
• Meeting minutes, status reports and Data Architecture announcements

Scoring

Description

The Data Management Governance must ensure that Data Architecture and Technology Architecture teams collaborate in governance to align with the organization's overall technology vision and strategy. This includes specific strategies for enterprise-wide platforms, data storage, and data integration infrastructures. Technology Architecture governance processes may involve design reviews, as well as permit-to-build, permit-to-use, and permit-to-send approvals.

Objectives

• Align Data Management governance structure and policies with the Technology Architecture and Data Management strategies.
• Ensure all technology development meets the Technology Architecture policy requirement to follow Data Management policy and standards.
• Ensure collaboration is driven by organizational requirements, with enhancements and new developments subject to architectural platform design, review, and approval.

Advice

Typically, technology governance is the responsibility of the technology group. However, because of the close relationship between the Data Management initiative, Data Architecture and technology implementation, it is imperative that Data Management facilitates collaboration with Data Architecture and the technology group. Together they create, support and enforce technology policy and standards that impact the Data Management initiative. This collaboration with technology is designed to ensure that implemented solutions align with business needs, that they comply with data restrictions and that they are harmonized with both technical and architectural standards. This collaboration should include design reviews to ensure that technology implementation follows Data Management policy and standards and that tollgates are in place to review the technical implementation. This governance should include collaboration on the data related technology platforms and tools that could impact data management activities.

Questions

• Is Technology Architecture governance aligned with Data Architecture and Data Management governance?
• Are Data Management initiative and Data Architecture involved in the technical design review process?
• Are authorizations to build, use and send data in place?
• Are legal and compliance involved in this effort?
• Is Technology Architecture policy aligned with the Data Management strategy and Data Management policy?

Artifacts

• Evidence of governance collaboration such as stakeholder meeting agendas, results, and escalation procedures
• Evidence of tollgate review process, for example, minutes and meeting outcomes
• Technology Architecture policy
• Data Management policy
• List of stakeholders and evidence of bi-directional communication on technical review and authorizations

Scoring

The activities in this capability must be aligned with the prioritization of data requirements performed in capability 3.1.2. This capability includes two primary activities, identifying and defining the data. Identifying the data includes:

1. Defining the logical data domains and the associated data that is critical to each domain
2. Mapping of physical data repositories to the logical data domains
3. Cataloguing the data in the repositories

Defining the data includes:

1. Modeling data to reflect core concepts, attributes, and their relationships
2. Describing the data within the context of the business capabilities or processes where the data is used
3. Creating taxonomies and ontologies to document relationships between the data

These activities guide identification, definition, organization, and cataloging of data requirements and data designs. Critical data identified during the business architecture activities must be described in a way that supports the design and implementation of data repositories and data architecture. Additionally, the data architecture must ensure that the data is designed in a manner that can be leveraged by the technical architecture to support the business requirements.

Description

A data domain is a logical representation of a specific category of data that has been formally designated and named. Data domains are logical constructs that represent the data itself rather than the physical repositories in which that data resides. Identifying domains helps pinpoint the data essential for executing business functions within those domains. Managing data within a domain structure helps ensure consistent ownership of shared data and thus minimizes conflicting understanding of what the data represents.

Objectives

• Involve business process subject matter experts in the identification of logical data domains.
• Identify and prioritize data domains.
• Structure logical data domains to contain related data regardless of where the data may be produced in the organization.

Advice

Identification of data domains must be driven by the business from the perspective of what data is needed to perform the required business functions. This activity will be closely aligned to sub-capabilities that are related to vocabulary / glossary management, 4.2.1, and data classification, 3.3.4. Data domains are defined to facilitate better understanding of the structure and organization of data that is associated with business or operational functions that curate the data. Defining data domains has benefits including:

• Forming a basis for communication and activities in a business context
• Supporting coordination between Business Architecture, Data Management/Data Architecture and Technology Architecture around common subjects or themes
• Facilitating consistent shared meaning and alignment around common business / operational concepts

The overall goal is to ensure the proper use of data and to get stakeholders to think about Data Management in terms of data content concepts rather than physical database repositories. All this needs to be based on an understanding of how the business functions operate. Once the logical data domains are defined, they must be described using ontologies, taxonomies, data models, or other relevant constructs, and mapped to their physical locations. 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 data consumer critical business processes. Metadata should be treated as a distinct data domain managed and maintained by the Data Management organization using quality standards comparable to those used for business domains.

Questions

• Has the business domain owner and the Data Architecture function been involved in identifying the authoritative data domains?
• Are appropriate business functions represented in the discussion?
• Is the distinction between data domains and data repositories clear?

Artifacts

• Policy indicating what data domains are and how they are to be used
• Criteria for determination of data domains
• Inventory of data domains
• List of stakeholders and evidence of bi-directional communication

Scoring

Description

Essential to the data domains are physical repositories of data that must be mapped to the data domains. The physical repositories may include application databases, spreadsheets, data warehouses / marts, data lakes, streaming data or data stored in cloud services, and particularly, systems of record and archives.

Objectives

• Create and maintain an inventory of physical data repositories.
• Map physical repositories to data domains.

Advice

The data elements in any data domain need to be mapped to where the data physically resides. The first step is the creation of the inventory of data repositories. For this activity, it doesn’t matter where the content resides; data may be external, streaming, master/slave and cloud based. What is important is that the repositories are inventoried and the data within them is mapped to the identified and approved data domains. Note that the mapping of repositories to domains will not necessarily be one-to-one. A single repository may contain data from two or more domains and a single domain may be instantiated in multiple repositories. This latter case is especially likely when a data domain represents data that is shared widely across the organization or where the organization is sufficiently complex requiring the existence of multiple versions of domains.

Questions

• Has the inventory of data repositories been compiled and verified?
• Have the identified and approved data domains been mapped to their physical location?

Artifacts

• Inventory of data repositories
• Mapping of identified and approved data domains to the physical location

Scoring

Description

Enterprise data domains and their key data elements must be clearly defined, modeled, and documented to capture core data relationships and ensure alignment to business requirements and process. Standard enterprise entity models should be mandated by data management policies.

Objectives

• Create comprehensive models of key enterprise data.
• Capture and document data object relationships.
• Develop enterprise entity definitions and models in support of business capabilities, processes and requirements.

Advice

Data modeling practices vary depending on an organization’s unique business challenges and the technical architecture or data platform decisions it has made. While traditionally associated with structuring data for relational database management systems (RDBMS), modern architectures—such as data lakes, NoSQL, and graph databases—have shifted the role and methods of data modeling. These newer technologies often do not require the same rigid modeling approach. Nonetheless, the organization of data to support business requirements remains essential. From a management—not implementation—perspective, organizations typically mandate the creation of conceptual and logical data models to capture business-relevant data relationships.

• Conceptual Data Models: A high-level simplified data model used to express data concepts important to the business process. Designed by data modelers to communicate with business process owners to ensure high-level agreement on core data concepts.
• Logical Data Models: These are more detailed data models that identify the data attributes and relationships required to support the business process, independent of any technology or solution. These models may be influenced by, if not necessarily descriptive of, the organization's existing technology stack and technical architecture, and intended to be comprehensible to business users.

In practice, logical models are often adapted to align with the physical, data, and technical architectures, as these functions collaborate to define models that reflect implementation needs—such as the distinctions between relational and semantic designs (e.g., graph or OWL). Once data models are established, they must be formally managed and governed by policy. This includes requirements for implementation, ongoing maintenance, and usage. Data modeling activities must be integrated into the organization’s change management processes, including change approvals, impact assessments, and controlled implementations.

Questions

• Have data models defining the entities in the organization-wide data domains been verified by business subject experts?
• Are data models documented, made accessible and used in existing and new systems?
• Is the terminology used when creating data models aligned to the organization standard glossary(ies), vocabulary, and other metadata requirements?
• Have policies and standards for managing data models been defined, verified, sanctioned, and made accessible?
• Has governance over data models been aligned with existing change management policies and practice?
• Has the modeling of terms, definitions, and relationships been verified by business stakeholders and stored as metadata?

Artifacts

• Policy and standards on use and maintenance of data models
• Data models
• List of stakeholders and evidence of bi-directional communication

Scoring

Description

Data should be identified by consistent, organization-wide identifiers and classified via approved taxonomic schemes to ensure accurate organization of that data. Establishing these schemes and methodologies is essential for defining relationships, standardizing data treatment across the organization, and aggregating data for analytical purposes. Unique and precise identifications and classifications are foundational concepts to ensure data is discoverable, understandable and consumable that aligns with the business and domain requirements.

Objectives

• Establish and standardize classification schemas considering both structured and unstructured data.
• Standardize how business elements are identified and cataloged.
• Formalize and standardize the implementation of reference data, classification vocabulary, taxonomies and ontologies considering industry standard identifiers.

Advice

Communicating context and semantic meaning is made possible through data classification schemas, reference vocabularies, and linked vocabularies organized into taxonomies or ontologies. Establishing classification schemas is critical to establishing standard approaches supporting data discovery and suitability or “fit for purpose” analysis. Unique and precise identification of data is a foundational capability. It is critical for master (operational) data management, regulatory reporting, risk analysis, as well as laying the foundation for artificial intelligence techniques as adopted. Data identification schemas are required to ensure that data associated with unique entities are readily identifiable when shared across the organization. Such data includes, but is not limited to, customer information, legal entity name, and product details. Additionally, correctly identifying and describing data is required for privacy management, information security management, data masking, data encryption, and risk analysis. Unique identification is a foundational tenet of data management that is governed by policy and enforced by standards. When reference data is used to classify other data, it is important that reference data be defined according to formal taxonomic methodologies and that each reference value be identified in a unique manner. Policies, procedures, and standards are needed to ensure critical tasks are performed as required; specifically related to identification of key data, the use of classification vocabularies, and application and mapping to taxonomy / ontology schemas. 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 broader data management processes within an organization.

Questions

• Have unique and precise vocabulary and schemas to describe data been established for all key data?
• Has policy been developed and approved to ensure these vocabularies and schemas are used in business applications?
• Have these standard vocabularies been published?
• Have appropriate classification schemas and taxonomies been defined?
• Has the organization approved and published standards for the vocabularies, schemas, taxonomies and ontologies selected?
• Does the organizational metadata model align and leverage the classification approaches adopted?
• Does the data catalog reference the classification approaches in a way that supports data discoverability and understanding the meaning and context of data?

Artifacts

• Policy about standard vocabulary, classification schemas, taxonomies and ontologies
• Inventory of identification, classifications, taxonomies and ontologies standards in use
• Cross-referencing and transformation documentation

Scoring

Description

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

Objectives

• Establish a catalog of data elements aligned to the data domain.
• Capture required standard metadata on the data elements.
• Make the data catalog accessible to stakeholders.

Advice

The catalog functions as a tool for communicating the available data within a specific domain while also providing essential details that enable consumers to both discover the data and to assess its relevance to their needs. Any cataloging effort requires an organization to determine which labels should be included. These labels must align with the information necessary to effectively manage key data in accordance with domain requirements. This is structured within a metadata model, which defines the essential attributes needed to comprehensively describe the data and outlines how those attributes are organized. The metadata cataloged must align to the established standards defined by the organization.

Questions

• Have the data elements aligned to a data domain been cataloged?
• Has the required metadata for those data elements been captured and cataloged?
• Does the metadata captured and cataloged fully describe the data elements at the level required by the relevant domain(s)?
• Is the metadata accessible to stakeholders in a data catalog?

Artifacts

• Data policy for metadata capture
• Data Catalog standards and guidelines
• Data Dictionary
• Metadata Dictionary

Scoring

The Data Management initiative must work in collaboration with the Technology function. Together they define the organization-wide data platform, storage and distribution infrastructures. This collaboration must be supported by mutual governance and policy.

Description

The role of the Technology function is to define and design the technical architecture needed to accommodate data requirements in collaboration with business process requirements. The Data Management initiative must work in collaboration with the Technology function. Together they define the database strategies, analytics platform approaches, middleware solutions, storage and retention technologies, information security considerations, and all other aspects of the holistic technology infrastructure needed to support the Data Management goals and objectives of the organization.

Objectives

• Ensure that Data Management is participating collaboratively in the Technology Strategy processes representing Data Management requirements, goals and objectives.
• Ensure that consideration is made for the incorporation of the latest technological advancements that relate to data management.

Advice

Technology strategy must ensure that the Data Management initiative can be supported by the Technology function. The Technology function is responsible for the operation of the technology infrastructure. It does not define data functionality or requirements. Evaluate the Technology Architecture strategy with Data Management objectives at the forefront. An obligation of the data architecture and Technology Architecture functions is to evaluate the business objectives and inform the senior business executives of opportunities to deploy innovative technology tools and methods like AI. The review should include opportunities to support Data Management initiative objectives and processes using these tools and methods.

Questions

• What are the mechanisms to ensure a formal, ongoing partnership between the Data Management Organization and the Technology function?
• Have the Data Architecture and Technology Architecture functions evaluated and communicated opportunities to deploy innovative technical tools and methods in support of the defined business objectives?

Artifacts

• Alignment of Technology Architecture and Data Management strategies, inclusive of the Data Architecture strategy
• List of stakeholders and evidence of bi-directional communication

Scoring

Description

The Data Management initiative must be engaged with Technology Architecture on the platform infrastructure to define and execute a strategy, an execution roadmap, and a governance structure. For a technology strategy to be sustainable it must have a budget commitment over the life of the designed roadmap.

Objectives

• Ensure Data Management is collaborating with Technology Architecture to inform the platform infrastructure strategy of Data Management requirements, goals and objectives.
• Ensure that all enhancements and new developments are subject to review and approval consistent with the defined platform infrastructure strategy and roadmap.

Advice

Technology Architecture is accountable for the platform infrastructure strategy and the associated execution roadmap. The strategy must be coordinated with business requirements for the Database Management Systems organization-wide. The Data Management organization should be a facilitator, providing coordination among the stakeholders to represent these business considerations. Prioritization of requirements is critical. Some opportunities for innovation in the organization-wide platform infrastructure involve new technology tools and methods. The infrastructure may benefit from the addition of AI, ML, Natural Language Processing (NLP), and knowledge graphs.

Questions

• What are the mechanisms to ensure coordination among business, data, and technology at the proper levels of the organization?
• What are the mechanisms for coordination and prioritization?

Artifacts

• Platform infrastructure requirements, strategy, and roadmap
• Evidence of alignment with the Data Management Strategy
• Platform Infrastructure policy and standards

Scoring

Description

The Data Management initiative must be engaged with Technology Architecture in the data storage infrastructure to define and execute a strategy, an execution roadmap and a governance structure. For a technology strategy to be sustainable, it must have a budget commitment over the life of the designed roadmap.

Objectives

• Ensure Data Management is collaborating with Technology Architecture to inform the data storage infrastructure strategy of Data Management requirements, goals, objectives, and policies.
• Ensure that all enhancements and new developments are subject to review and approval consistent with the defined storage management strategy and roadmap.

Advice

Technology Architecture is accountable for the data storage infrastructure strategy and the associated execution roadmap. The strategy must be coordinated with business requirements for activities like archiving capability, legal compliance considerations, retention and defensible destruction of data. The Data Management organization should be a facilitator, providing coordination among the stakeholders to represent these business considerations. Additionally, data storage, archive, and retrieval plans must be coordinated across the various stakeholders (business, data, technology, legal, and compliance). Prioritization of requirements is critical. Some opportunities for innovation in the organization-wide data storage infrastructure involve new technology tools and methods. Organizations should consider traditional (relational data bases, file systems, block storage), cloud-based (cloud objects, distributed file systems, hybrid and multi-cloud), NoSQL and Big Data (key-value stores, columnar, document, graph and time-series databases), edge and decentralized (edge computing storage, blockchain and decentralized), and in-memory and high-performance storage (RAM based storage, NVMe and SSD storage). There are also new opportunities for data to be encrypted, tokenized, anonymized, or pseudonymized at rest, in transit, and in storage.

Questions

• What mechanisms are in place to ensure coordination among business, data, and technology at the proper levels of the organization?
• Are additional legal requirements considered in the strategy, including but not limited to, masking, anonymization, and the full complement of personal privacy rights?
• What is the organization’s appetite for cloud data storage services?
• How will the organization manage data reconstruction from an archive?
• What are the mechanisms for coordination and prioritization?

Artifacts

• Storage requirements, strategy, and roadmap
• Evidence of alignment with the Data Management Strategy
• Evidence of collaboration, alignment, socialization, and approval between Data Management and Technology
• Data Storage policy and standards
• Evidence of storage consideration in SDLC tollgates at the onset of technology platform enhancements or new development

Scoring

Description

The Data Management initiative must be engaged with Technology Architecture in the data integration infrastructure to define and execute a strategy, an execution roadmap and a governance structure. For a technology strategy to be sustainable, it must have a budget commitment over the life of the designed roadmap.

Objectives

• Establish an integrated data integration strategy. Obtain agreement from business, data, and technology senior executive stakeholders.
• Ensure Data Management is collaborating with Technology Architecture to inform the data integration infrastructure strategy of Data Management requirements, goals and objectives.
• Ensure that all enhancements and new developments are subject to review and approval consistent with the defined data integration strategy and roadmap.

Advice

Technology Architecture is accountable for the data integration infrastructure strategy and the associated execution roadmap. The strategy must be coordinated with business requirements, and it must consider data access controls, information security restrictions, authoritative data provisioning points, and data as a service. The Data Management Organization should be a facilitator – providing coordination among the stakeholders to represent these business considerations. Prioritization of requirements is critical.

Questions

• What are the mechanisms to ensure coordination among business, data, and technology at the proper levels of the organization?
• What are the mechanisms for coordination and prioritization?

Artifacts

• Platform infrastructure requirements, strategy, and roadmap
• Evidence of alignment with the Data Management strategy
• Evidence of collaboration, alignment, socialization, and approval between Data Management and Technology
• Data integration infrastructure is represented in policy and standards
• Evidence of data integration infrastructure consideration in SDLC tollgates at the onset of technology platform enhancements or new development

Scoring

Description

The Data Management initiative, while not directly accountable, must be engaged in the contingency planning and testing for data access and maintenance in the event of an operational disruption.

Objectives

• Ensure Data Management collaboration with the technology function in support of defining, testing, and supporting contingency planning for operational disruption.
• Establish a full understanding of the data management dependencies that require support appropriately in contingency planning.

Advice

Operational risk requirements for data must be considered in the data requirements definition process. These requirements will define the technology architecture’s choice of data storage applications and data recovery strategies. When evaluating a data storage application, keep in mind that applications may hold data from multiple data domains. The most stringent data recovery requirements might be applied to all data in the application. The more stringent data recovery has a higher cost so this may inflate storage cost for all the data.

Questions

• What are the mechanisms to ensure collaboration between Data Management and Technology Architecture for contingency planning?
• Are all data dependencies defined and understood for recovery?

Artifacts

• Contingency/Disaster Recovery Plan
• Tests and results of Contingency/Disaster Recovery Plan

Scoring

The Data Management Technology Tool Stack is the set of technology tools needed to support data management business and operational requirements. The Data Management business process must define requirements for the Data Management Technology Tool Stack. Technology roadmaps and governance ensure the tools are deployed and adopted.

Description

The Data Management initiative must provide requirements for their software tools to the technology group. The Data Management Executive is accountable for the requirements and Technology is responsible for a data management technology strategy that delivers against those requirements. A technology tool roadmap must be developed to implement the Data Management technology infrastructure defined in the tool strategy.

Objectives

• Design and document an integrated Data Management technology tool strategy.
• Demonstrate support for the Data Management technology tool strategy by corporate policy.
• Develop an integrated technology tool roadmap in adherence to the technology tool strategy. Include guidelines for new development as well as decommission plans for non-standardized legacy tool implementations.
• Develop budgets aligned to the organization’s budget cycle processes.

Advice

Ensure that the Technology function has defined a tool strategy that incorporates Data Management Technology Tool requirements. The Data Management organization must ensure that the resources and skills to support the use of various tools are available (e.g., data catalog tool, data modeling tool, data transformation tools, metadata tool, glossary tool, Data Quality tool, analytics tools). This support capability requires coordination between business, data, and technology stakeholders to define the requirements for the Data Management tool strategy. Once the Data Management tool strategy is defined, it needs to be converted into an actionable implementation roadmap. Make sure the technology roadmap is practical, aligned to business priorities, and harmonized with internal procurement processes.

Questions

• Are Data Management and Technology collaborating in the development of requirements and strategy for Data Management Technology Tools?
• Are the policies and processes that govern this relationship defined and verified?
• Is the Data Management technology tool roadmap aligned with internal procurement processes?
• Is the Data Management technology tool roadmap shared with the Data Management Organization?

Artifacts

• Data Management Technology Tool strategy
• List of stakeholders and evidence of bi-directional communication
• Data Management Technology Tool roadmap aligned with the Data Management Strategy

Scoring

Description

Technology, in partnership with the Data Management, must define and govern the data management related tool stack.

Objectives

• Establish a Data Management technology tool governance structure with a complete set of associated policies. Confirm that the structure is aligned with the Data Management Strategy.
• Implement Data Management Technology tool governance structure across technology development teams.

Advice

Ensure the governance structure for Data Management technology tools is in place and operational, and that the governance policies and practices incorporate the Data Management initiative requirements. Different tools that perform the same functions can produce disparate data. Beyond this, the proliferation of tools can increase complexity, add cost and inhibit systems integration.

Questions

• What are the mechanisms for coordination between Technology Governance and Data Management Governance?
• How are Technology and Data Management working to keep abreast of innovation and new technology capabilities?
• Is there a mechanism for collaboration between Data Management and Technology?
• Is there an agreement between technology and business regarding the scope and controls associated with technology tools?

Artifacts

• Technology tool governance structure document
• Evidence of governance operations such as meetings and procedures, documents
• Policy and standards for data tool governance
• List of authorized Data Management tools
• Documentation of bi-directional communication between business, data and technology on tool selection and criteria for approval

Scoring