What You Should Know About Data Mining

Often called data or knowledge discovery, data mining is the process of analyzing data from various perspectives and summarizing it into useful information to help beef up revenue or cut costs. Data mining software is among the many analytical tools used to analyze data. It allows categorizing of data and shows a summary of the relationships identified. From a technical perspective, it is finding patterns or correlations among fields in large relational databases. Find out how data mining works and its innovations, what technological infrastructures are needed, and what tools like phone number validation can do.

Data mining may be a relatively new term, but it uses old technology. For instance, companies have made use of computers to sift through supermarket scanner data - volumes of them - and analyze years' worth of market research. These kinds of analyses help define the frequency of customer shopping, how many items are usually bought, and other information that will help the establishment increase revenue. These days, however, what makes this easy and more cost-effective are disk storage, statistical software, and computer processing power.

Data mining is mainly used by companies who want to maintain a strong customer focus, whether they're engaged in retail, finance, marketing, or communications. It enables companies to determine the different relationships among varying factors, including staffing, pricing, product positioning, market competition, and social demographics.

Data mining software, for example, vary in types: statistical, machine learning, and neural networks. It seeks any of the four types of relationships: classes (stored data is used for locating data in predetermined groups), clusters (data are grouped according to logical relationships or consumer preferences), associations (data is mined to identify associations), and sequential patterns (data is mined to estimate behavioral trends and patterns). There are different levels of analysis, including artificial neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction, and data visualization.

In today's world, data mining applications are available on all size systems from client/server, mainframe, and PC platforms. When it comes to enterprise-wide applications, the size usually ranges from 10 gigabytes to more than 11 terabytes. The two important technological drivers are the size of the database and query complexity. A more powerful system is required with more data being processed and maintained, and with more complex and greater queries.

Programmable XML web services like phone number validation will assist your company in improving the quality of your data needed for data mining. Used to validate phone numbers, a phone number validation service allows you to improve the quality of your contact database by eliminating invalid telephone numbers at the point of entry. Upon verification, phone number and other customer information can work wonders for your business and its constant improvement.




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Effectiveness of Web Data Mining Through Web Research

Web data mining is systematic approach to keyword based and hyperlink based web research for gaining business intelligence. It requires analytical skills to understand hyperlink structure of given website. Hyperlinks possess enormous amount of hidden human annotations that can help automatically understand the authority. If the webmaster provides a hyperlink pointing to another website or web page, this action is perceived as an endorsement to that webpage. Search engines highly focus on such endorsements to define the importance of the page and place them higher in organic search results.

However every hyperlink does not refer to the endorsement since the webmaster may have used it for other purposes, such as navigation or to render paid advertisements. It is important to note that authoritative pages rarely provide informative descriptions. For an instant, Google's homepage may not provide explicit self-description as "Web search engine."

These features of hyperlink systems have forced researchers to evaluate another important webpage category called hubs. A hub is a unique, informative webpage that offers collections of links to authorities. It may have only a few links pointing to other web pages but it links to a collection of prominent sites on a single topic. A hub directly awards authority status on sites that focus on a single topic. Typically, a quality hub points to many quality authorities, and, conversely, a web page that many such hubs link to can be deemed as a superior authority.

Such approach of identifying authoritative pages has resulted in the development of various popularity algorithms such as PageRank. Google uses PageRank algorithm to define authority of each webpage for a relevant search query. By analyzing hyperlink structures and web page content, these search engines can render better-quality search results than term-index engines such as Ask and topic directories such as DMOZ.

For any queries related to Web data mining and Web-based market research email us at info@outsourcingwebresearch.com

Richard Kaith is member of Data extraction services team at Outsourcing Web Research firm - an established BPO company offering effective Data mining, Data extraction and Web research services at affordable rates. For any queries visit us at http://www.outsourcingwebresearch.com




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What's Your Excuse For Not Using Data Mining?

In an earlier article I briefly described how data mining and RFM analysis can help marketers be more efficient (read... increased marketing ROI!). These marketing analytics tools can significantly help with all direct marketing efforts (multichannel campaign management efforts using direct mail, email and call center) and some interactive marketing efforts as well. So, why aren't all companies using it today? Well, typically it comes down to a lack of data and/or statistical expertise. Even if you don't have data mining expertise, YOU can benefit from data mining by using a consultant. With that in mind, let's tackle the first problem -- collecting and developing the data that is useful for data mining.

The most important data to collect for data mining include:

oTransaction data - For every sale, you at least need to know the product and the amount and date of the purchase.

oPast campaign response data - For every campaign you've run, you need to identify who responded and who didn't. You may need to use direct and indirect response attribution.

oGeo-demographic data - This is optional, but you may want to append your customer file/database with consumer overlay data from companies like Acxiom.

oLifestyle data - This is also an optional append of indicators of socio-economic lifestyle that are developed by companies like Claritas. All of the above data may or may not exist in the same data source. Some companies have a single holistic view of the customer in a database and some don't. If you don't, you'll have to make sure all data sources that contain customer data have the same customer ID/key. That way, all of the needed data can be brought together for data mining.

How much data do you need for data mining? You'll hear many different answers, but I like to have at least 15,000 customer records to have confidence in my results.

Once you have the data, you need to massage it to get it ready to be "baked" by your data mining application. Some data mining applications will automatically do this for you. It's like a bread machine where you put in all the ingredients -- they automatically get mixed, the bread rises, bakes, and is ready for consumption! Some notable companies that do this include KXEN, SAS, and SPSS. Even if you take the automated approach, it's helpful to understand what kinds of things are done to the data prior to model building.

Preparation includes:

oMissing data analysis. What fields have missing values? Should you fill in the missing values? If so, what values do you use? Should the field be used at all?

oOutlier detection. Is "33 children in a household" extreme? Probably - and consequently this value should be adjusted to perhaps the average or maximum number of children in your customer's households.

oTransformations and standardizations. When various fields have vastly different ranges (e.g., number of children per household and income), it's often helpful to standardize or normalize your data to get better results. It's also useful to transform data to get better predictive relationships. For instance, it's common to transform monetary variables by using their natural logs.

oBinning Data. Binning continuous variables is an approach that can help with noisy data. It is also required by some data mining algorithms.

More to come on data mining for marketers in my next article.

Jim Stafford has worked for leading companies in the Marketing Automation space (BI, data mining, campaign management and eMarketing) for over 10 years. He has held roles of Director - Database Marketing Solutions, Pre-Sales Manager, Product Manager, and Solution Architect at companies like Aprimo, Group1 Software, SAS, Siebel, SPSS and Unica. Mr. Stafford has consistently helped sales teams meet or beat established sales targets. He was the principal pre-sales contributor to Siebel's second largest MA sale with General Motors. Jim has had considerable exposure to many verticals including: Financial Services, Hospitality & Entertainment, Automotive, Communications, and Utilities. He is a seasoned expert at discovery and knows key industry trends. Jim has an M.A. Degree in Economics from the University of Maryland and has been a frequent speaker at annual National Center for Database Marketing and Direct Marketing Associations events. Visit [http://www.staffordsbsg.com/] to learn more about Jim and his company's services.




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Importance Of Data Mining In Today's Business World

What is Data Mining? Well, it can be defined as the process of getting hidden information from the piles of databases for analysis purposes. Data Mining is also known as Knowledge Discovery in Databases (KDD). It is nothing but extraction of data from large databases for some specialized work.

Data Mining is largely used in several applications such as understanding consumer research marketing, product analysis, demand and supply analysis, e-commerce, investment trend in stocks & real estates, telecommunications and so on. Data Mining is based on mathematical algorithm and analytical skills to drive the desired results from the huge database collection.

Data Mining has great importance in today's highly competitive business environment. A new concept of Business Intelligence data mining has evolved now, which is widely used by leading corporate houses to stay ahead of their competitors. Business Intelligence (BI) can help in providing latest information and used for competition analysis, market research, economical trends, consume behavior, industry research, geographical information analysis and so on. Business Intelligence Data Mining helps in decision-making.

Data Mining applications are widely used in direct marketing, health industry, e-commerce, customer relationship management (CRM), FMCG industry, telecommunication industry and financial sector. Data mining is available in various forms like text mining, web mining, audio & video data mining, pictorial data mining, relational databases, and social networks data mining.

Data mining, however, is a crucial process and requires lots of time and patience in collecting desired data due to complexity and of the databases. This could also be possible that you need to look for help from outsourcing companies. These outsourcing companies are specialized in extracting or mining the data, filtering it and then keeping them in order for analysis. Data Mining has been used in different context but is being commonly used for business and organizational needs for analytical purposes

Usually data mining requires lots of manual job such as collecting information, assessing data, using internet to look for more details etc. The second option is to make software that will scan the internet to find relevant details and information. Software option could be the best for data mining as this will save tremendous amount of time and labor. Some of the popular data mining software programs available are Connexor Machines, Free Text Software Technologies, Megaputer Text Analyst, SAS Text Miner, LexiQuest, WordStat, Lextek Profiling Engine.

However, this could be possible that you won't get appropriate software which will be suitable for your work or finding the suitable programmer would also be difficult or they may charge hefty amount for their services. Even if you are using the best software, you will still need human help in completion of projects. In that case, outsourcing data mining job will be advisable.

Scott Naxton is a freelance journalist having experience of many years writing articles and news releases on businesses like outsourcing, internet marketing, health and insurance. He is also associated with Outsourcing [http://www.kpoasia.com] and KPO [http://www.kpoasia.com]





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Data Loss Due to Damaged Internal Parts of the Hard Drive

Hard drives consist of several internal components that are very delicate and can get damaged at any point of time. Even small jerks may cause irrevocable damage to the drive and therefore to all the data stored in it. Since it is memory of a computer system, one needs to be very careful while shifting the computer from one place to another, as any mishap can prove costly if the it gets damaged. Generally, computer users do not take backup of the entire drive, which means all the important data that is now not accessible might just get lost. Damaged drives need to be handled even more cautiously and given proper treatment for recovery, which is done by Data Recovery Services companies.

Computers often need to be shifted from one work station to another in offices. While doing so, if the CPU falls and gets damaged, then, even the drive too might be in serious trouble as it may be subject to damage of several internal components like spindle motor, platters, etc. This may lead to a non-functional hard drive, resulting in complete non accessibility of data.

Why this happened?

When the drive falls, all the parts within get affected by the impact.
The read-write heads may scratch the platters
The head assembly may break.
The shaft motor could get damaged.
Any of the parts getting damaged has a domino effect and renders the drive non-usable.

How to resolve the problem?

Physical damage of the internal components of the hard drive, requires opening the hard drive to extract data and then saving it on another drive. This problem can be solved by our technicians. They have the knowledge and experience to fix the damaged hard drive completely to extract data from it to save it into another drive. The procedure of Data Recovery from a damaged drive is carried out under the sterilized and safe environment of Clean Room to ensure the safety and integrity of data stored on the hard drive.

Stellar Data Recovery Inc. is a well reputed company that offers world class Data Recovery Services for all kinds of data loss. The company is equipped with Class 100 Clean Room facility where it uses various indigenous techniques to recover data comprehensively in all scenarios of physical data loss. The company feels proud to have over 1,100,000 satisfied customers worldwide and provides recovery of data from all hard drives such as RAID, NAS, SAN, SCSI, etc.

Shaun Pattrik has 6 Years of Experience in the Software Technology field doing research in Stellar Data Recovery Inc. which offers data recovery, data recovery New Jersey, data recovery services and hard drive recovery services.




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Limitations and Challenges in Effective Web Data Mining

Web data mining and data collection is critical process for many business and market research firms today. Conventional Web data mining techniques involve search engines like Google, Yahoo, AOL, etc and keyword, directory and topic-based searches. Since the Web's existing structure cannot provide high-quality, definite and intelligent information, systematic web data mining may help you get desired business intelligence and relevant data.

Factors that affect the effectiveness of keyword-based searches include:
• Use of general or broad keywords on search engines result in millions of web pages, many of which are totally irrelevant.
• Similar or multi-variant keyword semantics my return ambiguous results. For an instant word panther could be an animal, sports accessory or movie name.
• It is quite possible that you may miss many highly relevant web pages that do not directly include the searched keyword.

The most important factor that prohibits deep web access is the effectiveness of search engine crawlers. Modern search engine crawlers or bot can not access the entire web due to bandwidth limitations. There are thousands of internet databases that can offer high-quality, editor scanned and well-maintained information, but are not accessed by the crawlers.

Almost all search engines have limited options for keyword query combination. For example Google and Yahoo provide option like phrase match or exact match to limit search results. It demands for more efforts and time to get most relevant information. Since human behavior and choices change over time, a web page needs to be updated more frequently to reflect these trends. Also, there is limited space for multi-dimensional web data mining since existing information search rely heavily on keyword-based indices, not the real data.

Above mentioned limitations and challenges have resulted in a quest for efficiently and effectively discover and use Web resources. Send us any of your queries regarding Web Data mining processes to explore the topic in more detail.




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Data Mining Software - Discover Software Modernization

Data mining software is usually an application that one uses and covers mostly with one's knowledge in the discovery of software modernization. Mining data software involves the understanding of the software artifacts that exist and the mining data tools. This process has very close relations with reverse engineering. The knowledge that one gains from studying data software that exists is usually presented in forms of models and by doing these queries one can be in a position to make his personal data mining software. With the knowledge that someone gains it must be applicable and one must also know the mining data tools that are suppose to be used apart from the soft wares. One can be able to know very widely about the mining data tools that are there in mining data software by doing computer science as a course. Computer science covers widely on what are the procedures, steps of mining data software and how can use the mining data tools.

This software is mostly used in making of databases schemes. Making of databases is not as easy as many would think it requires one to have some knowledge about computer engineering and the basic concepts of computers.;This software is mostly used in data crawling because it can be in a position to store data and one can be able to retrieve the data when needed.

The softwares are not that cheap they come in different varieties and it will depend on which information or the database on which one is coming up with.

Data mining software are usually in different levels there is the data level, design level, application level, architectural level, call graph level and program level it will depend on which level one is covering and this come together with mining data tools.

Data software's have increased rapidly through the introduction of computers and ERP definition. Computers hackers have been able to get the softwares at a very low price and this has made data mining to become very easy and quick to use in the shops and supermarkets and also government institutions. One cannot do data crawling without having the basic knowledge about data mining soft wares because soft wares are the programmes that are usually installed into the computer and without the programmes then no data can be processed.

There are a lot of challenges that come with the use of the mining soft ware. One can easily crush the software he is using or the softwares can easily break they are normally sold on CDS one can easily break it or loose it.

High chances of losing the data that someone is coming up with is very high because computers easily crash due to some difficulties that they experience or a virus can easily crush the computer.

Mining software take a very large space and in most of the computers. The reason behind this is because, data crawling use graphics. Graphics usually occupy a lot of space in terms of the size of the local disk. One is suppose to look for a computer that has very good memory. Data crawling is something that needs to be updated each and every time something appears along the way.

Victor C. has many hobbies and interests. As well being a keen blogger and article writer for many sites, he has also recently created a site focusing on data mining tools. The site is constantly being updated.



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