Active Data Warehouse. A combination of products, features, services, and business partnerships that support the Active Enterprise Intelligence business strategy. This term was coined by Teradata in 2001.
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An algorithm is mathematical “logic” or a set of rules used to make calculations. Starting with an initial input (which may be zero or null), the logic or rules are coded or written into software as a set of steps to be followed in conducting calculations, processing data or performing other functions, eventually leading to an output.
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An analytics platform is a full-featured technology solution designed to address the needs of large enterprises. Typically, it joins different “tools and analytics systems together with an engine to execute, a database or repository to store and manage the data, data mining processes, and techniques and mechanisms for obtaining and preparing data that is not stored. This solution can be conveyed as a software-only application or as a cloud-based software as a service (SaaS) provided to organizations in need of contextual information that all their data points to, in other words, analytical information based on current data records.
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Apache Hive is an open-source data warehouse infrastructure that provides tools for data summarization, query and analysis. It is specifically designed to support the analysis of large datasets stored in Hadoop files and compatible file systems, such as Amazon S3. Hive was initially developed by data engineers at Facebook in 2008, but is now used by many other companies.
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Behavioral Analytics is a subset of business analytics that focuses on understanding what consumers and applications do, as well as how and why they act in certain ways. It is particularly prevalent in the realm of eCommerce and online retailing, online gaming and Web applications.
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Big data is an all-encompassing term for any collection of data sets so large or complex that it becomes difficult to process them using traditional data-processing applications.
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Big data analytics refers to the strategy of analyzing large volumes of data … gathered from a wide variety of sources, including social networks, videos, digital images, sensors and sales transaction records. The aim in analyzing all this data is to uncover patterns and connections that might otherwise be invisible, and that might provide valuable insights about the users who created it. Through this insight, businesses may be able to gain an edge over their rivals and make superior business decisions.
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Business intelligence (BI) is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.
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Cascading is a platform for developing Big Data applications on Hadoop. It offers a computation engine, systems integration framework, data processing and scheduling capabilities. One important benefit of cascading is that it offers development teams portability so they can move existing applications without incurring the cost to rewrite them. Cascading applications run on and can be ported between different platforms, including MapReduce, Apache Tez and Apache Flink.
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Customer data platforms (CDPs), are marketer-managed systems that create a persistent, unified customer database, complementing existing software and marketing efforts while enabling an organization to build better customer relationships. CDPs have both champions and challengers; read on to learn more about the specifics of a customer data platform.
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Cloud computing refers to the practice of using a network of remote servers to store, manage and process data (rather than an on-premise server or a personal computer) with access to such data provided through the Internet (the cloud). Programs, applications and other services may also be hosted in the cloud, which frees companies from the task and expense of building and maintaining data centers and other infrastructure.
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Cluster analysis or clustering is a statistical classification technique or activity that involves grouping a set of objects or data so that those in the same group (called a cluster) are similar to each other, but different from those in other clusters. It is essential to data mining and discovery, and is often used in the context of machine learning, pattern recognition, image analysis and in bioinformatics and other sectors that analyze large data sets.
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Comparative analysis refers to the comparison of two or more processes, documents, data sets or other objects. Pattern analysis, filtering and decision-tree analytics are forms of comparative analysis. In healthcare, comparative analysis is used to compare large volumes of medical records, documents, images, sensor data and other information to assess the effectiveness of medical diagnoses.
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Connection analytics is an emerging discipline that helps to discover interrelated connections and influences between people, products, processes machines and systems within a network by mapping those connections and continuously monitoring interactions between them. It has been used to address difficult and persistent business questions relating to, for instance, the influence of thought leaders, the impact of external events or players on financial risk, and the causal relationships between nodes in assessing network performance.
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Concurrency or concurrent computing refers to the form of computing in which multiple computing tasks occur simultaneously or at overlapping times. These tasks can be handled by individual computers, specific applications or across networks. Concurrent computing is often used in Big Data environments to handles very large data sets. For it to work efficiently and effectively, careful coordination is necessary between systems and across Big Data architectures relative to scheduling tasks, exchanging data and allocating memory.
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Correlation analysis refers to the application of statistical analysis and other mathematical techniques to evaluate or measure the relationships between variables. It can be used to define the most likely set of factors that will lead to a specific outcome – like a customer responding to an offer or the performance of financial markets.
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The main tasks of data analysts are to collect, manipulate and analyze data, as well as to prepare reports, which may be include graphs, charts, dashboards and other visualizations. Data analysts also generally serve as guardians or gatekeepers of an organization's data, ensuring that information assets are consistent, complete and current. Many data analysts and business analysts are known for having considerable technical knowledge and strong industry expertise.
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Data analytics, also known as advanced analytics or big data analytics, is an autonomous or semi-autonomous inspection of data or content using sophisticated techniques and tools beyond those of traditional business intelligence (BI), to uncover deeper insights, make predictions, or produce recommendations. Techniques include data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks.
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Teradata Unified Data Architecture is the first comprehensive big data architecture. This framework harnesses relational and non-relational repositories via SQL and non-SQL analytics.
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Data cleansing, or data scrubbing, is the process of detecting and correcting or removing inaccurate data or records from a database.
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Data gravity appears when the amount of data volume in a repository grows and the number of uses also grows. At some point, the ability to copy or migrate data becomes onerous and expensive.
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A data lake and a data warehouse are both design patterns, but they are opposites. Data warehouses structure and package data for the sake of quality, consistency, reuse, and performance with high concurrency. Data lakes complement warehouses with a design pattern that focuses on original raw data fidelity and long-term storage at a low cost while providing a new form of analytical agility.
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Data latency is the ability to load and update data in near real-time while simultaneously supporting query workloads
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The data mart is a subject-oriented slice of the data warehouse logical model serving a narrow group of users. Many data marts only need a subset of data from the full tables in the data warehouse. For example, a mart may only have sales transactions, products, and inventory records. Most data marts only have 5-20 tables instead of 4,000.
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Data mining is the process of analyzing hidden patterns of data according to different perspectives for categorization into useful information, which is collected and assembled in common areas, such as data warehouses
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Data models that are tailored to specific industries or business functions can provide a strong foundation or “jump-start” for big data programs and investments.
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Considered the most basic type of analytics, descriptive analytics involves the breaking down of big data into smaller chunks of usable information so that companies can understand what happened with a specific operation, process or set of transactions.
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In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis.
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Ability to efficiently store and process petabytes of data stored natively and in object storage
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The data warehouse concept started in 1988 when Barry Devlin and Paul Murphy published their groundbreaking paper in the IBM Systems Journal. Their vision sparked a need for more specific definitions of database implementations, which Bill Inmon and Ralph Kimball provided in the early 1990s – and Gartner further clarified definitions in 2005. Now any discussion on the topic also includes how or where one is implemented, such as within the cloud, or spanning on-premises and cloud in a hybrid manner.
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Deep learning, also known as deep neural learning or deep neural network, is an artificial intelligence (AI) function that mimics how the human brain works to process data and create patterns that facilitate decision making. A subset of machine learning in artificial intelligence, deep learning has networks capable of learning unsupervised from unstructured or unlabeled data.
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Extract, Transform and Load (ETL) refers to the process in data warehousing that concurrently reads (or extracts) data from source systems; converts (or transforms) the data into the proper format for querying and analysis; and loads it into a data warehouse, operational data store or data mart).
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An extraordinarily large unit of digital data, one Exabyte (EB) is equal to 1,000 Petabytes or one billion gigabytes (GB). Some technologists have estimated that all the words ever spoken by mankind would be equal to five Exabytes.
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Finance analytics, also known as financial analytics, provides differing perspectives on the financial data of a given business, giving insights that can facilitate strategic decisions and actions that improve the overall performance of the business. Related to business intelligence and enterprise performance management, finance analytics impacts virtually all aspects of a business, playing a critical role in calculating profit, answering questions about a business, and enabling future business forecasting.
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Hadoop is a distributed data management platform or open-source software framework for storing and processing big data. It is sometimes described as a cut-down distributed operating system.
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Hybrid cloud, also known as hybrid cloud architecture, is the combination of on-premises and cloud deployment – whether public cloud, private cloud, or multi-cloud. Whether an organization’s resources include on-premises, private, public, or managed cloud, a hybrid cloud ecosystem can deliver the best of all worlds: on-prem when needed and cloud when needed.
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The Internet of Things, also known as IoT, is a concept that describes the connection of everyday physical objects and products to the Internet so that they are recognizable by (through unique identifiers) and can relate to other devices. The term is closely identified with machine-to-machine communications and the development of, for example, “smart grids” for utilities, remote monitoring and other innovations. Gartner estimates 26 billion devices will be connected by 2020, including cars, coffee makers.
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Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. It focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.
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Master Data Management (MDM) provides a unified view of data across multiple systems to meet the analytic needs of a global business. MDM creates singular views of master and reference data, whether it describes customers, products, suppliers, locations, or any other important attribute.
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Metadata is data that describes other data. Metadata summarizes basic information about data, which can make finding and working with particular instances of data easier. For example, author, date created and date modified and file size are very basic document metadata.
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A mixed workload is an ability to support multiple applications with different SLAs in a single environment
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MongoDB is a cross-platform, open-source database that uses a document-oriented data model, rather than a traditional table-based relational database structure. This type of database structure is designed to make the integration of structured and unstructured data in certain types of applications easier and faster.
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A branch of artificial intelligence, natural language processing (NLP) deals with making human language (in both written and spoken forms) comprehensible to computers.
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Pattern recognition occurs when an algorithm locates recurrences or regularities within large data sets or across disparate data sets. It is closely linked and even considered synonymous with machine learning and data mining.
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An extremely large unit of digital data, one Petabyte is equal to 1,000 Terabytes. Some estimates hold that a Petabyte is the equivalent of 20 million tall filing cabinets or 500 billion pages of standard printed text.
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Predictive analytics refers to the analysis of big data to make predictions and determine the likelihood of future outcomes, trends or events. In business, it can be used to model various scenarios for how customers react to new product offerings or promotions and how the supply chain might be affected by extreme weather patterns or demand spikes.
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A type or extension of predictive analytics, prescriptive analytics is used to recommend or prescribe specific actions when certain information states are reached or conditions are met.
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Python is an interpreted, object-oriented, high-level programming language with dynamic semantics developed by Guido van Rossum. It was originally released in 1991. Designed to be easy as well as fun, the name “Python” is a nod to the British comedy group Monty Python. Python has a reputation as a beginner-friendly language, replacing Java as the most widely used introductory language because it handles much of the complexity for the user, allowing beginners to focus on fully grasping programming concepts rather than minute details.
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R is an open-source programming language for statistical analysis. It includes a command line interface and several graphical interfaces. Popular algorithm types include linear and nonlinear modeling, time-series analysis, classification and clustering.
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Retail analytics is the analysis of data generated by retail operations with a goal of making business decisions that drive—or hinder—profitability. The use of retail analytics developed as a response to the retail transformation being driven by unprecedented changes in consumer behavior, intensified pressure on margins, the changing role of stores, and intensified competition for both on and offline channels.
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Risk management, sometimes referred to as risk mitigation, is the process of calculating the maximum acceptable level of overall risk to and from an activity, then using risk assessment techniques to pinpoint the initial level of risk and, if found to be excessive, developing a strategy to mitigate specific individual risks until the collective risk level is pared down to an acceptable level.
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RTIM, also known as Real Time Interaction Manager or Management, uses real-time customer interactions, predictive modeling, and machine learning to deliver consistent, personalized customer experiences across channels. Many users view RTIM as the fast lane to business value because it gives marketers immediate visibility into critical moments throughout the shopping experience. Marketing teams are increasingly relying on predictive analytics, AI, and real time decisioning to maximize customer satisfaction and engagement, personalize offers, and align shopper behavior with business objectives. They are collaborating with CIO organizations to integrate data, refine processes, exploit the full range of analytics approaches, and even reshape entire business models to enhance customer experience.
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Semi-structured data refers to data that is not captured or formatted in conventional ways, such as those associated with a traditional database fields or common data models. It is also not raw or totally unstructured and may contain some data tables, tags or other structural elements.
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Sentiment analysis involves the capture and tracking of opinions, emotions or feelings expressed by consumers in various types of interactions or documents, including social media, calls to customer service representatives, surveys and the like.
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Structured data refers to data sets with strong and consistent organization. Structured data is organized into rows and columns with known and predictable contents. Each column contains a specific data type, such as dates, text, money or percentages.
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A relatively large unit of digital data, one Terabyte (TB) equals 1,000 Gigabytes. It has been estimated that 10 Terabytes could hold the entire printed collection of the U.S. Library of Congress, while a single TB could hold 1,000 copies of the Encyclopedia Brittanica.
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Unstructured data refers to unfiltered information with no fixed organizing principle. It is often called raw data. Common examples are web logs, XML, JSON, text documents, images, video, and audio files.
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Big data – and the business challenges and opportunities associated with it – are often discussed or described in the context of multiple V’s:
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