Business Intelligence Home > MicroStrategy Business Intelligence Business Intelligence (BI) ułatwia pracownikom przedsiębiorstw podejmowanie lepszych decyzji dzięki szybkiemu dostarczaniu odpowiedzi na pytania biznesowe w oparciu o dane. System BI analizuje dane przechowywane w hurtowniach danych, operacyjnych bazach danych lub systemach ERP (np. SAP®, Oracle, JD Edwards, Peoplesoft) oraz prezentuje te dane za pomocą atrakcyjnych i łatwych do zrozumienia pulpitów menadżerskich i raportów. BI dostarcza informacji potrzebnych do podejmowania strategicznych decyzji planistycznych, podnoszenia efektywności operacyjnej i optymalizowania procesów biznesowych. Atuty MicroStrategy MicroStrategy ułatwia podejmowanie codziennych decyzji przez użytkowników biznesowych, informatyków i osoby kierujące przedsiębiorstwem. Skalowalność klasy korporacyjnej i duża wydajność Przedsiębiorstwa przechowują coraz większą ilość danych, a takżee tworzą coraz więcej aplikacji, z których korzysta coraz więcej użytkowników.
Data integration Combining data from different sources and providing a unified view Data integration involves combining data residing in different sources and providing users with a unified view of them.[1] This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example) domains. Data integration appears with increasing frequency as the volume, complexity (that is, big data) and the need to share existing data explodes.[2] It has become the focus of extensive theoretical work, and numerous open problems remain unsolved. Data integration encourages collaboration between internal as well as external users. History[edit] The data warehouse approach is less feasible for data sets that are frequently updated, requiring the extract, transform, load (ETL) process to be continuously re-executed for synchronization. where .
Predictive analytics Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events.[1][2] In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.[3] Predictive analytics is used in actuarial science,[4] marketing,[5] financial services,[6] insurance, telecommunications,[7] retail,[8] travel,[9] healthcare,[10] pharmaceuticals[11] and other fields. One of the most well known applications is credit scoring,[1] which is used throughout financial services. Definition[edit] Types[edit] Predictive models[edit] Descriptive models[edit] Decision models[edit] Applications[edit] Collection analytics[edit]
What is Internet of Things (IoT)? - Definition from WhatIs.com The Internet of Things (IoT) is an environment in which objects, animals or people are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. IoT has evolved from the convergence of wireless technologies, micro-electromechanical systems (MEMS) and the Internet. The concept may also be referred to as the Internet of Everything. In this Insider guide, InfoSec pros will learn about the risks related to the IoT and what they can do to mitigate them. A thing, in the Internet of Things, can be a person with a heart monitor implant, a farm animal with a biochip transponder, an automobile that has built-in sensors to alert the driver when tire pressure is low -- or any other natural or man-made object that can be assigned an IP address and provided with the ability to transfer data over a network. IPv6’s huge increase in address space is an important factor in the development of the Internet of Things.
Business Intelligence | BI.PL Aliasy: BI O Business Intelligence (BI) mówi się ostatnio coraz więcej, w bardzo różnym kontekście. BI stało się modne i powstało wokół niego wiele szumu, a różne osoby używają tego terminu w odniesieniu do bardzo różnych aspektów. Artykuł ten syntetyzuje i systematyzuje pojęcia i wiedzę związaną z Business Intelligence i Hurtowniami Danych w oparciu o kompletną architekturę rozwiązań tego typu. Pojęcie Business Intelligence rozumiane jest na wiele sposobów i pokrywa szerokie spektrum zagadnień, takich jak: praktyki, metodyki, narzędzia, czy technologie informatyczne związane z analizą danych. Najczęściej kojarzone jest z tzw. Nie ma jednej, powszechnie uznanej definicji BI. Jedna z definicji mówi, że BI to zbiór praktyk, metodyk, narzędzi i technologii informatycznych, służących zbieraniu i integrowaniu danych w celu dostarczania informacji i wiedzy właściwym osobom, we właściwym miejscu oraz we właściwym czasie. W ramach systemów BI pojawiają się również dane lokalizacyjne.
Chief data officer Top information processor in a corporation A chief data officer (CDO) is a corporate officer responsible for enterprise-wide governance and utilization of information as an asset, via data processing, analysis, data mining, information trading and other means. CDOs usually report to the chief executive officer (CEO), although depending on the area of expertise this can vary. Recently, countries like Canada, Estonia, France, Spain[1] and the United States have established this position of Chief Data Officer. Role definition[edit] The chief data officer title shares its abbreviation with the chief digital officer, but the two are not the same job. History and evolution[edit] The role of manager for data processing was not elevated to that of senior management prior to the 1980s. More recently, with the adoption of data science the chief data officer is sometimes looked upon as the key strategy person either reporting to the chief strategy officer or serving the role of CSO in lieu of one.
Data architecture In information technology, data architecture is composed of models, policies, rules or standards that govern which data is collected, and how it is stored, arranged, integrated, and put to use in data systems and in organizations.[1] Data is usually one of several architecture domains that form the pillars of an enterprise architecture or solution architecture.[2] Overview[edit] A data architecture should[neutrality is disputed] set data standards for all its data systems as a vision or a model of the eventual interactions between those data systems. Data integration, for example, should be dependent upon data architecture standards since data integration requires data interactions between two or more data systems. Essential to realizing the target state, Data Architecture describes how data is processed, stored, and utilized in an information system. The "data" column of the Zachman Framework for enterprise architecture – Physical data architecture[edit] Constraints and influences[edit]
Semantic Web The Semantic Web is a collaborative movement led by international standards body the World Wide Web Consortium (W3C).[1] The standard promotes common data formats on the World Wide Web. By encouraging the inclusion of semantic content in web pages, the Semantic Web aims at converting the current web, dominated by unstructured and semi-structured documents into a "web of data". The Semantic Web stack builds on the W3C's Resource Description Framework (RDF).[2] According to the W3C, "The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries".[2] The term was coined by Tim Berners-Lee for a web of data that can be processed by machines.[3] While its critics have questioned its feasibility, proponents argue that applications in industry, biology and human sciences research have already proven the validity of the original concept. History[edit] Purpose[edit] Limitations of HTML[edit] Semantic Web solutions[edit]
Business intelligence Business Intelligence (analityka biznesowa) jest pojęciem o bardzo szerokim znaczeniu. Najbardziej ogólnie można przedstawić je jako proces przekształcania danych w informacje, a informacji w wiedzę, która może być wykorzystana do zwiększenia konkurencyjności przedsiębiorstwa. Efektywne eksploatowanie narzędzi BI jest mocno uzależnione od utworzenia hurtowni danych, która pozwala na ujednolicenie i powiązanie danych zgromadzonych z różnorodnych systemów informatycznych przedsiębiorstwa. Utworzenie hurtowni danych zwalnia systemy transakcyjne od tworzenia raportów i umożliwia równoczesne korzystanie z różnych systemów BI. Koncepcja jest następująca: system BI generuje standardowe raporty lub wylicza Kluczowe wskaźniki efektywności działania przedsiębiorstwa (Key Performance Indicators) na podstawie których stawia się hipotezy, po czym weryfikuje się je poprzez wykonywanie szczegółowych "przekrojów" danych. Do tego służą różnego rodzaju narzędzia analityczne (np.
Bioinformatics Computational analysis of large, complex sets of biological data Early bioinformatics—computational alignment of experimentally determined sequences of a class of related proteins; see § Sequence analysis for further information. Bioinformatics includes biological studies that use computer programming as part of their methodology, as well as specific analysis "pipelines" that are repeatedly used, particularly in the field of genomics. Common uses of bioinformatics include the identification of candidates genes and single nucleotide polymorphisms (SNPs). History[edit] Historically, the term bioinformatics did not mean what it means today. Sequences[edit] Sequences of genetic material are frequently used in bioinformatics and are easier to manage using computers than manually. These are sequences being compared in a MUSCLE multiple sequence alignment (MSA). Goals[edit] The primary goal of bioinformatics is to increase the understanding of biological processes. Relation to other fields[edit]
XSLT XSLT (Extensible Stylesheet Language Transformations) is a language for transforming XML documents into other XML documents,[1] or other objects such as HTML for web pages, plain text or into XSL Formatting Objects which can then be converted to PDF, PostScript and PNG.[2] The original document is not changed; rather, a new document is created based on the content of an existing one.[3] Typically, input documents are XML files, but anything from which the processor can build an XQuery and XPath Data Model can be used, for example relational database tables, or geographical information systems.[1] XSLT is a Turing-complete language, meaning it can specify any computation that can be performed by a computer.[4][5] History[edit] Design and processing model[edit] Diagram of the basic elements and process flow of Extensible Stylesheet Language Transformations. Processor implementations[edit] Performance[edit] Most early XSLT processors were interpreters. XSLT and XPath[edit] XSLT media types[edit] <?
untitled Business intelligence, hurtownie danych i proces ETL Master data management Practice for controlling corporate data Master data management (MDM) is a discipline in which business and information technology work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise's official shared master data assets.[1][2] Drivers for master data management [edit] Other problems include (for example) issues with the quality of data, consistent classification and identification of data, and data-reconciliation issues. There are a number of root causes for master data issues in organisations. Business unit and product line segmentationMergers and acquisitions Business unit and product line segmentation As a result of business unit and product line segmentation, the same business entity (such as Customer, Supplier, Product) will be serviced by different product lines; redundant data will be entered about the business entity in order to process the transaction. Mergers and acquisitions People, process and technology