Data warehousing
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This document discusses the importance of data warehousing for multinational corporations (MNCs). It notes that MNCs often struggle to find, understand, and use the data they need because data is scattered across networks in many different versions and formats. A data warehouse integrates data from various sources into a single consistent store that is easy for end users to access and understand. It allows historical data analysis and "what if" scenario planning to help answer business questions and make better decisions. The document outlines key components and architectures of data warehouses and how they can provide summarized data to different departments through data marts.
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मैं कौन हूँ व कहा से आया हूँ आइये जरा गंभीरता से विचार करे मैं कौन व कहाँ से आया हूँ और कहा जाना है ?मैं पवित्र आत्मा चौरासी लाख योनिओं में भटकते -भटकते अपने पुण्य कर्मो के उदय होने पर इस दुर्लभ मनुष्य योनी में आया और मुझे मोक्ष जाना हैं/
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Data warehousing
- 1. Data WarehousingData Warehousing and its Importanceand its Importance in MNCsin MNCs
- 2. A producer wants to knowA producer wants to know 2 Which are our lowest/highest margin customers ? Which are our lowest/highest margin customers ? Who are my customers and what products are they buying? Who are my customers and what products are they buying? Which customers are most likely to go to the competition ? Which customers are most likely to go to the competition ? What impact will new products/services have on revenue and margins? What impact will new products/services have on revenue and margins? What product prom- -otions have the biggest impact on revenue? What product prom- -otions have the biggest impact on revenue? What is the most effective distribution channel? What is the most effective distribution channel?
- 3. Data, Data everywhereData, Data everywhere • An MNC can’t find the data it needs o data is scattered over the network o many versions, subtle differences 3 MNC can’t get the data it needs need an expert to get the data MNC can’t understand the data found available data poorly documented MNC can’t use the data found results are unexpected data needs to be transformed from one form to other
- 4. What are the users saying...What are the users saying... • Data should be integrated across the enterprise • Summary data has a real value to the organization • Historical data holds the key to understanding data over time • What-if capabilities are required 4
- 5. What is a Data Warehouse?What is a Data Warehouse? A single, complete and consistent store of current and historic data, obtained from a variety of different sources, and made available to end users in a way they can understand and use in business context. 5
- 6. What is Data Warehousing?What is Data Warehousing? A process of transforming data into information and making it available to users in a timely enough manner to make a difference. Technique for assembling and managing data, (from various sources) for the purpose of answering business questions. Thus making decisions that were not previous possible 6 Data Information
- 7. Warehouses are very LargeWarehouses are very Large Data BasesData Bases • Terabytes -- 10^12 bytes: • Petabytes -- 10^15 bytes: • Exabytes -- 10^18 bytes: • Zettabytes -- 10^21 bytes: • Zottabytes -- 10^24 bytes: 7 Walmart -- 24 Terabytes Geographic Info. Systems National Medical Records Weather images Intelligence Agency Videos
- 8. Data Warehouse PropertiesData Warehouse Properties o Subject Oriented o Used to analyze business o Summarized and refined o Snapshot data o Integrated Data o Ad-hoc access o Knowledge User (Manager) o Thousands of Users 8
- 9. Components of the WarehouseComponents of the Warehouse • Data Extraction and Loading • The Warehouse • Analyze and Query -- OLAP Tools • Metadata 9
- 10. Data Warehouse ArchitectureData Warehouse Architecture 10 Data Warehouse Engine Optimized Loader Extraction Cleansing Analyze Query Metadata Repository Relational Databases Legacy Data Purchased Data ERP Systems
- 11. Data Warehouse and Data MartsData Warehouse and Data Marts 11 OLAP Data Mart Lightly summarized Departmentally structured Organizationally structured Atomic Detailed Data Warehouse Data
- 12. From the Data Warehouse toFrom the Data Warehouse to Data MartsData Marts 12 Departmentally Structured Individually Structured Data Warehouse Organizationally Structured Less More History Normalized Detailed Data Information
- 13. Application AreasApplication Areas 13 Industry Application Finance Credit Card Analysis Insurance Claims, Fraud Analysis Telecommunication Call record analysis Transport Logistics management Consumer goods promotion analysis Data Service providers Value added data Utilities Power usage analysis
- 14. Thank You!!!Thank You!!! 14 A Presentation by:A Presentation by: Abhitanjay Chaudhary (C23)Abhitanjay Chaudhary (C23) Owais Ashraf (C41)Owais Ashraf (C41) Anant Prakash Gupta (C24)Anant Prakash Gupta (C24) Rajat Sharma (C20)Rajat Sharma (C20)