Business Intelligence Data Mining

Data mining can be technically defined as the automated extraction of hidden information from large databases for predictive analysis. In other words, it is the retrieval of useful information from large masses of data, which is also presented in an analyzed form for specific decision-making.

Data mining requires the use of mathematical algorithms and statistical techniques integrated with software tools. The final product is an easy-to-use software package that can be used even by non-mathematicians to effectively analyze the data they have. Data Mining is used in several applications like market research, consumer behavior, direct marketing, bioinformatics, genetics, text analysis, fraud detection, web site personalization, e-commerce, healthcare, customer relationship management, financial services and telecommunications.

Business intelligence data mining is used in market research, industry research, and for competitor analysis. It has applications in major industries like direct marketing, e-commerce, customer relationship management, healthcare, the oil and gas industry, scientific tests, genetics, telecommunications, financial services and utilities. BI uses various technologies like data mining, scorecarding, data warehouses, text mining, decision support systems, executive information systems, management information systems and geographic information systems for analyzing useful information for business decision making.

Business intelligence is a broader arena of decision-making that uses data mining as one of the tools. In fact, the use of data mining in BI makes the data more relevant in application. There are several kinds of data mining: text mining, web mining, social networks data mining, relational databases, pictorial data mining, audio data mining and video data mining, that are all used in business intelligence applications.

Some data mining tools used in BI are: decision trees, information gain, probability, probability density functions, Gaussians, maximum likelihood estimation, Gaussian Baves classification, cross-validation, neural networks, instance-based learning /case-based/ memory-based/non-parametric, regression algorithms, Bayesian networks, Gaussian mixture models, K-means and hierarchical clustering, Markov models and so on.


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Data Management Services

In recent studies it has been revealed that any business activity has astonishing huge volumes of data, hence the ideas has to be organized well and can be easily gotten when need arises. Timely and accurate solutions are important in facilitating efficiency in any business activity. With the emerging professional outsourcing and data organizing companies nowadays many services are offered that matches the various kinds of managing the data collected and various business activities. This article looks at some of the benefits that accrue of offered by the professional data mining companies.

Entering of data

These kinds of services are quite significant since they help in converting the data that is needed in high ideal and format that is digitized. In internet some of this data can found that is original and handwritten. In printed paper documents and or text are not likely to contain electronic or needed formats. The best example in this context is books that need to be converted to e-books. In insurance companies they also depend on this process in processing the claims of insurance and at the same time apply to the law firms that offer support to analyze and process legal documents.

EDC

That is referred to as electronic data. This method is mostly used by clinical researchers and other related organization in medical. The electronic data and capture methods are used in the utilization in managing trials and research. The data mining and data management services are given in upcoming databases for studies. The ideas contained can easily be captured, other services being done and the survey taken.

Data changing

This is the process of converting data found in one format to another. Data extraction process often involves mining data from an existing system, formatting it, cleansing it and can be installed to enhance both availability and retrieving of information easily. Extensive testing and application are the requirements of this process. The service offered by data mining companies includes SGML conversion, XML conversion, CAD conversion, HTML conversion, image conversion.

Managing data service

In this service it involves the conversion of documents. It is where one character of a text may need to be converted to another. If we take an example it is easy to change image, video or audio file formats to other applications of the software that can be played or displayed. In indexing and scanning is where the services are mostly offered.

Data extraction and cleansing

Significant information and sequences from huge databases and websites extraction firms use this kind of service. The data harvested is supposed to be in a productive way and should be cleansed to increase the quality. Both manual and automated data cleansing services are offered by data mining organizations. This helps to ensure that there is accuracy, completeness and integrity of data. Also we keep in mind that data mining is never enough.

Web scraping, data extraction services, web extraction, imaging, catalog conversion, web data mining and others are the other management services offered by data mining organization. If your business organization needs such services here is one that can be of great significance that is web scraping and data mining


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Spatial Data Mining Systems

Data mining systems are used for a variety of different purposes. Essentially, large amounts of data are stored in one particular spot, enabling organizations and companies to access information that will help them in their own marketing and surveillance strategies. By having access to all relevant data, a company can better employ their sales and production tactics. Companies and businesses can save large sums of money by researching past consumer behaviors and producing product in relation to how well it sold at certain times. This is just a small example of what data mining can do for a company.

Spatial data mining systems rely on the same principals. However, the data stored is related directly to special data. Spatial data mining systems are also used to detect patterns, but the patterns that are being looked for are geographical patterns. Up until this point geographical information systems and spatial data mining have existed as two separate technologies. Both systems have their own individual approaches to storing geographical data. Each system has derived from its own methods and traditions, making it difficult to cross the two. Geographical information systems tend to be much more basic and only provide the most simple form of functionality. Because there became a larger demand for geographically referenced data, the basic functions of GIS represented the massive need for more sophisticated methods of mining spatial data. There is a larger demand for geographical analysis and modeling as well as digital mapping and remote sensing.

Through spatial data mining, there have been numerous benefits experienced by those who make important decisions based on geographical information systems. Public and private sector organizations have recently become aware of the huge potential of the amount of information they possess in their thematic and geographical referenced databases. There are various types of companies who can benefit from geographical data. For example, those that are in the public health sector will use this data to determine the cause for epidemics such as disease clusters. In addition, some environmental agencies will use the information collected in these databases to understand the impact of land-use patterns that are in constant flux and how they relate to climate change. Geo-marketing companies will also find this information useful when they are conducting customer research regarding segmentation on spatial location.

However, spatial data mining systems force those who need them to face certain challenges. First of all, these databases tend to be extremely large and can be cumbersome to sort through when looking for specific information. Geographical information system datasets that already exist are usually split into featured and attributed components and this means that they are separated into hybrid data management systems. Both featured and attributed data systems require separate means of management. For example algorithmic requirements differ when it comes to relational data, which is in the attribute category and for topographical data, which falls under the feature category.

The two main systems for spatial data management are the raster and the vector. Depending on the needs of the data being used, it is important to analyze the benefits and downfalls of both systems.



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Data Mining and Financial Data Analysis

Introduction:

Most marketers understand the value of collecting financial data, but also realize the challenges of leveraging this knowledge to create intelligent, proactive pathways back to the customer. Data mining - technologies and techniques for recognizing and tracking patterns within data - helps businesses sift through layers of seemingly unrelated data for meaningful relationships, where they can anticipate, rather than simply react to, customer needs as well as financial need. In this accessible introduction, we provides a business and technological overview of data mining and outlines how, along with sound business processes and complementary technologies, data mining can reinforce and redefine for financial analysis.

Objective:

1. The main objective of mining techniques is to discuss how customized data mining tools should be developed for financial data analysis.

2. Usage pattern, in terms of the purpose can be categories as per the need for financial analysis.

3. Develop a tool for financial analysis through data mining techniques.

Data mining:

Data mining is the procedure for extracting or mining knowledge for the large quantity of data or we can say data mining is "knowledge mining for data" or also we can say Knowledge Discovery in Database (KDD). Means data mining is : data collection , database creation, data management, data analysis and understanding.

There are some steps in the process of knowledge discovery in database, such as

1. Data cleaning. (To remove nose and inconsistent data)

2. Data integration. (Where multiple data source may be combined.)

3. Data selection. (Where data relevant to the analysis task are retrieved from the database.)

4. Data transformation. (Where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance)

5. Data mining. (An essential process where intelligent methods are applied in order to extract data patterns.)

6. Pattern evaluation. (To identify the truly interesting patterns representing knowledge based on some interesting measures.)

7. Knowledge presentation.(Where visualization and knowledge representation techniques are used to present the mined knowledge to the user.)

Data Warehouse:

A data warehouse is a repository of information collected from multiple sources, stored under a unified schema and which usually resides at a single site.

Text:

Most of the banks and financial institutions offer a wide verity of banking services such as checking, savings, business and individual customer transactions, credit and investment services like mutual funds etc. Some also offer insurance services and stock investment services.

There are different types of analysis available, but in this case we want to give one analysis known as "Evolution Analysis".

Data evolution analysis is used for the object whose behavior changes over time. Although this may include characterization, discrimination, association, classification, or clustering of time related data, means we can say this evolution analysis is done through the time series data analysis, sequence or periodicity pattern matching and similarity based data analysis.

Data collect from banking and financial sectors are often relatively complete, reliable and high quality, which gives the facility for analysis and data mining. Here we discuss few cases such as,

Eg, 1. Suppose we have stock market data of the last few years available. And we would like to invest in shares of best companies. A data mining study of stock exchange data may identify stock evolution regularities for overall stocks and for the stocks of particular companies. Such regularities may help predict future trends in stock market prices, contributing our decision making regarding stock investments.

Eg, 2. One may like to view the debt and revenue change by month, by region and by other factors along with minimum, maximum, total, average, and other statistical information. Data ware houses, give the facility for comparative analysis and outlier analysis all are play important roles in financial data analysis and mining.

Eg, 3. Loan payment prediction and customer credit analysis are critical to the business of the bank. There are many factors can strongly influence loan payment performance and customer credit rating. Data mining may help identify important factors and eliminate irrelevant one.

Factors related to the risk of loan payments like term of the loan, debt ratio, payment to income ratio, credit history and many more. The banks than decide whose profile shows relatively low risks according to the critical factor analysis.

We can perform the task faster and create a more sophisticated presentation with financial analysis software. These products condense complex data analyses into easy-to-understand graphic presentations. And there's a bonus: Such software can vault our practice to a more advanced business consulting level and help we attract new clients.

To help us find a program that best fits our needs-and our budget-we examined some of the leading packages that represent, by vendors' estimates, more than 90% of the market. Although all the packages are marketed as financial analysis software, they don't all perform every function needed for full-spectrum analyses. It should allow us to provide a unique service to clients.

The Products:

ACCPAC CFO (Comprehensive Financial Optimizer) is designed for small and medium-size enterprises and can help make business-planning decisions by modeling the impact of various options. This is accomplished by demonstrating the what-if outcomes of small changes. A roll forward feature prepares budgets or forecast reports in minutes. The program also generates a financial scorecard of key financial information and indicators.

Customized Financial Analysis by BizBench provides financial benchmarking to determine how a company compares to others in its industry by using the Risk Management Association (RMA) database. It also highlights key ratios that need improvement and year-to-year trend analysis. A unique function, Back Calculation, calculates the profit targets or the appropriate asset base to support existing sales and profitability. Its DuPont Model Analysis demonstrates how each ratio affects return on equity.

Financial Analysis CS reviews and compares a client's financial position with business peers or industry standards. It also can compare multiple locations of a single business to determine which are most profitable. Users who subscribe to the RMA option can integrate with Financial Analysis CS, which then lets them provide aggregated financial indicators of peers or industry standards, showing clients how their businesses compare.

iLumen regularly collects a client's financial information to provide ongoing analysis. It also provides benchmarking information, comparing the client's financial performance with industry peers. The system is Web-based and can monitor a client's performance on a monthly, quarterly and annual basis. The network can upload a trial balance file directly from any accounting software program and provide charts, graphs and ratios that demonstrate a company's performance for the period. Analysis tools are viewed through customized dashboards.

PlanGuru by New Horizon Technologies can generate client-ready integrated balance sheets, income statements and cash-flow statements. The program includes tools for analyzing data, making projections, forecasting and budgeting. It also supports multiple resulting scenarios. The system can calculate up to 21 financial ratios as well as the breakeven point. PlanGuru uses a spreadsheet-style interface and wizards that guide users through data entry. It can import from Excel, QuickBooks, Peachtree and plain text files. It comes in professional and consultant editions. An add-on, called the Business Analyzer, calculates benchmarks.

ProfitCents by Sageworks is Web-based, so it requires no software or updates. It integrates with QuickBooks, CCH, Caseware, Creative Solutions and Best Software applications. It also provides a wide variety of businesses analyses for nonprofits and sole proprietorships. The company offers free consulting, training and customer support. It's also available in Spanish.

ProfitSystem fx Profit Driver by CCH Tax and Accounting provides a wide range of financial diagnostics and analytics. It provides data in spreadsheet form and can calculate benchmarking against industry standards. The program can track up to 40 periods.


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

Introduction

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.

Getting Started

In each case presented above, data mining yielded significant benefits to the business. Some were top-line results that increased revenues or expanded the customer base. Others were bottom-line improvements resulting from cost-savings and enhanced productivity. The natural next question is, "How can my organization get started and begin to realize the competitive advantages of DM?"

In our experience, pilot projects are the most successful vehicles for introducing data mining. A pilot project is a short, well-planned effort to bring DM into an organization. Good pilot projects focus on one very specific business need, and they involve business users up front and throughout the project. The duration of a typical pilot project is one to three months, and it generally requires 4 to 10 people part-time.

The role of the executive in such pilot projects is two-pronged. At the outset, the executive participates in setting the strategic goals and objectives for the project. During the project and prior to roll out, the executive takes part by supervising the measurement and evaluation of results. Lack of executive sponsorship and failure to involve business users are two primary reasons DM initiatives stall or fall short.

In reading this article, perhaps you've developed a vision and want to proceed - to address a pressing business problem by sponsoring a data mining pilot project. Twisting the old adage, we say "just because you should doesn't mean you can." Be aware that a capability assessment needs to be an integral component of a DM pilot project. The assessment takes a critical look at data and data access, personnel and their skills, equipment, and software. Organizations typically underestimate the impact of data mining (and information technology in general) on their people, their processes, and their corporate culture. The pilot project provides a relatively high-reward, low-cost, and low-risk opportunity to quantify the potential impact of DM.

Another stumbling block for an organization is deciding to defer any data mining activity until a data warehouse is built. Our experience indicates that, oftentimes, DM could and should come first. The purpose of the data warehouse is to provide users the opportunity to study customer and market behavior both retrospectively and prospectively. A data mining pilot project can provide important insight into the fields and aggregates that need to be designed into the warehouse to make it really valuable. Further, the cost savings or revenue generation provided by DM can provide bootstrap funding for a data warehouse or related initiatives.

Recapping, in this article we addressed the key questions executives have about data mining - what it is, what the benefits are, and how to get started. Armed with this knowledge, begin with a pilot project. From there, you can continue building the data mining capability in your organization; to expand your business, improve profitability, reduce costs, and market your products more effectively.



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Increasing Accessibility by Scraping Information From PDF

You may have heard about data scraping which is a method that is being used by computer programs in extracting data from an output that comes from another program. To put it simply, this is a process which involves the automatic sorting of information that can be found on different resources including the internet which is inside an html file, PDF or any other documents. In addition to that, there is the collection of pertinent information. These pieces of information will be contained into the databases or spreadsheets so that the users can retrieve them later.

Most of the websites today have text that can be accessed and written easily in the source code. However, there are now other businesses nowadays that choose to make use of Adobe PDF files or Portable Document Format. This is a type of file that can be viewed by simply using the free software known as the Adobe Acrobat. Almost any operating system supports the said software. There are many advantages when you choose to utilize PDF files. Among them is that the document that you have looks exactly the same even if you put it in another computer so that you can view it. Therefore, this makes it ideal for business documents or even specification sheets. Of course there are disadvantages as well. One of which is that the text that is contained in the file is converted into an image. In this case, it is often that you may have problems with this when it comes to the copying and pasting.

This is why there are some that start scraping information from PDF. This is often called PDF scraping in which this is the process that is just like data scraping only that you will be getting information that is contained in your PDF files. In order for you to begin scraping information from PDF, you must choose and exploit a tool that is specifically designed for this process. However, you will find that it is not easy to locate the right tool that will enable you to perform PDF scraping effectively. This is because most of the tools today have problems in obtaining exactly the same data that you want without personalizing them.

Nevertheless, if you search well enough, you will be able to encounter the program that you are looking for. There is no need for you to have programming language knowledge in order for you to use them. You can easily specify your own preferences and the software will do the rest of the work for you. There are also companies out there that you can contact and they will perform the task since they have the right tools that they can use. If you choose to do things manually, you will find that this is indeed tedious and complicated whereas if you compare this to having professionals do the job for you, they will be able to finish it in no time at all. Scraping information from PDF is a process where you collect the information that can be found on the internet and this does not infringe copyright laws.



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Data Conversion Services

Data conversion services have a unique place in this internet driven, fast-growing business world. Whatever be the field - educational, health, legal, research or any other - data conversion services play a crucial role in building and maintaining the records, directories and databases of a system. With this service, firms can convert their files and databases from one format or media to another.

Data conversion services help firms to convert their valuable data and information stored and accumulated in papers into digital format for long-term storage - for the purpose of archiving, easy searching, accessing and sharing.

Now there are many big and small highly competent business process outsourcing (BPO) companies providing a full range of reliable and trustworthy data conversion services to the clients worldwide. Most of these BPO firms are fully equipped with excellent infrastructural facilities and skilled manpower to provide data conversion services catering to the clients' expectations and specifications. These firms can effectively play an important role in improving a company's document/data lifecycle management. With the application of high speed scanners and data processors, these firms can expertly and accurately convert any voluminous and complex data into digital formats, all within the specified time and budget. Moreover, they use state-of-the-art encryption techniques to ensure privacy and security of data transmission over the Internet. The following are the important services offered by the companies in this area:

o Document scanning and conversion
o File format conversion
o XML conversion
o SGML conversion
o CAD conversion
o OCR clean up, ICR, OMR
o Image Conversion
o Book conversion
o HTML conversion
o PDF conversion
o Extracting data from catalog
o Catalog conversion
o Indexing
o Scanning from hard copies, microfilms, microfiche, aperture cards, and large-scale drawings

Thus, by entrusting a data conversion project to an expert outsourcing company, firms can enjoy numerous advantages in terms of quality, efficiency and cost. Some of its key benefits are:

o Avoids paper work
o Cuts down operating expenses and excessive staffing
o Helps to rely on core business activities
o Promotes business as effectively as possible
o Systemizes company's data in simpler format
o Eliminates data redundancy
o Easy accessibility of data at any time

If you are planning to outsource your data conversion work, then you must choose the provider carefully in order to reap the fullest benefits of the services.

Data conversion experts at Managed Outsource Solutions (MOS) provides full conversion services of paper, microfilm, aperture cards, and large-scale drawings, through scanning, indexing, OCR, quality control and export of the archive and books to electronic formats or the final imaging solution. MOS is a US company providing managed outsource solutions that are focused on several industries, including medical, legal, information technology and media.



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Effective Online Data Entry Services

The outsourcing market has many enthusiastic buyers who have paid a small amount to online data entry service providers. They carry the opinion that they have paid too low as against the work they have got done. Online services is helpful to a number of smaller business units who take these projects as their significant source of occupation.

Online data-entry services include data typing, product entry, web and mortgage research, data mining as well as extraction services. Service providers allot proficient workforce at your service who timely deliver best possible results. They have updated technology, guaranteeing 100% data security.

Few obvious benefits found by outsourcing online data entry:

    Business units receive quality online entry services from projects owners.
    Entering data is the first step for companies through which they get the understanding of the work that makes strategic decisions. The raw data represented by mere numbers soon turns to be a decision making factor accelerating the progress of the business.
    Systems used by these services are completely protected to maintain high level of security.
    As you increasingly obtain high quality of information the business executive of the company is expected to arrive at extraordinary decisions which influence progress in the company.
    Shortened turnaround time.
    Cutting down on cost by saving on operational overheads.

Companies are highly fascinated by the benefits of outsourcing your projects for these services, as it saves time as well as money.

Flourishing companies want to concentrate on their key business activities instead of exploring into such non-key business activities. They take a wise step of outsourcing their work to data-entry-services and keep themselves free for their core business functions.

One such company they opt for is Offshore Data Entry who provides 99.995 % accuracy for projects.



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Outsource Yellow Pages Data Entry Services to Flatworld Solutions

Whether you are looking for a good source of sales leads or hunting for vendors and partners for your business, yellow pages are one of the most reliable resources you have at your disposal. But sifting through tons of information in the yellow pages to finalize on what you want not only takes a lot of time but also robs you of your money and resources.

Data entry of business listings, pre-sales leads, addresses & Phone numbers is an efficient way to collate huge volumes of information from yellow pages so that you can use them for your business.

Flatworld Solutions might just have the answer to your yellow pages data entry needs; with our advanced database capturing software, we are capable of capturing names, addresses, phone numbers, e-mails and other details. We have been catering to a wide variety of organizations with varied business processes and data collection methods; proving our quality and speed with every one of these.
Why outsource yellow pages data entry?

Outsourcing has revolutionized the way in which businesses are automating their processes. Leading the pack of outsourcing destinations, India has a well qualified work force and the infrastructure necessary to take this job up efficiently.

Outsourcing not only gives you cost advantages, but it also gives you the necessary time and resources to invest back into the core objectives of your business.

Here are some of the reasons and benefits why you need to use data entry services for yellow pages and why outsourcing is best for the job:

    It is difficult to manually extract tons of data from Yellow pages. Outsourcing this tedious task saves time and resources through manual and automated data entry
    No need to spend on local staff as there is a well qualified and large work force available in India
    No hassles of management and maintenance that is normally found in manual data entry and collation
    You can take a huge load off your existing operations by outsourcing and focus your attention in marketing your services
    Turnaround time and quality is high – you can start your sales / pre-sales campaign within the quickest time possible once the data entry process begins. Even if you want to use the information from yellow pages for other business objectives, the time for the finished database to be ready is quick
    The time difference between India and America / Europe means while you sleep, we work to finish the assignment. This ensures faster operations at a lower cost

Outsource yellow pages data entry services to Flatworld Solutions

Flatworld Solutions has been providing Data Entry, processing and conversion services to over 5400 customers spread across 45 countries for over 12 years. We have a separate team for data entry from yellow pages that operate on strong processes.

Our cornerstone in yellow pages Data entry is the use of a slick Database Capture and Builder tool. This tool has been designed to gather information from yellow pages and other sources based on conditions that have been assigned to it, based on customer requirements.

This software can also be customized based on the scope and nature of your project. Strict testing is carried out to test the changes to the tool and then they are put to work.

In addition we also employ manual data entry methods to search for information based on various criteria which is then keyed in specially created templates. This is then checked for accuracy and errors by the quality assurance team.
The benefits of outsourcing yellow pages data processing / entry to Flatworld Solutions

    Quick delivery timeframes
    Cost-effective pricing schemes
    Customer support
    Maximum accuracy guaranteed through multiple rounds of quality testing
    Highly knowledgeable and trained data entry workforce
    Complete confidentiality of your database information
    Highly customized pricing schemes dependant on the complexity of the data entry job

Contact us to outsource yellow pages data entry services.



Source: http://www.flatworldsolutions.com/data-management/yellow-pages-data-entry-services.php

How We Get A Money From Yellow Pages Scraping Services?

Wherever you live in the world, every day, Google searches for keywords related to your environment or an environment based on the hit. Tickets, bars and restaurants, shops, festivals and fairs in the schools, a trip to the daily activities of work about, about, you need to do (well) to fill a need not being met.

Now call Google (and all search engines) are used. Yellow pages are outdated, and just about everything about everyone I know only online searches.

Here are some scenarios that the "local" Some people are looking for:

The best pizza in town for ABC?

* Anyone wishing to board animals on the site?

* Where can I rent a car in ABC?

* Where can I get the tickets at ABC?

* I can do for information about the ABC schools are closed?

* What equipment repair in ABC?

* Bachelor ABC tent open house for rent in the city?

* ABC Catering in the city?

* On ABC contractors license?

* Private tutors in ABC?

* Doctor, dentist or specialist at ABC?

I literally spent days and days and days can go by this list. I do not care where you live - your neighborhood, town, city, state and regional-day online looking for things, and if you read my blog you probably already have your own website. Limitations, most of them (if not all) topics or niches that are based on the goal of people around the world. "Large" instead of a few times to narrow your focus, and some very small, very concentrated thinking, creates a local Web sites.

Top 5 reasons to do this are:

1. Source of income: revenues consistently across the board you can build low-income streams per month. 1,000 visitors with web sites with 20,000 visitors per month for 2 months and 20 percent prefer? Put all your eggs in one basket.

2.maand, but 10 per year for up to 500 - not counting realization.

3. Low maintenance: Unlike a traditional blog, site or niche store bans - these little "mini sites" have little or no maintenance required after installation. This way only one or a few small sites may be successful in more than a dozen pages. If the content does not change, there is very little maintenance.

4. In most cases, a math tutor Boise Idaho ", or" Paris Texas, chair and tent rental for competitive phrases. "Believe me, it's very easy compared to this rank for" blue iPod Nano! "Most of these sites for you to half a dozen of the Left to make and drain.

5. Things in your environment, things that are the "Yellow Book" or zip code ever know about the spam sites specializing in online.

As insight. Objects and events that you have a national website or blog, or can be added to the local taste. Can you beat Expedia, hotels.

A "backyard" and that the past is to create a local site - Google loves original content. I turn so many people are content, article rewriting, scraping, blogging car, and try the system. And eBay Partner Network has canceled an account that surprises people - because so much quality out of nowhere at Sammy's strange websites (Content) are.


Source: http://www.articlesnatch.com/Article/How-We-Get-A-Money-From-Yellow-Pages-Scraping-Services-/3533074#.UbWKANiWbDc

Understanding Data Mining

Well begun is half done. We can say that the invention of Internet is the greatest invention of the century which allows for quick information retrieval. It also has negative aspects, as it is an open forum therefore differentiating facts from fiction seems tough. It is the objective of every researcher to know how to perform mining of data on the Internet for accuracy of data. There are a number of search engines that provide powerful search results.

Knowing File Extensions in Data Mining

For mining data the first thing is important to know file extensions. Sites ending with dot-com are either commercial or sales sites. Since sales is involved there is a possibility that the collected information is inaccurate. Sites ending with dot-gov are of government departments, and these sites are reviewed by professionals. Sites ending with dot-org are generally for non-profit organizations. There is a possibility that the information is not accurate. Sites ending with dot-edu are of educational institutions, where the information is sourced by professionals. If you do not have an understanding you may take help of professional data mining services.

Knowing Search Engine Limitations for Data Mining

Second step is to understand when performing data mining is that majority search engines have filtering, file extension, or parameter. These are restrictions to be typed after your search term, for example: if you key in "marketing" and click "search," every site will be listed from dot-com sites having the term "marketing" on its website. If you key in "marketing site.gov," (without the quotation marks) only government department sites will be listed. If you key in "marketing site:.org" only non-profit organizations in marketing will be listed. However, if you key in "marketing site:.edu" only educational sites in marketing will be displayed. Depending on the kind of data that you want to mine after your search term you will have to enter "site.xxx", where xxx will being replaced by.com,.gov,.org or.edu.

Advanced Parameters in Data Mining

When performing data mining it is crucial to understand far beyond file extension that it is even possible to search particular terms, for example: if you are data mining for structural engineer's association of California and you key in "association of California" without quotation marks the search engine will display hundreds of sites having "association" and "California" in their search keywords. If you key in "association of California" with quotation marks, the search engine will display only sites having exactly the phrase "association of California" within the text. If you type in "association of California" site:.com, the search engine will display only sites having "association of California" in the text, from only business organizations.

If you find it difficult it is better to outsource data mining to companies like Online Web Research Services


Source: http://ezinearticles.com/?Understanding-Data-Mining&id=5608012