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data warehouse model

Results 1 - 8 of 8Sort Results By: Published Date | Title | Company Name
Published By: Group M_IBM Q1'18     Published Date: Jan 23, 2018
In this paper, you'll learn how organizations are adopting increasingly sophisticated analytics methods, that analytics usage trends are placing new demands on rigid data warehouses, and what's needed is hybrid data warehouse architecture that supports all deployment models.
Tags : 
data warehouse, analytics, hybrid data warehouse, development model
    
Group M_IBM Q1'18
Published By: IBM     Published Date: Dec 12, 2006
This white paper outlines the components of the Banking Data Warehouse (BDW) and how they assist financial institutions in addressing the data modeling and data consolidation issues relating to the SOX regulations.
Tags : 
data warehousing, database warehousing, data consolidation, sarbanes oxley compliance, sox, compliance, banking, bdw, banking data warehouse, sarbanes-oxley act, security, ibm, data management, auditing, high availability, software compliance
    
IBM
Published By: IBM     Published Date: Dec 12, 2006
This white paper will outline the components of the Banking Data Warehouse (BDW) and how they assist financial institutions to address the data modeling and data consolidation issues relating to the Basel II Capital Accord. 
Tags : 
data warehousing, bdw, banking data warehousing, compliance, risk management, basel ii, ibm, security, it management, knowledge management, data management, auditing, encryption, policy based management, security management, security policies, software compliance, sarbanes oxley compliance
    
IBM
Published By: IBM     Published Date: Dec 30, 2008
Most long-standing data warehouses are designed to support a relatively small number of users who access information to support strategic decisions, financial planning and the production of standard reports that track performance. Today, many more users need to access information in context and on demand so that critical functions are optimized to run efficiently. Learn how to create a roadmap for a truly dynamic warehousing infrastructure, and move ahead of your competition with your business intelligence system
Tags : 
warehousing infrastructure, ibm, business intelligence, data warehouse, dynamic warehousing, data warehouse model, master data, data management
    
IBM
Published By: IBM     Published Date: Feb 02, 2009
A comprehensive solution for leveraging data in today's financial industry. Most organizations realize that the key to success lies in how well they manage data—and the banking industry is no exception. From customer statistics to strategic plans to employee communications, financial institutions are constantly juggling endless types of information.
Tags : 
ibm, information management software, leveraging data, dynamic warehousing, data management, improve customer service, real-time risk analysis, analytics capabilities, information on demand framework, ibm db2 warehouse, ibm master data management server, ibm omnifind, ibm industry data models, ibm balanced warehouses, oltp-based transactional data, enterprise applications, compliance
    
IBM
Published By: IBM     Published Date: Nov 08, 2017
In this paper, you'll learn how organizations are adopting increasingly sophisticated analytics methods, that analytics usage trends are placing new demands on rigid data warehouses, and what's needed is hybrid data warehouse architecture that supports all deployment models.
Tags : 
data warehouse, analytics, ibm, deployment models
    
IBM
Published By: Oracle Corporation     Published Date: May 11, 2012
By using the Oracle Exadata Database Machine as your data warehouse platform you have a balanced, high performance hardware configuration. This paper focuses on the other two corner stones, data modeling and data loading.
Tags : 
oracle, data warehousing, database, exadata, database machine, infrastructure, operation, operation costs, mobile, growth, payback, architecture, demands, enterprise applications, data management, design and facilities
    
Oracle Corporation
Published By: Oracle EMEA     Published Date: Apr 15, 2019
Oracle Autonomous Data Warehouse Cloud is more than just a new way to store and analyze data; it’s a whole new approach to getting more value from your data. Market leaders in every industry depend on analytics to reach new customers, streamline business processes, and gain a competitive edge. Data warehouses remain at the heart of these business intelligence (BI) initiatives, but traditional data-warehouse projects are complex undertakings that take months or even years to deliver results. Relying on a cloud provider accelerates the process of provisioning data-warehouse infrastructure, but in most cases database administrators (DBAs) still have to install and manage the database platform, then work with the line-of-business leaders to build the data model and analytics. Once the warehouse is deployed—either on premises or in the cloud—they face an endless cycle of tuning, securing, scaling, and maintaining these analytic assets. Oracle has a better way. Download this whitepaper to f
Tags : 
    
Oracle EMEA
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