Practic, avem de-a face cu coborârea din sferele abstracte a BI-ului tradiţional către „enterprise intelligence“; o formă de „democratizare“ a BI-ului a devenit accesibilă maselor de utilizatori finali, pe baza ideii că instrumentele specifice acestui concept (analiză, raportare, semnalare etc.) trebuie să permită şi să ofere suport pentru luarea deciziilor în timp real.
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Potrivit specialiştilor, „mutaţia“ menţionată reprezintă o evoluţie naturală, realizată sub presiunea pieţei, care impune luarea tot mai rapidă a unor decizii din ce în ce mai complexe la nivelul managementului operaţional în mod cotidian. Este vorba, practic, de o reorientare a conceptului de BI, de la tradiţionalul „data-centric“ spre mai pragmaticul „process-centric“, menit să permită un răspuns mai agil la provocările din piaţă.
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Astfel, aplicaţiile de BI operaţional nu mai sunt rezervate doar analiştilor de business din top management, ci sunt accesibile şi directorilor executivi, managerilor şi utilizatorilor finali cu putere decizională. Prin intermediul acestui nou concept, managerii departamentelor de vânzări şi staff-urile din centrele de suport beneficaiză de informaţii relaţionate cu lista de activităţi zilnice şi de workflow-uri şi ghiduri de analiză, care îi ajută să interpreteze şi să analizeze informaţiile pe baza cărora trebuie să ia deciziile.
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BI Brief - Four Legs of a Successful Business Intelligence (BI) Project Team - 0 views
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1. Project Sponsorship and Governance 2. Project Management 3. Development Team (Core Team) 4. Extended Project Team
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1. Project Sponsorship and Governance IT and the business should form a BI steering committee to sponsor and govern design, development, deployment, and ongoing support. It needs both the CIO and a business executive, such as CFO, COO, or a senior VP of marketing/sales to commit budget, time, and resources. The business sponsor needs the project to succeed. The CIO is committed to what is being built and how.
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2. Project Management Project management includes managing daily tasks, reporting status, and communicating to the extended project team, steering committee, and affected business users. The project management team needs extensive business knowledge, BI expertise, DW architecture background, and people management, project management, and communications skills. The project management team includes three functions or members: Project development manager - Responsible for deliverables, managing team resources, monitoring tasks, reporting status, and communications. Requires a hands-on IT manager with a background in iterative development. Must understand the changes caused by this approach and the impact on the business, project resources, schedule and the trade-offs. Business advisor - Works within the sponsoring business organization. Responsible for the deliverables of the business resources on the project's extended team. Serves as the business advocate on the project team and the project advocate within the business community. Often, the business advocate is a project co-manager who defers to the IT project manager the daily IT tasks but oversees the budget and business deliverables. BI/DW project advisor - Has enough expertise with architectures and technologies to guides the project team on their use. Ensures that architecture, data models, databases, ETL code, and BI tools are all being used effectively and conform to best practices and standards.
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Hadoop is a framework written in Java for running applications on large clusters of commodity hardware and incorporates features similar to those of the Google File System (GFS) and of the Map Reduce computing paradigm. Hadoop’s HDFS is a highly fault-tolerant distributed file system and, like Hadoop in general, designed to be deployed on low-cost hardware. It provides high throughput access to application data and is suitable for applications that have large data sets.
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Some of the Hadoop projects we will talk about are: HDFS : A distributed filesystem that runs on large clusters of commodity machines. Map Reduce: A distributed data processing model and execution environment that runs on large clusters of commodity machines. Pig: A data flow language and execution environment for exploring very large datasets. Pig runs on HDFS and MapReduce clusters. HBase: A distributed, column-oriented database. HBase uses HDFS for its underlying storage, and supports both batch-style computations using MapReduce and point queries (random reads). ZooKeeper: A distributed, highly available coordination service. ZooKeeper provides primitives such as distributed locks that can be used for building distributed applications. Oozie: Oozie is a workflow scheduler system to manage Apache Hadoop jobs.
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Oracle Linux as the operating system and Hadoop 1.1.2 or 1.2.0
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