Online Data Entry Services

Online data entry services are now commonly used by businesses and these services are generally offered by outsourcing companies with the required standards and specifications. As everything is becoming global, business entities need to manage their valuable and critical data in an accurate and organized manner in order to maintain their competitiveness in the global marketplace. They usually entrust their non core, repetitive and other support tasks to BPO firms who can offer affordable, reliable and trustworthy documentation services online.

Online data entry services have become immensely helpful in all fields where the data needs to be stored, maintained and used for future applications. Today, many firms have partnered with business process outsourcing companies to have an excellent data management system in their facilities. By integrating state-of-the-art technologies, unique processes and skilled data entry specialists, these firms deliver data entry services with accuracy, efficiency and effectiveness. They offer their services through safe and secure online platform. They deliver the final outputs in encrypted FTP upload, CD-R or CD-W or email. Thus, clients are assured that their data or information is free from unauthorized access, copying or downloading.

Business process outsourcing companies specializing in online data entry services offer a wide spectrum of services, tailored to the particular needs of each client. Some of them are listed below:

o Text, numeric or alphanumeric, image or hardcopy date entry
o Data entry from handwritten or printed materials such as books, newspapers, magazines
o Catalog and business card documentation
o E-books and e-magazines
o Data entry from insurance claims and property tax records
o Online listing of yellow pages
o For website content
o Documentation of surveys, questionnaires, company reports and airway bill entries
o Data capture/collection
o Online form processing and submission
o For mailing list/mailing label
o Email mining
o Typing manuscript into MS Word
o Online copying, pasting, editing, sorting, and indexing data
o Online medical and legal data entry
o Data entry of historical data

Outsourcing your documentation task to a BPO firm is a viable and economical choice. You can eliminate tedious and time consuming tasks from your regular routine. As data entry services are developing in tune with the giant leaps in technology, your firm can also utilize these services and stay competitive in the field. Moreover, you can reduce costs, improve productivity and give more importance to core and revenue generating functions.



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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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Data Extraction Services - A Helpful Hand For Large Organization

The data extraction is the way to extract and to structure data from not structured and semi-structured electronic documents, as found on the web and in various data warehouses. Data extraction is extremely useful for the huge organizations which deal with considerable amounts of data, daily, which must be transformed into significant information and be stored for the use this later on.

Your company with tons of data but it is difficult to control and convert the data into useful information. Without right information at the right time and based on half of accurate information, decision makers with a company waste time by making wrong strategic decisions. In high competing world of businesses, the essential statistics such as information customer, the operational figures of the competitor and the sales figures inter-members play a big role in the manufacture of the strategic decisions. It can help you to take strategic business decisions that can shape your business' goals..

Outsourcing companies provide custom made services to the client's requirements. A few of the areas where it can be used to generate better sales leads, extract and harvest product pricing data, capture financial data, acquire real estate data, conduct market research , survey and analysis, conduct product research and analysis and duplicate an online database..

The different types of Data Extraction Services:

    Database Extraction:
    Reorganized data from multiple databases such as statistics about competitor's products, pricing and latest offers and customer opinion and reviews can be extracted and stored as per the requirement of company.
    Web Data Extraction:
    Web Data Extraction is also known as data Extraction which is usually referred to the practice of extract or reading text data from a targeted website.

Businesses have now realized about the huge benefits they can get by outsourcing their services. Then outsourcing is profitable option for business. Since all projects are custom based to suit the exact needs of the customer, huge savings in terms of time, money and infrastructure are among the many advantages that outsourcing brings.

Advantages of Outsourcing Data Extraction Services:

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

By outsourcing, you can definitely increase your competitive advantages. Outsourcing of services helps businesses to manage their data effectively, which in turn would enable them to experience an increase in profits.



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What Happens When Municipalities Use Rich Data Mining Against Home Businesses to Collect Tax?

Do you will realize how many Americans run a small home business? The number is staggering, and did you know that 10% of our population is self-employed, and that is something like 30 million Americans. That same 30 million Americans, also represents a group that hires over 65% of our population in their small businesses. Folks that started these little firms might expand their business and eventually hire someone, grow their business larger, actually make it into a real company. I'd say that's a good thing, and it shows that the entrepreneurial spirit in the US is alive and well.

Many people don't seem to be aware of these figures or how important they are. Did you know that 10% of our population is self-employed? Don't worry, you're not the only one who hasn't figured this out, even the President of the United States doesn't understand, or obviously he wouldn't have made that political faux pas telling small business people that they didn't build that, or that they couldn't have built their business had it not been for the government providing such a wonderful civilization and society for them to participate in.

Yes, I was a little miffed when he said that as well, because it isn't true, and I've been self-employed my entire life and I've loved my country my entire adult life as well, as have you. Now then, many municipalities are stretched thin with their budgets. Often they owe 60% of all the money they take in, in legacy cost, that is to say pensions, retirement, and health care for people who have already retired from their city employment. That means only 40% of all the money they take collect taxes actually goes to the current city services.

How can any business, much less a government operate on 40% of its income? It can't, and perhaps that's why three cities in California have filed for bankruptcy, along with a couple of other big bankruptcy municipality cases; Birmingham Alabama and Harrisburg Pennsylvania. With city budgets stretched thin they have no choice but to collect more money, and that means finding more ways to tax more people. Most cities require that if you start a business you have to get a business license, and it is considered a tax.

In some cities these taxes are only a $100 or less depending on the type of business you run, but in other cities they can run as much as $500. Most people that start a small business, especially a little home-based business don't bother to register for their business license. They don't make enough money to even afford that when they first start. But guess what? Soon I am almost positive that all these municipalities will be running rich data mining programs, and/or pay other companies to give them information about anyone who resides in their city was running a business.

They will then of course check this data and all these names against all their business licenses. If you run a business and you don't have a business license but you are doing business online, or it is mentioned on your Facebook page, you will not only have to pay the business license registration fee, you will also be charged with a penalty which could but be two or three times that amount. The cities will then have more revenue to spend by attacking small businesses just barely getting off the ground. Welcome to the future of data mining and your government. Please consider all this and think on it.



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Data Mining in the 21st Century: Business Intelligence Solutions Extract and Visualize

When you think of the term data mining, what comes to mind? If an image of a mine shaft and miners digging for diamonds or gold comes to mind, you're on the right track. Data mining involves digging for gems or nuggets of information buried deep within data. While the miners of yesteryear used manual labor, modern data minors use business intelligence solutions to extract and make sense of data.

As businesses have become more complex and more reliant on data, the sheer volume of data has exploded. The term "big data" is used to describe the massive amounts of data enterprises must dig through in order to find those golden nuggets. For example, imagine a large retailer with numerous sales promotions, inventory, point of sale systems, and a gift registry. Each of these systems contains useful data that could be mined to make smarter decisions. However, these systems may not be interlinked, making it more difficult to glean any meaningful insights.

Data warehouses are used to extract information from various legacy systems, transform the data into a common format, and load it into a data warehouse. This process is known as ETL (Extract, Transform, and Load). Once the information is standardized and merged, it becomes possible to work with that data.

Originally, all of this behind-the-scenes consolidation took place at predetermined intervals such as once a day, once a week, or even once a month. Intervals were often needed because the databases needed to be offline during these processes. A business running 24/7 simply couldn't afford the down time required to keep the data warehouse stocked with the freshest data. Depending on how often this process took place, the data could be old and no longer relevant. While this may have been fine in the 1980s or 1990s, it's not sufficient in today's fast-paced, interconnected world.

Real-time EFL has since been developed, allowing for continuous, non-invasive data warehousing. While most business intelligence solutions today are capable of mining, extracting, transforming, and loading data continuously without service disruptions, that's not the end of the story. In fact, data mining is just the beginning.

After mining data, what are you going to do with it? You need some form of enterprise reporting in order to make sense of the massive amounts of data coming in. In the past, enterprise reporting required extensive expertise to set up and maintain. Users were typically given a selection of pre-designed reports detailing various data points or functions. While some reports may have had some customization built in, such as user-defined date ranges, customization was limited. If a user needed a special report, it required getting someone from the IT department skilled in reporting to create or modify a report based on the user's needs. This could take weeks - and it often never happened due to the hassles and politics involved.

Fortunately, modern business intelligence solutions have taken enterprise reporting down to the user level. Intuitive controls and dashboards make creating a custom report a simple matter of drag and drop while data visualization tools make the data easy to comprehend. Best of all, these tools can be used on demand, allowing for true, real-time ad hoc enterprise reporting.



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What is Data Mining? Why Data Mining is Important?

Searching, Collecting, Filtering and Analyzing of data define as data mining. The large amount of information can be retrieved from wide range of form such as different data relationships, patterns or any significant statistical co-relations. Today the advent of computers, large databases and the internet is make easier way to collect millions, billions and even trillions of pieces of data that can be systematically analyzed to help look for relationships and to seek solutions to difficult problems.

The government, private company, large organization and all businesses are looking for large volume of information collection for research and business development. These all collected data can be stored by them to future use. Such kind of information is most important whenever it is require. It will take very much time for searching and find require information from the internet or any other resources.

Here is an overview of data mining services inclusion:

* Market research, product research, survey and analysis
* Collection information about investors, funds and investments
* Forums, blogs and other resources for customer views/opinions
* Scanning large volumes of data
* Information extraction
* Pre-processing of data from the data warehouse
* Meta data extraction
* Web data online mining services
* data online mining research
* Online newspaper and news sources information research
* Excel sheet presentation of data collected from online sources
* Competitor analysis
* data mining books
* Information interpretation
* Updating collected data

After applying the process of data mining, you can easily information extract from filtered information and processing the refining the information. This data process is mainly divided into 3 sections; pre-processing, mining and validation. In short, data online mining is a process of converting data into authentic information.

The most important is that it takes much time to find important information from the data. If you want to grow your business rapidly, you must take quick and accurate decisions to grab timely available opportunities.

Outsourcing Web Research is one of the best data mining outsourcing organizations having more than 17 years of experience in the market research industry. To know more information about our company please contact us.



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What Poker Data Mining Can Do for a Player

Anyone who wants to be more successful in many poker rooms online should take a look at what poker data mining can do. Poker data mining involves looking into all of the past hands in a series of poker games. This can be used to help with reviewing the ways how a player plays the game of poker. This will help to determine how well someone is working when trying to play this exciting game.

Poker data mining works in that a player will review all of the past hands that a player has gotten into. This includes taking a look at the individual hands that were involved. Every single card, bet and movement will be recorded in a hand.

All of the hands can be combined to help with figuring out the wins and losses in a game alongside all of the strategies that had been used throughout the course of a game. The analysis will be used to determine how well a player has gone in a game.

The review will be used to figure out the changes in one's winnings over the course of time. This can be used in conjunction with different types of things that are going on in a game and how the game is being played. This will be used to help figure out what is going on in a game and to see what should be done correctly and what should not be handled.

The data mining that is used is handled by a variety of different kinds of online poker sites. Many of these sites will allow its customers to buy information on various previous hands that they have gotten into. This is used by all of these places as a means of helping to figure out how well a player has done in a game.

Not all places are going to offer support for poker data mining. Some of these places will refuse to work with it due to how they might feel that poker data mining will give a player an unfair advantage over other players who are not willing to pay for it. The standards that these poker rooms will have are going to vary. It helps to review policies of different places when looking to use this service.

Poker data mining can prove to be a beneficial function for anyone to handle. Poker data mining can be smart because of how it can help to get anyone to figure out how one's hand histories are working in a poker room. It will be important to see that this is not accepted in all places though. Be sure to watch for this when playing the game of poker and looking to succeed in it.



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Things to Remember Before Selecting a Home Based Data Entry Company

Online data entry work is considered as one of the most popular part time jobs across the globe especially in the US. The data entry job has gained immense popularity not only because of the scope of an additional income but also it is very convenient to do the job. You can start working in this sector from the comfort of your bed room, an internet café or from practically any place from where you can access the internet.

Even though you will find most of the required information on the websites, still it is preferable to clear any doubts, if needed. It is always advisable to be informed about various data entry job opportunities. If you have knowledge about the data entry market, you will be easily able to determine the legitimacy of a particular company you are planning to work for. There are few factors, which you need to consider before selection a particular company.

Registration Fees
Often you will find that companies are asking for a certain amount of registration fees for home based data entry work. Before paying such fees, ask them the purpose of the fees. Enquire whether you will receive any startup material or any software to start your job. Gather all the necessary information about the trail period and offers related to money back guarantee. Note down for how long this offer will be valid.

Methods And Terms Of Payment
You will only get paid on the work, which is efficiently completed by you. Once you have completed the work, the client will review it and only after complete satisfaction the payment will be made to you. Before you accept any project fix the rate and the available time period with the client. Know the payment scheme - whether it is weekly or bimonthly or monthly. Always ask for the modes of payment too as it is important in case the client and the agent is from two different countries. Options like direct deposit to bank account, moneybookers, paypal etc. are few choices, which are widely used by people to transfer online money.

Secret Charges
Some companies always keep hidden charges inform of taxes and fees. Before accepting a project make sure whether your employer are planning to levy any such charges on your income or not. Often data entry agents are surprised after they receive less amount of payment than what they were expecting. Sometimes bank transfer charges are even deducted from your fees. In order to avoid all complications, inquire carefully before you start to work.

Information Of References
After you discuss the business details of a home based data entry work with the representative of the company, don't forget to take details of that representative. Note down his first as well as last name. If the representative is unable to give you his real identity due to business policies, then get their agent number or code number. This is very important as it can be used as references in the later stage. You should also remember the date and time of the call.



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Digging Up Dollars With Data Mining - An Executive's Guide

Traditionally, organizations use data tactically - to manage operations. For a competitive edge, strong organizations use data strategically - to expand the business, to improve profitability, to reduce costs, and to market more effectively. Data mining (DM) creates information assets that an organization can leverage to achieve these strategic objectives.

In this article, we address some of the key questions executives have about data mining. These include:

    What is data mining?
    What can it do for my organization?
    How can my organization get started?

Business Definition of Data Mining

Data mining is a new component in an enterprise's decision support system (DSS) architecture. It complements and interlocks with other DSS capabilities such as query and reporting, on-line analytical processing (OLAP), data visualization, and traditional statistical analysis. These other DSS technologies are generally retrospective. They provide reports, tables, and graphs of what happened in the past. A user who knows what she's looking for can answer specific questions like: "How many new accounts were opened in the Midwest region last quarter," "Which stores had the largest change in revenues compared to the same month last year," or "Did we meet our goal of a ten-percent increase in holiday sales?"

We define data mining as "the data-driven discovery and modeling of hidden patterns in large volumes of data." Data mining differs from the retrospective technologies above because it produces models - models that capture and represent the hidden patterns in the data. With it, a user can discover patterns and build models automatically, without knowing exactly what she's looking for. The models are both descriptive and prospective. They address why things happened and what is likely to happen next. A user can pose "what-if" questions to a data-mining model that can not be queried directly from the database or warehouse. Examples include: "What is the expected lifetime value of every customer account," "Which customers are likely to open a money market account," or "Will this customer cancel our service if we introduce fees?"

The information technologies associated with DM are neural networks, genetic algorithms, fuzzy logic, and rule induction. It is outside the scope of this article to elaborate on all of these technologies. Instead, we will focus on business needs and how data mining solutions for these needs can translate into dollars.

Mapping Business Needs to Solutions and Profits

What can data mining do for your organization? In the introduction, we described several strategic opportunities for an organization to use data for advantage: business expansion, profitability, cost reduction, and sales and marketing. Let's consider these opportunities very concretely through several examples where companies successfully applied DM.

Expanding your business: Keystone Financial of Williamsport, PA, wanted to expand their customer base and attract new accounts through a LoanCheck offer. To initiate a loan, a recipient just had to go to a Keystone branch and cash the LoanCheck. Keystone introduced the $5000 LoanCheck by mailing a promotion to existing customers.

The Keystone database tracks about 300 characteristics for each customer. These characteristics include whether the person had already opened loans in the past two years, the number of active credit cards, the balance levels on those cards, and finally whether or not they responded to the $5000 LoanCheck offer. Keystone used data mining to sift through the 300 customer characteristics, find the most significant ones, and build a model of response to the LoanCheck offer. Then, they applied the model to a list of 400,000 prospects obtained from a credit bureau.

By selectively mailing to the best-rated prospects determined by the DM model, Keystone generated $1.6M in additional net income from 12,000 new customers.

Reducing costs: Empire Blue Cross/Blue Shield is New York State's largest health insurer. To compete with other healthcare companies, Empire must provide quality service and minimize costs. Attacking costs in the form of fraud and abuse is a cornerstone of Empire's strategy, and it requires considerable investigative skill as well as sophisticated information technology.

The latter includes a data mining application that profiles each physician in the Empire network based on patient claim records in their database. From the profile, the application detects subtle deviations in physician behavior relative to her/his peer group. These deviations are reported to fraud investigators as a "suspicion index." A physician who performs a high number of procedures per visit, charges 40% more per patient, or sees many patients on the weekend would be flagged immediately from the suspicion index score.

What has this DM effort returned to Empire? In the first three years, they realized fraud-and-abuse savings of $29M, $36M, and $39M respectively.

Improving sales effectiveness and profitability: Pharmaceutical sales representatives have a broad assortment of tools for promoting products to physicians. These tools include clinical literature, product samples, dinner meetings, teleconferences, golf outings, and more. Knowing which promotions will be most effective with which doctors is extremely valuable since wrong decisions can cost the company hundreds of dollars for the sales call and even more in lost revenue.

The reps for a large pharmaceutical company collectively make tens of thousands of sales calls. One drug maker linked six months of promotional activity with corresponding sales figures in a database, which they then used to build a predictive model for each doctor. The data-mining models revealed, for instance, that among six different promotional alternatives, only two had a significant impact on the prescribing behavior of physicians. Using all the knowledge embedded in the data-mining models, the promotional mix for each doctor was customized to maximize ROI.

Although this new program was rolled out just recently, early responses indicate that the drug maker will exceed the $1.4M sales increase originally projected. Given that this increase is generated with no new promotional spending, profits are expected to increase by a similar amount.

Looking back at this set of examples, we must ask, "Why was data mining necessary?" For Keystone, response to the loan offer did not exist in the new credit bureau database of 400,000 potential customers. The model predicted the response given the other available customer characteristics. For Empire, the suspicion index quantified the differences between physician practices and peer (model) behavior. Appropriate physician behavior was a multi-variable aggregate produced by data mining - once again, not available in the database. For the drug maker, the promotion and sales databases contained the historical record of activity. An automated data mining method was necessary to model each doctor and determine the best combination of promotions to increase future sales.



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Data Mining - A Short Introduction

Data mining is an integral part of data analysis which contains a series of activities that goes from the 'meaning' of the ideas, to the 'analysis' of the data and up to the 'interpretation' and 'evaluation' of the outcome. The different stages of the technique are as follows:

Objectives for Analysis: It is sometimes very difficult to statistically define the phenomenon we wish to analyze. In fact, the business objectives are often clear, but the same can be difficult to formalize. A clear understanding of the crisis and the goals is very important setup the analysis correctly. This is undoubtedly, one of the most complex parts of the process, since it establishes the techniques to be engaged and as such, the objectives must be crystal clear and there should not be any doubt or ambiguity.

Collection, grouping and pre-processing of the data: Once the objectives of the analysis are set and defined, we need to gather or choose the data needed for the study. At first, it is essential to recognize the data sources. Usually data are collected from the internal sources as the same are economical and more dependable and moreover these data also has the benefit of being the outcome of the experiences and procedures of the business itself.

Investigative analysis of the data and their conversion: This stage includes a preliminary examination of the information available. It involves a preliminary assessment of the significance of the gathered data. An exploratory and / or investigative analysis can highlight the irregular data. An exploratory analysis is important because it lets the analyst choose the most suitable statistical method for the subsequent stage of the analysis.

Choosing statistical methods: There are multiple statistical methods that can be put into use for the purpose of analysis, so it is very essential to categorize the existing methods. The choice statistical method is case specific and depends on the problem and also upon the type of information available.

Data analysis on the basis of chosen methods: Once the statistical method is chosen, the same must be translated into proper algorithms for working out the results. Ranges of specialized and non-specialized software are widely available for data mining and as such it is not always required to develop ad hoc computation algorithms for the most 'standard' purpose. However, it is essential that the people managing the data mining method well aware and have a good knowledge and understanding of the various methods of data analysis and also the different software solutions available for the same, so that they may adapt the same in times of need of the company and can flawlessly interpret the results.

Assessment and contrast of the techniques used and selection of the final model for analysis: It is of utmost necessity to choose the best 'model' from the variety of statistical methods accessible. The selection of the model should be based in contrast with the results obtained. When assessing the performance of a specific statistical method and / or type, all other dependent and / or relevant criterions should also be considered. The other criterions may be the constraints on the company both in terms of time and resources or it may be in terms of quality and the accessibility of data.

Elucidation of the selected statistical model and its employment in the decision making process: The scope of data mining is not limited to data analysis rather it is also includes the integration of the results so as to facilitate the decision making process of the company. Business awareness, the pulling out of rules and their use in the decision process allows us to proceed from the diagnostic phase to the phase of decision making. Once the model is finalized and tested with an information set, the categorization rule can be generalized. But the inclusion of the data mining process in the business should not be done in haste; rather the same should always be done slowly, setting out sensible and logical aims. The final aim of data mining is to be an integral supporting part of the company's decision making process.



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Internet Data Mining - How Does it Help Businesses?

Internet has become an indispensable medium for people to conduct different types of businesses and transactions too. This has given rise to the employment of different internet data mining tools and strategies so that they could better their main purpose of existence on the internet platform and also increase their customer base manifold.

Internet data-mining encompasses various processes of collecting and summarizing different data from various websites or webpage contents or make use of different login procedures so that they could identify various patterns. With the help of internet data-mining it becomes extremely easy to spot a potential competitor, pep up the customer support service on the website and make it more customers oriented.

There are different types of internet data_mining techniques which include content, usage and structure mining. Content mining focuses more on the subject matter that is present on a website which includes the video, audio, images and text. Usage mining focuses on a process where the servers report the aspects accessed by users through the server access logs. This data helps in creating an effective and an efficient website structure. Structure mining focuses on the nature of connection of the websites. This is effective in finding out the similarities between various websites.

Also known as web data_mining, with the aid of the tools and the techniques, one can predict the potential growth in a selective market regarding a specific product. Data gathering has never been so easy and one could make use of a variety of tools to gather data and that too in simpler methods. With the help of the data mining tools, screen scraping, web harvesting and web crawling have become very easy and requisite data can be put readily into a usable style and format. Gathering data from anywhere in the web has become as simple as saying 1-2-3. Internet data-mining tools therefore are effective predictors of the future trends that the business might take.



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Some of the Main Techniques For Data Mining

Data mining is the process of extracting relationships from large data sets. It is an area of Computer Science that has received significant commercial interest. In this article I will detail a few of the most common methods of data mining analysis.

Association rule discovery: Association rule discovery methods are used to extract associations from data sets. Traditionally, the technique was developed on supermarket purchase data. An association rule is a rule of the form X -> Y. An example of this may be "If a customer purchases milk this implies (->) that the customer will also purchase bread". An association rule has associated with it a support and a confidence value. The support is the percentage of all entries (or transactions in this case) that have all the items. For example, the percentage of all transactions in which milk and bread were purchased. The confidence is the percentage of the transactions that satisfy the left hand side of the rule that also satisfy the right hand side of the rule. For example, in this case, the confidence would be the percentage of purchases that purchased milk which also purchased bread. Association discovery methods will extract all possible association rules from a data set for which the user has specified a minimum support and confidence.

Cluster Analysis: Cluster analysis is the process of taking one or more numerical fields and assigning clusters their values. These clusters represent groups of points which are close to each other. For example, if you watch a documentary on space, you will see that galaxies contain a lot of stars and planets. There are many galaxies in space, however the stars and planets all occur in clusters that are the galaxies. That is, the stars and planets are not randomly located in space but are clumped together in groups that are galaxies. A cluster analysis method is used to find these sorts of groups. If a cluster analysis method was applied to the stars in space, it may find that each galaxy is a cluster and assign a unique cluster identification to each star in a given galaxy. This cluster identification then becomes another field in the data set and can be used in further data mining analysis. For example, you might use a cluster id field to form association rules to other fields in the data set.

Decision Trees: Decision trees are used to form a tree of decisions in a data set to help predict a value. For example, if you were looking at a data set that was used to predict weather a potential loan applicant would be a credit risk, a tree of decisions would be formed based on factors in the data set. The tree may contain decisions such as whether the applicant had defaulted on a loan before, the age of the applicant, whether the applicant was employed or not, the applicants income and the total repayments on the loan. You could then follow this tree of decisions to say for example, if an applicant has never defaulted on a loan before, the applicant is employed, their income is in the top 15 percentile for the country and the loan amount relatively low then there is a very low risk of default.

These are some of the more common techniques for data mining analysis amongst a large group of data mining techniques that a commonly applied to analyzing large data sets. These techniques have proved beneficial to gather useful information and relationships from data that may otherwise be too large to interpret well.


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Offshore Data Entry Provides Unlimited Growth Opportunities

As the world becomes a smaller place, business relations between different countries continue to be one of the major cementing factors in maintaining international relations.
The ever expanding offshore data entry industry is one such field which provides ample scope for such business interactions between different nations. Currently, the rapidly developing countries such as India and China are important players and very much responsible for the expansion of the offshore data entry industry.

The term 'offshore' is used to describe the banks, investments, deposits and corporations that are situated in a foreign location. Such an organization generally moves to a foreign destination for the purpose of avoiding payment of taxes or ease of regulations as maybe the case. The corporations then outsource the services of an external organization in another offshore country that takes care of the data entry, data conversion, documentation, processing and such other services.

In today's industrial sector, the offshore data entry services is one of the fastest growing
industry. The reason for such phenomenal growth can be related to many advantages such as lower rates for the services offered, highly professional and efficient workforce, tailored solutions to cater to the clients need and the required skills to meet the specific requirements of the job.

The concept of data entry has also been revolutionized with the constant up-gradation and innovation in the digital world. Each and every multinational company requires accurate database and information to conduct its business efficiently and successfully. The offshore data entry industry has therefore gained tremendous importance due to this crucial database requirement. The offshore data entry company's efficient service of gathering, compiling, processing and providing a voluminous amount of data on a day to day basis to the multinational companies ensures its heavy demand in the global market.

The convenience of the internet provides the ideal facility for the online compilation and processing of the offshore data. Also in countries such as India and China the volume of such data entry work is very high and the rates thereby constantly sharpening the skills of the professionals while the rates are comparatively lower than the Western world. Hence these countries form a favorable destination for the offshore data entry industry. The UK, US, France and many more such countries now form a regular client base for the offshore data entry industry in India, China, etc.

The offshore data entry done by competent, computer savvy professionals ensure availability of accurate information that has been expertly processed and compiled. This data is a crucial management resource that enables optimum decision making by the multinational banks, corporations, institutions, etc. for whom the data is either a regular or a temporary requirement.

The general characteristics of an offshore data entry job are that the work has high amount of information content, can be done over the telephone and transmitted over the internet, is easy to set up and is repeatable in nature. The major wage difference between the countries also becomes an important deciding factor. Hence, as the need for accurate and relevant data continues to increase the offshore data entry industry will continue charter its expansion in the recent times.



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Data Mining Questions? Some Back-Of-The-Envelope Answers

Data mining, the discovery and modeling of hidden patterns in large volumes of data, is becoming a mainstream technology. And yet, for many, the prospect of initiating a data mining (DM) project remains daunting. Chief among the concerns of those considering DM is, "How do I know if data mining is right for my organization?"

A meaningful response to this concern hinges on three underlying questions:

    Economics - Do you have a pressing business/economic need, a "pain" that needs to be addressed immediately?
    Data - Do you have, or can you acquire, sufficient data that are relevant to the business need?
    Performance - Do you need a DM solution to produce a moderate gain in business performance compared to current practice?

By the time you finish reading this article, you will be able to answer these questions for yourself on the back of an envelope. If all answers are yes, data mining is a good fit for your business need. Any no answers indicate areas to focus on before proceeding with DM.

In the following sections, we'll consider each of the above questions in the context of a sales and marketing case study. Since DM applies to a wide spectrum of industries, we will also generalize each of the solution principles.

To begin, suppose that Donna is the VP of Marketing for a trade organization. She is responsible for several trade shows and a large annual meeting. Attendance was good for many years, and she and her staff focused their efforts on creating an excellent meeting experience (program plus venue). Recently, however, there has been declining response to promotions, and a simultaneous decline in attendance. Is data mining right for Donna and her organization?

Economics - Begin with economics - Is there a pressing business need? Donna knows that meeting attendance was down 15% this year. If that trend continues for two more years, turnout will be only about 60% of its previous level (85% x 85% x 85%), and she knows that the annual meeting is not sustainable at that level. It is critical, then, to improve the attendance, but to do so profitably. Yes, Donna has an economic need.

Generally speaking, data mining can address a wide variety of business "pains". If your company is experiencing rapid growth, DM can identify promising new retail locations or find more prospects for your online service. Conversely, if your organization is facing declining sales, DM can improve retention or identify your best existing customers for cross-selling and upselling. It is not advisable, however, to start a data mining effort without explicitly identifying a critical business need. Vast sums have been spent wastefully on mining data for "nuggets" of knowledge that have little or no value to the enterprise.

Data - Next, consider your data assets - Are sufficient, relevant data available? Donna has a spreadsheet that captures several years of meeting registrations (who attended). She also maintains a promotion history (who was sent a meeting invitation) in a simple database. So, information is available about the stimulus (sending invitations) and the response (did/did not attend). This data is clearly relevant to understanding and improving future attendance.

Donna's multi-year registration spreadsheet contains about 10,000 names. The promotion history database is even larger because many invitations are sent for each meeting, both to prior attendees and to prospects who have never attended. Sounds like plenty of data, but to be sure, it is useful to think about the factors that might be predictive of future attendance. Donna consults her intuitive knowledge of the meeting participants and lists four key factors:

    attended previously
    age
    size of company
    industry

To get a reasonable estimate for the amount of data required, we can use the following rule of thumb, developed from many years of experience:

Number of records needed ≥ 60 x 2^N (where N is the number of factors)

Since Donna listed 4 key factors, the above formula estimates that she needs 960 records (60 x 2^4 = 60 x 16). Since she has more than 10,000, we conclude Yes, Donna has relevant and sufficient data for DM.

More generally, in considering your own situation, it is important to have data that represents:

    stimulus and response (what was done and what happened)
    positive and negative outcomes

Simply put, you need data on both what works and what doesn't.

Performance - Finally, performance - Is a moderate improvement required relative to current benchmarks? Donna would like to increase attendance back to its previous level without increasing her promotion costs. She determines that the response rate to promotions needs to increase from 2% to 2.5% to meet her goals. In data mining terms, a moderate improvement is generally in the range of 10% to 100%. Donna's need is in this interval, at 25%. For her, Yes, a moderate performance increase is needed.

The performance question is typically the hardest one to address prior to starting a project. Performance is an outcome of the data mining effort, not a precursor to it. There are no guarantees, but we can use past experience as a guide. As noted for Donna above, incremental-to-moderate improvements are reasonable to expect with data mining. But don't expect DM to produce a miracle.



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India Outsourcing Accounting Is Leading The Outsourcing Revolution

It really seems pretty difficult to portray India through words. India has a very rich culture and that is the reason why India is developing with an escalating pace even after suffering from so many years of dependence. Now India has become the fastest growing economy and contributing predominantly in global issues. India shares a large involvement in global market and the overall credit of these achievements goes to liberal globalization policies that helped India in developing as favorite destination for outsourcing. Today India is a leading name in outsourcing; whether it is for IT solutions, customer care, management or accounting India is doing extremely well in every field. In many aspects India is the true global leader; accounting services are one of those aspects which is leveraging the global market. India outsourcing accounting has now become an another name of ideal accounting services as businesses from all over the world are getting great benefits from it.

For every business leader, India accounting outsourcing sounds like progress and brighter future prospects. India is leveraging the outsourcing revolution as its education level, suitability, means of correspondence and cost effectiveness makes it the preferred choice of variety of businesses. Till now no other country is able to challenge the position of India an in global market. With time it may be challenged but attributes like extensive use of English, large population and unbeatable expertise make it different from others. India has the power to keep on excelling to provide the best of service to outside clients. India outsourcing accounting is well equipped with highly qualified accounting professionals and financial experts. India is the country with influential accounting and financial skills that offer businesses an ample scope to grow as they get all whatever their business requires.

Existing Indian policies are also inviting business to outsource their accounting and bookkeeping requirements with India. India has the most liberal foreign exchange and globalization policies that give India outsourcing accounting more opportunities to benefit businesses. As businesses form every country of world can easily come to India and get their accounting task done without any problem, India outsourcing accounting is flourishing day by day. It is true that Indian politics is full of surprises but theses changes in governing authorities never affected India outsourcing accounting. Contemporary FDI policies support foreign investors and clients the most as it offers the maximum profit to them.

Cost structure of India outsourcing accounting is favorable for every client. Cost is another big advantage of outsourcing accounting services to India. India outsourcing accounting is moderately cheap and cost cutting. Foreign clients find every level of expertise in India and at desired cost, conversely if they hire the same level services in their own country they will have to pay just double. Linguistic skill is also a big problem with other countries but India outsourcing accounting experts are well-versed with English. No doubt that India is the true leader of the outsourcing development and inviting more and more foreign clients to find better future prospects with cost effective solutions.


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RFM - A Precursor to Data Mining

RFM was initially utilized by marketers in the B-2-C space - specifically in industries like Cataloging, Insurance, Retail Banking, Telecommunications and others. There are a number of scoring approaches that can be used with RFM. We'll take a look at three:

RFM - Basic Ranking
RFM - Within Parent Cell Ranking
RFM - Weighted Cell Ranking

Each approach has experienced proponents that argue one over the other. The point is to start somewhere and experiment to find the one that works best for your company and your customer base. Let's look at a few examples.

RFM - Basic Ranking

This approach involves scoring customers based on each RFM factor separately. It begins with sorting your customers based on Recency, i.e., the number of days or months since their last purchase. Once sorted in ascending order (most recent purchasers at the top), the customers are then split into quintiles, or five equal groups. The customers in the top quintile represent the 20% of your customers that most recently purchased from you.

This process is then undertaken for Frequency and Monetary as well. Each customer is in one of the five cells for R, F, and M

Experience tells us that the best prospects for an upcoming campaign are those customers that are in Quintile 5 for each factor - those customers that have purchased most recently, most frequently and have spent the most money. In fact, a common approach to creating an aggregated score is to concatenate the individual RFM scores together resulting in 125 cells (5x5x5).

A customer's score can range from 555 being the highest, to 111 being the lowest.

RFM - Within Parent Cell Ranking

This approach is advocated by Arthur Middleton Hughes - one of the biggest proponents of RFM analysis. It begins like the one above, i.e., all customer are initially grouped into 5 cells based on Recency. The next step takes customers in a given Recency cell - say cell number 5, and then ranks those customers based on Frequency. Then customers in the 55 (RF) cell are ranked by monetary value.

RFM - Weighted Ranking

Weightings used by RFM practitioners vary. For example some advocate adding the RFM score together - thus giving equal weight to each factor. Consequently, scores can range from 15 (5+5+5) to 3 (1+1+1). Another weighting arrangement often used is, 3xR + 2xF + 1xM. In this case, scores can range from 30 to 3.

So which to use? In reality, there are many other permutations of approaches that are being used today. Best-practice marketing analytics requires a fine mix of mathematical and statistical science, creativity and experimentation. Bottom line, test multiple scoring methods to see which works best for your unique customer base.

Establishing a Score Threshold

After a test or production campaign, you will find that some of the cells were profitable while some were not. Let's turn to a case study to see how you can establish a threshold that will help maximize your profitability. This study comes from Professor Charlotte Mason of the Kenan-Flagler Business School and utilizes a real-life marketing study performed by The BookBinders Book Club (Source:Recency, Frequency and Monetary (RFM) Analysis, Professor Charlotte Mason, Kenan-Flagler Business School, University of North Carolina, 2003).

BookBinders is a specialty book seller that utilizes multiple marketing channels. BookBinders traditionally did mass marketing and wanted to test the power of RFM. To do so, they initially did a random mailing to 50,000 customers. The customers were mailed an offer to purchase The Art History of Florence. Response data was captured and a "post-RFM" analysis was completed. This "post analysis" was done by freezing the files of the 50,000 test customers prior to the actual test offer. Thus, the impact of this test campaign did not effect the analysis by coding many (the actual buyers) of the 50,000 test subjects as the most recent purchasers. The results firmly support the use of RFM as a highly effective segmentation approach.



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Basics of Web Data Mining and Challenges in Web Data Mining Process

Today World Wide Web is flooded with billions of static and dynamic web pages created with programming languages such as HTML, PHP and ASP. Web is great source of information offering a lush playground for data mining. Since the data stored on web is in various formats and are dynamic in nature, it's a significant challenge to search, process and present the unstructured information available on the web.

Complexity of a Web page far exceeds the complexity of any conventional text document. Web pages on the internet lack uniformity and standardization while traditional books and text documents are much simpler in their consistency. Further, search engines with their limited capacity can not index all the web pages which makes data mining extremely inefficient.

Moreover, Internet is a highly dynamic knowledge resource and grows at a rapid pace. Sports, News, Finance and Corporate sites update their websites on hourly or daily basis. Today Web reaches to millions of users having different profiles, interests and usage purposes. Every one of these requires good information but don't know how to retrieve relevant data efficiently and with least efforts.

It is important to note that only a small section of the web possesses really useful information. There are three usual methods that a user adopts when accessing information stored on the internet:

• Random surfing i.e. following large numbers of hyperlinks available on the web page.
• Query based search on Search Engines - use Google or Yahoo to find relevant documents (entering specific keywords queries of interest in search box)
• Deep query searches i.e. fetching searchable database from eBay.com's product search engines or Business.com's service directory, etc.

To use the web as an effective resource and knowledge discovery researchers have developed efficient data mining techniques to extract relevant data easily, smoothly and cost-effectively.



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