Apr 26, 2024 10:39:50 AM
Hello, old or future friend of data. There are no silly questions on this page. Just a simple data glossary filled to the brim with definitions for data terms, covering the oh-so-easy-to-confuse areas of data analytics, data development, big data, DataOps, data business, and more.
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Basic data terms & definitions
Big data: Large and complex datasets that exceed the capacity of traditional data processing methods, requiring specialized tools and technologies for storage, analysis, and extraction of valuable insights.
Customer Data Platform (CDP): A centralized system that collects, integrates, and organizes customer data from various sources to create unified customer profiles for marketing, sales, service, and analysis purposes.
Data: Information collected, stored, and processed for various purposes, often in the form of numbers, text, images, or other formats.
Data architecture: The overall design, structure, and organization of data assets, including databases, data models, integration methods, and storage systems, to support data management and use.
Database: A structured collection of data organized for efficient storage, retrieval, processing, and management.
Datacenter: A facility or physical location used to house servers, storage devices, networking equipment, and other IT infrastructure to store, manage, and process data.
Data democratization: The process of making data more accessible and available to a broader audience within an organization, enabling non-technical users to access and utilize data for decision-making.
Data engineering: The process of designing, constructing, and maintaining the systems and architecture for efficient data processing, storage, and retrieval.
Data governance: The overall management and framework of policies, processes, and controls established to ensure the availability, integrity, security, and usability of data within an organization.
Data integrity: The accuracy, consistency, and reliability of data throughout its lifecycle, ensuring that it remains unaltered and trustworthy.
Data management: The practice of planning, controlling, organizing, and governing data assets throughout their lifecycle to ensure accessibility, security, quality, and usability.
Data maturity: The level of an organization's capability and readiness to manage and use data effectively throughout its operations and decision-making processes.
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Data migration: The process of transferring data from one system to another or from one format to another.
Data normalization: The process of organizing data in databases to minimize redundancy and dependency.
Data privacy: Concerned with the proper handling of sensitive information to ensure individuals' privacy rights are protected.
Data quality: The accuracy, completeness, consistency, relevance, and reliability of data to meet specific requirements or business needs.
Dataset: A structured collection of related data records or information grouped together for analysis or reference.
Data science: A multidisciplinary field that employs scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data.
Data warehouse (DWH): A centralized repository that stores data from various sources to support data analysis and reporting.
Enterprise data warehouse (EDW): A centralized repository that stores integrated, historical, and comprehensive data from various sources within an organization for analytics and reporting purposes.
Master data management (MDM): The process of creating and managing a single, consistent, accurate, and authoritative source of truth for an organization's key data entities, such as customers, products, or employees.
Qualitative data: Non-numeric information that describes qualities, attributes, or characteristics, obtained through observations, interviews, or open-ended responses.
Quantitative data: Numeric information used for statistical analysis.
Raw data: Unprocessed and unorganized data that has not undergone any transformation or analysis.
Semi-structured data: Data that doesn't conform to a specific data model but has some structural properties (e.g., JSON, XML).
Structured data: Formatted data that follows a specific, predefined data model, making it easily searchable and processable by databases.
Data analytics terms
Business intelligence (BI): A set of tools, technologies, and processes used to collect, analyze, and present business information and insights to support decision-making within an organization.
Dashboard: A visual display of key performance indicators (KPIs), metrics, or data points, often in real-time, to monitor and track the status of an organization, process, or system.
Data analytics: The process of examining data sets to uncover insights, trends, and patterns to make informed decisions or derive meaningful conclusions.
Data aggregation: The process of combining and summarizing data from multiple sources or datasets into a cohesive and more manageable form for analysis or reporting.
Data mining: The process of discovering and extracting patterns, trends, or valuable information from large datasets using various techniques, such as machine learning, statistics, or algorithms.
Data visualization: The process of presenting data in graphical or visual formats to make it easier to understand and interpret.
Descriptive analytics: Analyzing past data to understand and summarize what has occurred within an organization, often involving simple reporting and data aggregation.
Predictive analytics: Utilizing historical data, statistical algorithms, and machine learning techniques to forecast future outcomes or behavior.
Prescriptive analytics: Utilizing various data and computational methods to recommend actions or strategies that optimize outcomes based on predictive and descriptive analysis.
Real-time data: Information that is processed, analyzed, and made available instantly or near-instantly, providing the most current and up-to-date information available at any given moment.
Unstructured data: Data that does not have a predefined format, like text, images, or videos.
Technical data terminology
API (Application Programming Interface): A set of rules that allows different software applications to communicate with each other.
Continuous integration (CI): The automated data development practice of frequently merging and validating changes made to data-related code and artifacts, aiming to detect integration issues early in the development process.
Continuous delivery (CD): The practice of ensuring that code changes, data pipelines, or data-related artifacts that pass through the CI process are automatically tested, prepared, and made available for deployment to production or other environments in a reliable, consistent, and efficient manner.
Database schema: The logical structure or blueprint that defines the organization, relationships, and constraints of data stored in a database.
Data catalog: A centralized repository or tool that indexes, organizes, and manages metadata and information about available datasets, making it easier for users to discover, understand, and access data assets within an organization.
Data cleansing: The process of identifying and correcting errors or inconsistencies in datasets.
Data deployment: The process of implementing and making data available in a specific environment or system for use by applications or users.
Data extraction: The process of retrieving or pulling specific data subsets from databases or sources for further processing or analysis.
Data fabric: An architectural approach that enables unified and seamless data access, integration, and management across distributed and diverse data sources.
Data ingestion: The process of collecting and importing raw data from various sources into a storage or processing system for analysis or storage.
Data integration: Combining data from different sources to provide a unified view, often involving ETL processes or integration tools.
Data lake: A large storage repository that holds a vast amount of raw data in its native format until it's needed.
Data lakehouse: An architectural approach that combines the features of a data lake with those of a data warehouse to provide a unified platform for storing, managing, and analyzing data.
Data lifecycle: The sequence of stages through which data passes from its initial creation or acquisition, through processing and storage, to its eventual archiving or deletion.
Data lineage: The record or history of data's origins, movements, transformations, and relationships throughout its lifecycle.
Data loading: The process of inserting or loading transformed data into a target database or system.
Data mart: A smaller, specialized subset of a data warehouse containing data focused on specific business functions or departments for easier access and analysis.
Data mesh: An architectural paradigm focused on decentralized data ownership and domain-oriented distributed architecture to enable scalable and flexible data management within organizations.
Data modeling: The process of creating a conceptual or logical representation of data entities, relationships, and attributes to facilitate understanding and database design.
Data orchestration: The practice of managing and coordinating data workflows, processes, and tasks to ensure seamless and efficient data operations.
Data pipeline: A series of automated processes and tools used to extract, transform, and load (ETL) data from multiple sources into a destination such as a data warehouse or application.
Data source: The origin or location from which data is collected or obtained, such as databases, files, sensors, APIs, or applications.
Data (tech) stack: The collection of tools, technologies, and software used in combination to manage and process data within an organization.
Data stream: Continuous and real-time flow of data from various sources to target destinations, enabling immediate processing, analysis, or action on fresh incoming data.
Data transformation: The process of converting raw data into a standardized, structured format suitable for analysis or storage.
Data vault: A modeling technique used in data warehousing that maintains historical data in its purest form without modification, enabling traceability and agility in adapting to changing business requirements.
ELT (Extract, Load, Transform): A data integration process where data is first extracted from various sources, then loaded into a destination system, and finally transformed or processed as needed.
ETL (Extract, Transform, Load): The process of extracting data from various sources, transforming it into a consistent format, and loading it into a destination.
Metadata: Data that describes and provides information about other data, such as data descriptions, attributes, data origin, structure, and usage, aiding in data management, understanding, and governance.
NoSQL (Not only SQL): A term for databases that use different data models than traditional relational databases.
SQL (Structured Query Language): A programming language used for managing and manipulating relational databases.
DataOps terms
Data as a product: Treating data assets as valuable, consumable products by focusing on their quality, usability, and delivery to fulfill specific business needs or objectives throughout the lifecycle.
Data development: The creation, design, and implementation of data-related assets, including databases, ETL processes, data pipelines, and data models.
Data operations: The processes, activities, and tasks involved in managing, processing, and maintaining data throughout its lifecycle.
DataOps: A data development and operations methodology that emphasizes collaboration, automation, and integration of data-related processes across teams to improve the quality and speed of data analytics and delivery.
DataOps management: The practices, strategies, and leadership involved in implementing and overseeing DataOps methodologies within an organization to optimize data work.
DataOps platform: A toolset or environment that supports and facilitates the principles of DataOps, providing capabilities for data integration, management, automation, and collaboration.
Data platform: A technology infrastructure or environment that supports the storage, processing, integration, and analysis of data from various sources.
Data warehouse automation (DWA): The use of automated tools and processes to streamline and accelerate the design, construction, and management of data warehouses.
DevOps (in the data context): A cultural and technical approach that combines data engineering, data management, and IT operations practices to streamline and automate processes involved in managing data infrastructure, pipelines, and analytics workflows.
Time-to-value (TTV): The duration or time taken to deliver meaningful insights, actionable results, or beneficial outcomes from data-related initiatives or projects.
Bet you didn't read every single one?
Well, it’s good to leave some for another time. Perhaps bookmark this page for future reference, or share it with an unsuspecting colleague, who could use a refresher on data terminology.
Here’s a fun bit of data: the word ‘data’ is mentioned exactly 203 times in this article. With that, it’s probably a good time to move from definitions to actions...
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