> The Data “Everything” Matrix

Comprehensive Guide to Data Terminology, Activities, Tools, and Responsibilities

The guide defines 12 key data activities, including Data Curation, Data Classification, Data Wrangling, Data Preparation, Data Lineage, Data Engineering, Data Science, Data Observability, Data Compliance, Data Management, Data Quality, and Data Governance. For each activity, the document outlines the key tasks involved, common tools and technologies used, and the desired outcomes or business benefits.

A feature you might find particularly useful is the focus on responsibility mapping. For every data activity, it highlights which roles within a corporate data team are typically accountable for execution, oversight, or maintenance. These roles span technical, operational, and compliance functions, covering Data Stewards, Data Engineers, Compliance Officers, Privacy Officers, and Chief Data Officers, among others.

Term	Definition	Key Activities	Common Tools/Technologies	Outcomes/Goals	Staff/Responsibility
Data Curation	The process of selecting, organizing, and maintaining data to ensure it remains accessible, reliable, and relevant over time.	Validating data sources, consolidating data sets, documenting metadata	Data catalogs, metadata management tools	Improved data discoverability and trustworthiness	Data Curator Data Steward Data Librarian Business Analyst Information Governance Manager
Data Classification	The systematic categorization of data based on sensitivity, usage, and compliance requirements to protect and manage it effectively.	Defining classification schemes, labeling sensitive data, applying access controls	Data classification software, DLP tools, security suites	Enhanced data security and compliance	Data Security Analyst Data Steward Compliance Officer Privacy Officer Information Security Manager
Data Wrangling	The process of cleaning, transforming, and enriching raw data into a more usable format for analysis.	Parsing, merging, filtering, formatting, handling missing values	Python/R scripts (pandas, dplyr), ETL tools	Readily analyzable and consistent datasets	Data Analyst Data Engineer Data Scientist ETL Developer
Data Preparation	The set of tasks making data suitable for analysis or modeling, including cleaning, normalization, and integration.	Data cleansing, data transformation, feature engineering, integration	ETL/ELT pipelines, data integration platforms	Faster, more accurate analytics and modeling workflows	Data Engineer Data Analyst Data Scientist BI Developer ETL Developer
Data Lineage	A record of the data’s origin, transformations, and usage as it moves through systems and processes.	Tracking data flow, mapping source-to-target transformations, documenting data movement	Lineage tracing tools, metadata management platforms	Transparency, traceability, and regulatory compliance	Data Steward Data Governance Lead Metadata Specialist Data Quality Analyst Information Architect
Data Engineering	Designing, building, and maintaining the infrastructure and systems that reliably deliver clean, consistent, and organized data.	Pipeline development, system architecture, performance optimization	Cloud platforms (AWS, GCP), Spark, Kafka, Airflow	Scalable, robust, and high-performance data pipelines	Data Engineer Cloud Engineer Data Platform Engineer Solutions Architect DevOps Engineer
Data Science	Applying statistical and computational methods to extract insights, make predictions, and drive decisions from data.	Exploratory analysis, modeling, machine learning, experimentation	Python/R, Jupyter Notebooks, TensorFlow, PyTorch	Actionable insights, predictive models, and informed decisions	Data Scientist Machine Learning Engineer AI Specialist Research Scientist Quantitative Analyst
Data Observability	Monitoring and understanding the health, reliability, and performance of data and data systems.	Automated data quality checks, anomaly detection, lineage monitoring	Observability platforms, APM tools, logging/monitoring systems	Improved reliability, early detection of issues, faster incident response	Data Reliability Engineer Data Quality Analyst Data Engineer Monitoring Specialist DevOps Engineer
Data Compliance	Ensuring data management practices align with legal, regulatory, and organizational policies.	Auditing data usage, applying privacy measures (e.g., GDPR), maintaining regulatory documentation	Compliance management software, governance platforms	Legal adherence, minimized risk of fines, enhanced trust	Compliance Officer Privacy Officer Legal Counsel Data Protection Officer (DPO) Risk Manager
Data Management	The overarching set of practices for handling data throughout its lifecycle, from ingestion to retirement.	Data governance, storage optimization, security management, archiving	Data management suites, data warehouses, master data management tools	Efficient, secure, and cost-effective data operations	Data Manager Data Governance Lead Data Operations Manager Data Steward Chief Data Officer (CDO)
Data Quality	Assessing and ensuring data is accurate, complete, reliable, timely, and consistent.	Validation checks, cleansing routines, deduplication, standardization	Data quality software, validation frameworks	High-confidence analytics, improved decision-making	Data Quality Analyst Data Steward Data Engineer Quality Assurance (QA) Specialist, Data Governance Analyst
Data Governance	Establishing the policies, standards, and oversight needed to manage data responsibly, ethically, and securely.	Policy definition, stewardship roles, compliance enforcement, access control	Governance frameworks, data stewardship platforms	Strategic, responsible, and compliant data usage	Chief Data Officer (CDO) Data Governance Lead Data Steward Compliance Officer Data Governance Committee