Below, you will find definitions to some of the terms that we come across frequently and have a meaningful tie-in to the Business Intelligence and Data Integration space.
Agile BI/Data Warehousing ? A blend of Agile project management principles practiced within the BI/Data Warehousing space. The approach addresses a need to increase program agility by reducing the time intervals necessary to deliver value to the business.
Analytic Database ? An analytic database is a database technology specifically designed to support business intelligence (BI) and analytic applications, typically as part of a data warehouse or data mart solution. Types of Analytic databases include: Columnar databases, data warehouse appliances, in-memory databases, massively parallel processing (MPP) databases, and online analytical processing (OLAP) databases
Analytics ? Discovery and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics rely on the simultaneous application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight.
Big Data ? A collection of data sets so large and complex that it becomes awkward to work with using on-hand database management tools. Big data usually includes data sets with sizes beyond the ability of commonly-used software tools to capture, manage, and process within a tolerable elapsed time. Big data sizes range from a few dozen terabytes to many petabytes of data in a single data set. With this difficulty, a new platform of ?big data? tools has arisen such as the Apache Hadoop Big Data Platform.
BI ? Business Intelligence ? The ability for an organization to take all its capabilities and convert them into knowledge. Computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments or associated costs and incomes. BI evolved from the decision support systems which began in the 1960s and developed throughout the mid-1980s. In 1989, Howard Dresner (later a Gartner Group analyst) proposed ?business intelligence? as an umbrella term to describe ?concepts and methods to improve business decision making by using fact-based support systems.? It was not until the late 1990s that this usage was widespread.
BI In the Cloud ? Business intelligence software or solutions architected for multitenant operation on a public or private cloud. Typically, such solutions are licensed on a subscription basis. Some of the goals or perceived benefits include lower initial investment, scalability, reduced IT footprint, and reliable recurring expense.
BPM ? Business performance management ? a set of management and analytic processes that enable the management of an organization?s performance to achieve one or more pre-selected goals. Synonyms for BPM include ?corporate performance management (CPM)? and ?enterprise performance management (EPM)?. BPM includes three main activities: selection of goals, identification of metrics for the goals, actions to be taken in response to metrics.
Columnar database ? A database management system (DBMS) that stores data tables as sections of columns of data rather than as rows of data, like most relational DBMSs. This has advantages for data warehouses, customer relationship management (CRM) systems, and library card catalogs, and other ad-hoc inquiry systems where aggregates are computed over large numbers of similar data items. Examples of databases with columnar-oriented storage capabilities include: Aster Data Systems, Greenplum, Hive Intelligence, Infobright, Paraccel, SAP HANA, Teradata, and Vertica.
Data discovery ? The process of locating critical data throughout an enterprise. This includes developing an understanding of the nuances of the data such as key structures, data cleanliness, data history, source of record, data format, protocols to obtain data, etc.
Data architecture ? Models, policies, rules or standards that govern which data is collected, and how it is stored, arranged, integrated, and put to use. A Data Architecture often defines the target state of data and the planning needed to achieve the target state.
Data integration ? A key component of data warehousing and business intelligence, data integration (DI) focuses on combining data residing in different sources and providing users with a unified view of these data. DI is often achieved by either data virtualization or extract, transform & load (ETL) technologies.
Data mining ? A field at the intersection of computer science and statistics. It is the process that attempts to discover patterns in large data sets by harnessing methods of artificial intelligence, statistics, and database systems. The term is frequently misused to mean any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics). In the proper use of the word, the key term is discovery, commonly defined as ?detecting something new?.
Data modeling (dimensional) ? A set of techniques and concepts used in data warehouse design. It is considered to be different from entity-relationship modeling (ER).
Data Virtualization ? Process of abstracting disparate systems (databases, applications, file repositories, websites, data services vendors, etc.) through a single data access layer (which may be any of several data access mechanisms). Companies like Composite, Informatica, Oracle, and IBM have solutions in the data virtualization space.
Data visualization ? The study of the visual representation of data, meaning ?information that has been abstracted in some schematic form, including attributes or variables for the units of information?. There are a wide variety of technologies employed to achieve data visualization such as: Tableau, SAS, R, Google Fusion Tables, Many Eyes, Color Brewer, etc.
Data warehouse appliance ? An integrated set of servers, storage, operating system(s), DBMS and software specifically pre-installed and pre-optimized for data warehousing (DW). Most DW appliance vendors use massively parallel processing (MPP) architectures to provide high query performance and platform scalability. Technology vendors include Netezza, DATAllegro, Greenplum, HP Neoview, and XtremeData.
ETL ? Extract transform & load ? A process in database usage and especially in data warehousing that involves: Extracting data from outside sources(E), Transforming it to fit operational needs (which can include quality levels) (T), Loading it into the end target (database, more specifically, operational data store, data mart or data warehouse) (L)
Hadoop ? An open-source software framework that supports data-intensive distributed applications. It enables applications to work with thousands of computational independent computers and petabytes of data. Hadoop was derived from Google?s MapReduce and Google File System (GFS) papers. Many organizations are using Hadoop to run large distributed computations such as Facebook, Yahoo, Amazon.com, LinkedIn, Twitter, eBay, Apple, Netflix, etc.
In-memory analytics ? An alternative to in-database or disk-based business intelligence, in-memory analytics increases speed, performance, and reliability when querying data. Queries and data reside in the server?s random access memory (RAM). The technology has grown along with the adoption of 64-bit architectures, which can support larger amounts of RAM, and consequently larger data sets. Technology vendors include Tableau, SAP HANA, IBM TM1, Oracle TimesTen, Spotfire, QlikView,
Lean BI ? The application of Lean manufacturing principles to the BI/DI/Data Warehouse space. A set of principles that can be used to become more efficient and effective while still focused on delivering value within a BI organization. The principles focus on generating additional value by accomplishing more with existing resources and eliminating waste. Waste in BI programs is defined as any activity, task, process, mapping, object, code, report or data that absorbs resources but creates no incremental value to the customer.
Mobile BI/Analytics ? A set of technologies and processes that focuses on the distribution of business data to mobile devices such as smartphones and tablet computers. Recent prevalence of Mobile BI has been encouraged by a change from the ?wired world? to a wireless world with the advantage of smartphones which has led to a new era of mobile computing, especially in the field of BI.
MPP ? Massively parallel processing ? The use of a large number of processors (or separate computers) to perform a set of coordinated computations in parallel.
OLAP ? Online analytic processing ? An approach to swiftly answer multi-dimensional analytical (MDA) queries. OLAP tools enable users to interactively analyze multidimensional data from multiple perspectives. OLAP consists of three basic analytical operations: consolidation (roll-up), drill-down, and slicing and dicing.
Predictive Analytics ? A variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events. Notable predictive analytic vendors include: SAS, Information Builders, SPSS, Spotfire, and Pervasive
Prescriptive analytics ? Synthesizes big data, mathematical sciences, business rules, and machine learning to make predictions and then suggests decision options to take advantage of the predictions. Prescriptive analytics is the third phase of business analytics (BA) which includes descriptive, predictive and prescriptive analytics.
Text analytics / Text Mining ? In short, the process of deriving high-quality information from text. Typically employed as a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence.
Source: http://datasourceconsulting.com/bidi-industry-terms/
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