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Software Functionality Revealed in Detail
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 data mining machine learning


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 machine learning  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.

Compare Now

Documents related to » data mining machine learning

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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TEC Industry Watch: Enterprise Software News for the Week of June 11, 2012


SOFTWARE SELECTIONSJeanswest selects Manhattan Associates Industry tags: Fashion/retail "Manhattan Associates once again shows the strength of its SCALE platform; Jeanswest was in much need of a supply chain solution that could help them streamline logistics across and between three continents. Jeanswest will now be able to better manage its operations across its branded stores and distribution

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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 extract useful information, is harder still. Even so, this data deluge has great potential for good—as long as consumers, companies, and governments make the right choices about when to restrict the flow of data, and when to encourage it. Find out more.

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The Intelligence of Social Media (Part 2)


In the first part of this blog, I mentioned that sentiment analysis measures the polarity of opinion—positive, negative, or neutral—regarding a subject, a product, a service, etc. Two main approaches can be used to perform sentiment analysis or text mining: a knowledge-based approach, which uses linguistic models to classify sentiments; and a learning-based approach, which uses

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Data Quality: A Survival Guide for Marketing


Even with the finest marketing organizations, the success of marketing comes down to the data. Ensuring data quality can be a significant challenge, particularly when you have thousands or even millions of prospect records in your CRM system and you are trying to target the right prospect. Data quality, data integration, and other functions of enterprise information management (EIM) are crucial to this endeavor. Read more.

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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% 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 two architectures, and illustrates the key drivers of both the capex and opex savings of the improved architecture.

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Data Masking: Strengthening Data Privacy and Security


Many business activities require access to real production data, but there are just as many that don’t. Data masking secures enterprise data by eliminating sensitive information, while maintaining data realism and integrity. Many Fortune 500 companies have already integrated data masking technology into their payment card industry (PCI) data security standard (DSS) and other compliance programs—and so can you.

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Unified Data Management: A Collaboration of Data Disciplines and Business Strategies


In most organizations today, data are managed in isolated silos by independent teams using various data management tools for data quality, integration, governance, and so on. In response to this situation, some organizations are adopting unified data management (UDM), a practice that holistically coordinates teams and integrates tools. This report can help your organization plan and execute effective UDM efforts.

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Data Quality Trends and Adoption


While much of the interest in data quality (DQ) solutions had focused on avoiding failure of data management-related initiatives, organizations now look to DQ efforts to improve operational efficiencies, reduce wasted costs, optimize critical business processes, provide data transparency, and improve customer experiences. Read what DQ purchase and usage trends across UK and US companies reveal about DQ goals and drivers.

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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 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."

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