Big Data Terminology: 80 Terms Every Marketer Should Know

Almost every organization leverages big data to gain insights and make informed decisions. For marketers, big data offers unmatched opportunities to refine strategies, optimize campaigns, and enhance customer experiences.

Why is big data valuable for marketers? 

Big data offers marketers unparalleled benefits, from deeper customer insights to predictive analytics, enhancing every aspect of marketing efforts.

• Deeper customer insights: Marketers gain a comprehensive understanding of their audience by analyzing data such as demographics and behavior patterns, allowing for more effective, targeted campaigns.

• Personalized marketing campaigns: Data analytics enable precise audience segmentation, helping marketers create personalized content and offers that resonate, increasing engagement and conversions.

• Predictive analytics: Big data helps forecast trends and behaviors, allowing marketers to adjust strategies proactively and stay competitive.

• Enhanced marketing ROI: By tracking KPIs and campaign performance through data analysis, marketers can optimize their spend, focus on the most effective channels, and maximize ROI.

• Improved customer experience: Real-time data analysis allows marketers to customize interactions, delivering a seamless, tailored customer experience that fosters brand loyalty and advocacy.

While the value of big data is clear, navigating its terminology can feel overwhelming. That’s why having a solid grasp of key terms is essential for leveraging your data effectively. From foundational concepts to advanced definitions, this guide will walk you through 80 essential big data terms every marketer should know, ensuring you have the knowledge needed to confidently harness big data in your marketing efforts.

So, let's get started...

Big data terminology: Definitions every marketer should know

1. Abstraction layer

A translation layer that transforms high-level requests into low-level functions and actions. Data abstraction sees the essential details needed to perform a function removed, leaving behind the complex, unnecessary data in the system. The complex, unneeded data is hidden from the client, and a simplified representation is presented. 

A typical example of an abstraction layer is an API (application programming interface) between an application and an operating system. 

2. API

API is an acronym used for Application Programming Interface, a software connection between computers or computer programs. APIs are not databases or servers but rather the code and rules that allow access to and sharing of information between servers, applications, etc. 

Source: ColorWhistle

3. Aggregation

Data aggregation refers to the process of collecting data and presenting it in a summarised format. The data can be gathered from multiple sources to be combined for a summary.

4. Algorithms

In computer science, an algorithm is a set of well-defined rules that solve a mathematical or computational problem when implemented. Algorithms are used to carry out calculations, data processing, machine learning, search engine optimisation, and more. 

5. Analytics

Systems and techniques of computational analysis and interpretation of large amounts of data or statistics. Analytics are used to derive insights, spot patterns, and optimise business performance.

Source: G2

6. Applications

An application is any computer software or program designed to be used by end-users to perform specific tasks. Applications or apps can be desktop, web, or mobile-based.  

AI refers to the development of computer systems capable of performing tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, language understanding, and perception. Modern AI technologies, such as machine learning and deep learning, enable systems to recognize patterns, make decisions, and adapt over time with minimal human intervention. Applications range from chatbots and recommendation systems to autonomous vehicles and complex data analytics in marketing and business.

Binary classification is a technique used to identify whether a set of two elements are in one group or another based on classification rules. For example, binary classification techniques are used to determine whether a disease is present in medical data. In computing, it determines whether a piece of content should be included in search results based on its relevance or value to the users.

Business intelligence is a process of collecting and preparing internal and external data for analysis; this often includes data visualisation techniques (graphs, pie charts, scatter plots, etc.) presented on business intelligence dashboards. By harnessing business intelligence, organisations can make faster, more informed business decisions. 

Source: MPercent Academy

10. Byte

In computing, a byte is a unit of data that is eight binary digits (bits) long. A byte is a unit of memory size; a single byte is the smallest unit of storage; thus, in computing, we usually refer to gigabytes (GB, one billion bytes) and terabytes (TB, one trillion bytes). 


11. C

C is a programming language, and it’s one of the oldest programming languages around. Despite its age, it continues to be one of the most prevalent as it powers systems like Microsoft Windows and Mac. 


12. CPU

This acronym stands for Central Processing Unit. A CPU is often referred to as the brains of a computer - you will find one in your phone, smartwatch, tablet, etc. Despite being one of many processing systems within a computer, a CPU is vitally important as it controls the ability to perform calculations, take actions and run programs. 


13. Cascading 

Cascading is a type of software designed for use with Hadoop for the creation of data-driven applications. Cascading software creates an abstraction layer that enables complex data processing workflows and masks the underlying complexity of MapReduce processes. 


14. Cleaning data

Cleaning data improves data quality by removing errors, corruptions, duplications, and formatting inconsistencies from datasets.


15. Cloud

Cloud technology, or The Cloud as it is often referred to, is a network of servers that users access via the internet and the applications and software that run on those servers. Cloud computing has removed the need for companies to manage physical data servers or run software applications on their own devices - meaning that users can now access files from almost any location or device. 

The cloud is made possible through virtualisation - a technology that mimics a physical server but in virtual, digital form, A.K.A virtual machine. 

• Random access 
• Sequential access

Data capture refers to collecting information from either paper or electronic documents and converting it into a format that a computer can read. Data capture can be automated to reduce the need for manual data entry and accelerate the process. 

21. Data governance 

Data governance is a framework for managing data quality, accessibility, and security across an organization. It defines roles, responsibilities, and processes for ensuring data accuracy, consistency, and compliance with regulations. Effective data governance helps organizations maximize data value, maintain data integrity, and reduce risks by setting policies for data use, privacy, and protection. This ensures that data remains a reliable asset for informed decision-making and strategic initiatives.

Data ingestion is the process of moving data from various sources into a central repository such as a data warehouse where it can be stored, accessed, analysed, and used by an organisation.

The practice of ensuring data remains accurate, valid and consistent throughout the entire data life cycle. Data integrity incorporates logical integrity (a process) and physical integrity (a state). 

24. Data lake

A data lake is a centralized repository that allows you to store all structured and unstructured data at any scale. Unlike traditional databases, data lakes keep raw data in its native format until it's needed, making it easier to run analytics, machine learning, and other data processing tasks. They are often used in conjunction with cloud platforms like AWS, Azure, and Google Cloud, enabling scalable and cost-efficient data storage and analysis for businesses.


25. Data management

Data management is an overarching strategy of data use that guides organisations to collect, store, analyse and use their data securely and cost-effectively via policies and regulations.


26. Data processing 

The process of transforming raw data into a format that can be read by a machine or, in other words, turning data into something usable. Once processed, businesses can use data to glean insights and make decisions. 

27. Data serialisation

Data serialisation is a data translation process that enables complex or large data structures or object states to be changed to formats that can be more easily stored, transferred and distributed. After serialisation and the chosen data action, the byte sequence can create an identical clone of the original - a process known as deserialisation.


28. Data storage

Refers to collecting and recording data to be retained for future use on computers or other devices. In its most common form, data storage occurs in three ways: file storage, block storage, and object storage.

Data tagging is a type of categorisation process that allows users to better organise types of data (websites, blog posts, photos, etc.) using tags or keywords. 

Data visualisation sees large amounts of data translated into visual formats such as graphs, pie charts, scatter charts, etc. Visualisations can be better understood by the human brain and accelerate the rate of insight retrieval for organisations. 

31. Data warehouse

A centralised repository of information that enterprises can use to support business intelligence (BI) activities such as analytics. Data warehouses typically integrate historical data from various sources.

Decision trees are visual representations of processes and options that help machines mark complex predictions or decisions when faced with many choices and outcomes. Decision trees are directional acyclic graphs made up of branch nodes, edges, and leaf nodes with all data flowing in one direction.

Source: Edureka!

Deep learning is a function of artificial intelligence and machine learning that mimics the processes of the human brain to make decisions, process data, and create patterns. It can be used to process huge amounts of unstructured data that would take human brains years to understand. Deep learning algorithms can recognise objects and speech, translate languages, etc.

An acronym used to describe a process within data integration: Extract, Transform and Load.

An acronym used to describe a process within data integration: Extract, Load, and Transform.

In computing, encoding refers to assigning numerical values to categories. For example, male and female would be encoded to be represented by 1 and 2. 

There are two main types of encoding:

• Binary 
• Target-based

The term fault tolerance describes the ability of a system, for example, a computer or a cloud cluster, to continue operating uninterrupted despite one or more of its components failing. 

Fault tolerance is developed to ensure a high level of availability and that no business is impacted by a loss of critical systems or continuity. Fault tolerance is achieved by utilising backup components in hardware, software, and power solutions.

38. Flume

Flume is open-source software that facilitates the collecting, aggregating and moving of huge amounts of unstructured, streaming data such as log data and events.  Flume has a simple and flexible architecture, moving data from various servers to a centralised data store.

39. GPS

GPS is an acronym for Global Positioning System, which is a navigation system that uses data from satellites and algorithms to synchronise location, space, and time data. GPS utilises three key segments: satellites, ground control, and user equipment.  

Source: MarketsandMarkets

• Distributed-File System - allows data to be easily stored in any format across a large number of storage devices.
• MapReduce - reads and translates data into the right format for analysis (map) and carrying out mathematical calculations (reduce).
• Hadoop Common - provides the baseline tools needed for users systems, e.g. Windows, to retrieve data from Hadoop.
• YARN - a management module that handles the systems that carry out storage and analysis.
  
  

Source: SAS

Hardware is the physical component of any computer system, for example, the wiring, circuit board, monitor, keyboard, mouse, desktop, etc.

In statistics, dimensionality refers to how many attributes a dataset has. Thus, high dimensionality refers to a dataset with an exceedingly large amount of attributes. When high-dimensional data occurs, calculations become extremely difficult because the number of features outweighs the number of observations. 

Website analysis (e.g. ranking, advertising and crawling) is a good example of high dimensionality.

46. Hive

Hive is an open-source data warehouse software system that allows developers to carry out advanced work on Hadoop distributed file systems (HDFS) and MapReduce. Hive makes working with these tools easier by facilitating the use of a more simplified Hive-Query Language (HQL), thus, reducing the need for developers to know or write complex java code.

47. Information retrieval (IR)

A software program that handles the organisation, storage, and retrieval of information, usually of a text-based format, from large documentation repositories. A simple example of IR is search engine queries that we all carry out on Google.

Integration is the process of combining data from multiple disparate sources to achieve a unified view of the data for easier, more valuable operations or business intelligence. 

There are five main forms of data integration: 

• Manual
• Middleware
• Data warehouse
• Application-based
• Uniform access
  
  

The internet of things (IoT) refers to an ecosystem of physical objects that are connected to the internet and generate, collect, and share data. With advancing technologies enabling smaller and smaller microchips, the IoT has transformed previously benign objects into smart devices that can submit insights without the need for human interaction.


50. Java

Java is a high-level programming language that is specifically designed to reduce programming dependencies. However, it is also used as a computing platform. Java is widely regarded as fast, secure, and reliable.


51. Latency

Data latency refers to the time it takes for a data query to be fully processed by a data warehouse or business intelligence platform. There are three main types of data latency: zero-data latency (real-time), near-time data latency (batch consolidation), some-time data latency (data is only accessed and updated when needed).


52. Machine learning

Machine learning or ML is a branch of artificial intelligence that focuses on building algorithms and models that enable computers to learn from and make decisions based on data. Instead of being explicitly programmed, these systems use statistical methods to identify patterns and improve performance over time. ML applications include recommendation engines, fraud detection, and predictive analytics. It encompasses various techniques such as supervised learning (learning from labelled data), unsupervised learning (finding patterns in unlabeled data), and reinforcement learning (learning through trial and error).


53. Mining

Mining or data mining, as it is commonly known, refers to the practice of using computer programs to identify patterns, trends and anomalies within large amounts of data and using these findings to predict future outcomes.


54. NoSQL

NoSQL is also referred to as non-SQL or not-only SQL. It is a database design approach that extends storage and querying capabilities beyond what is possible from the traditional tabular structures found in a regular relational database.

Instead, NoSQL databases use a JSON document to house data within one structure. This is a non-relational design that can handle unstructured data as it does not require a schema.


55. Non-relational database

A database system that does not use the tabular system of rows and columns.


56. Neural networks

A set of algorithms that work to recognise relationships between huge sets of data by mimicking the processes of the brain. The word neural refers to neurons in the brain which act as information messengers. 

Neural networks automatically adapt to change without the need to redesign their algorithms and thus have been widely taken up in the design of financial trading software.

Source: Investopedia

Open-source refers to the availability of certain types of code to be used, redistributed and even modified for free by other developers. This decentralised software development model encourages collaboration and peer production.


58. Pattern recognition

One of the cornerstones of computer science, pattern recognition, uses algorithms and machine learning to identify patterns in large amounts of data.


59. Pig

Pig is a high-level scripting language that is used to create programs that run on Hadoop.


60. Pixel

Pixels are small pieces of HTML code that are used to track users' behaviours online, for example, when they visit a website or open an email. 

61. Predictive analytics 

Predictive analytics involves using historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes. It analyzes patterns and trends in data to make predictions about future events, such as customer behavior, market trends, or potential risks. Businesses use predictive analytics for applications like demand forecasting, personalized marketing, and fraud detection, helping them make data-driven decisions to optimize strategies and operations.

62. Programming language

A programming language is a set of formal language formatted using sets of strings that instruct a computer to perform specific tasks. Programmers use languages to develop applications. There are numerous programming languages, the most common of which are Python and Java.


63. Python

Python is a high-level programming language with dynamic semantics used to develop applications at a rapid pace. Python prioritises readability making it easy to learn and cheaper due to a lessened need for program maintenance.


64. Query

In computing, a query is a request for information or a question directed toward a database. The queried data may be returned in the form of SQL (structured query language) or data visualisations such as graphs, pictorial representations, etc.


65. R

R is a free software environment for statistical computing and graphics.


66. RAM

An acronym used for Random Access Memory, which essentially refers to the short-term memory of a computer. RAM stores all of the information that a computer may need in the present and near future; this information is everything currently running on a device for example any web browser in use or game that you’re currently playing. 

RAM’s fast-access capabilities make it beneficial for short-term storage, unlike a hard drive device which is slower but preferred for long term storage.


67. Relational database

A relational database exists to house and identify data items that have pre-defined relationships with one another. Relational databases can be used to gain insights into data in relation to other data via sets of tables with columns and rows. In a relational database, each row in the table has a unique ID referred to as a key.


68. SQL

SQL stands for Structured Query Language and is used to communicate with a database. SQL is the standard language used for a relational database.


69. Scalability

Scalability in databases refers to the ability to accommodate rapidly changing amounts of data processing needs. Scalability concerns both rapid increases in data (scaling up) and decreases in demand for data processing (scaling down). Scalability ensures that the rate of processing is consistent despite the volume of data being handled.


70. Schema on-read

A method of data analysis that applies a schema to data sets as they are extracted from a database rather than when they are pulled into that database. A data lake applies an on-read schema, allowing it to house unstructured data.


71. Schema on-write

A method of data analysis that applies a schema to data sets as they are ingested into a database. A data warehouse uses an on-write schema, meaning that data is transformed into a standardised format for storage and is ready for analysis.


72. Semi-structured data

Semi-structured data does not reside in a relational database (rows and columns); however, it still has some form of organisational formatting that enables it to be more easily processed, such as semantic tags.


73. Software

The opposite of hardware, software is a virtual set of instructions, codes, data, or programs used to perform operations via a computer.

Spark is a data processing and analysis framework that can quickly perform processing tasks on very large data sets or distribute tasks across multiple computers. 

Spark’s architecture consists of two main components: 

• Drivers - convert the user’s code into tasks to be distributed across worker nodes
• Executors - run on those nodes and carry out the tasks assigned to them
  
  

Structured data can be formatted into rows and columns, and whose elements can be mapped into clear, pre-defined fields. Typical examples of structured data are names, addresses, telephone numbers, geolocations, etc. Unstructured data does not have a pre-defined structure or data model and is not organised in a predefined format. Examples include images, video files, audio files, etc.

76. User Interface (UI)

A user interface or UI is the location of human-computer interaction; they are the display screens at the front end of applications that mask the code that works behind the scenes. A user interface is designed with usability in mind to ensure that any user can easily understand and navigate the interface as this impacts user experience. 

Part of the 4 Vs of big data, variety refers to the huge variety of data formats that data can now exist in.


78. Velocity

Part of the 4Vs of big data, velocity refers to the rapid speed at which large amounts of data can be processed.


79. Veracity

Part of the 4Vs of big data, veracity refers to the trustworthiness of big data in terms of integrity, accuracy, privacy, etc.


80. Volume

Part of the 4Vs of big data, volume refers to the huge amount of data being generated globally each data.

Big data is a vast and complex field that is constantly evolving, and for that reason, it’s important to understand the basic terms and the more technical vocabulary so that your marketing can evolve with it. But understanding these terms is only the first step—using a reliable tool to analyze and manage your data is crucial for leveraging its full potential. Hurree is the perfect solution, offering a powerful AI-powered platform for data integration, visualization, and analysis to help you turn complex data into actionable insights.