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Documents related to » data mining warehousing


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.

DATA MINING WAREHOUSING: a result set back. Data Mining Tools: Tools that automatically search for patterns in data. These tools are usually driven by complex statistical formulas. The easiest way to distinguish data mining from the various forms 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
8/18/2002

Business Process Overview: Warehousing Solutions
Business Process Overview: Warehousing Solutions. Search for Templates and Other Software to Delineate Your Evaluation Concerning the Business Process and Warehousing Solutions Warehousing processes are critical for the success of a distribution business. These processes are highly visible to your customers, and have a direct impact on their opinion of your company. If you cannot get the goods to your customers in the most efficient way, you risk losing their business. Your warehousing and logistics operations must translate into reliability and service.

DATA MINING WAREHOUSING: Journey The Importance of Data Representation: Best Practices in Creating a Usable Report Better Business Outcomes with Business Analytics Business Process Overview: Warehousing Solutions If you receive errors when attempting to view this white paper, please install the latest version of Adobe Reader. IBS is a world-leading supplier of business systems and supply chain management solutions to distribution and manufacturing companies. Effective warehouse management IBS warehouse management software
11/16/2006 2:23:00 PM

Six Steps to Manage Data Quality with SQL Server Integration Services
Six Steps to Manage Data Quality with SQL Server Integration Services. Read IT Reports Associated with Data quality. Without data that is reliable, accurate, and updated, organizations can’t confidently distribute that data across the enterprise, leading to bad business decisions. Faulty data also hinders the successful integration of data from a variety of data sources. But with a sound data quality methodology in place, you can integrate data while improving its quality and facilitate a master data management application—at low cost.

DATA MINING WAREHOUSING: Six Steps to Manage Data Quality with SQL Server Integration Services Six Steps to Manage Data Quality with SQL Server Integration Services Source: Melissa Data Document Type: White Paper Description: Without data that is reliable, accurate, and updated, organizations can’t confidently distribute that data across the enterprise, leading to bad business decisions. Faulty data also hinders the successful integration of data from a variety of data sources. But with a sound data quality methodology in
9/9/2009 2:32:00 PM

Who Needs Warehousing Management and How Much Thereof?
The warehouse is no longer merely a static storage facility. It now has to use virtually real-time data to closely match supply to demand, eliminate the need to hold excess inventory, and increase the flow of goods throughout the supply chain.

DATA MINING WAREHOUSING: to use virtually real-time data to closely match supply to demand, eliminate the need to hold excess inventory, and increase the flow of goods throughout the supply chain. Therefore, because broader supply chain execution (SCE) software (with warehouse management at its core) can handle these complex requirements, postponing many light manufacturing operations (e.g., final assembly, customized packing, labeling, engraving, etc.) from shop floors to warehouses and distribution centers (DC) has become a
8/30/2005

EMC to Buy Data General
WALTHAM, Mass., August 9th, 1999 (Reuters) - High-end data storage leader EMC Corp. (NYSE:EMC) on Monday moved to grab a chunk of the mid-range storage business with a deal to buy Data General Corp. (NYSE:DGN) for $1.1 billion in stock, the companies said.

DATA MINING WAREHOUSING: EMC to Buy Data General EMC to Buy Data General R. Krause - August 13, 1999 Read Comments EMC to Buy Data General WALTHAM, Mass., August 9th, 1999 (Reuters) High-end data storage leader EMC Corp. (NYSE:EMC) on Monday moved to grab a chunk of the mid-range storage business with a deal to buy Data General Corp. (NYSE:DGN) for $1.1 billion in stock, the companies said. EMC, which holds a 35 percent share of the $12 billion high-end storage market, said it will lend its considerable distribution and support
8/13/1999

Spend Data Warehouse “On Steroids”
It’s only lately that people have been questioning the value of information they’re able to garner from within “spend data” warehouses. Why can t we leverage traditional tools to give the sourcing and purchasing community what they want? To understand the limitations of traditional data-cleansing technology, and why spend data necessitates special algorithms, we need to start with the basics.

DATA MINING WAREHOUSING: Spend Data Warehouse “On Steroids” Spend Data Warehouse “On Steroids” Source: Zycus Document Type: White Paper Description: It’s only lately that people have been questioning the value of information they’re able to garner from within “spend data” warehouses. Why can t we leverage traditional tools to give the sourcing and purchasing community what they want? To understand the limitations of traditional data-cleansing technology, and why spend data necessitates special algorithms, we need
4/5/2007 1:58:00 PM

Data-driven Design
Creating and maintaining a successful digital experience that drives business results requires the right research insight, design, technology, and ongoing optimization. Forrester conducted an online survey of 209 digital experience professionals in the US to evaluate current practices around Web site monitoring and digital experiences. Read about the adoption, benefits, and challenges of current data-driven design processes.

DATA MINING WAREHOUSING:   Business Intelligence and Data Management,   Web Authoring,   Business-to-Consumer Web Sales,   Web Site Monitoring Related Industries:   Industry Independent Related Keywords:   Extractable,   Data Driven Design,   Forrester,   Analytics,   User Experience Design,   Business,   Value,   Metrics,   Website Development,   web development,   web site development tools,   web page development Source: Extractable Learn more about Extractable Readers who downloaded this white paper also
5/15/2012 1:00:00 PM

Role of In-memory Analytics in Big Data Analysis
Organizations today need to handle and manage increasingly large volumes of data in various formats and coming from disparate sources. Though the benefits to be gained from analysis of such big data are immense, so are the inherent challenges, including need for rapid analysis. In his article, TEC BI analyst Jorge García discusses how in-memory analytics helps address these challenges and reap the benefits hidden in big data.

DATA MINING WAREHOUSING: In-memory Analytics in Big Data Analysis Role of In-memory Analytics in Big Data Analysis Jorge García - March 16, 2012 Read Comments In a previous article , I discussed big data and the need for a big data solution to handle, manage, and derive valuable insight from large volumes of data in various formats and coming from disparate sources. Here I discuss the role that in-memory technologies plays in big data analysis and the potential of this technology to change the business intelligence (BI)
3/20/2012 10:25:00 AM

The Data Explosion
RFID and wireless usage will drive up data transactions by ten fold over the next few years. It is likely that a significant readdressing of the infrastructure will be required--in the enterprise and the global bandwidth.

DATA MINING WAREHOUSING: The Data Explosion The Data Explosion Ann Grackin - October 20, 2004 Read Comments Introduction Traffic on the World Wide Web continues to grow. Traffic on your S mall S mart F ast devices continues to grow. Ok, I admit it. I bought the cell phone that takes pictures. I didn t know if it was useful; but being a technophile, I went for it. And rapidly it all came to me! I tried on a new cool jacket ... I crooned over it ... but for that much money, I wasn t sure. Should I really buy this? Enter the pic in
10/20/2004

Why Systems Fail - The Dead-end of Dirty Data
If your data does not reflect reality, the system can never be effective. In today’s world of collaboration, showing a trading partner dirty data is giving them the wrong message and tearing down the trust called for in a collaborating partnership.

DATA MINING WAREHOUSING: The Dead-end of Dirty Data Why Systems Fail - The Dead-end of Dirty Data Olin Thompson - July 4, 2003 Read Comments Introduction If your data does not reflect reality, the system can never be effective. In today s world of collaboration, showing a trading partner dirty data is giving them the wrong message and tearing down the trust called for in a collaborating partnership. What is dirty data? When reality and the data in your system do not agree, you have dirty data. It may be as simple as having 1000
7/4/2003

About Big Data
TEC analyst Jorge Garcia discusses the key issues surrounding big data, the different ways to manage it, and the major vendors offering big data solutions. There may not be a consensus with respect to just how big

DATA MINING WAREHOUSING: About Big Data About Big Data Jorge García - November 18, 2011 Read Comments There is no general consensus with respect to how big big data is—some companies deal with data volumes in the order of terabytes or even petabytes—but not many people will disagree that managing these huge amounts of data represents a challenge. It’s fair to say that we’re dealing with big data when traditional relational databases and systems are no longer sufficient. Things as simple as data storage and movement
11/18/2011 2:08:00 PM


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