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Data Mining
Data Mining (DM), also called Knowledge-Discovery in Databases (KDD) or Knowledge-Discovery and Data Mining, is the process of automatically searching large volumes of data for patterns such as association rules. It is a fairly recent topic in computer science but applies many older computational techniques from statistics, information retrieval, machine learning and pattern recognition.
Have you considered the suitability of spreadsheets for business planning and more importantly, the danger they pose to the security and competitiveness of your organization? If not, here are six questions you should ask yourself.
With the ranks of mobile and remote workers expanding every day, cost-effective endpoint security is a growing business concern that enterprise IT organizations avoid at their peril. A solid approach to enterprise-wide protection focuses equally on safeguarding data and preventing unauthorized access. Download this paper to learn more.
This paper will outline the value and methods involved in data mining across both quantitative and qualitative data. In addition, it will describe the data transformations necessary before doing such work, and the tools that are particularly valuable for mining mixed data types.
Nearly all companies realize that the way to gain a competitive advantage is to obtain better data, interpret them quickly, and distribute them in easier-to-use formats. However, there are many obstacles to the effective use of data and few companies surmount them all-a fact that results in a lot of unused corporate data. How are companies using information to beat their rivals and create a more level playing field? This report from the Economist Intelligence Unit examines how their practices are evolving and offers examples of data use at some highly successful companies.
The list of initiatives that are data-enabled-indeed, data-intensive-vary from company to company, from industry to industry. But the common denominator is an increasing awareness that these and other programs can't succeed without clean, meaningful, and available information. In this white paper, Baseline Consulting's Jill Dyché discusses how the data conversation is changing from philosophical questioning to hard-core tactics, from "What do we need?" to "Where do we start?" It covers the most common reasons for data governance failure, as well as the components of data governance that will inform the right strategy and give companies a means of determining where and how to begin their data governance journeys.
Published By: IBM Software
Published Date: Feb 07, 2011
See what BI can do for your company in this complete four-chapter Shortcut Guide to BI success. Learn practical, affordable tips for creating or expanding your BI strategy.
This white paper discusses what drives organizations in both heavily-regulated and less regulated industries to retain email and other electronic content.
Published By: IBM Software
Published Date: Nov 02, 2010
Learn how leading insurance companies are using data mining techniques to target claims with the greatest likelihood of adjustment, improving audit accuracy, and saving time and resources.
Proactive Data Management Controls Data Growth. To take control, organizations must begin with the conscious decision not to let data growth proceed unexamined.
Vendors in this market have been challenged in their attempts to gain market visibility and awareness for their products and for the benefits they can provide compared with just throwing more hardware at the problem.
Forget spreadsheets. Organizations that are winning in this down economy are using automated analytical tools to take a more scientific approach to decision making through observation, experimentation and measurement to improve their business processes.
Published By: Vertica
Published Date: Aug 16, 2010
The Vertica Analytic Database is the only database built from scratch to handle today's heavy business intelligence workloads. In customer benchmarks, Vertica has been shown to manage terabytes of data running on extraordinarily low-cost hardware and answers queries 50 to 200 times faster than competing row-oriented databases and specialized analytic hardware. This document summarizes the key aspects of Vertica's technology that enable such dramatic performance benefits, and compares the design of Vertica to other popular relational systems.
Published By: Vertica
Published Date: Aug 15, 2010
If you are responsible for BI (Business Intelligence) in your organization, there are three questions you should ask yourself:
- Are there applications in my organization for combining operational processes with analytical insight that we can't deploy because of performance and capacity constraints with our existing BI environment?
Published By: Vertica
Published Date: Mar 15, 2010
Revenue assurance analysts at a top-tier US-based carrier studied this every day. Primarily
focused on detecting fraud, revenue sharing contract violations and incomplete revenue collections,
they had the need to query and analyze call detail record (CDR) databases that grow by millions of
new CDRs every day.
Published By: Vertica
Published Date: Mar 15, 2010
In a world of growing data volumes and shrinking IT budgets, it is critical to think differently about the efficiency of your database and storage infrastructure. The Vertica Analytic Database is a high-performance, scalable and cost-effective solution that can bring dramatic savings in
hardware, storage and operational costs.