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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

implementation 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. It can be applied in any Read More

Mining Industry (ERP & CMMS)
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Documents related to » implementation data mining


Four Critical Success Factors to Cleansing Data
Quality data in the supply chain is essential in when information is automated and shared with internal and external customers. Dirty data is a huge impediment

implementation data mining  within budget. Find an implementation including metrics of data integrity and process and you will be hard pressed. Project teams would rather perform system configuration than perform manual data review and validation. No wonder, according to Ovum Research, 50 % of data warehousing projects fail. If more companies would incorporate the concepts of data quality and data integrity into their project lifecycle, the IT department would have less data to clean up post golive. We have all heard this before: Read More
How the Mining Industry Benefits from ERP Systems
Integrated enterprise resource planning software normalizes the reporting requirements for a mining company’s various departments. This article loosely shows

implementation data mining  mining companies, as its implementation will eliminate the business errors and reporting confusion that a smaller mining company start-up faces as it grows in profitability. Read More
Komatsu Employs “Mod Squad” For Logility Implementation
At the Connections 2000 Logility user conference in New Orleans, users recounted implementation stories. Few were as striking as that of the Komatsu’s DRP

implementation data mining  of the Komatsu's DRP implementation modifications, a testament to human ingenuity. Komatsu America is the second largest supplier of construction and mining equipment in the Western Hemisphere. It sells a complete line of hydraulic excavators, bulldozers, wheel loaders, dump trucks, graders, and crushers. In October 1998, the company began parallel implementations of Logility Voyager Solutions for distribution requirements planning (DRP) and inventory planning (IP). In two months, Komatsu had gone live Read More
Data Quality: Cost or Profit?
Data quality has direct consequences on a company's bottom-line and its customer relationship management (CRM) strategy. Looking beyond general approaches and

implementation data mining  foundation of successful CRM implementation and accurate customer intelligence. Past experience and research show that 50 to 70 percent of many CRM initiatives should be devoted to data quality. Consequently poor data quality hampers a company's ability to realize the return from their investment in a truly integrated CRM. Data quality, therefore, should not be considered a one-time exercise. It has to be integrated as a core element in managing a business. Defective data quality leads to customer Read More
Data Enrichment and Classification to Drive Data Reliability in Strategic Sourcing and Spend Analytics
Spend analytics and performance management software can deliver valuable insights into savings opportunities and risk management. But to make confident

implementation data mining  Enrichment and Classification to Drive Data Reliability in Strategic Sourcing and Spend Analytics Spend analytics and performance management software can deliver valuable insights into savings opportunities and risk management. But to make confident decisions and take the right actions, you need more than just software—you also need the right data. This white paper discusses a solution to help ensure that your applications process reliable data so that your spend analyses and supplier risk Read More
Data Mining with MicroStrategy: Using the BI Platform to Distribute Data Mining and Predictive Analytics to the Masses
Data mining and predictive analysis applications can help you make knowledge-driven decisions and improve efficiency. But the user adoption of these tools has

implementation data mining  Mining with MicroStrategy: Using the BI Platform to Distribute Data Mining and Predictive Analytics to the Masses Data mining and predictive analysis applications can help you make knowledge-driven decisions and improve efficiency. But the user adoption of these tools has been slow due to their lack of business intelligence (BI) functionality, proactive information distribution, robust security, and other necessities. Now there’s an integrated enterprise BI system that can deliver data mining and Read More
MSI Data


implementation data mining  Data 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

implementation data mining  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
New Data Protection Strategies
One of the greatest challenges facing organizations is the protection of corporate data. The issues complicating data protection are compounded by increased

implementation data mining  Data Protection Strategies One of the greatest challenges facing organizations is the protection of corporate data. The issues complicating data protection are compounded by increased demand for data capacity and higher service levels. Often these demands are coupled with regulatory requirements and a shifting business environment. Learn about data protection strategies that can help organizations meet these demands while maintaining flat budgets. Read More
Custom Implementation
PDG Group Model 1004

implementation data mining  Implementation PDG Group Model 1004 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

implementation data mining  the organization. Step 6: Implementation At this stage, the recommended data quality solution is put into practice, possibly on a limited basis, for a trial period. The point person tracks costs and manpower usage to compare with those of a similar period before the data quality initiative. Step 7: Validation After a trial period, the results are collected and reported back to management. At this point, the effectiveness of the data quality initiative can be evaluated and any adjustments, if necessary, Read More
Collaborative Quality: Ensuring the Success of Your SAP Software Implementation
As the pressure grows to justify information technology (IT) expenditure and tightly manage risk in sophisticated, integrated software implementation projects

implementation data mining  of Your SAP Software Implementation As the pressure grows to justify information technology (IT) expenditure and tightly manage risk in sophisticated, integrated software implementation projects, those tasked with project management often feel exposed and vulnerable. No one wants to put his or her organization or career at risk over a technology rollout. This paper explores how to deliver software solutions that bring significant business benefits. Read More
TCO Analysis of a Traditional Data Center vs. a Scalable, Containerized Data Center
Standardized, scalable, pre-assembled, and integrated data center facility power and cooling modules provide a total cost of ownership (TCO) savings of 30

implementation data mining  Analysis of a Traditional Data Center vs. a Scalable, Containerized Data Center Standardized, scalable, pre-assembled, and integrated data center facility power and cooling modules provide a total cost of ownership (TCO) savings of 30% compared with traditional, built-out data center power and cooling infrastructure. Avoiding overbuilt capacity and scaling the design over time contributes to a significant percentage of the overall savings. This white paper provides a quantitative TCO analysis of the Read More
Data Storage in the Cloud--Can You Afford Not To?
In Data Storage in the Cloud Can You Afford Not To?

implementation data mining  Storage in the Cloud--Can You Afford Not To? Spiraling data storage requirements are straining many companies' abilities to keep pace. One obvious solution is storage in the cloud—but concerns regarding security, speed, and accessibility remain. Or do they? In Data Storage in the Cloud—Can You Afford Not To? , you'll discover a cloud storage solution that offers you high-security dual-level encryption (SSLv3 and 256-bit AES) data accessibility from anywhere lower costs due to the economies of Read More
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