Introduction

As the demands, volumes, and varieties of data continue to expand due to advancements in technologies such as artificial intelligence, organizations across all industry sectors are increasingly recognizing the importance of comprehending their data and associated practices. The Business Data Knowledge component presents capabilities to help an organization improve awareness of data through a formal education program, capture common understanding of the business knowledge in a business glossary, and collect information about critical data to ensure availability, quality, and trustworthiness to the organization. By prioritizing these efforts, organizations can ensure they are well-equipped to navigate the complexities of the evolving data landscape and leverage their data assets for strategic advantage.

Definition

Business Data Knowledge addresses methods and practices for building and continuously maintaining an organization’s data awareness and shared understanding of its data. The desired result is a unique data ecosystem supporting a positive data culture embraced by the organization’s employees, each with a clear understanding of their own and others’ responsibility for data.

Scope

• Establish a formal data education and training program for Data Management.
• Collaborate with Data Management stakeholders to design and implement sustainable processes for defining business terms aligned with Data Architecture blueprints and models.
• Establish a sustainable metadata management approach and program.
• Establish a permanent repository of information and data knowledge accessible by the enterprise.

Value Proposition

Incorporating these practices is crucial for achieving operational excellence and maximizing the value derived from data as a strategic asset:

• Orchestrate successful data education programs by promoting shared data understanding and creating an environment of cross-organization collaboration around data, foster a positive improvement in organizational data knowledge.
• Identify, document, and share information regarding the internal stakeholders who define, produce, and utilize specific data, will improve effective coordination, collaboration, and communication related to this data. This practice helps mitigate operational, financial, and reputational risks linked to the use of incorrect data for analytics, decision-making, and regulatory reporting.
• Systemically record information about critical data elements, datasets, and documents, demonstrate a deeper understanding and a more effective utilization of this data. This practice lays the groundwork for automated data life cycle management ensuring the quality of information essential for complex business processes.
• Sustainably maintain data definitions, metadata and data provenance will be better prepared to respond to regulatory compliance and effective risk management. Further, data utilized in artificial intelligence and machine learning applications is anticipated to need comprehensive documentation of data sources and transformations.

Overview

The meaning of data becomes unclear as it is transferred, copied, and renamed. This creates problems because organizations rely on various business applications with distinct data models. These models usually depend on glossaries, which can cause misalignment across different applications. Consequently, the same terms may have different meanings, or different terms may refer to the same concepts. Front-office and back-office processes often suffer from this issue, leading to integration challenges and difficulty reusing data across new applications. Effective data management relies on establishing shared definitions of business data terms within a controlled vocabulary that is consistently used throughout the organization. This principle also extends to data management terminology. A business glossary serves as a key resource, defining each piece of valuable data or term by clearly articulating what it represents in the real world, thereby creating an easily understandable label. More precise data definitions are often needed to capture the context and associated information of labels. Classification techniques help define, deduplicate, organize, and interrelate data items and business terms, enhancing the value extracted from data. The goal is to systematically document and convey the contextualized meaning of data terms. Effective metadata management structures this process by digitizing definitions and capturing additional information through data attributes and tags. Metadata includes business, operational, technical, descriptive, structural, and administrative aspects, crucial for both manual and automated data processing. High-quality metadata is essential for effective data asset management and enables automation. The breadth and quality of an organization’s metadata significantly influences its overall data management capabilities. As data volumes and business demands grow, manual data management becomes impractical. Therefore, successful practices ensure automated governance of data creation, handling, and usage, supported by clear ownership and focused metadata management. A Data Education Program for all employees is crucial for building business data knowledge, enabling effective data management, and deriving value from data assets. Effective management of business terminology and metadata is most successful in organizations where all levels of staff are familiar with the data strategy, principles of data management, and the necessary organizational mindset.

Core Questions

• Has a Data Management Education Program been defined and documented?
• Is the Data Management Education Program supported by a corporate Education Team as appropriate?
• Is there a common approach for business term definition and glossary use defined for the organization?
• Is the Metadata Management Approach available to and approved by all relevant stakeholders?

Core Artifacts

• Data Education Program Approach and policy
• Business Glossary Approach
• Metadata Management Approach and policy

The Data Education Program provides a foundation for realizing value from shared data assets. It fosters an informed ‘data culture’ through the required data skills, a shared vocabulary for data, and employee understanding of the value of data to the organization. A comprehensive Data Education Program can assist in enhancing data literacy and improving collaboration across the organization regarding data objectives.

Description

A program is established to educate the organization in data concepts, skills and accountabilities.

Objectives

• Support the development and measurement of data education for the organization.
• Align the education program to the organization’s Data and Data Management Strategies, policy and standards.
• Design and operationalize formal training programs based on prioritized requirements.
• Reinforce the organization’s data-related roles and responsibilities to ensure everyone understands their own and others’ accountability for data and data ethics.
• Share data improvements and training updates across the organization.

Advice

A shift in organizational culture is necessary for effective data management that maximizes the long-term value of the data assets. The quality education program builds and sustains the organization’s understanding of data principles, skills, collaboration, and shared responsibility—ensuring alignment with data management policies. The Data Education Program should encompass a comprehensive range of topics to educate the entire organization. This should include conceptual training such as understanding why data assets are valuable, defining data domains, and skills development to support data management roles and foster a collaborative data management culture. The program should also provide an operational framework that guides stakeholders on where to find instructions, support, policies, standards, reference materials and additional training opportunities. The goal is to consistently reinforce the objectives of the organization’s Data Management initiative. Education on the core data concepts of the organization should be readily available to all levels of the organization. Focused specialized training can be developed to address gaps in data knowledge and skills across the organization, based on education needs identified through surveys, focus groups or other assessments. The goal is to promote a positive data culture shift, top down and bottom up, enhancing data literacy. As appropriate, the Data Education Program should be integrated into the organization’s general training function with close involvement of the Data Management function. Consider integrating industry best practices and external Data Management certifications to encourage in-depth training in relevant skills. Look for opportunities to partner with Human Resources—for example, by integrating data education into professional development plans, promotion criteria, or employee retention strategies. Clear communication regarding the requirement to participate in the Data Management education program is essential. Support from senior management helps to reinforce the program’s importance throughout the organization and secures adequate funding. Beyond formal training, it may be beneficial for the Data Management team to actively and creatively disseminate key data management messages using various engagement methods and channels to connect with the relevant employees. Substantial advantages can be gained by employing modern tools like chatbots to address various Data Management inquiries from staff while also assessing engagement levels and identifying educational gaps. Additionally, in the use of metrics is key to monitoring and improving the quality, coverage, and effectiveness of the education program.

Questions

• Is there a set of requirements and priorities for Data Management Education aligned to the organization structure, the roles defined for Data Management activities, and the Data Management strategy?
• Is there a formal training program covering the specialist Data Management role holders as well as broader stakeholders at all levels across the organization, including executive and non-permanent staff?
• Are the Data Management Education Program goals incorporated into the Data Management strategy?
• Does the approach to education and training address Data Management strategy, policy and standards in collaboration with other organizational units and control functions?
• Is participation in Data Management Education Program supported by policy?
• Is the Data Management Education Program supported by a broad and effective communication approach with a feedback loop to capture and action potential training improvements?
• Is the Data Management Education Program aligned and supported by the organization’s central training function, if applicable?

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
• Data Education Program Approach and Plan
• Data Education policy
• Data Education curricula and materials
• Data Education execution documentation
• Data Education Program communications
• Data Education administration standards and procedures
• Active log of new Data Management education requirements
• Data Education measurement and monitoring reports

Scoring

Description

A comprehensive catalog of data education and reference materials has been developed to support the success of the education program. It is essential to measure participation and gather feedback from the organization to enhance the overall educational experience.

Objectives

• Establish common access to the data education catalog, education materials, and authoritative data reference information across the organization.
• Structure an education catalog based on organizational data-related roles and responsibilities to ensure everyone understands their own and others’ accountability for data and data ethics.
• Provide facilities for feedback and suggestions for improvement to data educational program.

Advice

The Data Education Program offerings should be described and available through a catalog or other media that are easy to access at all levels and roles (including temporary) across the organization. This may allow self-service discovery of what is available, recommended and suited to their needs. The Data Management Team oversees the usage of training and reference materials by the organization, ensuring their relevance and addressing educational needs. If your organization has a corporate training function, the Data Management Team should partner to design and incorporate the Data Education Program into the standard training environment. A widely available, well-publicized website or intranet page can provide ready access to the catalog, to Data Management course delivery schedules, and links to static reference materials.

Questions

• Is a catalog of data education classes, training materials and reference material available and communicated throughout the organization?
• Does the catalog include recommended role-specific training courses?

Artifacts

• A data education catalog and supporting media
• A portal for the Data Education Program’s education, policy and reference information
• A feedback log/suggestion for data education improvements
• Data education broadcast messages and posts with links to authoritative reference materials
• Data Education Program use reporting (e.g., participation in mandatory training, adherence to policies, standards and requirements)

Scoring

Business glossaries serve as the official source of clear and precise definitions, fostering a common understanding of business terms and labels within a controlled vocabulary for shared data usage across the organization. This approach ensures consistency and comparability across various activities, helping the organization enhance its operational processes and unlock greater value from its data assets.

Description

A standard approach to management of business glossary has been established. The plan and procedures for identifying and managing the business glossary requirements involve defining, harmonizing, approving, publishing, maintaining and making individual terms accessible.

Objectives

• Establish a unified process for managing business glossary content, terms, and data terminology to promote clear ownership and a shared understanding throughout the organization.
• Designate roles and responsibilities to facilitate the process, ensuring that accountability, engagement, and participation are upheld by relevant stakeholders, experts, and individuals in key positions.
• Align the management of the business glossary within the larger data management and governance framework, especially with architecture, metadata, and change management.

Advice

A published, standard process for creating, updating, and maintaining business terms should be established, in alignment with data management policies. Engaging stakeholders to collaboratively define business terms fosters a shared understanding of data meanings. However, achieving alignment can be challenging due to resistance from users, especially with existing systems. A practical approach in fragmented environments involves prioritizing harmonization based on legal or business meanings instead of uniform naming conventions across all systems. Clearly assigning and communicating the roles and responsibilities for managing the glossary is crucial for promoting collaboration and ensuring stakeholder understanding in alignment with overall data management and governance practices. The business typically oversees the content in the business glossary. However, it is advised to include all stakeholders—such as other business functions and data and technology professionals—with the data management function facilitating rather than controlling the process. The implementation of automated tools, such as chatbots and AI language models, can significantly improve the management of definitions. Moreover, maintaining clear justification for business term definitions, supported by change approvals, ensures relevance and consistency in addressing broader aspects like taxonomies, ontologies, and knowledge graphs. The use of analytical tools is increasingly critical for leveraging these definitions effectively.

Questions

• Does a data management policy mandate the maintenance, use and reference to curated glossary, controlled vocabulary and business term definitions?
• Do clear standard process(es) exist for change management and maintenance of glossaries, terms and definitions organization-wide, considering business domains and central functions?
• How is the participation of parties with relevant business, technology and data management expertise ensured, as well as stakeholders’ ownership interest in the definitions?
• Are all the various roles and responsibilities for business glossary management communicated to participants and mutually understood and accepted by them?
• Does the change management process include a communication and feedback capability to ensure broad participation, coordination, collaboration, and consensus to build on definitions?

Artifacts

• Policy mandating glossary, business terms and vocabulary definitions
• Standards for maintenance of glossary, business terms and vocabulary
• Process or procedures for business terms and vocabulary management maintenance and change management
• Defined roles and responsibilities for glossary, business terms and vocabulary management tasks
• Work products that demonstrate process adoption (e.g., meeting minutes, change logs, issue registry, escalations, domain coverage metrics)

Scoring

Description

Glossary business terms and related semantic definitions are maintained using a common repository approach making the information accessible in manual and machine-readable format. The repository is maintained as the authoritative source of truth for business term definitions and is widely available to facilitate consistent use in business, technology and data management activities.

Objectives

• Ensure the common repository is used across the organization as the authoritative source for business term definitions and related semantic definitions.
• Provide access to business term definitions and related semantic definitions (e.g., taxonomy, ontology) to relevant stakeholders for approved uses.
• Support sustainable repository governance requirements through category, owner assignments, version control, auditability, and change tracking information.
• Enable interoperability of the repository with appropriate data domain, data management, and analytics environments.

Advice

Organizations often start by creating a shared repository for defining business terms, utilizing alphabetical glossaries of terms and labels. Over time, they advance to more sophisticated capabilities to manage complexity and facilitate automated access to detailed data definitions and controlled vocabulary information. To meet more sophisticated needs, options such as taxonomies, ontologies, and knowledge graphs are becoming increasingly essential in the context of artificial intelligence. A useful best practice is to develop thoroughly analyzed use cases for how information from the repository will be utilized and prioritize adding features that provide incremental business value based on these cases. Frequently, overly ambitious scopes or 'boil the ocean' strategies lacking sufficient business backing have resulted in unsustainable initiatives and diminished credibility for the data management efforts. Effective governance of the repository and its contents through policy, standards and procedures is essential, requiring collaboration between business and technical experts to guarantee the quality and usability of the information. After a foundational service is set up, a key factor for success is consistent communication from the Data Management organization on the purpose and value of the repository to a broad audience, maintaining a strong user base. To achieve this, securing support from executive leadership across business, data domain, and technology teams is crucial.

Questions

•  Is the common repository of business terms and semantic definitions accessible and searchable by stakeholders?
•  Is the common repository an authoritative reference for discovery?
•  Are instructions for using the repository easily accessible?
•  Are automated workflows or equivalent controls used to enforce governance procedures for the common repository to avoid incomplete, duplicative and ambiguous definitions?
• Is the repository integrated with other analytics and data management systems using standard integration mechanisms such as APIs?
• Is monitoring, analysis, and reporting on usage of the central repository performed?

Artifacts

• The Business Term Glossary repository
• User guides or training manuals for the common repository
• Operational runbook or procedures for managing the repository
• Access control policies and user permissions procedures
• Version control logs showing the audit trail history of changes and updates to terms
• Repository integration documentation
• Repository usage metrics and reporting

Scoring

Robust metadata capabilities are essential for effectively managing a data ecosystem and unlocking business value from data assets. It provides the information needed for knowledge workers to discover, comprehend, utilize and capitalize on data. The scope of metadata is broad, encompassing but not limited to architecture, technology integration, data governance controls, privacy, business process efficiency, and analytics/AI. The quality of metadata accessible within an organization, along with its effective application, serves as a key determinant of how successful data assets, both structured and unstructured, can be managed. The importance of metadata management cannot be overstated. Effective metadata management is essential for formally documenting an organization’s insights regarding its data assets, facilitating their organization and the extraction of value. High-quality metadata is crucial for effective data asset management and enables automation. The breadth and quality of an organization’s metadata significantly influences its overall data management capabilities. As data volumes and business demands for data grow, manual data management becomes impractical. Therefore, successful practices ensure automated governance of data creation, handling, and usage, supported by clear ownership and focused metadata management. It is essential to plan for the sustainable management of the metadata repository. Data management professionals should leverage their understanding of industry-standard tools and techniques and work closely with the Technology Team regarding tool stack decisions. As you establish the metadata standards, consideration should be given to the unique requirements for unstructured and cloud-based data such as, continuous cataloging and processes for cataloging, and scanning metadata to ensure governance compliance. Consider carefully the cost of metadata repository management as ambitious metadata capture initiatives may not provide value. Often it may be better to focus on an agile approach and smaller incremental projects. Innovation opportunities can be considered. Ancillary tools, such as chatbots and small language models, can help with stakeholder engagement and collaboration in the creation and maintenance of quality metadata across the organization, helping to break data silos.

Description

A Metadata Management Approach documents the strategy for an organization to collect, store, utilize and manage metadata on an enterprise basis, aligning with the data management strategy and establishing a clear approach for interoperability if multiple metadata repositories are necessary. To ensure its successful implementation and sustainability, a comprehensive plan needs to be created. This plan will outline the steps necessary to establish, maintain, and continuously improve the metadata management repository.

Objectives

• Establish a Metadata Management Approach and plan.
• Ensure Metadata Management Approach is consistently aligned with overall Data Management.
• Align Metadata Management Approach with data and technical architectural requirements.
• Establish a clear scope, priorities and a plan for collection of metadata covering structured and unstructured data.

Advice

The Metadata Management Approach should be founded on the principle that the Data Management Team takes ownership of its own data domain, Metadata, and recognizes its importance to data management. While many organizations correctly prioritize business data domains where high-quality data is crucial, they often overlook the same standards for metadata. Since metadata is vital to data management, it is important that the metadata strategy reflects this significance and receives the attention it deserves. Ideally, metadata collection and maintenance should be incorporated into the everyday processes of data management and the data life cycle. Whenever requirements are gathered or updated for projects related to data, the collection or enrichment of the associated metadata should be the standard. Building out a comprehensive metadata environment takes time and is best executed in incremental stages that allow for the evolution and enhancement to the metadata model. The metamodel, at its base, should have a minimum set of standards defined for the data objects (e.g., assets, products, domains, sources, elements) that exist in an organization. As data objects are prioritized by the organization, based on business needs and criticality, the metamodel will require a more comprehensive set of metadata to be captured. Metadata should be captured for all data in the organization including more traditional structured data through to unstructured data. As the data environment for an organization matures, the metadata collected will evolve and provide additional benefits (e.g. enhanced data discovery, cost-effectiveness for data initiatives, improved data lineage, and provenance). Enabling the automation of metadata collection and updates to facilitate improved data management addresses the challenges posed by increasing data volumes and complexity, as manual approaches become impractical. The Metadata Management Approach should be reviewed and updated on a regular basis to ensure commitment to priorities and initiatives.

Questions

• Is the Metadata Management Approach available to and approved by all relevant stakeholders?
• Is the Metadata Management Approach aligned with data strategy and data management strategy requirements and supported by funding?
• Is the Metadata Management Approach clear on the expected outcomes from management and usage of metadata?
• Does the Metadata Management Approach call for Metadata standards in alignment with internal data domain standards and relevant external industry standards for Metadata?
• Does the Metadata Management Approach set the requirement for clear role accountabilities for Metadata management and Metadata content management?

Artifacts

• Metadata Management Approach and shared vision document
• Evidence of Data management communications to relevant stakeholders regarding Metadata Management Approach
• Documentation of Metadata roles and responsibilities

Scoring

Description

A common metadata repository is important for an organization to address the challenges of understanding, comprehending, and managing its data. In more complex data environments where multiple metadata repositories may exist, it is critical that the repositories are interoperable to share information across the environments.

Objectives

• Ensure common repositories are integrated and accessible for curating and sharing metadata that sustains the organization’s data management capabilities, covering structured and unstructured data types.
• Support advanced data management requirements through the metadata repository in relation to the scale and sophistication of the organization’s data ecosystem.

Advice

Successful metadata repositories rely on a combination of strategy, policies, standards, processes, and strong cross-organizational communication. A shared repository can exist both virtually and physically. It is important to create a common metadata information model based on established standards to support the organization and enable the interoperability of multiple metadata repositories as necessary. With the increasing volume and types of data being consumed in organizations, automated discovery and maintenance of metadata are necessary to support commonality and interoperability. After establishing a common metadata information model and standards, these will need to be maintained through policies requiring the organization to adhere to the standards. Any adjustments to the standards or the information model should follow change management requirements defined by the data governance framework. These requirements may include change approvals, impact analysis, controlled implementation, and rollout. When establishing the metadata standards, consideration should be given to the unique requirements for unstructured and cloud-based data, such as continuous cataloging and processes for cataloging and scanning metadata to ensure governance compliance.

Questions

• Is the use of the common metadata repository (virtual or physical) supported by policy?
• Is a metadata reporting and query ability available to stakeholders and consumers of metadata?
• Are metadata repositories integrated (if more than one exists in complex environments)?

Artifacts

• Metadata Information Model
• Plan for implementing and developing the common repository
• Common Metadata repository
• Metadata repository standards, procedures and change governance documents
• Metadata repository reports
• Data lineage linked or integrated with metadata repository
• Audit trail logs showing the history of changes and updates to metadata assets in the repository.

Scoring