DMG GLOSSARY OF TERMS AND DEFINITIONS
Accessibility: The extent to which data is available, or easily and quickly retrievable.
Authoritative Data Element: A Data Management Group (DMG) approved unit of data collected by an authoritative source system.
Authoritative Source Data Business Unit: The business unit (section, area, office, etc.) in which the data steward for a given authoritative data collection system resides.
Authoritative Source System: The system which is declared the official source of data even though those data may be redundantly collected and reported in other systems.
Business Rules: Describe the operations, definitions and constraints that apply to an organization in achieving its goals. Business rules govern the business processes.
Business User: Any individual, who in the course of carrying out their role as assigned by the agency, requires and is granted access to specific data elements for the purpose of completing a given task or assignment.
Collection: Human or machine based capture of data that is submitted in response to an agency requirement for data. Transfer of data between systems is not a data collection under this definition, nor are mathematical operations that may be involved to produce an actual data element required for submission to the Agency.
Completeness: Data does not need anything added. Data is considered complete if it represents the complete list of eligible persons or units and not just a fraction of the list.
Correctness: Data matches the specification for that field.
Data Accuracy: The data matches its actual (true) value. Data accuracy is also known as validity.
Data Audit: The process used to determine the accuracy, timeliness, and relevance of data. A data audit may be used to determine data quality.
Data Certification: The final approval of data submitted as defined by the source system rules and responsibilities.
Data Cleansing: The act of data validating and/or data auditing and correcting (or removing) corrupt or inaccurate records from a record set, table, or database.
Data Dictionary: Formal names and definitions for data fields. The data dictionary may also include cross-references between the disparate data name and the common data name, definitions of changes that occur to the data field in upstream and downstream processes, and information about the accuracy of the data.
Data Integration: The ability to combine data in an automated (machine to machine) framework (semantic harmony) from multiple sources and databases while maintaining the integrity and reliability of the data, and providing users with unified view of these data.
Data Naming Lexicon: Common words and abbreviations for the data naming taxonomy.
Data Naming Taxonomy: A common language for naming data that ensures unique names for all data in the common data architecture.
Data Processing Improvements: The tracking of data through an operational process and making adjustments to processes for higher data quality.
Data Profiling: The process of examining the data available in an existing data source (e.g. a database or a file) and collecting statistics and information about that data.
Data Publishers: Information systems that publish data for which they are authoritative.
Data Quality: The measure of data for accuracy, correctness, timeliness, completeness, and relevancy.
Data Quality Assurance: The process of profiling the data to discover inconsistencies and other anomalies, in the data and performing data cleansing activities to improve data quality.
Data Quality Policy: A declaration defining the roles and responsibilities of the information steward, and guidelines for implementing data quality processes.
Data Quality Program: An enterprise-level data quality initiative with clear business direction, objectives, and goals, management infrastructure that properly assigns responsibilities for data, an operational plan for improvement, and program administration.
Data Subscribers: Non-authoritative systems that require authoritative data with the authority to direct the use of authorized data sources.
Data Validation: The process of ensuring that a program operates on clean, correct and useful data. The system uses routines, often called "validation rules" or "check routines", that check for correctness, completeness, meaningfulness, and security of data that are input to the system.
Data Verification: The process of ensuring data matches its actual (true) value.
Delivery Security Liaison: Person(s) within each source system who must determine how much access is appropriate and assign the relevant role(s) to the individual(s) that authorize(s) acceptable read/write privileges.
Disclose: To permit access to, release, transfer, or otherwise communicate, personally-identifiable information contained in education records to any party through oral, written, or electronic means.
Ease of Manipulation: The extent to which data is easy to manipulate and apply to different tasks.
Education Records: Information or data recorded in any medium that contain information directly related to a student and maintained by any NC DPI employee, agent, or contractor.
Free-of-Error: The extent to which data is correct and reliable.
Information Quality: The degree to which data are transformed into information to resolve uncertainty or meet a need.
Information Steward: Person accountable for the integrity of some part of the information resource.
Integrity: Data are protected from deliberate bias or manipulation for political or personal reasons.
Interpretability: The extent to which data is in appropriated languages, symbols, and units, and the definitions are clear.
Legacy System: An old computer system or application program that continues to be used because the user (typically an organization) does not want to replace or redesign it.
Memorandum of Agreement (MOA): Written documentation of a set of agreements and expectations between two or more parties used by DPI when sharing confidential or personally identifiable data with researchers or individuals.
Metadata: Data about the data. They include names, definitions, logical and physical data structure, data integrity, data accuracy, and other data about the organization's data resource.
Objectivity: The extent to which data is unbiased, unprejudiced, and impartial.
Personally Identifiable Information (PII): Any information about an individual maintained by an agency, including any information that can be used to distinguish or trace an individual's identity such as name, social security number, date and place of birth, mother's maiden name, biometric records, and any other personal information that is linked or linkable to an individual.
Precision: Data has sufficient detail.
Regulatory requirements: Rules used to maintain compliance with a statute.
Relevance: Data are actually used for something useful.
Reliability: Data generated by a system are based on protocols and procedures that do not change according to who is using them and when or how often they are used. Data can be considered broadly reliable if the same results (or similar) can be gained by different users asking the same questions of the same system. The data are reliable because they are measured and collected consistently.
Snapshot: A static view of data representing a period of time.
Source System: The system used to collect or calculate data.
Statutory requirements: Laws passed by a legislative body and set forth in a formal document.
Subject Matter Expert: The subject matter expert is typically a knowledgeable business analyst whose understanding of the business is necessary to understand data, define business rules, and measure data quality.
Timeliness: Data are current enough to be valid.
Validation Rules: In place as a check for correctness, meaningfulness, and security of data that are input to the system.
Value-Added: The extent to which data is beneficial and provides advantages from its use.