Importance of Data Mining Services in Business

Data mining is used in re-establishment of hidden information of the data of the algorithms. It helps to extract the useful information starting from the data, which can be useful to make practical interpretations for the decision making.
It can be technically defined as automated extraction of hidden information of great databases for the 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. Although data mining is a relatively new term, the technology is not. It is thus also known as Knowledge discovery in databases since it grip searching for implied information in large databases.
It is primarily used today by companies with a strong customer focus - retail, financial, communication and marketing organizations. It is having lot of importance because of its huge applicability. It is being used increasingly in business applications for understanding and then predicting valuable data, like consumer buying actions and buying tendency, profiles of customers, industry analysis, etc. It is used in several applications like market research, consumer behavior, direct marketing, bioinformatics, genetics, text analysis, e-commerce, customer relationship management and financial services.

However, the use of some advanced technologies makes it a decision making tool as well. It 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, scientific tests, genetics, financial services and utilities.

Data mining consists of major elements:

    Extract and load operation data onto the data store system.
    Store and manage the data in a multidimensional database system.
    Provide data access to business analysts and information technology professionals.
    Analyze the data by application software.
    Present the data in a useful format, such as a graph or table.

The use of data mining in business makes the data more related in application. There are several kinds of data mining: text mining, web mining, relational databases, graphic data mining, audio mining and video mining, which are all used in business intelligence applications. Data mining software is used to analyze consumer data and trends in banking as well as many other industries.




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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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Unleash the Hidden Potential of Your Business Data With Data Mining and Extraction Services

Every business, small or large, is continuously amassing data about customers, employees and nearly every process in their business cycle. Although all management staff utilize data collected from their business as a basis for decision making in areas such as marketing, forecasting, planning and trouble-shooting, very often they are just barely scratching the surface. Manual data analysis is time-consuming and error-prone, and its limited functions result in the overlooking of valuable information that improve bottom-lines. Often, the sheer quantity of data prevents accurate and useful analysis by those without the necessary technology and experience. It is an unfortunate reality that much of this data goes to waste and companies often never realize that a valuable resource is being left untapped.

Automated data mining services allow your company to tap into the latent potential of large volumes of raw data and convert it into information that can be used in decision-making. While the use of the latest software makes data mining and data extraction fast and affordable, experienced professional data analysts are a key part of the data mining services offered by our company. Making the most of your data involves more than automatically generated reports from statistical software. It takes analysis and interpretation skills that can only be performed by experienced data analysis experts to ensure that your business databases are translated into information that you can easily comprehend and use in almost every aspect of your business.

Who Can Benefit From Data Mining Services?

If you are wondering what types of companies can benefit from data extraction services, the answer is virtually every type of business. This includes organizations dealing in customer service, sales and marketing, financial products, research and insurance.

How is Raw Data Converted to Useful Information?

There are several steps in data mining and extraction, but the most important thing for you as a business owner is to be assured that, throughout the process, the confidentiality of your data is our primary concern. Upon receiving your data, it is converted into the necessary format so that it can be entered into a data warehouse system. Next, it is compiled into a database, which is then sifted through by data mining experts to identify relevant data. Our trained and experienced staff then scan and analyze your data using a variety of methods to identify association or relationships between variables; clusters and classes, to identify correlations and groups within your data; and patterns, which allow trends to be identified and predictions to be made. Finally, the results are compiled in the form of written reports, visual data and spreadsheets, according to the needs of your business.

Our team of data mining, extraction and analyses experts have already helped a great number of businesses to tap into the potential of their raw data, with our speedy, cost-efficient and confidential services. Contact us today for more information on how our data mining and extraction services can help your business.




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Data Mining, Visual Analytics, and The Human Component!

With all the massive amounts of data we are collecting from the Internet, well, it is just amazing the things we can do with it all. Of course, those concerned about privacy, well, you can understand why organizations like the Electronic Freedom Foundation is often fit to be tied. Still, think of all the good that can become of all this data? Let me explain.

You see, with the right use of visual analytics and various data mining strategies, we will be able to do nearly anything we need too. And, yes, I guess it goes without saying that I have a ton of thoughts on Visual Analytics of the Internet, Mobile Ad Hoc networking, and Social Networks along with some concepts for DARPAs plan for "crowd sourcing" innovation, it makes perfect sense to me, as each participant becomes basically a "neuron" and we use the natural neural network scheme.

What we need is a revolution in data mining visual analytics, so the other day I spent 20-minutes considering this and here are my thoughts. I propose an entirely new concept herein. Okay so let me explain my concept. But first let me briefly describe the bits and pieces of ideas and concepts I borrowed from to come up with this;

    There is an only UFO or Sci Fi tale I read, where the alien race said; "There is a whole new world waiting for you if you dare to take it,"
    Taking the "it" part of that line and calling "it" = "IT" as in Information Technologies.
    Next, combining that "IT" or "It entity" with that old Christian apocalyptic "mark of the beast" and the old computer system in Belgium 30-years ago claiming to be big enough to track every world transaction, also nick-named the beast.
    Then combining that concept with V. Bush's concept of "recording a life" or the later "life log theory" from Bell Labs.
    Then using the concept of the eRepublic, where government is nothing more than a networked website.
    Then considering the thought of Bill Gate's concepts in "the Road Ahead" where the digital nervous system of a corporation was completely and fully integrated.
    Combined with SAPs, and Oracles enterprise solutions
    Combined with Google's data bases
    Combined with the Pangaea Project for kids to collaborate in elementary school around the world and programming the AI computer, using a scheme designed by Carnegie Mellon to crowd source the teaching of an AI system. "eLearning Collaborative Networks like Quorum or Pangaea"
    Combined with IBMs newest mind map visualization recently in the news..
    Combined with these following thoughts of mine:

    My Book; "The Future of Truck Technologies," and 3D and 4D Transportation Computer Modeling; Page; 201.
    My Book; "Holographic Technologies," specifically; Data Visualization Schemes; Page 57 Chapter 5.
    My Article on 3D and 4D Mind Maps for Tracking and Analyzing.
    My Article on Mind Maps of the Future and Online style Think Tanks
    My Article on Stair Step Mentorship for Human Learning in the Future and Never Aging Societies.

Okay now let me explain the premise of my concept for Visual Analytics;

First, forget this whole idea of a 2D mind mapping concept or chart used to show links between terrorist players, cells, assets, acquaintances, etc., the way it is laid out currently - make it 3D, actually make it 4D and 5D where some layers can only be seen by a select few, and let's say a 6D level that can only be accessed by an AI super computer [why; because I don't trust humans, they can't be trusted, i.e. WikiLeak, leaker for instance].

Next ALL the data is stored within in the sphere. But to access the data on the outer side of the sphere, picture Earth's surface, the ball or sphere (with grids like a map of the globe) rolls around on a giant grid paper. When you want to look at a particular event, person, subject, or whatever, a particular point on the sphere's grid touches a corresponding point on the grid paper it rolls on, the grid paper it rolls on can wrap around and morph itself to the sphere or contour itself so the next corresponding piece of information on the surface can be accessed, rolling or spinning.

Picture a selectric typewriter ball on a shaft as a 2D model to consider this, now make it all 3D in your mind, and the paper molds around the sphere as it accesses, or in the case of a selectric typewriter it types. Now the Sphere is hollow inside containing layers, just like the earth, crust, mantel, and core. Information goes deep or across, every piece of information is connected, think about the earliest string theory models for this.

Great thing about my visualization concept is I believe all this math exists, even though in reality string theory is mostly bunk, but the math to get there makes this possible. As the information goes deep, think about the iPad touch screen, or the Microsoft restaurant "menu on a table" concept, or the depictions of Minority Reports, moving of the screens by way of motion gestures, I believe Lockheed also has this concept up and running for air-traffic control systems, prototype versions, perhaps the military is already using it, as it has massive applications for the net centric battlespace visualization too.

Okay so, some levels go through a frame-burst scenario taking you into another level, where the data generally stored at the almost infinite number of grid points and cross connected to every other is nothing more than a nucleus with additional data spinning around it. But the user cannot access all that information, without clearances, the AI system has access to all of it, while a sorting system is a series of search features within search features, with non-linked data also. You can't break into it; it's not connected to the users' interface at all, think of the hidden data as electrons unattached around the data. The data is known to exist but cannot be accessed that would be the 5D level, and 6D level no human may get too, but the data exists.

You know that surfer dude in Hawaii that came up with the "Grand Theory of the Universe" why not use his model for our visualization, in spherical form, again, the mathematics for all this already exists.

You see, what I need is a way to find people like me, I want to find these thinkers and innovators to take it all to the next level, and if the visualization is there, we can find; The Good Guys, Bad Guys, and the Future all at once. Why do I want a "Neural Network" visualization system in a sphere? It seems to me that this is how the brain does things, and what we are doing here is creating a Collective Brain, using each individual assigned to an "ever-expanding" unit of data, along a carrier or flow.

Remember when Microsoft Labs came out with that really cool way to travel through the Universe and look at all the celestial bodies along the way, using all the Hubble Pictures collected? It's kind of like that, you travel to the information, discover as you travel and it piques your curiosity as you go triggering your own brain waves, and splashing the users minds with chemical rewards as they go, as they discover more information, expanding their understanding as well, it just seems to me this is how it all works anyway.

Think of that old Sci Fiction concept where the Earth and our solar system are merely an atom of a chemical compound within a cell of the human body, all we can see is all the other compounds around us because everything is so small, thus, we cannot see the whole picture and what appears to be an entire universe would only be a few thousand cells close enough for us to see. And time itself is slow, as the electrons or planets moving around the atom appears to take a year to circle the nucleus instead of 10,000 times a second.

So, combining all these types of thoughts, this is how I envision how the future visualization tools would work.

Now then, using the whole concept of connecting the dots for information or even building an AI search feature scouring the system at speeds of terabytes a second, the AI computer can become the innovator, thanks to the user asking the question, and all the neurons (individual humans) with all their data putting in the information. You just need the best questions, you get instance answers.

Okay so, take this concept one step further; the AI super computer's operation is a "brain wave" and that brain wave is assigned a number, you can have as many brain waves, as the internet has IP addresses, with whatever scheme for that you choose. And your query can search the former queries too. The user's questions are as important as the data itself.

Thus, it helps us find the innovators, the question askers, once we know that, we have the opportunity for unlimited instant knowledge. Data visualization can take us there, and it removes all the fog of uncertainty, and answers most all the questions we could ever hope to ask, and comes up with its own questions as well. Does this make sense?

This is the type of visualization I need to faster access information, and I can solve all the problems, even the ones humans refuse to solve, or doom themselves to repeateth. That's my preliminary thought on this - may we start such a dialogue on the topic? If so, email me, and I hope you enjoyed today's dialogue?




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Advantages of Online Data Entry Services

People all over the world are enthusiastic to buy online data entry services as they find it cost effective. Most of them have an impression that they get quality services against the prices they have to pay. Entering data online is of a great help to business units of all sizes as they consider them as their main basis of profession.

Online data entering and typing services providers have skilled resources at their service who deliver quality work timely. These service providers have modernized technology, assuring cent percent security of data. Online data entry services include the following:

    Data entry
    Data Processing
    Product entry
    Data typing
    Data mining, Data capture/collection
    Business Process Outsourcing
    Data Conversion
    Form Filling
    Web and mortgage research
    Extraction services
    Online copying, pasting, editing, sorting, as well as indexing data
    E-books and e-magazines data entry

Get companies world wide quality services to business units of all sizes, some of the common input formats are:

    PDF
    TIFF
    GIF
    XBM
    JPG
    PNG
    BMP
    TGA
    XML
    HTML
    SGML
    Printed documents
    Hard copies, etc

Benefits of outsourcing online data entering services:

Major benefits of data entry for business units is that they get the facts and figures which helps in taking strategic decisions for the organization. The data projected by numbers turns to be a factor of evaluation that accelerates the progress of the business. Online data typing services maintain high level of security by using systems that are highly protected.

The business organization progresses because of right decisions taken with the help of superior quality data available.

    Save operational overhead expense.
    Saves time and space.
    Accurate services can be accessed.
    Eliminating the paper documents.
    Cost effective.
    Data accessible from anywhere in the world.
    100% work satisfaction.
    Access to professional and experienced data typing services.
    Adequate knowledge of wide range industrial needs.
    Use of highly advance technologies for quality results.

Business organizations find themselves blessed because of the benefits they receive out of outsourcing their projects on online data entering and typing services, because it not only saves their time but also saves a huge amount of money.

Upcoming business companies can focus on their key business functions instead of dealing with non-key business activities. They find it sensible to outsource their confidential and crucial projects to trustworthy online data entry services and remain free for their key business activities. These companies have several layers of quality control which assures 99.9% quality on projects on online data entry.



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Advantages of Data Mining in Various Businesses

Data mining techniques have advantages for several types of businesses, as well as there are more to be discovered over time. Since the era of the computer, things have been changing pretty quickly and every new step in the technology is equivalent to a revolution. Communication itself has not been enough. As compared to the present times, the data analyzers in the past have not achieved the chance to go further with the data they have in hand. Today, this data isn't used for selling more of a product but to foresee future risks as well as prevent them.

All are benefiting from modern these techniques even from smaller to large enterprises. They can now predict the outcome of a particular marketing campaign by analyzing them. However, in order for these techniques to be successful, the data must be arranged accurately. If your data is disseminated, you need to bring it in a meeting and then feed into the systems for the algorithms to figure it out. To put it shortly, no matter how small or big your business might be you always need to have the right system when collecting data from your customers, transactions and all business activities.

Advantages of Data Mining For Businesses

Businesses can truly benefit from its latest techniques; however, in the future, data mining techniques are expected to be even more concise and effective than they are today. Here are the essential techniques that you need to understand:

· Big companies providing the free web based email services can use data mining techniques to catch spam emails from their customer's inboxes. Their software uses a technique to assess whether an email is a spam or not. These techniques are first tested and validated before they are finally used. This is to ensure they are producing the correct results.

· Large retail stores and even shopping malls could make use of these techniques by registering and recording the transactions made by their customers. When customers are buying particular sets of product, it can give them a good understanding of placing these items in the aisle. If they want to change the order and placement of the item on weekends, it could be found out after analyzing the data on their database.

· Companies manufacturing edible or drinkable products could easily use data mining techniques to increase their sales in a particular area and launch new products based on the information they've obtained. That's why the conventional statistical analysis is rigid in scenarios wherein consumer behavior is in question. However, these techniques still manages to give you good analysis for any situations.

· In call centers, the human interaction is at its peak because people are talking with another people at all times. Customers respond differently when they talk to a female representative as opposed to talking to a male representative. The response of customers to an infomercial is different from their response to an ad in the newspaper. Data could be used for the benefit of the business and is best understood with the use of data mining techniques.

· Data mining techniques are also being used in sports today for analyzing the performances of players in the field. Any game could be analyzed with the help of these techniques; even the behaviors of players could be changed on the field through this.

In short, data mining techniques are giving the organizations, enterprises and smaller businesses the power of focusing on their most productive areas. These techniques also allow stores and companies to innovate their current selling techniques by unveiling the hidden trends of their customer's behavior, background, price of the products, placement, closeness to the related products and many more.



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Benefits and Advantages of Data Mining

One definition given to data mining is the categorization of information according to the needs and preferences of the user. In data mining, you try to find patterns within a big volume of available data. It is a potent and popular technology for different industries. Data mining can even be compared to the difficult task of looking for a needle in the haystack. The greatest challenge is not obtaining information but uncovering connections and information that have not been known in the past.

Yet, data mining tools can only be utilized efficiently provided you possess huge amounts of information in repository. Almost all of corporate organizations already hold this information. One good example is the list of potential clients for marketing purposes. These are the consumers to whom you can sell commodities or services. You have greater chances of generating more revenues if you know these potential customers in the inventory and determine consumption behavior. There are benefits that you need to know regarding data mining.

    Data mining is not only for entrepreneurs. The process is cut out for analysis as well and can be employed by government agencies, non-profit organizations, and basketball teams. In short, the data must be made more specific and refined according to the needs of the group concerned.

    This unique method can be used along with demographics. Data mining combined with demographics enables enterprises to pursue the advertising strategy for specific segments of customers. That form of advertising that is related directly to behavior.

    It has a flexible nature and can be used by business organizations that focus on the needs of customers. Data mining is one of the more relevant services because of the fast-paced and instant access to information together with techniques in economic processing.

However, you need to prepare ahead of time the data used for mining. It is essential to understand the principles of clustering and segmentation. These two elements play a vital part in marketing campaigns and customer interface. These components encompass the purchasing conduct of consumers over a particular duration. You will be able to separate your customers into categories based on the earnings brought to your company. It is possible to determine the income that these customers will generate and retention opportunities. Simply remember that nearly all profit-oriented entities will desire to maintain high-value and low-risk clients. The target is to ensure that these customers keep on buying for the long-term.




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Online Data Entry and Data Mining Services

Data entry job involves transcribing a particular type of data into some other form. It can be either online or offline. The input data may include printed documents like Application forms, survey forms, registration forms, handwritten documents etc.

Data entry process is an inevitable part of the job to any organization. One way or other each organization demands data entry. Data entry skills vary depends upon the nature of the job requirement, in some cases data to be entered from a hard copy formats and in some other cases data to be entered directly into a web portal. Online data entry job generally requires the data to be entered in to any online data base.

For a super market, data associate might be required to enter the goods which have sold in a particular day and the new goods received in a particular day to maintain the stock well in order. Also, by doing this the concerned authorities will get an idea about the sale particulars of each commodity as they requires. In another example, an office the account executive might be required to input the day to day expenses in to the online accounting database in order to keep the account well in order.

The aim of the data mining process is to collect the information from reliable online sources as per the requirement of the customer and convert it to a structured format for the further use. The major source of data mining is any of the internet search engine like Google, Yahoo, Bing, AOL, MSN etc. Many search engines such as Google and Bing provide customized results based on the user's activity history. Based on our keyword search, the search engine lists the details of the websites from where we can gather the details as per our requirement.

Collect the data from the online sources such as Company Name, Contact Person, Profile of the Company, Contact Phone Number of Email ID Etc. are doing for the marketing activities. Once the data is gathered from the online sources into a structured format, the marketing authorities will start their marketing promotions by calling or emailing the concerned persons, which may result to create a new customer. So basically data mining is playing a vital role in today's business expansions. By outsourcing the data entry and its related works, you can save the cost that would be incurred in setting up the necessary infrastructure and employee cost.



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New Method of Market Segmentation - Combining Segmentation With Data Mining

Marketers have the ability to get high-fidelity information on their target markets through market segmentation. Market segmentation is the process of categorizing potential customers based on certain variables, such as age, gender, and income. A market segment is a group of customers that will react in the same way to a particular marketing campaign. By gathering this information, marketers can tailor their campaigns to groups of prospects to build stronger relationships with them.

Marketers gather this demographic information through surveys, usually when the customer submits a product rebate or willingly participates in a customer satisfaction survey. Over the majority of the past few decades, market segmentation consisted of differentiating prospects based on very simple variables: income, race, location, etc. While this is definitely important information to have on your target market, modern market segmentation takes into account more integrated information.

Modern segmentation breaks the market into target clusters that take into account not only standard demographics, but also other factors such as population density, psychographics, and buying and spending habits of customers. By focusing on these variables in addition to standard demographics, you can gain deeper insight into customer behavior.

Using standard demographics, you can tailor your marketing pieces to specific groups of people. But, by including these more sophisticated variables in your segmentation process, you can determine achieve a higher degree of "lift" or return on your segmentation efforts.

Segmenting your market on these factors helps you realize your total opportunity and revenue potential. It can enable you to better compete with similar product or service providers and lets you know where you stand within the game. It can help you target untapped market opportunities and allow you to better reach and retain customers.

Market segmentation depends on the gathering of high-quality, usable data. Many companies exist to gather and sell massive databases of targeted customer information, as well as providing consultation services to help you make sense of data bought or already owned. The key to the process is determining the best way to split up data.

There are essentially two methods for categorizing customers. Segments can either be determined in advance and then customers are assigned to each segment, or the actual customer data can be analyzed to identify naturally occurring behavioral clusters. Each cluster forms a particular market segment.

The benefit of cluster-based segmentation is that as a market's behavior changes, you can adapt your campaigns to better suit the cluster. The latest techniques blend cluster-based segmentation with deeper customer information acquired via data mining. Data mining uses algorithms to interrogate data within a database, and can produce information such as buying frequency and product types.

This new method of market segmentation, combining segmentation with data mining, provides marketers with high quality information on how their customers shop for and purchase their products or services. By combining standard market segmentation with data mining techniques you can better predict and model the behavior of your segments.



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Data Entry - Outsource or Keep It Local?

Data entry services are usually something of a contentious subject, both in the public's eye and also in the data entry community itself, primarily because of the location from which the services are obtained. Some clients know that a lot of data entry is outsourced to developing countries such as India and Pakistan, and this can often put them off, particularly when the entry is in the English language and many clients would prefer the data inputting to be done by someone whose first language is English.

It's also a raging debate within the data entry industry itself, and there is a conflict between providing well priced services that are outsourced to other countries, or work that is completed locally by native English speakers but who will inevitably charge more.

As an experienced member or a reputable data entry supplier, my experience with outsourced services has been positive for the most part; however, it isn't always the case, much like with anything else in life. Clients are justifiably concerned that the entry work that will come back will not be of an acceptable quality and I'm sure this has happened before.

However there are a few steps that can be taken in order to ensure that the service supplied is not just an adequate one, but an excellent one, and they are mostly common sense, but it's still worthwhile to keep a list of a few things to remember:

- Request a sample

Requesting a sample is always a good idea and not just of any work but of your work itself so that you can see that the work was completed successfully. Any company that knows what they are doing will offer this as a good will gesture as well as to show you that their service is of good quality. This allows you to assess the quality of the output and it may even be a good idea to get a few samples at once just to see what works best for you.

- Check their credentials and experience

There's nothing wrong with start-up companies by any means, but in this case you may want to check their experience and credentials as a company. Here is the UK, it's a very good sign when a data entry company has the ICO, which guarantees higher quality of data entry as well as superior data protection. The ISO 9001:2000 is also one to look out for as this means that the company's work has been approved by the governing body of the industry, the ISO and is checked on regularly.

- Don't be put off by a lack of testimonials

Just because a company doesn't have testimonials doesn't mean they aren't a good company. In this industry, a lot of larger and smaller sized companies for that matter don't always want it to be known who they're outsourcing their work to, let alone that they're outsourcing at all. Therefor it is difficult for companies to come up with a good testimonials list at the best of times.

- Visit or arrange a meeting with the company

Human instinct can be your best friend. Visit the company premises if possible and see if you can see any work being produced or just to check that they are a good establishment that is well set up.

Those are a few steps to take to ensure that you get the right service for you and one that, if you are looking at needing a continuous supply of data entry work completing can become a business partnership to last for years. The argument regarding home-based data entry and outsourcing will go on and on, but the best thing you can do is to trust in the company you choose, make sure to get a sample before committing and judge for yourself when you get (hopefully) great results back.



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Data Mining and Financial Data Analysis

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.



Source: http://ezinearticles.com/?Data-Mining-and-Financial-Data-Analysis&id=2752017

Customer Relationship Management (CRM) Using Data Mining Services

In today's globalized marketplace Customer relationship management (CRM) is deemed as crucial business activity to compete efficiently and outdone the competition. CRM strategies heavily depend on how effectively you can use the customer information in meeting their needs and expectations which in turn leads to more profit.

Some basic questions include - what are their specific needs, how satisfied they are with your product or services, is there a scope of improvement in existing product/service and so on. For better CRM strategy you need a predictive data mining models fueled by right data and analysis. Let me give you a basic idea on how you can use Data mining for your CRM objective.

Basic process of CRM data mining includes:
1. Define business goal
2. Construct marketing database
3. Analyze data
4. Visualize a model
5. Explore model
6. Set up model & start monitoring

Let me explain last three steps in detail.

Visualize a Model:
Building a predictive data model is an iterative process. You may require 2-3 models in order to discover the one that best suit your business problem. In searching a right data model you may need to go back, do some changes or even change your problem statement.

In building a model you start with customer data for which the result is already known. For example, you may have to do a test mailing to discover how many people will reply to your mail. You then divide this information into two groups. On the first group, you predict your desired model and apply this on remaining data. Once you finish the estimation and testing process you are left with a model that best suits your business idea.

Explore Model:
Accuracy is the key in evaluating your outcomes. For example, predictive models acquired through data mining may be clubbed with the insights of domain experts and can be used in a large project that can serve to various kinds of people. The way data mining is used in an application is decided by the nature of customer interaction. In most cases either customer contacts you or you contact them.

Set up Model & Start Monitoring:
To analyze customer interactions you need to consider factors like who originated the contact, whether it was direct or social media campaign, brand awareness of your company, etc. Then you select a sample of users to be contacted by applying the model to your existing customer database. In case of advertising campaigns you match the profiles of potential users discovered by your model to the profile of the users your campaign will reach.

In either case, if the input data involves income, age and gender demography, but the model demands gender-to-income or age-to-income ratio then you need to transform your existing database accordingly.



Source: http://ezinearticles.com/?Customer-Relationship-Management-%28CRM%29-Using-Data-Mining-Services&id=4641198

Collecting Data With Web Scrapers

There is a large amount of data available only through websites. However, as many people have found out, trying to copy data into a usable database or spreadsheet directly out of a website can be a tiring process. Data entry from internet sources can quickly become cost prohibitive as the required hours add up. Clearly, an automated method for collating information from HTML-based sites can offer huge management cost savings.

Web scrapers are programs that are able to aggregate information from the internet. They are capable of navigating the web, assessing the contents of a site, and then pulling data points and placing them into a structured, working database or spreadsheet. Many companies and services will use programs to web scrape, such as comparing prices, performing online research, or tracking changes to online content.

Let's take a look at how web scrapers can aid data collection and management for a variety of purposes.

Improving On Manual Entry Methods

Using a computer's copy and paste function or simply typing text from a site is extremely inefficient and costly. Web scrapers are able to navigate through a series of websites, make decisions on what is important data, and then copy the info into a structured database, spreadsheet, or other program. Software packages include the ability to record macros by having a user perform a routine once and then have the computer remember and automate those actions. Every user can effectively act as their own programmer to expand the capabilities to process websites. These applications can also interface with databases in order to automatically manage information as it is pulled from a website.

Aggregating Information

There are a number of instances where material stored in websites can be manipulated and stored. For example, a clothing company that is looking to bring their line of apparel to retailers can go online for the contact information of retailers in their area and then present that information to sales personnel to generate leads. Many businesses can perform market research on prices and product availability by analyzing online catalogues.

Data Management

Managing figures and numbers is best done through spreadsheets and databases; however, information on a website formatted with HTML is not readily accessible for such purposes. While websites are excellent for displaying facts and figures, they fall short when they need to be analyzed, sorted, or otherwise manipulated. Ultimately, web scrapers are able to take the output that is intended for display to a person and change it to numbers that can be used by a computer. Furthermore, by automating this process with software applications and macros, entry costs are severely reduced.

This type of data management is also effective at merging different information sources. If a company were to purchase research or statistical information, it could be scraped in order to format the information into a database. This is also highly effective at taking a legacy system's contents and incorporating them into today's systems.



Source: http://ezinearticles.com/?Collecting-Data-With-Web-Scrapers&id=4223877

Data Mining Explained

Data mining is the crucial process of extracting implicit and possibly useful information from data. It uses analytical and visualization techniques to explore and present information in a format which is easily understandable by humans.

Data mining is widely used in a variety of profiling practices, such as fraud detection, marketing research, surveys and scientific discovery.

In this article I will briefly explain some of the fundamentals and its applications in the real world.

Herein I will not discuss related processes of any sorts, including Data Extraction and Data Structuring.

The Effort
Data Mining has found its application in various fields such as financial institutions, health-care & bio-informatics, business intelligence, social networks data research and many more.

Businesses use it to understand consumer behavior, analyze buying patterns of clients and expand its marketing efforts. Banks and financial institutions use it to detect credit card frauds by recognizing the patterns involved in fake transactions.

The Knack
There is definitely a knack to Data Mining, as there is with any other field of web research activities. That is why it is referred as a craft rather than a science. A craft is the skilled practicing of an occupation.

One point I would like to make here is that data mining solutions offers an analytical perspective into the performance of a company depending on the historical data but one need to consider unknown external events and deceitful activities. On the flip side it is more critical especially for Regulatory bodies to forecast such activities in advance and take necessary measures to prevent such events in future.

In Closing
There are many important niches of Web Data Research that this article has not covered. But I hope that this article will provide you a stage to drill down further into this subject, if you want to do so!

Should you have any queries, please feel free to mail me. I would be pleased to answer each of your queries in detail.



Source: http://ezinearticles.com/?Data-Mining-Explained&id=4341782

Data Entry Services in India Are Getting Famous in the World!

Outsourcing has become the most profitable business in the world. This business is growing in India and other part of the world. These services are getting famous in the world and most of the business owners are saving their lots of money by doing outsourcing to different countries where India comes in top in the outsourcing. By outsourcing your offline and online information entry jobs, your company will maintain properly organized and up-to-date records of the employees and other important stuff. These jobs are usually done in the home environment.

India is very popular in providing the BPO services for their customers. There is large scale of BPO service providers running their business in India. The employees working in these offices are also very competent and trained. Data entry services in India is very popular all around the world because of having the access of BPO experts and the web data extraction experts.

What these BPO services provide you?

There are many business across the globe running on the outsource services, BPO services in India provides the ease of life to the business owner want quick and fast data entry work.

There are many well reputed firms working in India and doing their best to finish and deliver comes punctually. They're professional well equipped with the newest technology and software and more importantly with the professional labor work. They are fully trained and expert in their niche so if a business owner take the services then they get the in time work and quality. When you will select any BPO expert then you will find the following data entry expertise in these professional companies.

1. You will find the handwritten material with the help of experts.
2. Knowledge entry of e-books, directories, image files and etc.
3. You will also get the best services of data processing.
4. Business card knowledge entry
5. Bills and survey services which will help you to Maintain and correct records.
6. Alpha numeric data entry services
7. Data entry free trails.

Thousand of online BPO jobs are also available on the Indian big job portals and other data entry work. These services and work force reduce your workload and will enhance your productivity of your business. Outsourcing the right choice by any business owner because it reduces your total cost and you get the perfect and reliable work. When you approach to any professional service provider firm in India then it reduce the turnaround time and you get the professional data entry services.


Source: http://ezinearticles.com/?Data-Entry-Services-in-India-Are-Getting-Famous-in-the-World!&id=4708858

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.

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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Advantages of Outsourcing Your Data Entry Processes

Aside from the call centers boom in many developing countries, another expanding and growing trend in many businesses is outsourcing data entry services. Globalization and the present technological innovations have made it easier and possible to outsource back office functions even to data entry staff from half the world away. Now, not only large companies can take advantage of this business strategy, even small and medium businesses are realizing the benefits.

Data entry primarily deals with the creation of soft copies of documents from different resources. It could either be for online or offline. For online, virtual staff is required to log in to the client's website to input data and store it up in an online database maintained by the client company. On the other hand, offline staff don't require internet connection as the sources will be made available offline or will be provided with a hard copy.

Understandably, data is considered as one of the most important tool of any company. It is essential to have appropriate management in order for the business to run smoothly and effortlessly, thus the rise in demand for reliable data handling and acquiring it. And presently, many Business Process Outsourcing or BPO companies have started outsourcing other services than call center and telemarketing and have looked into entering this segment of the industry.

More than reliable and efficient data handling, small and medium business may also derive these benefits:

• Costs savings from local staff benefits such as health care, insurance and other employee issues. Expansion of business is what drives many companies so as to likewise avoid such employee issues.

• World class quality and timely output delivery with outsourcing, so small business can give more attention to core business issues.

• It provides businesses the advantage of minimizing infrastructure and management costs. They won't need office space or pay outrageous salaries to local employees just to handle mundane and repetitive related tasks.

Hiring virtual staff like data encoders could be a bit disconcerting especially for first timers. There are several offshore locations that could offer you affordable services. Developing countries such as India, China and the Philippines are the top choices for outsourcing. But if you really want to be sure about the competitiveness of your offshore virtual staff, and then why not consider the third largest English speaking nation in the world?

One can easily notice that most multinational companies turn to the Philippines for their BPO needs. Filipinos are easy to deal with mainly due to their excellent English speaking abilities, high educational attainment and corporate experience as well as their affinity to the Western culture. Filipinos can easily understand how the West thinks, feels and behaves, thus they can easily adapt to the needs and requirement of their Western offshore employees.

Large companies may need a building full of hundreds offline professionals to handle their mountain of back office functions. But for small businesses, the best option would be is to hire online staff or an online typist. Consider how many offshore staff you need for your back office jobs and start searching for that perfect data entry staff to provide your typing services or encoding service needs.


Source: http://ezinearticles.com/?Advantages-of-Outsourcing-Your-Data-Entry-Processes&id=4604826

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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