Terminology

A

• Aggregation – a process of searching, gathering and presenting data
• Algorithms – a mathematical formula that can perform certain analyses on data
• Analytics – the discovery of insights in data
• Anomaly detection – the search for data items in a dataset that do not match a projected pattern or expected behaviour. Anomalies are also called outliers, exceptions, surprises or contaminants and they often provide critical and actionable information.
• Anonymization – making data anonymous; removing all data points that could lead to identify a person
• Application – computer software that enables a computer to perform a certain task
• Artificial Intelligence – developing intelligence machines and software that are capable of perceiving the environment and take corresponding action when required and even learn from those actions.

B

• Behavioural Analytics – analytics that informs about the how, why and what instead of just the who and when. It looks at humanized patterns in the data
• Big Data Scientist – someone who is able to develop the algorithms to make sense out of big data
• Big data startup – a young company that has developed new big data technology
• Biometrics – the identification of humans by their characteristics
• Brontobytes – approximately 1000 Yottabytes and the size of the digital universe tomorrow. A Brontobyte contains 27 zeros
• Business Intelligence – the theories, methodologies and processes to make data understandable

C

• Classification analysis – a systematic process for obtaining important and relevant information about data, also meta data called; data about data.
• Cloud computing – a distributed computing system over a network used for storing data off-premises
• Clustering analysis – the process of identifying objects that are similar to each other and cluster them in order to understand the differences as well as the similarities within the data.
• Cold data storage – storing old data that is hardly used on low-power servers. Retrieving the data will take longer
• Comparative analysis – it ensures a step-by-step procedure of comparisons and calculations to detect patterns within very large data sets.
• Complex structured data – data that are composed of two or more complex, complicated, and interrelated parts that cannot be easily interpreted by structured query languages and tools.
• Computer-generated data – data generated by computers such as log files
• Concurrency – performing and executing multiple tasks and processes at the same time
• Correlation analysis – the analysis of data to determine a relationship between variables and whether that relationship is negative (- 1.00) or positive (+1.00).
• Customer Relationship Management – managing the sales and business processes, big data will affect CRM strategies

D

• Dashboard – a graphical representation of the analyses performed by the algorithms
• Data aggregation tools – the process of transforming scattered data from numerous sources into a single new one.
• Data analyst – someone analysing, modelling, cleaning or processing data
• Database – a digital collection of data stored via a certain technique
• Database-as-a-Service – a database hosted in the cloud on a pay per use basis, for example Amazon Web Services
• Database Management System – collecting, storing and providing access of data
• Data centre – a physical location that houses the servers for storing data
• Data cleansing – the process of reviewing and revising data in order to delete duplicates, correct errors and provide consistency
• Data custodian – someone who is responsible for the technical environment necessary for data storage
• Data ethical guidelines – guidelines that help organizations being transparent with their data, ensuring simplicity, security and privacy Data feed – a stream of data such as a Twitter feed or RSS Data marketplace – an online environment to buy and sell data sets Data mining – the process of finding certain patterns or information from data sets
• Data modelling – the analysis of data objects using data modelling techniques to create insights from the data
• Data set – a collection of data
• Data virtualization – a data integration process in order to gain more insights. Usually it involves databases, applications, file systems, websites, big data techniques, etc.)
• De-identification – same as anonymization; ensuring a person cannot be identified through the data
• Discriminant analysis – cataloguing of the data; distributing data into groups, classes or categories. A statistical analysis used where certain groups or clusters in data are known upfront and that uses that information to derive the classification rule.
• Distributed File System – systems that offer simplified, highly available access to storing, analysing and processing data
• Document Store Databases – a document-oriented database that is especially designed to store, manage and retrieve documents, also known as semi structured data.

E

• Exploratory analysis – finding patterns within data without standard procedures or methods. It is a means of discovering the data and to find the data sets main characteristics.
• Exabytes – approximately 1000 petabytes or 1 billion gigabytes. Today we create one Exabyte of new information globally on a daily basis.
• Extract, Transform and Load (ETL) – a process in a database and data warehousing meaning extracting the data from various sources, transforming it to fit operational needs and loading it into the database

F

• Failover – switching automatically to a different server or node should one fail
• Fault-tolerant design – a system designed to continue working even if certain parts fail

G

• Gamification – using game elements in a non game context; very useful to create data therefore coined as the friendly scout of big data
• Graph Databases – they use graph structures (a finite set of ordered pairs or certain entities), with edges, properties and nodes for data storage. It provides index-free adjacency, meaning that every element is directly linked to its neighbour element.
• Grid computing – connecting different computer systems from various location, often via the cloud, to reach a common goal

H

• Hadoop – an open-source framework that is built to enable the process and storage of big data across a distributed file system
• HBase – an open source, non-relational, distributed database running in conjunction with Hadoop
• HDFS – Hadoop Distributed File System; a distributed file system designed to run on commodity hardware
• High-Performance-Computing (HPC) – using supercomputers to solve highly complex and advanced computing problems

I

• In-memory – a database management system stores data on the main memory instead of the disk, resulting is very fast processing, storing and loading of the data
• Internet of Things – ordinary devices that are connected to the internet at any time any where via sensors

J

• Juridical data compliance – relevant when you use cloud solutions and where the data is stored in a different country or continent. Be aware that data stored in a different country has to oblige to the law in that country.

K

• KeyValue Databases – they store data with a primary key, a uniquely identifiable record, which makes easy and fast to look up. The data stored in a KeyValue is normally some kind of primitive of the programming language.

L

• Latency – a measure of time delayed in a system
• Legacy system – an old system, technology or computer system that is not supported any more
• Load balancing – distributing workload across multiple computers or servers in order to achieve optimal results and utilization of the system
• Location data – GPS data describing a geographical location
• Log file – a file automatically created by a computer to record events that occur while operational

M

• Machine2Machine data – two or more machines that are communicating with each other
• Machine data – data created by machines via sensors or algorithms
• Machine learning – part of artificial intelligence where machines learn from what they are doing and become better over time
• MapReduce – a software framework for processing vast amounts of data
• Massively Parallel Processing (MPP) – using many different processors (or computers) to perform certain computational tasks at the same time
• Metadata – data about data; gives information about what the data is about.
• MongoDB – an open-source NoSQL database
• Multi-Dimensional Databases – a database optimized for data online analytical processing (OLAP) applications and for data warehousing.
• MultiValue Databases – they are a type of NoSQL and multidimensional databases that understand 3 dimensional data directly. They are primarily giant strings that are perfect for manipulating HTML and XML strings directly

N

• Natural Language Processing – a field of computer science involved with interactions between computers and human languages
• Network analysis – viewing relationships among the nodes in terms of the network or graph theory, meaning analysing connections between nodes in a network and the strength of the ties.
• NewSQL – an elegant, well-defined database system that is easier to learn and better than SQL. It is even newer than NoSQL
• NoSQL – sometimes referred to as ‘Not only SQL’ as it is a database that doesn’t adhere to traditional relational database structures. It is more consistent and can achieve higher availability and horizontal scaling.

O

• Object Databases – they store data in the form of objects, as used by object-oriented programming. They are different from relational or graph databases and most of them offer a query language that allows objects to be found with a declarative programming approach.
• Object-based Image Analysis – analysing digital images can be performed with data from individual pixels, whereas object-based image analysis uses data from a selection of related pixels, called objects or image objects.
• Operational Databases – they carry out regular operations of an organisation and are generally very important to a business. They generally use online transaction processing that allows them to enter, collect and retrieve specific information about the company.
• Optimization analysis – the process of optimization during the design cycle of products done by algorithms. It allows companies to virtually design many different variations of a product and to test that product against pre-set variables.
• Ontology – ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts
• Outlier detection – an outlier is an object that deviates significantly from the general average within a dataset or a combination of data. It is numerically distant from the rest of the data and therefore, the outlier indicates that something is going on and generally, therefore, requires additional analysis.

P

• Pattern Recognition – identifying patterns in data via algorithms to make predictions of new data coming from the same source.
• Petabytes – approximately 1000 terabytes or 1 million gigabytes. The CERN Large Hydron Collider generates approximately 1 petabyte per second
• Platform-as-a-Service – a service providing all the necessary infrastructure for cloud computing solutions
• Predictive analysis – the most valuable analysis within big data as they help predict what someone is likely to buy, visit, do or how someone will behave in the (near) future. It uses a variety of different data sets such as historical, transactional, social or customer profile data to identify risks and opportunities.
• Privacy – to seclude certain data / information about oneself that is deemed personal
• Public data – public information or data sets that were created with public funding

Q

• Quantified Self – a movement to use the application to track one's every move during the day in order to gain a better understanding about one's behaviour
• Query – asking for information to answer a certain question

R

• Re-identification – combining several data sets to find a certain person within anonymized data
• Regression analysis – to define the dependency between variables. It assumes a one-way causal effect from one variable to the response of another variable.
• RFID – Radio Frequency Identification; a type of sensor using wireless non-contact radio-frequency electromagnetic fields to transfer data
• Real-time data – data that is created, processed, stored, analysed and visualized within milliseconds
• Recommendation engine – an algorithm that suggests certain products based on previous buying behaviour or buying behaviour of others
• Routing analysis – finding the optimized routing using many different variables for a certain means of transport in order to decrease fuel costs and increase efficiency.

S

• Semi-structured data – a form a structured data that does not have a formal structure like structured data. It does however have tags or other markers to enforce hierarchy of records.
• Sentiment Analysis – using algorithms to find out how people feel about certain topics
• Signal analysis – it refers to the analysis of measurement of time varying or spatially varying physical quantities to analyse the performance of a product. Especially used with sensor data.
• Similarity searches – finding the closest object to a query in a database, where the data object can be of any type of data.
• Simulation analysis – a simulation is the imitation of the operation of a real-world process or system. A simulation analysis helps to ensure optimal product performance taking into account many different variables.
• SQL – a programming language for retrieving data from a relational database
• Structured data – data that is identifiable as it is organized in structure like rows and columns. The data resides in fixed fields within a record or file or the data is tagged correctly and can be accurately identified.

T

• Terabytes – approximately 1000 gigabytes. A terabyte can store up to 300 hours of high-definition video
• Time series analysis – analysing well-defined data obtained through repeated measurements of time. The data has to be well defined and measured at successive points in time spaced at identical time intervals.
• Topological Data Analysis – focusing on the shape of complex data and identifying clusters and any statistical significance that is present within that data.
• Transactional data – dynamic data that changes over time
• Transparency – consumers want to know what happens with their data and organizations have to be transparent about that

U

• Un-structured data – unstructured data is regarded as data that is in general text heavy, but may also contain dates, numbers and facts.

V

• Value – all that available data will create a lot of value for organizations, societies and consumers. Big data means big business and every industry will reap the benefits from big data.
• Variability – it means that the meaning of the data can change (rapidly). In (almost) the same tweets for example a word can have a totally different meaning
• Variety – data today comes in many different formats: structured data, semi-structured data, unstructured data and even complex structured data
• Velocity – the speed at which the data is created, stored, analysed and visualized
• Veracity – organizations need to ensure that the data is correct as well as the analyses performed on the data are correct. Veracity refers to the correctness of the data
• Visualization – with the right visualizations, raw data can be put to use. Visualizations of course do not mean ordinary graphs or pie-charts. They mean complex graphs that can include many variables of data while still remaining understandable and readable
• Volume – the amount of data, ranging from megabytes to brontobytes

W

• Weather data – an important open public data source that can provide organisations with a lot of insights if combined with other sources

X

• XML Databases – XML Databases allow data to be stored in XML format. XML databases are often linked to document-oriented databases. The data stored in an XML database can be queried, exported and serialized into any format needed.

Y

• Yottabytes – approximately 1000 Zettabytes, or 250 trillion DVD’s. The entire digital universe today is 1 Yottabyte and this will double every 18 months.

Z

• Zettabytes – approximately 1000 Exabytes or 1 billion terabytes. Expected is that in 2016 over 1 zettabyte will cross our networks globally on a daily basis.²