Data governance in business refers to the overall management framework and set of processes that ensure the availability, usability, integrity, and security of data across an organization.

Data quality in business refers to the accuracy, completeness, consistency, reliability, and timeliness of data used for decision-making, operations, analysis, and reporting within an organization.

Data architecture in business refers to the design and structure of an organization's data assets and the framework that governs how data is collected, stored, organized, accessed, and managed. 

Data strategy in business refers to a comprehensive plan that outlines how an organization will leverage its data assets to achieve its overarching business objectives.

Data auditing in business refers to the process of examining and assessing the quality, integrity, and reliability of an organization's data assets. It involves systematically reviewing and analyzing data to identify any discrepancies, errors, or inconsistencies, as well as ensuring compliance with regulatory requirements and internal policies

In a business context, data performance refers to the efficiency, effectiveness, and reliability of data-related processes, systems, and infrastructure in supporting organizational objectives and delivering value. 

In business, data management refers to the process of acquiring, storing, organizing, processing, and maintaining data assets effectively and efficiently throughout their lifecycle. It encompasses a range of activities and practices aimed at ensuring that data is accurate, secure, accessible, and compliant with relevant regulations and policies.

Data compliance in business refers to the adherence to regulations, laws, policies, and standards governing the collection, storage, processing, and sharing of data. It involves ensuring that an organization's data practices align with legal requirements, industry standards, and internal policies to protect the privacy, security, and integrity of sensitive information.

In business, data availability refers to the accessibility and readiness of data for authorized users or systems when needed. It encompasses the ability to retrieve, access, and utilize data promptly and reliably to support operational activities, decision-making processes, and strategic initiatives within an organization.

In business, data principles refer to the fundamental beliefs or guidelines that govern the management, use, and treatment of data within an organization. These principles establish the overarching framework for how data is handled, ensuring that it is managed ethically, securely, and effectively to support business objectives and stakeholder needs.

In business, data requirements refer to the specific needs, specifications, and criteria for the data that are necessary to support business processes, operations, and decision-making. These requirements outline the characteristics, format, quality standards, and accessibility criteria that data must meet to fulfill the needs of users and stakeholders within the organization.

In business, data triage refers to the process of prioritizing and categorizing data based on its importance, urgency, and relevance to the organization's objectives or operations. It involves swiftly assessing and classifying data to determine the appropriate actions or responses required, particularly in situations where there is a high volume of data or when dealing with data-related issues.

In business, data scoring refers to the process of assigning numerical or qualitative scores to data based on predefined criteria or algorithms to evaluate its quality, relevance, or suitability for specific purposes. Data scoring helps organizations make informed decisions by quantifying and ranking the attributes or characteristics of data according to their importance or desired outcomes.

In business, data integration refers to the process of combining data from multiple disparate sources or systems to provide a unified view of information across an organization. It involves harmonizing data formats, structures, and semantics to facilitate seamless data exchange, analysis, and decision-making.

In business, data matching refers to the process of identifying and linking similar or related records across different datasets or systems. It involves comparing data elements, such as names, addresses, or unique identifiers, to determine if they correspond to the same entity or object.

In business, data merging refers to the process of combining multiple datasets into a single unified dataset, typically based on common identifiers or key attributes. This consolidation allows organizations to aggregate and integrate data from various sources to create a comprehensive and coherent view of information.

In business, data unmerging refers to the process of separating previously merged data back into its original, distinct datasets or records. This may be necessary when errors are discovered in the merging process, or when there's a need to revert to the original datasets for analysis or other purposes.

In business, data uniformity refers to the consistency and standardization of data across various systems, formats, and sources within an organization. It involves ensuring that data elements are structured, formatted, and represented in a consistent manner to facilitate accurate analysis, reporting, and decision-making.

In business, data ownership refers to the assignment of responsibility and accountability for managing and maintaining specific datasets or data assets within an organization. It involves identifying individuals or organizational units who have the authority to make decisions about the collection, storage, access, and usage of data.

In business, data stewardship refers to the practice of managing and overseeing the lifecycle of data assets within an organization to ensure their quality, integrity, and effective use. Data stewards are individuals or teams responsible for maintaining and safeguarding specific datasets or data domains, acting as custodians of the data and advocates for its proper management.