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OLAP teknolojileri ve iş zekasını araştırmada OLAP teknolojilerinin kullanımı

Başlık çevirisi mevcut değil.

  1. Tez No: 104199
  2. Yazar: ÖZGÜL ASLAN
  3. Danışmanlar: DR. HALİL HALEFŞAN SÜMEN
  4. Tez Türü: Yüksek Lisans
  5. Konular: İşletme, Business Administration
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2001
  8. Dil: Türkçe
  9. Üniversite: İstanbul Teknik Üniversitesi
  10. Enstitü: Fen Bilimleri Enstitüsü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 133

Özet

OLAP TEKNOLOJİLERİ ve İŞ ZEKASINI ARTTIRMADA OLAP TEKNOLOJİLERİNİN KULLANIMI ÖZET OLAP yazılım teknolojisinin bir kategorisidir. Kullanıcıların anlayabileceği şekilde gerçek kurumsal boyutluluğu etkileyen ham veriden dönüştürülerek elde edilen stratejik bilginin çok boyutlu görüntülerine hızlı, tutarlı ve interaktif erişim sağlayarak analistlere, yöneticilere yeni kavrayışlar kazandırmaktadır. OLAP işlevselliği konsolide edilmiş kurumsal verinin çok boyutlu analizi ile karakterize edilir. OLAP araçlarının odak noktası bilginin temelini oluşturan çok boyutlu analizdir. Bu amacı başarmak için OLAP araçları veri depolama ve veri sunumu için çok boyutlu modeller kullanır. Veri çok boyutlu bir uzayda birkaç boyut içeren küpler içersinde düzenlenir. Tipik OLAP işlemleri özetler-toplamlar, detay açma (drill down), küpün belli bir parçasını seçme (slice and dice) ve ekranda verinin çok boyutlu görüntüsünü döndürmeyi içerir. OLAP ve veri ambarı birbirinin tamamlayıcısıdır. Veri ambarı ilişkisel teknolojiye dayanmakta olup veriyi depolar ve yönetir. OLAP veri ambarmdaki veriyi stratejik bilgiye dönüştürür. OLAP uygulamaları çok farklı organizasyonel fonksiyonları kapsar. Finans departmanı bütçeleme, faaliyet tabanlı maliyetlendirme, finansal performans analizi ve finansal modelleme için satış departmanı satış analizi ve tahminleme için pazarlama departmanı pazar araştırma analizi, satış tahminleme, promosyon analizi, pazar/müşteri segmentasyonu, fiyatlandırma, karlılık analizinde (ürüne, müşteriye, pazara, bölgeye, sektöre vs göre); üretim planlama departmanı ise üretim planlama, hata ve kalite analizi uygulamalarında kullanmaktadur. Bu uygulamaların hepsi yöneticilere organizasyonun stratejik yönetimi hakkında etkili karar vermeyi sağlayacak bilgiyi sunar. Başarılı OLAP uygulamalarının anahtar göstergesi gerekli olan bilgiyi sağlama yeteneğidir. OLAP etkili karar vermek için tam zamanında bilgi sağlama yeteneğine sahiptir. Başarılı OLAP uygulamaları yöneticilerin, geliştiricilerin ve tüm işletmenin iş verimliliğini arttırır. Kullanıcılar, yöneticiler raporlama, analiz, modelleme gereksinimlerini İT departmanına bağlı olmadan kendileri karşılayabilirler. Yöneticiler etkili karar vermek için stratejik bilgiye OLAP sistemleri kullanarak tam zamanında erişirler. Gerçek iş problemlerini modelleme yeteneği, insan kaynağım verimli kullanmayı, işletmenin, pazar taleplerine çok hızlı yanıt vermesini sağlar. IXOLAP TECHNOLOGY and USE of OLAP TECHNOLOGY to IMPROVE BUSINESS INTELLIGENCE SUMMARY Online Analytical Processing (OLAP) is a category of software technology that enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user. OLAP functionality is characterized by dynamic multidimensional analysis of consolidated enterprise data supporting end user analytical and navigational activities including: calculations and modeling applied across dimensions, through hierarchies and/or across members, trend analysis over sequential time periods, slicing subsets for on-screen viewing, drill-down to deeper levels of consolidation, reach-through to underlying detail data, rotation to new dimensional comparisons in the viewing area. The focus of OLAP tools is to provide multidimensional analysis to the underlying information. To achieve this goal, this tools employ multidimensional models for the storage and presentation of data. Data is organized in cubes which are defined over a multidimensional space, consisting of several dimensions. Each dimension r comprises of a set of aggregation levels. Typical OLAP operations include the aggregation or de-aggregation of information (roll up and drill-down) along a dimension, the selection of specific parts of a cube and the reorientation of the multidimensional view of the data on the screen (pivoting). OLAP and Data Warehouses are complementary. A Data Warehouse stores and manages data. OLAP transforms Data Warehouse data into strategic information. OLAP ranges from basic navigation and browsing (often known as“slice and dice”), to calculations, to more serious analyses such as time series and complex modeling. As decision-makers exercise more advanced OLAP capabilities, they move from data access to information to knowledge. OLAP applications span a variety of organizational functions. Finance departments use OLAP for applications such as budgeting, activity-based costing (allocations), financial performance analysis, and financial modeling. Sales analysis and forecasting are two of the OLAP applications found in sales departments. Among other applications, marketing departments use OLAP for market research analysis, sales forecasting, promotions analysis, customer analysis, and market/customer segmentation. Typical manufacturing OLAP applications include production planning and defect analysis.

Özet (Çeviri)

OLAP TECHNOLOGY and USE of OLAP TECHNOLOGY to IMPROVE BUSINESS INTELLIGENCE SUMMARY Online Analytical Processing (OLAP) is a category of software technology that enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user. OLAP functionality is characterized by dynamic multidimensional analysis of consolidated enterprise data supporting end user analytical and navigational activities including: calculations and modeling applied across dimensions, through hierarchies and/or across members, trend analysis over sequential time periods, slicing subsets for on-screen viewing, drill-down to deeper levels of consolidation, reach-through to underlying detail data, rotation to new dimensional comparisons in the viewing area. The focus of OLAP tools is to provide multidimensional analysis to the underlying information. To achieve this goal, this tools employ multidimensional models for the storage and presentation of data. Data is organized in cubes which are defined over a multidimensional space, consisting of several dimensions. Each dimension r comprises of a set of aggregation levels. Typical OLAP operations include the aggregation or de-aggregation of information (roll up and drill-down) along a dimension, the selection of specific parts of a cube and the reorientation of the multidimensional view of the data on the screen (pivoting). OLAP and Data Warehouses are complementary. A Data Warehouse stores and manages data. OLAP transforms Data Warehouse data into strategic information. OLAP ranges from basic navigation and browsing (often known as“slice and dice”), to calculations, to more serious analyses such as time series and complex modeling. As decision-makers exercise more advanced OLAP capabilities, they move from data access to information to knowledge. OLAP applications span a variety of organizational functions. Finance departments use OLAP for applications such as budgeting, activity-based costing (allocations), financial performance analysis, and financial modeling. Sales analysis and forecasting are two of the OLAP applications found in sales departments. Among other applications, marketing departments use OLAP for market research analysis, sales forecasting, promotions analysis, customer analysis, and market/customer segmentation. Typical manufacturing OLAP applications include production planning and defect analysis.Important to all of the above applications is the ability to provide managers with the information they need to make effective decisions about an organization's strategic directions. The key indicator of a successful OLAP application is its ability to provide information as needed, i.e., its ability to provide“just-in-time”information for effective decision-making. This requires more than a base level of detailed data. Successful OLAP applications increase the productivity of business managers, developers, and whole organizations. The inherent flexibility of OLAP systems means business users of OLAP applications can become more self-sufficient. Managers are no longer dependent on IT to make schema changes, to create joins, or worse. Perhaps more importantly, OLAP enables managers to model problems that would be impossible using less flexible systems with lengthy and inconsistent response times. More control and timely access to strategic information equal more effective decision-making. IT developers also benefit from using the right OLAP software. Although it is possible to build an OLAP system using software designed for transaction r processing or data collection, it is certainly not a very efficient use of developer time. By using software specifically designed for OLAP, developers can deliver applications to business users faster, providing better service. Faster delivery of applications also reduces the applications backlog. OLAP reduces the applications backlog still further by making business users self- sufficient enough to build their own models. However, unlike standalone departmental applications running on PC networks, OLAP applications are dependent on Data Warehouses and transaction processing systems to refresh their source level data. As a result, IT gains more self-sufficient users without relinquishing control over the integrity of the data. IT also realizes more efficient operations through OLAP. By using software designed for OLAP, IT reduces the query drag and network traffic on transaction systems or the Data Warehouse. Lastly, by providing the ability to model real business problems and a more efficient use of people resources, OLAP enables the organization as a whole to respond more quickly to market demands. Market responsiveness, in turn, often yields improved revenue and profitability. XI

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