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Software Functionality Revealed in Detail
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 olap data warehousing analysis

Software Functionality Revealed in Detail

We’ve opened the hood on every major category of enterprise software. Learn about thousands of features and functions, and how enterprise software really works.

Get free sample report
Compare Software Solutions

Visit the TEC store to compare leading software by functionality, so that you can make accurate and informed software purchasing decisions.

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Documents related to » olap data warehousing analysis

Thinking Radically about Data Warehousing and Big Data: Interview with Roger Gaskell, CTO of Kognitio


Managing data—particularly in large numbers—still is and probably will be the number one priority for many organizations in the upcoming years. Many of the traditional ways to store and analyze large amounts of data are being replaced with new technologies and methodologies to manage the new volume, complexity, and analysis requirements. These include new ways of developing data warehousing, the

olap data warehousing analysis  create and populate physical OLAP cubes. The underlying data is held as low-level transactional data in standard relational structures, but to the application layer it appears as a “virtual cube.” By virtualizing the cube structures, organizations can still get a holistic view of their data but with zero latency given that large virtual cubes can be created within just seconds. Moreover, users can benefit from one single version of the truth; instead of contending with multiple physical cubes that Read More

Customer Relationship Analysis Firm Extends Reach


thinkAnalytics signs a partnering agreement with one of the largest information technology services companies in North America. Why does CGI expect thinkAnalytics’ software to make a difference to its customers?

olap data warehousing analysis  by Gentia Software, an OLAP (On-Line Analytical Processing) specialist, in 1998; thinkAnalytics was recently launched as a separate company. The company''s product suite comprises a variety of analytic tools that sit on top of common middleware and analysis functions. The middleware functions do not replace traditional ETL (Extract/Transform/Load) functions but rather augments them to provide intelligent data cleanup. Data cleanup encompasses such functions as null replacement, data scaling and deduping. Read More

A Definition of Data Warehousing


There is a great deal of confusion over the meaning of data warehousing. Simply defined, a data warehouse is a place for data, whereas data warehousing describes the process of defining, populating, and using a data warehouse. Creating, populating, and querying a data warehouse typically carries an extremely high price tag, but the return on investment can be substantial. Over 95% of the Fortune 1000 have a data warehouse initiative underway in some form.

olap data warehousing analysis  of OLAP is that OLAP can only answer questions you know to ask, data mining answers questions you didn''t necessarily know to ask. Data Visualization Tools: Tools that show graphical representations of data, including complex three-dimensional data pictures. The theory is that the user can see trends more effectively in this manner than when looking at complex statistical graphs. Some vendors are making progress in this area using the Virtual Reality Modeling Language (VRML). Metadata Management: Read More

Best Practices for a Data Warehouse on Oracle Database 11g


Companies are recognizing the value of an enterprise data warehouse (EDW) that provides a single 360-degree view of the business. But to ensure that your EDW performs and scales well, you need to get three things right: the hardware configuration, the data model, and the data loading process. Learn how designing these three things correctly can help you scale your EDW without constantly tuning or tweaking the system.

olap data warehousing analysis  Warehousing , Data Warehousing OLAP , Resource Data Warehouse , Land and Resource Data Warehouse , Data Warehouse Web Site , Data Mining , Data Mart , Data Warehouse Architecture , Data Warehouse Concepts , Data Warehouse Tutorial , Data Warehouse Definition , OLAP , Business Intelligence , Huge Data Warehouse , Data Warehouse Appliance . NOTE: The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. Read More

A One-stop Event for Business Intelligence and Data Warehousing Information


The Data Warehousing Institute (TDWI) hosts quarterly World Conferences to help organizations involved in data warehousing, business intelligence, and performance management. These conferences supply a wealth of information aimed at improving organizational decision-making, optimizing performance, and achieving business objectives.

olap data warehousing analysis  included online analytical processing (OLAP), dashboards, scorecards, and data mining, as well as analytic applications. This focus allows organizations to gain insight into areas within BI and the different aspects of insight that analytics can provide. Organizations that require a subset of BI can identify how their needs can be met, by identifying requirements based on the topics presented. Additionally, they can take advantage of the trade show to identify those vendors that meet their needs, or Read More

ESG - Riverbed Whitewater: Optimizing Data Protection to the Cloud


Riverbed Whitewater leverages WAN optimization technology to provide a complete data protection service to the cloud. The appliance-based solution is designed to integrate seamlessly with existing backup technologies and cloud storage provider APIs. Read this ESG Lab report on hands-on testing of the Riverbed Whitewater appliance for ease of use, cost-effective recoverability, data assurance, and performance and scalability.

olap data warehousing analysis  - Riverbed Whitewater: Optimizing Data Protection to the Cloud Riverbed Whitewater leverages WAN optimization technology to provide a complete data protection service to the cloud. The appliance-based solution is designed to integrate seamlessly with existing backup technologies and cloud storage provider APIs. Read this ESG Lab report on hands-on testing of the Riverbed Whitewater appliance for ease of use, cost-effective recoverability, data assurance, and performance and scalability. Read More

Garbage in, Garbage out: Getting Good Data out of Your BI Systems


Find out in Garbage In, Garbage Out: Getting Good Data Out of Your BI Systems.

olap data warehousing analysis  in, Garbage out: Getting Good Data out of Your BI Systems Garbage in, garbage out. Poor quality data leads to bad business decisions. You need high quality data in your business intelligence (BI) system to facilitate effective analysis—to make the right decisions at the right time. But how do you achieve this? Find out in Garbage In, Garbage Out: Getting Good Data Out of Your BI Systems . In this Focus Brief , you''ll learn about the steps in the data delivery cycle, the problems can occur at Read More

Data Quality Trends and Adoption


While much of the interest in data quality (DQ) solutions had focused on avoiding failure of data management-related initiatives, organizations now look to DQ efforts to improve operational efficiencies, reduce wasted costs, optimize critical business processes, provide data transparency, and improve customer experiences. Read what DQ purchase and usage trends across UK and US companies reveal about DQ goals and drivers.

olap data warehousing analysis  Quality Trends and Adoption While much of the interest in data quality (DQ) solutions had focused on avoiding failure of data management-related initiatives, organizations now look to DQ efforts to improve operational efficiencies, reduce wasted costs, optimize critical business processes, provide data transparency, and improve customer experiences. Read what DQ purchase and usage trends across UK and US companies reveal about DQ goals and drivers. Read More

Linked Enterprise Data: Data at the heart of the company


The data silos of today's business information systems (IS) applications, and the pressure from the current economic climate, globalization, and the Internet make it critical for companies to learn how to manage and extract value from their data. Linked enterprise data (LED) combines the benefits of business intelligence (BI), master data management (MDM), service-oriented architecture (SOA), and search engines to create links among existing data, break down data walls, provide an open, secure, and long-term technological environment, and reduce complexity—read this white paper to find out how.

olap data warehousing analysis  Enterprise Data: Data at the heart of the company The data silos of today''s business information systems (IS) applications, and the pressure from the current economic climate, globalization, and the Internet make it critical for companies to learn how to manage and extract value from their data. Linked enterprise data (LED) combines the benefits of business intelligence (BI), master data management (MDM), service-oriented architecture (SOA), and search engines to create links among existing data, Read More

Data Quality: A Survival Guide for Marketing


Even with the finest marketing organizations, the success of marketing comes down to the data. Ensuring data quality can be a significant challenge, particularly when you have thousands or even millions of prospect records in your CRM system and you are trying to target the right prospect. Data quality, data integration, and other functions of enterprise information management (EIM) are crucial to this endeavor. Read more.

olap data warehousing analysis  Quality: A Survival Guide for Marketing Even with the finest marketing organizations, the success of marketing comes down to the data. Ensuring data quality can be a significant challenge, particularly when you have thousands or even millions of prospect records in your CRM system and you are trying to target the right prospect. Data quality, data integration, and other functions of enterprise information management (EIM) are crucial to this endeavor. Read more. Read More

The Fast Path to Big Data


Today, most people acknowledge that big data is more than a fad and is a proven model for leveraging existing information sources to make smarter, more immediate decisions that result in better business outcomes. Big data has already been put in use by companies across vertical market segments to improve top- and bottom-line performance. As unstructured data becomes a pervasive source of business intelligence, big data will continue to play a more strategic role in enterprise information technology (IT). Companies that recognize this reality—and that act on it in a technologically, operationally, and economically optimized way—will gain sustainable competitive advantages.

olap data warehousing analysis  Fast Path to Big Data Today, most people acknowledge that big data is more than a fad and is a proven model for leveraging existing information sources to make smarter, more immediate decisions that result in better business outcomes. Big data has already been put in use by companies across vertical market segments to improve top- and bottom-line performance. As unstructured data becomes a pervasive source of business intelligence, big data will continue to play a more strategic role in enterprise Read More

How the Right Mix of Static Analysis and Dynamic Analysis Technologies Can Strengthen Application Security


In searching for tools to implement an effective application-security strategy, managers have a choice between two technological approaches: dynamic analysis and static analysis. Available in a variety of freeware and commercial automated tools, both approaches promise comprehensive detection of security vulnerabilities. But a truly effective strategy may require a mix of both.

olap data warehousing analysis   Read More

Data Evolution: Why a Comprehensive Data Management Platform Supersedes the Data Integration Toolbox


Today’s organizations have incredible amounts of information to be managed, and in many cases it is quickly spiraling out of control. To address the emerging issues around managing, governing, and using data, organizations have been acquiring quite a toolbox of data integration tools and technologies. One of the core drivers for these tools and technologies has been the ever-evolving world of the data warehouse.

olap data warehousing analysis  Evolution: Why a Comprehensive Data Management Platform Supersedes the Data Integration Toolbox Today’s organizations have incredible amounts of information to be managed, and in many cases it is quickly spiraling out of control. To address the emerging issues around managing, governing, and using data, organizations have been acquiring quite a toolbox of data integration tools and technologies. One of the core drivers for these tools and technologies has been the ever-evolving world of the data ware Read More

Data Warehouse vs. Data Mart-Approaches to Implementing a Data Integration Solution


There continues to be a wide variety of different approaches to building a solid data management solution for your organization, and just as many consulting firms across the globe willing to help you build them. However, it’s imperative for each data management solution to specialize to the unique needs of your organization’s business users, across varying functional areas.

olap data warehousing analysis  Warehouse vs. Data Mart-Approaches to Implementing a Data Integration Solution There continues to be a wide variety of different approaches to building a solid data management solution for your organization, and just as many consulting firms across the globe willing to help you build them. However, it’s imperative for each data management solution to specialize to the unique needs of your organization’s business users, across varying functional areas. Read More