Why Outsourcing Data Mining Services?

Are huge volumes of raw data waiting to be converted into information that you can use? Your organization's hunt for valuable information ends with valuable data mining, which can help to bring more accuracy and clarity in decision making process.

Nowadays world is information hungry and with Internet offering flexible communication, there is remarkable flow of data. It is significant to make the data available in a readily workable format where it can be of great help to your business. Then filtered data is of considerable use to the organization and efficient this services to increase profits, smooth work flow and ameliorating overall risks.

Data mining is a process that engages sorting through vast amounts of data and seeking out the pertinent information. Most of the instance data mining is conducted by professional, business organizations and financial analysts, although there are many growing fields that are finding the benefits of using in their business.

Data mining is helpful in every decision to make it quick and feasible. The information obtained by it is used for several applications for decision-making relating to direct marketing, e-commerce, customer relationship management, healthcare, scientific tests, telecommunications, financial services and utilities.

Data mining services include:

    Congregation data from websites into excel database
    Searching & collecting contact information from websites
    Using software to extract data from websites
    Extracting and summarizing stories from news sources
    Gathering information about competitors business

In this globalization era, handling your important data is becoming a headache for many business verticals. Then outsourcing is profitable option for your business. Since all projects are customized to suit the exact needs of the customer, huge savings in terms of time, money and infrastructure can be realized.

Advantages of Outsourcing Data Mining Services:

    Skilled and qualified technical staff who are proficient in English
    Improved technology scalability
    Advanced infrastructure resources
    Quick turnaround time
    Cost-effective prices
    Secure Network systems to ensure data safety
    Increased market coverage

Outsourcing will help you to focus on your core business operations and thus improve overall productivity. So data mining outsourcing is become wise choice for business. Outsourcing of this services helps businesses to manage their data effectively, which in turn enable them to achieve higher profits.



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Data Mining Models - Tom's Ten Data Tips

What is a model? A model is a purposeful simplification of reality. Models can take on many forms. A built-to-scale look alike, a mathematical equation, a spreadsheet, or a person, a scene, and many other forms. In all cases, the model uses only part of reality, that's why it's a simplification. And in all cases, the way one reduces the complexity of real life, is chosen with a purpose. The purpose is to focus on particular characteristics, at the expense of losing extraneous detail.

If you ask my son, Carmen Elektra is the ultimate model. She replaces an image of women in general, and embodies a particular attractive one at that. A model for a wind tunnel, may look like the real car, at least the outside, but doesn't need an engine, brakes, real tires, etc. The purpose is to focus on aerodynamics, so this model only needs to have an identical outside shape.

Data Mining models, reduce intricate relations in data. They're a simplified representation of characteristic patterns in data. This can be for 2 reasons. Either to predict or describe mechanics, e.g. "what application form characteristics are indicative of a future default credit card applicant?". Or secondly, to give insight in complex, high dimensional patterns. An example of the latter could be a customer segmentation. Based on clustering similar patterns of database attributes one defines groups like: high income/ high spending/ need for credit, low income/ need for credit, high income/ frugal/ no need for credit, etc.

1. A Predictive Model Relies On The Future Being Like The Past

As Yogi Berra said: "Predicting is hard, especially when it's about the future". The same holds for data mining. What is commonly referred to as "predictive modeling", is in essence a classification task.

Based on the (big) assumption that the future will resemble the past, we classify future occurrences for their similarity with past cases. Then we 'predict' they will behave like past look-alikes.

2. Even A 'Purely' Predictive Model Should Always (Be) Explain(ed)

Predictive models are generally used to provide scores (likelihood to churn) or decisions (accept yes/no). Regardless, they should always be accompanied by explanations that give insight in the model. This is for two reasons:

    buy-in from business stakeholders to act on predictions is of eminent importance, and gains from understanding
    peculiarities in data do sometimes arise, and may become obvious from the model's explanation


3. It's Not About The Model, But The Results It Generates

Models are developed for a purpose. All too often, data miners fall in love with their own methodology (or algorithms). Nobody cares. Clients (not customers) who should benefit from using a model are interested in only one thing: "What's in it for me?"

Therefore, the single most important thing on a data miner's mind should be: "How do I communicate the benefits of using this model to my client?" This calls for patience, persistence, and the ability to explain in business terms how using the model will affect the company's bottom line. Practice explaining this to your grandmother, and you will come a long way towards becoming effective.

4. How Do You Measure The 'Success' Of A Model?

There are really two answers to this question. An important and simple one, and an academic and wildly complex one. What counts the most is the result in business terms. This can range from percentage of response to a direct marketing campaign, number of fraudulent claims intercepted, average sale per lead, likelihood of churn, etc.

The academic issue is how to determine the improvement a model gives over the best alternative course of business action. This turns out to be an intriguing, ill understood question. This is a frontier of future scientific study, and mathematical theory. Bias-Variance Decomposition is one of those mathematical frontiers.

5. A Model Predicts Only As Good As The Data That Go In To It

The old "Garbage In, Garbage Out" (GiGo), is hackneyed but true (unfortunately). But there is more to this topic. Across a broad range of industries, channels, products, and settings we have found a common pattern. Input (predictive) variables can be ordered from transactional to demographic. From transient and volatile to stable.

In general, transactional variables that relate to (recent) activity hold the most predictive power. Less dynamic variables, like demographics, tend to be weaker predictors. The downside is that model performance (predictive "power") on the basis of transactional and behavioral variables usually degrades faster over time. Therefore such models need to be updated or rebuilt more often.

6. Models Need To Be Monitored For Performance Degradence

It is adamant to always, always follow up model deployment by reviewing its effectiveness. Failing to do so, should be likened to driving a car with blinders on. Reckless.

To monitor how a model keeps performing over time, you check whether the prediction as generated by the model, matches the patterns of response when deployed in real life. Although no rocket science, this can be tricky to accomplish in practice.

7. Classification Accuracy Is Not A Sufficient Indicator Of Model Quality

Contrary to common belief, even among data miners, no single number of classification accuracy (R2, Gini-coefficient, lift, etc.) is valid to quantify model quality. The reason behind this has nothing to do with the model itself, but rather with the fact that a model derives its quality from being applied.

The quality of model predictions calls for at least two numbers: one number to indicate accuracy of prediction (these are commonly the only numbers supplied), and another number to reflect its generalizability. The latter indicates resilience to changing multi-variate distributions, the degree to which the model will hold up as reality changes very slowly. Hence, it's measured by the multi-variate representativeness of the input variables in the final model.

8. Exploratory Models Are As Good As the Insight They Give

There are many reasons why you want to give insight in the relations found in the data. In all cases, the purpose is to make a large amount of data and exponential number of relations palatable. You knowingly ignore detail and point to "interesting" and potentially actionable highlights.

The key here is, as Einstein pointed out already, to have a model that is as simple as possible, but not too simple. It should be as simple as possible in order to impose structure on complexity. At the same time, it shouldn't be too simple so that the image of reality becomes overly distorted.

9. Get A Decent Model Fast, Rather Than A Great One Later

In almost all business settings, it is far more important to get a reasonable model deployed quickly, instead of working to improve it. This is for three reasons:

    A working model is making money; a model under construction is not
    When a model is in place, you have a chance to "learn from experience", the same holds for even a mild improvement - is it working as expected?
    The best way to manage models is by getting agile in updating. No better practice than doing it... :)


10. Data Mining Models - What's In It For Me?

Who needs data mining models? As the world around us becomes ever more digitized, the number of possible applications abound. And as data mining software has come of age, you don't need a PhD in statistics anymore to operate such applications.

In almost every instance where data can be used to make intelligent decisions, there's a fair chance that models could help. When 40 years ago underwriters were replaced by scorecards (a particular kind of data mining model), nobody could believe that such a simple set of decision rules could be effective. Fortunes have been made by early adopters since then.




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Data Mining And Importance to Achieve Competitive Edge in Business

What is data mining? And why it is so much importance in business? These are simple yet complicated questions to be answered, below is brief information to help understanding data and web mining services.

Mining of data in general terms can be elaborated as retrieving useful information or knowledge for further process of analyzing from various perspectives and summarizing in valuable information to be used for increasing revenue, cut cost, to gather competitive information on business or product. And data abstraction finds a great importance in business world as it help business to harness the power of accurate information thus providing competitive edge in business. May business firms and companies have their own warehouse to help them collect, organize and mine information such as transactional data, purchase data etc.

But to have a mining services and warehouse at premises is not affordable and not very cost effective to solution for reliable information solutions. But as if taking out of information is the need for every business now days. Many companies are providing accurate and effective data and web data mining solutions at reasonable price.

Outsourcing information abstraction services are offered at affordable rates and it is available for wide range of data mine solutions:

• taking out business data
• service to gather data sets
• digging information of datasets
• Website data mining
• stock market information
• Statistical information
• Information classification
• Information regression
• Structured data analysis
• Online mining of data to gather product details
• to gather prices
• to gather product specifications
• to gather images

Outsource web mining solutions and data gathering solutions has been effective in terms of cost cutting, increasing productivity at affordable rates. Benefits of data mining services include:

• clear customer, service or product understanding
• less or minimal marketing cost
• exact information on sales, transactions
• detection of beneficial patterns
• minimizing risk and increased ROI
• new market detection
• Understanding clear business problems and goals

Accurate data mining solutions could prove to be an effective way to cut down cost by concentrating on right place.



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Data Mining

I just love the so called political correct terminology used today. How they decide it's really a political correct term is beyond me. Why don't they call it what it is; misleading terminology, whitewashing, sugar coating. Example: collaterial damage ie. innocent people fatally killed in the wake of combat.

Now the new term for collecting information on people to compile marketing lists so that we can all receive more junk mail, marketing phone calls and faxes is a cute little term called Data Mining.

This is a quote from a banking website on the subject:
Let me try to clarify this. We are wanting to contact our commercial customers' payors that do not bank with us. We would get the information off their checks when our commercial customers deposit them. For example, I use ABC Cleaners and pay by check but I bank at a different bank than the Cleaners. The Cleaner's bank would take my information off my check to contact me.

Data mining has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data" [1] and "The science of extracting useful information from large data sets or databases" [2]. Although it is usually used in relation to analysis of data, data mining, like artificial intelligence, is an umbrella term and is used with varied meaning in a wide range of contexts.

The Kirchman Corporation, a leading provider of automation software and compliance services to the banking industry, with over 800 financial institutions worldwide recently publised a 4 page gloss executive summary on Data Mining in the banking industry.

[The Kirchman Corporation became a Metavante subsidiary in May 2004 and operates within the Metavante Financial Services Group. Metavante is a subsidiary of Marshall & Ilsley Corporation (NYSE: MI) Kirchman Bankway®, the easiest-to-use and most customer-focused core bank software on the market, provides a comprehensive package of business solutions for use by the smallest de novo to the largest multi-billion-dollar bank holding company. Operating on IBM and Sun Microsystems hardware platforms, Kirchman Bankway® makes it easy for everyone from the teller to the CEO to access everything they need to know about their customers, right at their fingertips.[www.kirchman.com, Orlando. FL]

Data Mining software is used to analyse customer data and trends in banking as well as many other industries. Chase analyzed the attributes and habits of its checking account customers for clues that might reveal how to set the minimum balance requirement to retain profitable customers. The staff used data mining to develop profiles of customer groups whose members consistently had trouble maintaining minimum balances.

I'm sure you no where I'm headed with this. If Banks use Data Mining to predict trends in one area of services, it's not a far leap to say they would also use data mining to add courtesy bounce protection to customers who have a low balance and would be more likley to overdraft. Would it be too far of a leap to say banks might even use data mining in deciding what customers may get their account processed high to low?



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Data Mining, Not Just a Method But a Technique

Web data mining is segregating probable clients out of huge information available on the Internet by performing various searches. It could be well organized and structured, or raw, depending on the use of the data. Web data mining could be done using a simple database program or investing money in a costly program.

Start collecting basic contact information of probable clients, such as: names, addresses, landline and cell phone numbers, email addresses and education or occupation if required.

CART and CHAID data mining

While collecting data you will find that tree-shaped structures that represent decisions. These derived decisions give rules for the classification of data collected. Precise decision tree methods include Classification and Regression Trees also know as CART data mining and Chi Square Automatic Interaction Detection also known as CHAID data mining. CART and CHAID data mining are decision tree techniques used for classification of data collected. They provide a set of rules that could be applied to unclassified data collected in prediction. CART segments a dataset creating two-way splits whereas CHAID segments using chi square tests creating multi-way splits. CART requires less data preparation compared to CHAID.

Understanding customer's actions

Keep a track of customer's actions like: what does he buy, when does he buy, why does he buy, what is the use of his buying, etc. Knowing such simple things about your customer will help you to understand needs of your customer better and thus process of data mining services will be easier and quality data would be mined. This will increase your personal relations with your customer which would finally result in a better professional relationship.

Following demography

Mine the data as per demography, dependent on geography as well as socio economic background of business location. You can use government statistics as the source of your data collection. Keeping it in mind you can go ahead with the understanding of the community existing and thus the data required.

Use your informal conversation in serving your clients better

Use minute details of your conversation and understanding with your customers to serve them. If essential, conduct surveys, send a professional gift or use some other object that helps you understand better in fulfilling customer needs. This will increase the bonding between you and your customer and you will be able to serve your customer better in providing data mining services.

Insert the collect information in a desktop database. More the information is collected you will find that you can prepare specific templates in feeding information. Using a desktop database, it is easier to make changes later on as and when required.

Maintaining privacy

While performing, it is essential to ensure that you or your team members are not violating privacy laws in gathering or providing the data information. Once trust is lost, you may also loose the customer, because trust is the base of any relationship, let it be a business relation.



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Know What the Truth Behind Data Mining Outsourcing Service

We came to that, what we call the information age where industries are like useful data needed for decision-making, the creation of products - among other essential uses for business. Information mining and converting them to useful information is a part of this trend that allows companies to reach their optimum potential. However, many companies that do not meet even one deal with data mining question because they are simply overwhelmed with other important tasks. This is where data mining outsourcing comes in.

There have been many definitions to introduced, but it can be simply explained as a process that involves sorting through large amounts of raw data to extract valuable information needed by industries and enterprises in various fields. In most cases this is done by professionals, professional organizations and financial analysts. He has seen considerable growth in the number of sectors or groups that enter my self.
There are a number of reasons why there is a rapid growth in data mining outsourcing service subscriptions. Some of them are presented below:

A wide range of services

Many companies are turning to information mining outsourcing, because they cover a wide range of services. These services include, but are not limited to data from web applications congregation database, collect contact information from different sites, extract data from websites using the software, the sort of stories from sources news, information and accumulate commercial competitors.

Many companies fall

Many industries benefit because it is fast and realistic. The information extracted by data mining service providers of outsourcing used in crucial decisions in the field of direct marketing, e-commerce, customer relationship management, health, scientific tests and other experimental work, telecommunications, financial services, and a whole lot more.

A lot of advantages

Subscribe data mining outsourcing services it's offers many benefits, as providers assures customers to render services to world standards. They strive to work with improved technologies, scalability, sophisticated infrastructure, resources, timeliness, cost, the system safer for the security of information and increased market coverage.

Outsourcing allows companies to focus their core business and can improve overall productivity. Not surprisingly, information mining outsourcing has been a first choice of many companies - to propel the business to higher profits.



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An Easy Way For Data Extraction

There are so many data scraping tools are available in internet. With these tools you can you download large amount of data without any stress. From the past decade, the internet revolution has made the entire world as an information center. You can obtain any type of information from the internet. However, if you want any particular information on one task, you need search more websites. If you are interested in download all the information from the websites, you need to copy the information and pate in your documents. It seems a little bit hectic work for everyone. With these scraping tools, you can save your time, money and it reduces manual work.

The Web data extraction tool will extract the data from the HTML pages of the different websites and compares the data. Every day, there are so many websites are hosting in internet. It is not possible to see all the websites in a single day. With these data mining tool, you are able to view all the web pages in internet. If you are using a wide range of applications, these scraping tools are very much useful to you.

The data extraction software tool is used to compare the structured data in internet. There are so many search engines in internet will help you to find a website on a particular issue. The data in different sites is appears in different styles. This scraping expert will help you to compare the date in different site and structures the data for records.

And the web crawler software tool is used to index the web pages in the internet; it will move the data from internet to your hard disk. With this work, you can browse the internet much faster when connected. And the important use of this tool is if you are trying to download the data from internet in off peak hours. It will take a lot of time to download. However, with this tool you can download any data from internet at fast rate.There is another tool for business person is called email extractor. With this toll, you can easily target the customers email addresses. You can send advertisement for your product to the targeted customers at any time. This the best tool to find the database of the customers.

However, there are some more scraping tolls are available in internet. And also some of esteemed websites are providing the information about these tools. You download these tools by paying a nominal amount.



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