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• ESG data collection: Beginning steps and best practices

  Sustainability initiatives won't succeed without quality data. Following an ESG data collection framework and best practices ensures program and reporting success. Continue Reading

• 5V's of big data

  The 5 V's of big data -- velocity, volume, value, variety and veracity -- are the five main and innate characteristics of big data. Continue Reading

• database administrator (DBA)

  A database administrator (DBA) is the information technician responsible for directing or performing all activities related to maintaining a successful database environment. Continue Reading

• database management system (DBMS)

  A database management system (DBMS) is system software for creating and managing databases, allowing end users to create, protect, read, update and delete data in a database. Continue Reading

• Assemble the 6 layers of big data stack architecture

  Assemble the six layers of a big data stack architecture to address the challenges organizations face with big data, which include increases in data size, speed and structure. Continue Reading

• Definitions to Get Started

  • 5V's of big data
  • database administrator (DBA)
  • database management system (DBMS)
  • OLAP (online analytical processing)

  • C++
  • dimension
  • disambiguation
  • consumer privacy (customer privacy)

  View All Definitions

• OLAP (online analytical processing)

  OLAP (online analytical processing) is a computing method that enables users to easily and selectively extract and query data in order to analyze it from different points of view.Continue Reading

• C++

  C++ is an object-oriented programming (OOP) language that is viewed by many as the best language for creating large-scale applications. C++ is a superset of the C language.Continue Reading

• dimension

  In data warehousing, a dimension is a collection of reference information that supports a measurable event, such as a customer transaction.Continue Reading

• disambiguation

  Disambiguation is the process of determining a word's meaning -- or sense -- within its specific context.Continue Reading

• consumer privacy (customer privacy)

  Consumer privacy, also known as customer privacy, involves the handling and protection of the sensitive personal information provided by customers in the course of everyday transactions.Continue Reading

• ACID (atomicity, consistency, isolation, and durability)

  In transaction processing, ACID (atomicity, consistency, isolation, and durability) is an acronym and mnemonic device used to refer to the four essential properties a transaction should possess to ensure the integrity and reliability of the data ...Continue Reading

• snowflaking (snowflake schema)

  In data warehousing, snowflaking is a form of dimensional modeling in which dimensions are stored in multiple related dimension tables.Continue Reading

• data virtualization

  Data virtualization is an umbrella term used to describe an approach to data management that allows an application to retrieve and manipulate data without requiring technical details about the data.Continue Reading

• conformed dimension

  In data warehousing, a conformed dimension is a dimension that has the same meaning to every fact with which it relates.Continue Reading

• dimension table

  In data warehousing, a dimension table is a database table that stores attributes describing the facts in a fact table.Continue Reading

• 15 ways AI influences the data management landscape

  AI, NLP and machine learning advancements have become core to data management processes. Ask tool vendors how they use -- or fail to use -- AI in these 15 areas.Continue Reading

• How to create a data quality management process in 5 steps

  Data quality requires accurate and complete data that fits task-based needs. These five steps establish a data quality management process to ensure data fits its purpose.Continue Reading

• Hadoop

  Hadoop is an open source distributed processing framework that manages data processing and storage for big data applications in scalable clusters of computer servers.Continue Reading

• data lakehouse

  A data lakehouse is a data management architecture that combines the key features and the benefits of a data lake and a data warehouse.Continue Reading

• How to approach data mesh implementation

  Data mesh takes a decentralized approach to data management, setting it apart from data lakes and warehouses. Organizations can transition to data mesh with progressive steps.Continue Reading

• data analytics (DA)

  Data analytics (DA) is the process of examining data sets to find trends and draw conclusions about the information they contain.Continue Reading

• data pipeline

  A data pipeline is a set of network connections and processing steps that moves data from a source system to a target location and transforms it for planned business uses.Continue Reading

• master data management (MDM)

  Master data management (MDM) is a process that creates a uniform set of data on customers, products, suppliers and other business entities from different IT systems.Continue Reading

• Essential skills for data-centric developers

  To become more data-driven, organizations need data-centric developers. Developers can learn a mix of technical and interpersonal skills to be an attractive candidate for the job.Continue Reading

• Data steward responsibilities fill data quality role

  Data stewards tie together data operations. From quality to governance to boosting collaboration, data stewards are valuable members of any data effort.Continue Reading

• Enhance data governance with distributed data stewardship

  Data stewardship and distributed stewardship models bring different tools to data governance strategies. Organizations need to understand the differences to choose the best fit.Continue Reading

• Data lakes: Key to the modern data management platform

  Data lakes influence the modern data management platform at all levels. Organizations can gain faster insights, save costs, improve governance and boost self-service data access.Continue Reading

• data integration

  Data integration is the process of combining data from multiple source systems to create unified sets of information for both operational and analytical uses.Continue Reading

• transcription error

  A transcription error is a type of data entry error commonly made by human operators or by optical character recognition (OCR) programs.Continue Reading

• MongoDB

  MongoDB is an open source NoSQL database management program.Continue Reading

• Data stewardship: Essential to data governance strategies

  As data governance gets increasingly complicated, data stewards are stepping in to manage security and quality. Without one, organizations lose speed, quality info and opportunity.Continue Reading

• data warehouse

  A data warehouse is a repository of data from an organization's operational systems and other sources that supports analytics applications to help drive business decision-making.Continue Reading

• database (DB)

  A database is a collection of information that is organized so that it can be easily accessed, managed and updated.Continue Reading

• 18 top big data tools and technologies to know about in 2023

  Numerous tools are available to use in big data applications. Here's a look at 18 popular open source technologies, plus additional information on NoSQL databases.Continue Reading

• database replication

  Database replication is the frequent electronic copying of data from a database in one computer or server to a database in another -- so that all users share the same level of information.Continue Reading

• DataOps

  DataOps is an Agile approach to designing, implementing and maintaining a distributed data architecture that will support a wide range of open source tools and frameworks in production.Continue Reading

• Comparing DBMS vs. RDBMS: Key differences

  A relational database management system is the most popular type of DBMS for business uses. Find out how RDBMS software differs from other DBMS technologies.Continue Reading

• What is data management and why is it important?

  Data management is the process of ingesting, storing, organizing and maintaining the data created and collected by an organization, as explained in this in-depth look at the process.Continue Reading

• data mesh

  Data mesh is a decentralized data management architecture for analytics and data science.Continue Reading

• data observability

  Data observability is a process and set of practices that aim to help data teams understand the overall health of the data in their organization's IT systems.Continue Reading

• What key roles should a data management team include?

  These 10 roles, with different responsibilities, are commonly a part of the data management teams that organizations rely on to make sure their data is ready to use.Continue Reading

• Data tenancy maturity model boosts performance and security

  A data tenancy maturity model can boost an organization's data operations and help improve the protection of customer data. Improvement is tracked through tiers of data tenancy.Continue Reading

• data quality

  Data quality is a measure of the condition of data based on factors such as accuracy, completeness, consistency, reliability and whether it's up to date.Continue Reading

• What is a data warehouse analyst?

  Data warehouse analysts help organizations manage the repositories of analytics data and use them effectively. Here's a look at the role and its responsibilities.Continue Reading

• Data observability benefits entire data pipeline performance

  Data observability benefits include improving data quality and identifying issues in the pipeline process, but also has challenges organizations must solve for success.Continue Reading

• OPAC (Online Public Access Catalog)

  An OPAC (Online Public Access Catalog) is an online bibliography of a library collection that is available to the publicContinue Reading

• primary key (primary keyword)

  A primary key, also called a primary keyword, is a column in a relational database table that's distinctive for each record.Continue Reading

• How to reap the benefits of data integration, step by step

  A new book lays out a strong case for data integration and guides readers in how to carry out this essential process.Continue Reading

• How to build an effective DataOps team

  More organizations are turning to DataOps to bolster their data management operations. Learn how to build a team with the right people to ensure DataOps success.Continue Reading

• data warehouse as a service (DWaaS)

  Data warehouse as a service (DWaaS) is an outsourcing model in which a cloud service provider configures and manages the hardware and software resources a data warehouse requires, and the customer provides the data and pays for the managed service.Continue Reading

• database as a service (DBaaS)

  Database as a service (DBaaS) is a cloud computing managed service offering that provides access to a database without requiring the setup of physical hardware, the installation of software or the need to configure the database.Continue Reading

• data catalog

  A data catalog is a software application that creates an inventory of an organization's data assets to help data professionals and business users find relevant data for analytics uses.Continue Reading

• Key roles and responsibilities of the modern chief data officer

  Chief data officer roles and responsibilities are expanding beyond data strategy, as they are increasingly tasked with cultivating a data-driven culture.Continue Reading

• What is data lineage? Techniques, best practices and tools

  Organizations can bolster data governance efforts by tracking the lineage of data in their systems. Get advice on how to do so and how data lineage tools can help.Continue Reading

• The evolution of the chief data officer role

  Chief data officers are taking on additional responsibilities beyond data management as they strive to transform organizations' data culture and focus on value creation.Continue Reading

• Cloud DBA: How cloud changes database administrator's role

  Cloud databases change the duties and responsibilities of database administrators. Here's how the job of a cloud DBA differs from what an on-premises one does.Continue Reading

• data classification

  Data classification is the process of organizing data into categories that make it is easy to retrieve, sort and store for future use.Continue Reading

• 10 trends shaping the chief data officer role

  As data use increases and organizations turn to business intelligence to optimize information, these 10 chief data officer trends are shaping the role.Continue Reading

• DBMS keys: 8 types of keys defined

  Here's a guide to primary, super, foreign and candidate keys, what they're used for in relational database management systems and the differences among them.Continue Reading

• How to build a data catalog: 10 key steps

  A data catalog helps business and analytics users explore data assets, find relevant data and understand what it means. Here are 10 important steps for building one.Continue Reading

• What is data governance and why does it matter?

  Data governance (DG) is the process of managing the availability, usability, integrity and security of the data in enterprise systems, based on internal data standards and policies that also control data usage.Continue Reading

• How to evaluate and optimize data warehouse performance

  Organizations build data warehouses to satisfy their information management needs. Data warehouse optimization can help ensure that these warehouses achieve their full potential.Continue Reading

• 6 key steps to develop a data governance strategy

  Data governance shouldn't be built around technology, but the other way around. Existing infrastructure, executive support, data literacy, metrics and proper tools are essential.Continue Reading

• 7 best practices for successful data governance programs

  A comprehensive, companywide data governance program strengthens data infrastructure, improves compliance initiatives, supports strategic intelligence and boosts customer loyalty.Continue Reading

• 3 considerations for a data compliance management strategy

  A data compliance management strategy is key for organizations to protect data the right way. Different positions have responsibility to ensure industry regulations are met.Continue Reading

• Data Dredging (data fishing)

  Data dredging -- sometimes referred to as data fishing -- is a data mining practice in which large data volumes are analyzed to find any possible relationships between them.Continue Reading

• 5 key elements of data tenancy

  Data tenancy is a key piece of any data protection scheme and can be crafted around five building blocks to provide safe, secure data access to users.Continue Reading

• data stewardship

  Data stewardship is the management and oversight of an organization's data assets to help provide business users with high-quality data that is easily accessible in a consistent manner.Continue Reading

• What a big data strategy includes and how to build one

  Companies analyze stores of big data to improve how they operate. But those efforts will bring diminishing returns without a big data strategy. Here's how to build one.Continue Reading

• How big data collection works: Process, challenges, techniques

  Taming large amounts of data from multiple sources and deriving the greatest value to ensure trusted business decisions hinge on a foolproof system for collecting big data.Continue Reading

• Structured Query Language (SQL)

  Structured Query Language (SQL) is a standardized programming language that is used to manage relational databases and perform various operations on the data in them.Continue Reading

• Self-service data preparation: What it is and how it helps users

  Using self-service tools to properly prepare data simplifies analytics and visualization tasks for business users and speeds complex modeling processes for data scientists.Continue Reading

• data validation

  Data validation is the practice of checking the integrity, accuracy and structure of data before it is used for a business operation.Continue Reading

• data profiling

  Data profiling refers to the process of examining, analyzing, reviewing and summarizing data sets to gain insight into the quality of data.Continue Reading

• data preprocessing

  Data preprocessing, a component of data preparation, describes any type of processing performed on raw data to prepare it for another data processing procedure.Continue Reading

• data cleansing (data cleaning, data scrubbing)

  Data cleansing, also referred to as data cleaning or data scrubbing, is the process of fixing incorrect, incomplete, duplicate or otherwise erroneous data in a data set.Continue Reading

• big data

  Big data is a combination of structured, semistructured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications.Continue Reading

• data modeling

  Data modeling is the process of creating a simplified diagram of a software system and the data elements it contains, using text and symbols to represent the data and how it flows.Continue Reading

• tree structure

  A tree data structure is an algorithm for placing and locating files (called records or keys) in a database.Continue Reading

• data mart (datamart)

  A data mart is a repository of data that is designed to serve a particular community of knowledge workers.Continue Reading

• data lake

  A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed for analytics applications.Continue Reading

• compliance

  Compliance is the state of being in accordance with established guidelines or specifications, or the process of becoming so.Continue Reading

• dark data

  Dark data is digital information an organization collects, processes and stores that is not currently being used for business purposes.Continue Reading

• semantic technology

  Semantic technology is a set of methods and tools that provide advanced means for categorizing and processing data, as well as for discovering relationships within varied data sets.Continue Reading

• Apache Flink

  Apache Flink is a distributed data processing platform for use in big data applications, primarily involving analysis of data stored in Hadoop clusters.Continue Reading

• Apache Spark

  Apache Spark is an open source parallel processing framework for running large-scale data analytics applications across clustered computers.Continue Reading

• multimodel database

  A multimodel database is a data processing platform that supports multiple data models, which define the parameters for how the information in a database is organized and arranged.Continue Reading

• data silo

  A data silo exists when an organization's departments and systems cannot, or do not, communicate freely with one another and encourage the sharing of business-relevant data.Continue Reading

• data fabric

  A data fabric is an architecture and software offering a unified collection of data assets, databases and database architectures within an enterprise.Continue Reading

• data architect

  A data architect is an IT professional responsible for defining the policies, procedures, models and technologies to be used in collecting, organizing, storing and accessing company information.Continue Reading

• NoSQL database types explained: Column-oriented databases

  Learn about the uses of column-oriented databases and the large data model, data warehouses and high-performance querying benefits the NoSQL database brings to organizations.Continue Reading

• How to choose exactly the right data story for your audience

  A data practitioner has two jobs: tell the right data story and in the right way to win over project stakeholders, data expert Larry Burns says in his latest book.Continue Reading

• Quiz: Test your understanding of the Hadoop ecosystem

  This quiz will test your knowledge of Hadoops basics including framework, capabilities and related technologies.Continue Reading

• stream processing

  Stream processing is a data management technique that involves ingesting a continuous data stream to quickly analyze, filter, transform or enhance the data in real time.Continue Reading

• 7 data modeling techniques and concepts for business

  Three types of data models and various data modeling techniques are available to data management teams to help convert data into valuable business information.Continue Reading

• 9 steps to a dynamic data architecture plan

  Learn the nine steps to a comprehensive data architecture plan, including C-suite support, data personas, user needs, governance, catalogs, SWOT, lifecycles, blueprints and maps.Continue Reading

• How to build a successful cloud data architecture

  As enterprises vacate the premises and migrate their operations skyward, a cloud data architecture can provide the long-term flexibility to improve workflows, costs and security.Continue Reading

• columnar database

  A columnar database is a database management system (DBMS) that stores data in columns instead of rows.Continue Reading

• relational database

  A relational database is a collection of information that organizes data points with defined relationships for easy access.Continue Reading

• Db2

  Db2 is a family of database management system (DBMS) products from IBM that serve a number of different operating system (OS) platforms.Continue Reading