Data Management: Data (Notes)

Disclaimer: This is work in progress intended to consolidate information from various sources. 
Last updated: 29-Mar-2024

Data

• {definition} raw, unrelated numbers or entries that represent facts, concepts,  events,  and/or associations 
• categorized by
• domain
• {type} transactional data
• {type} master data
• {type} configuration data
• {subtype}hierarchical data
• {subtype} reference data
• {subtype} setup data
• {subtype} policy
• {type} analytical data
• {subtype} measurements
• {subtype} metrics
• {subtype} 
• structuredness
• {type} structured data
• {type} semi-structured data
• {type} unstructured data
• statistical usage as variable
• {type} categorical data (aka qualitative data)
• {subtype} nominal data
• {subtype} ordinal data
• {subtype} binary data
• {type} numerical data (aka quantitative data)
• {subtype} discrete data
• {subtype} continuous data
• size
• {type} small data
• {type} big data
• {concept} transactional data
• {definition} data that describe business transactions and/or events
• supports the daily operations of an organization
• commonly refers to data created and updated within operational systems
• support applications that automated key business processes 
• usually stored in normalized tables
• {concept} master data
• {definition}"data that provides the context for business activity data in the form of common and abstract concepts that relate to the activity" [2]
• the key business entities on which transaction are executed
• the dimensions around on which analysis is conducted
• used to categorize, evaluate and aggregate transactional data
• can be shared across more than one transactional applications
• there are master data similar to most organizations, but also master data specific to certain industries 
• often appear in more than one area within the business
• represent one version of the truth
• can be further divided into specialized subsets
• {concept} master data entity
• core business entity used in different applications across the organization, together with their associated metadata, attributes, definitions, roles, connections and taxonomies 
• may be classified within a hierarchy
• the way they describe, characterize and classify business concepts may actually cross multiple hierarchies in different ways
• e.g. a party can be an individual, customer, employee, while a customer might be an individual, party or organization
• do not change as frequent like transactional data
• less volatile than transactional data 
• there are master data that don’t change at all 
• e.g. geographic locations
• strategic asset of the business 
• needs to be managed with the same diligence as other strategic assets
• {concept} metadata 
• {definition} "data that defines and describes the characteristics of other data, used to improve both business and technical understanding of data and data-related processes" [2]
• data about data
• refers to 
• database schemas for OLAP & OLTP systems
• XML document schemas
• report definitions
• additional database table and column descriptions stored with extended properties or custom tables provided by SQL Server
• application configuration data
• {concept} analytical data
• {definition} data that supports analytical activities 
• e.g. decision making, reporting queries and analysis 
• comprises
• numerical values
• metrics
• measurements
• stored in OLAP repositories 
• optimized for decision support 
• enterprise data warehouses
• departmental data marts
• within table structures designed to support aggregation, queries and data mining 
• {concept} hierarchical data 
  • {definition} data that reflects a hierarchy 
  • relationships between data are represented in hierarchies
  • typically appears in analytical applications
  • {concept} hierarchy
  • "a classification structure arranged in levels of detail from the broadest to the most detailed level" [2]
  • {concept} natural hierarchy
  • stem from domain-based attributes
  • represent an intrinsic structure of the dat
  • they are natural for the data
  • e.g. product taxonomy (categories/subcategories)
  • useful for drilling down from a general to a detailed level in order to find reasons, patterns, and problems
  • common way of analyzing data in OLAP applications
  • common way of filtering data in OLTP applications
  • {concept} explicit hierarchy
  • organize data according to business needs
  • entity members can be organized in any way
  • can be ragged
  • the hierarchy can end at different levels
  • {concept} derived hierarchy
  • domain-based attributes form natural hierarchies 
  • relationships between entities must already exist in a model
  • can be recursive
• {concept} structured data
• {definition} "data that has a strict metadata defined"
• {concept} unstructured data 
• {definition} data that doesn't follow predefined metadata
• involves all kinds of documents 
• can appear in a database, in a file, or even in printed material
• {concept} semi-structured data 
• {definition} structured data stored within unstructured data,
• data typically in XML form
• XML is widely used for data exchange
• can appear in stand-alone files or as part of a database (as a column in a table)
• useful when metadata (the schema) changes frequently, or there’s no need for a detailed relational schema

References:
[1] The Art of Service (2017) Master Data Management Course 

[2] DAMA International (2011) "The DAMA Dictionary of Data Management",