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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

data warehousing job  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 Read More

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Documents related to » data warehousing job


Optimizing Gross Margin over Continously Cleansed Data
Imperfect product data can erode your gross margin, frustrate both your customers and your employees, and slow new sales opportunities. The proven safeguards

data warehousing job  as a result of data errors Reduce time spent with vendors on purchase order errors Sales and Marketing Save time spent in providing product information to customers Save hours in communicating production promotions to customers Increase sales based on improved relationships with your customers by providing accurate, up-todate product information Improve your customer loyalty Inventory Management and Warehousing Save labor in processing incorrect shipments Reduce cost due to fewer inaccuracies about Read More
Metagenix Reverse Engineers Data Into Information
Metagenix’ MetaRecon reverse engineers metadata information by examining the raw data contained in the source(s) rather than depending on the data dictionaries

data warehousing job  Reverse Engineers Data Into Information Metagenix Reverse Engineers Data Into Information M. Reed - February 15, 2001 Event Summary Metagenix, Inc. has designed its flagship product, MetaRecon to, as they put it, Decipher Your Data Genome . The product reverse engineers all of the metadata ( data about data ) from data sources and generates information that is very helpful to developers in designing specifications for a new data store, and assists greatly in preparing for cleansing and Read More
Distilling Data: The Importance of Data Quality in Business Intelligence
As an enterprise’s data grows in volume and complexity, a comprehensive data quality strategy is imperative to providing a reliable business intelligence

data warehousing job  When embarking on a data warehousing or business intelligence project, it is essential for organizations to emphasize the quality of data that is used for analysis and subsequent decision making. As data captured from a multitude of sources makes its way to an enterprise data warehouse or data marts, a data quality framework creates a screening process that measures the purity of the data and corrects any inconsistencies found. This article walks the reader through a typical data quality strategy by Read More
A Road Map to Data Migration Success
Many significant business initiatives and large IT projects depend upon a successful data migration. But when migrated data is transformed for new uses, project

data warehousing job  | ETL Tools | Data Warehousing ETL | Powercenter ETL | ETL Documentation | IT Projects | One-time Movement of Data | Business Objects | SAP | Business Objects SAP | SAP Company | Migration Tasks | Migration Assessment | MDM | Master Data Management | MDM ETL | Master Data Management MDM | ETL Architecture | SOA MDM | MDM Products | MDM Solution | MDM Tools | Online Data Migration | Master Data Management Services | MDM Data Quality | Business Objects ETL | Real Time ETL | Business Objects MDM | Road Map 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

data warehousing job  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
The Necessity of Data Warehousing
An explanation of the origins of data warehousing and why it is a crucial technology that allows businesses to gain competitive advantage. Issues regarding

data warehousing job  Necessity of Data Warehousing The Necessity of Data Warehousing M. Reed - August 2, 2000 Why the market is necessary Data warehousing is an integral part of the information age . Corporations have long known that some of the keys to their future success could be gleaned from their existing data, both current and historical. Until approximately 1990, many factors made it difficult, if not impossible, to extract this data and turn it into useful information. Some examples: Data storage peripherals such Read More
The Use of Ceiling-Ducted Air Containment in Data Centers
Ducting hot IT-equipment exhaust to a drop ceiling can be an effective air management strategy, improving the reliability and energy efficiency of a data center

data warehousing job  Ceiling-Ducted Air Containment in Data Centers Ducting hot IT-equipment exhaust to a drop ceiling can be an effective air management strategy, improving the reliability and energy efficiency of a data center. Typical approaches include ducting either individual racks or entire hot aisles and may be passive (ducting only) or active (include fans). This paper examines available ducting options and explains how such systems should be deployed and operated. Practical cooling limits are established and Read More
Types of Prefabricated Modular Data Centers
Data center systems or subsystems that are pre-assembled in a factory are often described with terms like prefabricated, containerized, modular, skid-based, pod

data warehousing job  of Prefabricated Modular Data Centers Data center systems or subsystems that are pre-assembled in a factory are often described with terms like prefabricated, containerized, modular, skid-based, pod-based, mobile, portable, self-contained, all-in-one, and more. There are, however, important distinctions between the various types of factory-built building blocks on the market. This paper proposes standard terminology for categorizing the types of prefabricated modular data centers, defines and Read More
Paperless Warehousing
Paperless Warehousing Pty Limited was incorporated in 1988 by two of the existing Directors with a vision to overhaul traditional warehousing operations by

data warehousing job  Warehousing Paperless Warehousing Pty Limited was incorporated in 1988 by two of the existing Directors with a vision to overhaul traditional warehousing operations by improving the logistics processes inside and outside the four walls of a warehouse thus providing Companies with a market advantage. Based in Sydney, Australia, Paperless Warehousing offers a wealth of experience, solidarity and dependability in Warehouse and Transport Management Systems. All our people are employees and are Read More
Beware of Legacy Data - It Can Be Lethal
Legacy data can be lethal to your expensive new application – two case studies and some practical recommendations.

data warehousing job  of Legacy Data - It Can Be Lethal Beware of Legacy Data It Can Be Lethal Featured Author - Jan Mulder - August 23, 2002 Introduction The term legacy is mostly used for applications. For example, according to the Foldoc dictionary, legacy is: A computer system or application program which continues to be used because of the cost of replacing or redesigning it and often despite its poor competitiveness and compatibility with modern equivalents. The implication is that the system is large, monolithic Read More
Data Warehousing in the Big Data Era: Are You BIReady?
Netherlands-based BIReady automates the process of designing, deploying, and maintaining a data warehousing solution, allowing the optimization of all necessary

data warehousing job  deploying, and maintaining a data warehousing solution, allowing the optimization of all necessary BI and analytics tasks. Read this TEC product note from TEC senior BI and data management analyst Jorge Garcia to learn more about how BIReady is meeting its goal of helping companies become BI ready. 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.

data warehousing job  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 each step, Read More
Data Loss Prevention Best Practices: Managing Sensitive Data in the Enterprise
While a great deal of attention has been given to protecting companies’ electronic assets from outside threats, organizations must now turn their attention to

data warehousing job  Best Practices: Managing Sensitive Data in the Enterprise While a great deal of attention has been given to protecting companies’ electronic assets from outside threats, organizations must now turn their attention to an equally dangerous situation: data loss from the inside. Given today’s strict regulatory standards, data loss prevention (DLP) has become one of the most critical issues facing executives. Fortunately, effective technical solutions are now available that can help. Read More
Scalable Data Quality: A Seven-step Plan for Any Size Organization
Every record that fails to meet standards of quality can lead to lost revenue or unnecessary costs. A well-executed data quality initiative isn’t difficult, but

data warehousing job  Data Quality: A Seven-step Plan for Any Size Organization Melissa Data’s Data Quality Suite operates like a data quality firewall – instantly verifying, cleaning, and standardizing your contact data at point of entry, before it enters your database. Source : Melissa Data Resources Related to Scalable Data Quality: A Seven-step Plan for Any Size Organization : Data quality (Wikipedia) Scalable Data Quality: A Seven-step Plan for Any Size Organization Data Quality is also known as : Customer Read More

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