Data Management Concepts - Vocabulary Flashcards

These vocabulary flashcards cover fundamental terms, roles, processes, benefits, challenges, and best practices presented in the lecture on Data Management Concepts.

Data Management

The practice of managing data as a valuable resource through strategies and methods that access, integrate, cleanse, govern, store and prepare data for analytics across its entire lifecycle.

Data Strategy

An organization’s overarching plan that guides how data will be accessed, integrated, governed, stored, secured and used to create value.

Data Governance

A subset of data management that develops and enforces policies, standards, definitions and controls to ensure data consistency, quality, availability, integrity and security.

Database Management

The use of tools and technologies to create, maintain and alter databases—the foundational structures that store and organize data.

Data Architecture

The overall structure of an organization’s data assets and how those assets fit into the broader enterprise architecture.

Data Modeling and Design

The process of analyzing requirements and creating data models that map relationships, workflows and structures for analytics systems.

Data Storage and Operations

The physical hardware, systems and activities used to store, back up and manage data.

Data Security

Measures that protect data from unauthorized access, alteration or destruction while ensuring privacy and compliance.

Data Integration

Combining data from disparate sources into a unified, structured form for analysis or operational use.

Master Data Management (MDM)

A discipline that uses a common master file to establish a single, authoritative definition of core entities and their attributes, eliminating ambiguity and redundancy.

Reference Data

Standardized data values used to categorize or classify other data, reducing redundancy and errors.

Metadata

Data that describes other data, such as headers, definitions or context, enabling easier discovery, management and usage.

Data Quality

The degree to which data is accurate, complete, consistent, timely and fit for its intended purpose.

Data Warehouse

A centralized repository that integrates data from multiple sources for reporting and business intelligence.

Data Lake

A storage repository that holds raw, unprocessed data in its native format until it is needed for analytics.

Business Intelligence (BI)

Technologies and practices that analyze data in warehouses or marts to support business decision-making.

Data Steward

A role responsible for managing the quality, definition and lifecycle of specific data assets.

Data Asset

Any piece of data that has value to an organization and therefore must be properly managed and protected.

Data Ethics

Principles that guide the responsible collection, use, sharing and disposal of data, respecting privacy and societal norms.

Extract, Transform, Load (ETL)

The process of moving data: extracting it from sources, transforming it into the desired format, and loading it into a target system.

Data Lifecycle

The series of stages data passes through: Plan, Acquire, Maintain, Access, Evaluate, Archive, and QA/QC.

Data Maintenance

Processing data for analysis, creating metadata, and ensuring future accessibility and usability.

Data Cleansing

Detecting and correcting errors and inconsistencies in data to improve its quality.

Data Discovery

Identifying and cataloging data assets to understand what data exists and where it resides.

Data Enrichment

Enhancing existing data by adding relevant information from external or internal sources.

Data Integrity

The accuracy, consistency and reliability of data throughout its lifecycle.

Data Privacy

Policies and technologies that protect personally identifiable information (PII) and ensure compliance with regulations.

Data Access

The ability of authorized users or systems to retrieve and use data as needed.

Data Erasure

Securely deleting data so it cannot be recovered, often required by privacy regulations.

Data Subsetting

Creating smaller, representative datasets from larger databases for testing or analysis.

Business Continuity Planning

Preparing processes and resources to ensure data availability and integrity during disruptions.

Data Management Professional

Any individual working in any facet of data management, from technical roles (DBA, programmer) to strategic roles (Data Steward, CDO).

Certified Data Management Professional (CDMP)

A certification offered by DAMA International that validates expertise in data management.

Data Governance Framework

An overarching structure that aligns policies, roles, processes and metrics to manage and control enterprise data.

Data Management Challenges

Common obstacles such as unknown data inventories, expanding data tiers, evolving compliance rules, data repurposing issues and diverse storage systems.

Data Management Best Practices

Recommendations like adding discovery layers, automating transformations, using autonomous technology, and applying common query layers for multi-store access.

Autonomous Data Capabilities

AI- and machine-learning-powered functions that automatically monitor queries and optimize indexes to maintain performance.

Data Science Environment

A toolset that automates data transformation and model evaluation to speed hypothesis testing and reuse of data.

Compliance Requirements

Legal and regulatory obligations—often multijurisdictional—governing the handling of data, especially PII.

Machine Learning

Algorithms that enable computers to learn from data, widely used in predictive and prescriptive analytics.

Cloud Computing

Delivering computing services—servers, storage, databases and analytics—over the internet to host and manage data.

Data Warehouse vs. Data Mart

A warehouse stores enterprise-wide integrated data, while a mart is a smaller, subject-specific subset designed for a particular business line.

Data Quality Assurance (QA/QC)

Preventive and corrective processes that ensure data defects are avoided or detected and resolved.

Data Management Life Cycle – Plan Phase

Stage where project goals, data products, roles and quality controls are documented before data is collected.

Data Management Life Cycle – Acquire Phase

Stage involving collection of new data, processing legacy data, or contracting partners to gather data.

Data Management Life Cycle – Archive Phase

Long-term storage of data and documentation of methods needed to read or interpret it in the future.

DAMA International

The professional organization that publishes the Data Management Body of Knowledge (DMBOK) and promotes data management standards.