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The Truth about Data Mining
It is now imperative that businesses be prudent. With rising volumes of data, traditional analytical techniques may not be able to discover valuable data

data mining techniques  Truth about Data Mining A business intelligence (BI) implementation can be considered two-tiered. The first tier comprises standard reporting, ad hoc reporting, multidimensional analysis, dashboards, scorecards, and alerts. The second tier is more commonly found in organizations that have successfully built a mature first tier. Advanced data analysis through predictive modeling and forecasting defines this tier—in other words, data mining. Data mining has a significantly broad reach and application. Read More
Mining Industry (ERP & CMMS)
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Documents related to » data mining techniques


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 mining techniques  analysis techniques such as data mining (statistical analysis to discover trends in the data), data visualization (graphical display of query results), or multi-dimensional analysis (the so called slice and dice ). Will the architecture be two-tiered or three-tiered? Three-tiered architectures offload some of the processing to an application server which sits between the database server and the end-user. Will the tool employ a push or a pull technology? ( Push technology publishes the queries to Read More
Data, Data Everywhere: A Special Report on Managing Information
The quantity of information in the world is soaring. Merely keeping up with, and storing new information is difficult enough. Analyzing it, to spot patterns and

data mining techniques  online, are masters of data mining (or business intelligence , as it is now known). By analysing basket data , supermarkets can tailor promotions to particular customers' preferences. The oil industry uses supercomputers to trawl seismic data before drilling wells. And astronomers are just as likely to point a software query tool at a digital sky survey as to point a telescope at the stars. There's much further to go. Despite years of effort, law-enforcement and intelligence agencies' databases are 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 mining techniques  that are not obvious. Data mining is the process of discovering information from enterprise data that is otherwise hidden. For instance, an online bookstore suggests additional books based on what a user adds to his or her cart by examining evidence from other comparable buyers. This is done through the use of association rules applied to historical sales data. Although the primary purpose of mining data is to gain business insight, it can be applied to discover anomalies in data. Consider a Web-based Read More
Data Mining: The Brains Behind eCRM
Data mining has emerged from obscure beginnings in artificial intelligence to become a viable and increasingly popular tool for putting data to work. Data

data mining techniques  of products and services. Data mining has emerged from obscure beginnings in artificial intelligence to become a viable and increasingly popular tool for putting data to work. What is eCRM? Customer Relationship Management is an information industry term for methodologies and software that help companies manage customer relationships in a structured way. For example, an enterprise might build a database about its customers that describe relationships in sufficient detail. Ideally, the information in the Read More
Mining & Quarrying
Mining is the extraction from the earth of materials used in different human activities (industry, trade, energy production, etc.). There are four major types

data mining techniques  & Quarrying Mining is the extraction from the earth of materials used in different human activities (industry, trade, energy production, etc.). There are four major types of materials: precious metals and minerals (gold, diamonds, silver, etc.); materials used to produce energy (coal, uranium, etc.); base metals (copper, iron, etc.); and building materials (stone, sand, gravel) extracted from quarries, which are open-pit mines. There are two major types of activities specific to the mining industry: Read More
Top Software for Mining Companies
For your own customized comparison reports, select a category below: ERP & CMMS for Mining. EAM for Mining. Accounting Software Systems.

data mining techniques  Software for Mining Companies Comparing software solutions for mining companies can be difficult and time-consuming—but at TEC we make it quick and easy In just seconds, you can get free side-by-side software comparison reports. Choose from all the leading vendors—including Mincom, IFS, Microsoft, SAP, Lawson, Pronto Software, Infor, Mapcon Technologies, Bell Business Solutions, TARGIT, IBM, QlikTech International, Oracle—and many more. For your own customized comparison reports , select a 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 mining techniques  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
Master Data Management and Accurate Data Matching
Have you ever received a call from your existing long-distance phone company asking you to switch to its service and wondered why they are calling? Chances are

data mining techniques  Data Management and Accurate Data Matching Have you ever received a call from your existing long-distance phone company asking you to switch to its service and wondered why they are calling? Chances are the integrity of its data is so poor that the company has no idea who many of its customers are. The fact is, many businesses suffer from this same problem. The solution: implement a master data management (MDM) system that uses an accurate data matching process. Read More
Evoke Software Releases Axio Data Integration Product
Evoke Software Corporation has announced the release of Axio™, an e-business integration product designed to web-enable multiple different data sources into a

data mining techniques  Software Releases Axio Data Integration Product Evoke Software Releases Axio Data Integration Product M. Reed - June 27, 2000 Event Summary Evoke Software has created a new product based on their existing Migration Architect product. It will be known as Axio and is designed to provide rapid e-business integration with existing corporate operational systems, new e-commerce applications, customer relationship management, and/or data warehousing. Axio is designed to automatically discover information in 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 mining techniques  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
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 mining techniques  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
10 Errors to Avoid When Building a Data Center
In the white paper ten errors to avoid when commissioning a data center, find out which mistakes to avoid when you're going through the data center...

data mining techniques  Avoid When Building a Data Center Proper data center commissioning can help ensure the success of your data center design and build project. But it's also a process that can go wrong in a number of different ways. In the white paper Ten Errors to Avoid when Commissioning a Data Center , find out which mistakes to avoid when you're going through the data center commissioning process. From bringing in the commissioning agent too late into the process, to not identifying clear roles for commissioning team 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 mining techniques  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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