Data - a collection of facts, numbers, words, symbols, measurements, observations, or other useful information. It can be Qualitative (descriptive or categorical only) or Quantitative (measurable or countable).

Data Catalog - collection and classification of metadata.

Data Governance - the ability to use demographic, socioeconomic, and spatial data to make better-informed policy decisions for programs and services.

Data Inventory – types of data an organization currently collects, and what data it needs, but does not currently collect.

Data Literacy - the ability to read, understand, create and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case, application, and resulting value.

Data Management - the processes, tools, and enabling technology that support Data Governance.

Data Maturity – level of sophistication of an organization’s data management and governance policies, procedures, and practices. In the absence of written policies and procedures, it defaults to what is actually being done.

Data Maturity Assessment – process by which organizations identify current practices and policies, data systems, data inventory, data catalog, and prohibitions and restrictions around legal or ethical sharing of data.

DMBoK – Data Management International’s Data Management Body of Knowledge model. It contains 9 core domains:  

• Data Architecture Management 
• Data Development 
• Database Operations Management 
• Data Security Management 
• Reference and Master Data Management 
• Data Warehousing and Business Intelligence Management 
• Document and Content Management 
• Metadata Management 
• Data Quality Management 

Gap Analysis - comparison between the current state of an organization’s data management policies and practices and its aspirational state.

Integration – unification of multiple data systems and technologies into one. This will not be DAFS’s approach, because there are currently over 1,800 disparate data systems, and centralizing all of this data into a single entity would likely create a security concern.

Interoperability - the ability of disparate data systems to work effectively together and share data. DAFS’s vision of the Data Management and Governance Practice is to support interoperability among the State’s various data systems, rather than merge them into one (integration).

Metadata – “data about data.” This includes the purpose, date of creation, quality, location, author, file size and name. Much of this information can be de-identified to promote legal and ethical sharing of data.

Prohibitions and Restrictions - the laws, policies, and other safeguards or barriers that prevent an organization from sharing data.

Qualitative Data - describes traits or categories of data that cannot be measured or counted. Examples include but are not limited to: different flavors of ice cream, hair color, species of flower, etc.

Quantitative Data - numerical data that can be measured or counted. Examples include but are not limited to: cost, duration, distance, number of customers served, and temperature.

Semi-Structured Data - information without a standardized format or predefined model, making it harder to use for decision-making. Examples include but are not limited to: social media posts, short-form video content, and JSON (JavaScript Object Notation) files.

Structured Data - information organized and stored in fixed formats, such as relational databases. Examples include but are not limited to: Microsoft Excel spreadsheets, SQL (Structured Query Language) tables, and payment information from a retail Point of Sale system.

Unstructured Data - information that does not have a predefined framework or organizational format. Unstructured Data constitutes the bulk (between 80 to 90 percent) of most organization's data. Examples include but are not limited to: audio recordings, video files, PDFs, and Microsoft Word documents.