DATA WAREHOUSE 

(Index)

DATA ALTRUISM:

D.A refers to individuals and companies giving their consent or permission to make available data that they generate - voluntarily and without reward - to be used in the public interest.

ENCODED DATA:

DATA ERROR:

The aforementioned term refers to errors caused by failures in data conversion or failures caused by the Application Services processing.

MASTER DATA:

Master data is the core data that is absolutely essential for running operations within a business enterprise or unit.

DATA FEED:

The aforementioned term refers to a mechanism for delivering data streams from a server to a client automatically or on demand.

HOLISTIC DATA (APPROACH):

The holistic data approach is a way for businesses to improve data analysis and integration; enabling information to be distributed across the company more efficiently and consistently.

DATA FEDERATION:

The aforementioned terminology refers to a software process that allows multiple databases to function as one.

DATA MINIMIZATION:

The principle of "data minimisation" means that a data controller should limit the collection of personal information to what is directly relevant and necessary to accomplish a specified purpose. They should also retain the data only for as long as is necessary to fulfil that purpose.

DATA TIERING:

Data tiering is the process whereby data is shifted from one storage tier to another as its state changes dynamically from hot to cold and vice versa.

DATA LIFE CYCLE:

The data life cycle, also called the information life cycle, refers to the entire period of time that data exists in your system. 

SIMULTANEOUS VOICE AND DATA:

The aforementioned terminology refers to the concurrent transmission of voice and data by modem over a single analog telephone line.

SENSITIVE DATA:

Sensitive data, also known as special category data, is information that must be protected against unauthorized access and treated with extra security.

DATA CITATION:

Data citation is the practice of referencing data products used in research. 

DATA [TRANSMISSION] CIRCUIT:

(As regarding telecommunications) a data transmission circuit is the transmission media and the intervening equipment used for the data transfer between data terminal equipment (DTEs).

DATA CIRCUIT:

A data circuit is the method that you use connect to the internet, or even to connect your local network between two (or more) offices. 

DATA COMPRESSION:

Data compression is a reduction in the number of bits needed to represent data.

PRODUCTION DATA:

Production data is data that is essential to completing day-to-day business tasks and processes.

BASIC DATA:

Basic data means data such as name, surname, address, phone number, mobile phone number, fax number, e-mail address, car license plate.

DATA MEDIA:

This term refers to hardware or any other physical system or device used to store, process, read, amend or control any computer programs, text, digital media, voice or images.

DATA MEDIUM:

The aforementioned term refers to means any medium whatsoever for storing or transmitting data or information, whether physically or electronically, including books, printed documents or records, video recordings, audio recordings, magnetic disks, optical disks, computer tapes, magnetic tapes, digital tapes, magnetic files, cloud storage, online repositories, any network or other use of the Internet, computer hard drives, flash drives, and any other form of computer storage or transmission.

DATA FIDELITY:

(In Healthcare) The aforementioned term refers to the accuracy with which data quantifies and embodies the characteristics of the source.

DATA DOMAIN:

The aforementioned term refers to a logical grouping of items of interest to the organization, or areas of interest within the organization.

WEB DATA:

(On the Internet) Web Data means the data and any content made available on publicly accessible networks that we process on your behalf as a result of your use of the Program or the Service.

MULTIDATA:

Meaning consisting of several items or types of data.

DATA STREAM:

A data stream is defined in IT as a set of digital signals used for different kinds of content transmission.

DATA SYNERGY:

Data synergy is a term coined for this chapter******* and describes data from multiple sources or disciplines that, when combined, is more valuable than any of the sources were on their own.

*******Source: https://link.springer.com/chapter/10.1007/978-3-319-99097-2_5#:~:text=Data%20synergy%20is%20a%20term,sources%20were%20on%20their%20own.

CHARACTER DATA:

CHARACTER DATA STRUCTURE:

(**)Source: https://www.javatpoint.com/character#:~:text=A%20character%20is%20a%20display,help%20of%20compiled%20machine%20language.

DATA SYNTHESIS:

Data Synthesis refers to the process of taking raw data and organizing it into a format that is easy to understand and analyze.

MEDICAL IMAGE DATA:

The data, on which medical visualization methods and applications are based, are acquired with scanning devices, such as computed tomography (CT) and magnetic resonance imaging (MRI).

DATA TRACK:

As pertaining to (computer science), one of the circular magnetic paths on a magnetic disk that serve as a guide for writing and reading data.

DATA TABLE:

A data table is a visual instrument comprised of labeled columns and rows, used to arrange information contained in a computer's database.

DATA TRANSMISSION:

Data transmission is the transfer of data from one digital device to another. 

DATA POINT:

A data point is a discrete unit of information.

DATA COMMUNICATIONS:

Data communications (DC) is the process of using computing and communication technologies to transfer data from one place to another, or between participating parties.

DATA SIGNAL:

Data signal refers to a pulse or frequency of electricity or light that represents data as it travels over a network, a computer channel or wireless.

OPEN 'DATA' PORTAL:

Open data portals are web-based interfaces designed to make it easier to find reusable information.

DATA STORE:

A datastore is a repository for storing, managing and distributing data sets on an enterprise level. 

DATA SCHEMA:

A database schema is an abstract design that represents the storage of your data in a database.

DATA MODEL:

Data models are visual representations of an enterprise's data elements and the connections between them.

DATA WRANGLING:

Data wrangling is the process of removing errors and combining complex data sets to make them more accessible and easier to analyze.

UI DATA BINDING:

The aforementioned term refers to a way to associate fields on a form with fields in a data store; it is a software design pattern to simplify development of GUI applications.

DATA NORMALIZATION:

Data normalization/Data normalisation is the organization of data to appear similar across all records and fields.

DATA GRID:

A data grid is a set of computers that directly interact with each other to coordinate the processing of large amounts of data.

DATA BINDING:

Data binding is the process that establishes a connection between the app UI and the data it displays.

DATA AUTOMATION:

Data automation is the process of updating data on your open data portal programmatically, rather than manually.

DATA NETWORK:

The aforementioned terminology is a system designed to transfer data from one network access point to one other or more network access points via data switching, transmission lines, and system controls.

CACHED 'DATA':

This term refers to information stored on your computer or device after you visit a website.

MACROECONOMICS DATA:

 The aforementioned consist mainly of the aggregate values of economic flows either at the level of the total economy, such as GDP and National Income, or at lower levels of aggregation such as the income, expenditure, and saving of the household or government sectors.

EMPIRICAL DATA:

Also known as (empirical evidence), relies on practical experience rather than theories.

POPULATION DATA: 

Population data is defined as a set of individuals who share a characteristic or set of these. A population is mainly determined by geographies, such as all people in California, or all people in the United States. Demographers (people who study human populations) categorize this as the natural population.

Source: https://www.questionpro.com/blog/population-data/#:~:text=Population%20data%20is%20defined%20as,this%20as%20the%20natural%20population.

DATA TEAM:

Data Team (employees/workers and/or stakeholders) work together to use data to plan and make decisions about programs and services.

DATA LAKE:

A data lake is a central location that holds a large amount of data in its native, raw format.

DATA MESH:

A data mesh is a decentralized data architecture that organizes data by a specific business domain.

SOCIAL DATA:

The aforementioned terms refers to [data] that is publicly available information shared by social media users, like their location, language, and content shared.

CORPORATE DATA:

This term refers to any and all data maintained by any of the Companies including, but not limited to, data related to its finances, Taxes, Employees, customers, suppliers and the Business.

DATA ORGANIZATION:

D.O is the practice of categorizing and classifying data to make it more usable.

ACTUARIAL 'DATA':

Actuarial data refers to the statistics used to calculate various sorts of risk that insurance companies insure people against.

REVENUE FROM 'DATA':

The aforementioned term means all data, notes, invoices, receipts and other documentation pertaining to [the] Gross Revenue. 

PARTICIPANT 'DATA':

Participant Data means any and all Transaction Data submitted or otherwise reported to the Company by a Participant regarding any and all transactions entered into by such Participant.

DATABANK:

This term refers to a large repository of computer data on a particular topic, sometimes formed from more than one database, and accessible by many users.

DATA EXTRACTION:

UNSTRUCTURED DATA:

Unstructured data is a conglomeration of many varied types of data that are stored in their native formats.

STRUCTURED DATA:

METADATA:

The aforementioned term refers to a set of data that describes and gives information about other data.

DATA CATALOG:

The data catalog shows the data assets of an organization and where they're located (inventory-style).

DATA CENTER:

DC refers to a facility that provides shared access to applications and data using a complex network, compute, and storage infrastructure.

DATA LIMIT (CAP):

A Data Cap is known as a limit imposed on the amount of data that can be transferred to an electronic device.

DATA PROFILING:

BANDWIDTH (of DATA):

The aforementioned term refers to the maximum amount of data transmitted over an internet connection in a given amount of time.

DATA SYSTEM:

DS refers to computer, electronic or telecommunications or network systems of any variety (including data bases, websites, hardware, software, storage, switching and interconnection devices and mechanisms, whether on-premises or provided as a service by a third party).

DATA MINING:

The aforementioned term refers to the practice of analysing large databases in order to generate new information.

DATA DISTRIBUTION:

DD is a function that specified all possible values for a variable and also quantifies the relative frequency*.

*Probability of how often they occur.

REAL-TIME 'DATA':

The aforementioned term refers to that is presented as it is acquired.

DATA-HANDLING:

Data-handling is the process of ensuring that research data is stored, archived or disposed off in a safe and secure manner during and after the conclusion of a research project.

DATA PLATFORM:

A 'data' platform is an integrated set of technologies that collectively meets an organization's end-to-end data needs. 

It enables the acquisition, storage, preparation, delivery, and governance of your data, as well as a security layer for users and applications.

PERSONAL DATA:

Aforementioned term incl. information that can be assigned to a determinable person.

DATA CONCERNING HEALTH:

The aforementioned term refers to personal data related to the physical or mental health of a natural person, including the provision of healthcare services, which reveal information about his or her health status. 

Not limited to:-

Any data related to a person's past, current or future health status and includes data from medical devices and fitness trackers (eg, the number of steps taken by the user or athletic performance). 

MEDICAL DATA:

Medical data contains information on a person's state of health and the medical treatment that they have received.

DATA FORMAT:

Data format is the definition of the structure of data within a database or file system that gives the information its meaning.

DATA ENCRYPTION:

 [DATA] encryption is the process of scrambling information and rendering it unintelligible, such that only someone with a "key" can decipher and read it. 

An algorithm encrypts the data and the encryption key enables the receiving party to decrypt it.

Data prior to encryption is referred to as plaintext, while the scrambled information is referred to as ciphertext.

DATA DEFINITION COMMAND: 

Focusing on 'bordering' DDC's are used for defining the data in the database.

DATA VOLUME:

Data volume is the amount of data that is stored or used by networks, organizations, infrastructure, processes, tools or individuals.

DATA INTEGRITY:

Data integrity is the overall accuracy, completeness, and consistency of data; 'safety of data'.

E-DISCOVERY:

E-discovery is a form of digital investigation that attempts to find evidence in email, business communications and other data that could be used in litigation or criminal proceedings.

DATA CLEANSING:

DC, also known as data cleaning and data scrubbing, is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.

DATA CURATION:

Data curation is the process of creating, organizing and maintaining data sets so they can be accessed and used by people looking for information.

DATA FABRIC:

Data fabric is defined as an emerging approach to handling data using a network-based architecture instead of point-to-point connections.

DATA TRANSFORMATION:

Data transformation is the process of changing the format, structure, or values of data. 

DATA COLLECTION:

Data collection is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes.

DATA SECURITY:

Data security is the practice of protecting digital information from unauthorized access, corruption, or theft throughout its entire lifecycle.

DATABASE:

The aforementioned term refers to a collection of independent works, data or other materials arranged in a systematic or methodical way and individually accessible by electronic or other means.

DATA PROGRAM:

Data program means an ordered set of electronic data representing coded instructions or statements that when executed by a computer causes the device to process electronic data.

BIG 'DATA':

The aforementioned term refers to extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.

DATA RETENTION:

DR is a risk mitigation strategy; ensuring:

1. One retains certain data for as long as each applicable law requires; and

2. refers to the necessary cost of doing business,

DATA ENTRY:

Data entry is the process of entering information or updating records in a database or computer system.

DIGITAL DATA:

Refers to 'Data' that represents other forms of data using specific machine language systems that can be interpreted by various technologies.

DATA RECONCILIATION******:

DR is a term typically used to describe a verification phase during a data migration where the target data is compared against original source data to ensure that the migration architecture has transferred the data correctly.

******Data validation and reconciliation in full.

DATA MIGRATION:

Data Migration is the process of moving data from one location to another, one format to another, or one application to another.

DATA IN TRANSIT:

Also known as Data in Motion, is data actively moving from one location to another such as across the internet or through a private network.

INACTIVE DATA:

Data that is not frequently used, but still needs to be preserved for business or compliance reasons.

DATA AT RESET:

Data at rest is data that is not actively moving from device to device or network to network such as data stored on a hard drive, laptop, flash drive, or archived/stored in some other way.

DATA INVENTORY:

Also referred to as a 'data map', focuses on simplifying the determination of 'privacy' impact.

DATA STORAGE:

CLOUD DATABASE:

The aforementioned term refers to a database service built and accessed through a cloud platform.

DATA BREACH:

DATA GOVERNANCE:

Data governance is a system of "people, processes, and technologies" that manages and protects data assets and defines who has authority and control over those data assets

DATA GOVERNANCE FRAMEWORK:

A data governance framework is the collection of rules, processes, and role delegations that ensure privacy and compliance in an organization's enterprise data management.

DATA MANAGEMENT (Procurement):

DM enacts those (Data Governance) policies and procedures to compile and use that data for decision-making; including (but not limited to):-

1. Customer Relationship Management System or CRM;

2. Marketing technology systems;

3. Data warehouse systems; and

4. Analytics tools. 

DATA PROCESSOR:

According to various sources the above term:- describes a person or entity that collects, uses, or discloses personal data on behalf of, or under the instructions of, the data controller.

DATA CONTROLLER:

Methodology at the forefront, it is suggested that the aforementioned term describes the entity deciding on certain key elements of and determining the purposes and means ("the why and how") of the processing.

SOURCE CODE (DATA):

Written IN human-readable form, usually a programming language; "any other particular tangible expression of an idea, obtaining copyright protection when written, created or COMPILED". 

DATA SHARING:

Data subjects/personal information

DATA TRANSFERS:

In-line with promoting Environmental, Social & Governance; Transfers enable effective oversight and regulation of business to ensure consumer protection and sound governance. Data would be known to include the distribution of [digital] ASSETS* in a [ledger].

DATA FLOWS: 

[CLAUSES/ once being developed/[plr]and/or[drafted] protect(encrypt) it. (data flow)[in a trading agreement/ i.e proforma or ____] Protected by trade/trading agreements.[samples](pre-contractual agreements / commercial dealings / intent to create legal relations](commerce incl. electronically)

DATA CAPTURE: 

The aforementioned term refers to the action or process of gathering data, especially from an automatic device, control system, or sensor.

Examples: PHP/EDU/DEI/

DATA FOUNDATION:

Properly attended to (robust), Data Foundation(s) is to provide integrated, trusted and timely data from which reporting and analytics can be performed.

ELECTRONIZATION: 

ionizing digital

DATA ACQUISITION:

Data acquisition (DAQ) is the process of sampling signals that measure real-world physical phenomena and the subsequent conversion of them into a digital form to be be processed by a computer with its associated software.

DATA PROTECTION:

Encrypted (bearer token)[CcTLD]

DATA WAREHOUSE:

Synonyms incl. archive/database

DATA FILE:

A data file is a computer file which stores data to be used by a computer application or system, including input and output data. 

DATA AVAILABILITY:

Two strands govern the consensus pertaining to the meaning of Data Availability:- a) Public Sector Data; and b) Data Custodians towards accredited users. 

Third parties can move from Public Sector Data (as third parties) (engaging) through Data Custodians (Accessible time of dataholders/rightsholder [i.e Data Controllers]) and become Accredited Users once accessibility as pertaining to 'data' can be reused by 'others' (through electronic means) on grounds of 'fair access' using the 'data' whilst avoiding abuse of the 'rights' dataholders/rightsholder enjoy. 'Purpose' is ascertained from the aforementioned, therefore consideration of the proper means/manner and/or modes to obtaining [the 'data' in question] refers to = DATA AVAILABILITY.

DATA AVAILABILITY STATEMENT:

A data availability statement (also sometimes called a 'data access statement') tells the reader where the research data associated with a paper is available, and under what conditions the data can be accessed; including links to (where applicable) to the data set.

DATA ORCHESTRATION:

Data orchestration is the practice of acquiring, cleaning, matching, enriching, and making data accessible across technology systems.

DATA SOLUTION:

Data Solution means any product, service or other solution which: (a) is modified or enhanced by, incorporated with, developed or created using, derived from or derives benefit from, or involves the supply or the making available of, the Data or any part of the Data.

CAPITAL 'DATA':

The aforementioned terminology refers to organizational data that is available to be used toward an end goal.

PERIODIC 'DATA':

The aforementioned term refers to a data set that repeats the same pattern over time.

DATA SEMANTICS:

The aforementioned term refers to the study of the meaning and use of specific pieces of data in computer programming and other areas that employ data.

MARKET DATA:

Broadly speaking, the aforementioned term is used in reference to the financial information necessary for carrying out research, analysing, trading and accounting for financial instruments of all asset classes on world markets.

DATA LIBRARY(IES):

Conversion from singular to plural requires. A Ton* [data library]

COMPOSITE DATA TYPES:

Also referred to as compound data type, are data types that have one or more fields dynamically linked to fields in another data type. 

DATA OBJECTS:

As distinct from (physical objects)[things in possession], Data Objects refer to 'personal property****'.

****i.e PATENTS

DATA SUBJECTS:

A 'data subject' means an identified or identifiable natural person (article 4(1), GDPR)

DATA ACCESS:

PHI/PI

DATASET:

A synthetic dataset is an "artificial" dataset containing computer-generated data instead of real-word records.

DATA QUALITY:

DATA *ASSETS:

Not qualified to M&A [transactions]; loosely and broadly speaking:- 'Asset' (status) can be derived from 'DATA' as follows:

i. within its [(commercial)] value;

ii. ['DATA'] open for access (i.e within the public sector); 

iii. ENCRYPTED (i.e premiums/securitization) to the rendering of 'service' and/ or portfolio (exhibitionism)[holdings];

iiii. Traction toward [registration](brand activating 'action') of 'trade'[mark] recognized as 'procurement***' [likely for consideration: fee(s)];

iv. Higher securitization probability of designating 'use' thereof [(in relation to ['DATA'])];

v. Certification surrounding proper delegating 'prowess' as pertaining to data loosely and broadly speaking.

***i.e Project management exp.

DATA POOLING:

DATA MONETIZATION:

Data monetization involves selling rights in data to a third party.

DATA ISSUES:

The key features of what is (seen)[and known as] 'data' issue (pertaining to 'source code' data) is the 'joint' (mindset)[i.e pertaining to ascertaining consensus] for the mechanism (human-readable/written) to be recognized as such (i.e WHICH [data] processor?)

A few systems [assets***** (colloquially known as RegTech)] to recognize:

1. WWW2;

2. SaaS; and

3. Trademarks/secrets.

*****https://www.investopedia.com/terms/r/regtech.asp#:~:text=Regtech%2C%20or%20RegTech%2C%20consists%20of,also%20known%20as%20regulatory%20technology

DATA PRUNING:

Dataset pruning is the process of removing sub-optimal tuples from a dataset to improve the learning of a machine learning.

TECHNOLOGICAL DATA:

The aforementioned terminology means any technical data or information which Neurogen possesses and has rights to license hereunder relating to ADCI, the processes for the preparation thereof and Product which may be necessary or useful to exercise the rights granted under this Agreement and to obtain regulatory approval of the Product in the Territory. The term includes without limitation specifications or equipment necessary for preparation of ADCI, manufacture, methods of production, and formulation of the Product, the chemical and physical properties of ADCI, preclinical and clinical studies including safety and efficacy data relative to current and future therapeutic indications for the Product, procedures of testing and requirements of control. The term shall also include copies of all registration dossiers submitted to drug regulatory authorities relating to the Product.

Source: https://www.lawinsider.com/dictionary/technological-data

DATA PREPARATION:

Data preparation is the process of gathering, combining, structuring and organizing data so it can be used in business intelligence (BI), analytics and data visualization applications.

DATA STEWARD:

A Data Steward refers to the lead role in a data governance project; working with the business to define the programme's objectives.

DATA SCIENCE:

DATA DOMAIN:

(RAW) SENSORY DATA:

The physical effects of the external world on our subjective senses (raw sensory stimuli) which are selectively filtered through. 

DATA VISUALIZATION:

The aforementioned term refers to the representation of information in the form of a chart, diagram, picture, etc.

DATA STORYTELLING:

HARD DATA:

The aforementioned term includes technology-generated, meaning it comes from applications and technological devices and is also collected in quantitative research.

UX DATA ANALYSIS:

UX data analysis is a name for a process of transforming raw data into valuable information.

DATA SERVICES:

Data services in [IT] is a term for a third-party services that help to manage data for clients.

DATABASE DEVELOPER:

 A Database Developer also known as database designers or database programmers, are responsible for the design, programming, construction, and implementation of new databases, as well as modifying existing databases for platform updates and changes in user needs.

DATA ENGINEER:

Data Engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance.

DATA ANALYST:

Data Analyst collects, organises and studies data to provide business insight. In this role, you will: apply tools and techniques for data analysis and data visualisation (including the use of business information tools) identify, collect and migrate data to and from a range of systems.