Data mining News
July 19, 2017
The technical lead on OpenText Magellan offers some insight into how the company's newest tool came together and how it can be used for fraud detection, CRM and more.
June 07, 2017
As Trump negates regulations against mining of customer data, consumers are mixed when it comes to trusting AI to act on those insights, Pegasystems research shows.
March 30, 2017
SAP Labs director Seema Thomas discusses how big data infuses mature technologies -- such as AI, CRM and IoT -- with cutting-edge insights to drive better sales
November 01, 2016
A growing number of network managers have their heads in the cloud, and that's a good thing. Cloud-based networking technologies are emerging to help solve a number of enterprise problems, and organizations are busy exploring what "the networked cloud" will mean for them. While some network pros are dipping cautious toes into the cloud-based networking waters with individual, subscription-based services that integrate with their existing infrastructure, others are rolling out technology that lets them manage the migration of their applications to the cloud with a high degree of visibility and control. This edition of Network Evolution examines how cloud-based networking can make network managers' lives easier in a new era of IT.
Also in this issue, find out whatever happened to the legacy voice engineers of yore, since voice over IP technology threatened to render their traditional circuit switching skills more or less obsolete in the 1990s. We catch up with a few such professionals who -- far from phoning it in -- learned new skills that took their careers in unexpected directions.
And last but not least, we explore Microsoft's upcoming Azure Stack release -- and whether it could become a major player in the software-defined networking space.
Data mining Get Started
Bring yourself up to speed with our introductory content
Process mining software is a type of analytics software that mines the event logs of enterprise applications for trends and patterns that can help improve understanding of the processes and make them more efficient. Continue Reading
Creating a successful data science program enables you to look deeper into your organization's data for analytics uses. Take this quiz to see how much you know about the data science process. Continue Reading
You might just now be reading about sentiment analysis tools for social media, yet software implementation promises deeper customer insights that drive sales and marketing. Continue Reading
Evaluate Data mining Vendors & Products
Weigh the pros and cons of technologies, products and projects you are considering.
Oracle Advanced Analytics and other data analytics tools, including Oracle R Enterprise and Oracle Data Miner, enable business users to explore large volumes of data. Continue Reading
The IBM SPSS Modeler and IBM SPSS Statistics predictive analytics tools help users and data analysts build predictive models and manage the analytics process. Continue Reading
By boosting the productivity of human and capital assets, IoT location-based services can drive new business opportunities. Continue Reading
Manage Data mining
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The difference between traditional data governance and rules surrounding the mining of customer data is an extra layer to prevent the 'stalker effect' that turns away business. Continue Reading
Predictive modeling, machine learning and other advanced analytics applications help dig the business value out of big data systems -- but for many users, it takes a lot of tools and effort. Continue Reading
The challenges encountered in deriving business benefits from big data are huge, but so are the rewards. Hadoop and related technologies are easing those challenges to the point where companies are willing to graduate from experimental to full-blown big data analytics deployments. Still, the march toward that goal can be long and arduous, and not just from a technological and architectural standpoint. Before taking the plunge, big data users, including data scientists, managers and evangelists, are faced with the sometimes monumental task of justifying big data's return on investment to business executives focused on competition, profit margins and allocation of funds. "For a lot of organizations like ours, big data has not yet become a core foundation of running the business," said Beata Puncevic, director of analytics, data engineering and data management at Blue Cross Blue Shield of Michigan. Yet, actionable insights gained from big data analytics can be indispensable in driving revenue, reducing costs and developing new products.
This handbook on big data analytics examines the trials and tribulations of big data users who are on the front lines, devising and implementing partial and full-blown applications. In the first feature, editor Craig Stedman interviews battle-tested IT and analytics warriors from Blue Cross, Macy's and Progressive Insurance who reveal the business challenges in justifying the worthiness of big data applications. In the second feature, Stedman explains how real-time big data analytics is helping companies like Comcast and eBay to move quickly on massive amounts of incoming information. And in the third feature, reporter Ed Burns spotlights the decisions at Neilsen and Nasdaq to run or not to run big data systems in the cloud.Continue Reading
Problem Solve Data mining Issues
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It seems like everything's in motion these days. Streaming data is no exception. With the avenues of information proliferating, it's critical that companies extract value from incoming data in real time, enabling them to devise successful business strategies and remain competitive. IDC reports that the amount of data created is growing at a burgeoning rate of 40% annually and will reach 44 trillion gigabytes by 2020. Needless to say, collecting, storing and analyzing this data can require a nonstop effort of herculean proportions. Cloud streaming analytics can help companies get a better grip on that relentless endeavor and mine true business value.
In the first feature of this guide, News Writer Joel Shore reports how Time Warner Cable used real-time cloud streaming analytics as a service to gain better insight into its customer base and identify new marketing opportunities for its clients. The second feature by Shore raises the question of whether big data is too big for cloud-based analytics. While companies amass and store large amounts of data, the percentage of collected data that's actually accessed for analytics purposes is alarmingly microscopic. In the third feature, IT consultant Swathija Raman delineates five principles of mastering software analytics, logging and reporting in relation to the internet of things and how these principles apply equally to a multitude of business formats.Continue Reading
In some organizations, corporate lawyers are tasked with protecting data from analytics abuse. But they need to avoid tying the hands of data scientists. Continue Reading
Cisco network security involves numerous users and products; Martin Roesch explains why the huge amount of data that results from this is a good thing. Continue Reading