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 data mining modeling techniques


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 modeling 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.

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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 » data mining modeling techniques

Statistica


StatSoft's flagship product line is the STATISTICA suite of analytics software products and solutions. STATISTICA provides the most comprehensive array of data analysis, data management, data visualization, and data mining procedures. Its techniques include the widest selection of predictive modeling, clustering, classification, and exploratory techniques in one software platform.  

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Predictive Analytics; the Future of Business Intelligence


Business intelligence (BI) is evolving as it grows in popularity. Within BI, there is a shift from traditional analytics to predictive analytics, and predictive analytics is emerging as a distinct new software sector.

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Analyse This


Enterprise applications have long been providing the means for businesses to collect required data and deliver it to the right people. Now that sales and marketing professionals are empowered with the right tools to better serve their customers and gather insights on all customers' interactions, the question to ask is what's next? We see the answer in a tight integration between Enterprise applications and Analytics.

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Predictive Analytics and the Evolution of BI


In the last couple of years, the popularity of predictive analytics offerings has increased significantly, as many companies looking to improve their existing business intelligence (BI) and analytics faculties are looking to the field of predictive analysis to enhance these capabilities, and in some cases even replace them. But, is predictive analysis really the new BI? Is it a replacement for the

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DynaFlow Modeling & Workflow Solutions


DynaFlow Modeling and Workflow Solutions Inc., founded in 1997, focusses on the domains of enterprise modeling, workflow management, and knowledge management, in relation to business re-engineering initiatives and ERP implementations. DynaFlow's mission is to support total process management solutions such as Baan-DEM, target enterprise methodology, and its own EZ-Process product suite. Additionallly, DynaFlow focusses on modeling with respect to projects related to ERP, B2B and B2C, and ISO. DynaFlow is based in Quebec, Canada.

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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.

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Increasing Sales and Reducing Costs across the Supply Chain-Focusing on Data Quality and Master Data Management


Nearly half of all US companies have serious data quality issues. The problem is that most are not thinking about their business data as being valuable. But in reality data has become—in some cases—just as valuable as inventory. The solution to most organizational data challenges today is to combine a strong data quality program with a master data management (MDM) program, helping businesses leverage data as an asset.

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A Roadmap to Data Migration Success


Many large business initiatives and information technology (IT) projects depend upon the successful migration of data—from a legacy source, or multiple sources, to a new target database. Effective planning and scoping can help you address the associated challenges and minimize risk for errors. This paper provides insights into what issues are unique to data migration projects and to offer advice on how to best approach them.

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Containerized Power and Cooling Modules for Data Centers


Standardized, pre-assembled, and integrated data center facility power and cooling modules are at least 60% faster to deploy, and provide a first cost savings of 13% or more compared with traditional data center power and cooling infrastructure. Facility modules, also referred to in the data center industry as containerized power and cooling plants, allow data center designers to shift their thinking from a customized “construction” mentality to a standardized “site integration” mentality. This white paper compares the cost of both scenarios, presents the advantages and disadvantages of each, and identifies which environments can best leverage the facility module approach.

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Re-think Data Integration: Delivering Agile BI Systems with Data Virtualization


Today’s business intelligence (BI) systems have to change, because they’re confronted with new technological developments and new business requirements, such as productivity improvement and systems as well as data in the cloud. This white paper describes a lean form of on-demand data integration technology called data virtualization, and shows you how deploying data virtualization results in BI systems with simpler and more agile architectures that can confront the new challenges much easier.

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