Using Forensic Social Media Data Mining to Discover Work Comp Fraud

As an employer, most of your employee based work comp claims are completely legitimate and should be handled in the best interest of the injured employee. Unfortunately, some are also fraudulent, causing increasing costs on baseless claims. Historically, it's been challenging to contest some of these claims, though recently a new road has opened, allowing enlightened employers to more rapidly travel this road to a truthful outcome.

Employers should now consider the usefulness of Facebook, YouTube, LinkedIn and other social media sites which can contain posts negating the claims of allegedly injured workers participating in activities that are beyond the restrictions placed by the treating physician. These posts can happen on any given day, clearly elucidating a fraudulent claim. For example, let's say that an employee is out of work based on a "work comp" (workers compensation) claim which has restrictions, yet they post links, discussions, comments and photos that are clearly incriminating. This is an area in which companies should be mining regularly to protect your company against out of scope workers compensation claims.

Recently, a transportation attorney based in central Pennsylvania was a guest speaker at an insurance transportation web seminar. His topic was the aggressive defense of trucking lawsuits and he elaborated extensively on an example of forensic social media investigation to assist in the aggressive defense of frivolous lawsuits. I recall that the metrics were impressive; one example noted that a $250,000 claim which was reduced to $2,500 when forensic social media data mining found New Years Eve photos proving the claimant's mobility to be much greater than stipulated in the law suit.

Social media offers a surprising if not inadvertent glimpse into the nuances of the lifestyles of anyone using it, and in certain cases, it also offers important evidence into the veracity of work comp and work comp lawsuit based claims. Employers should be aware of this avenue and investigate accordingly.



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Limitations and Challenges in Effective Web Data Mining

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

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

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

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

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


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Data Entry Outsourcing Companies

Data entry outsourcing companies are required by most businesses. The turn of the 21st century saw a spate of growth in outsourcing data entry tasks worldwide. Businesses opted to go digital and had a huge amount of data that was required to be fed into computers. Data consisted of their past and current records so as to enable companies to project into future trends with exactness. They were ill-equipped to handle the colossal amount of data on their own, and sought external assistance. This, in turn, encouraged the establishment of commercial organizations whose core activity consisted of helping other businesses enter their bulk of data for a fee.

Such tasks include word processing, numeric data, and transcription. Companies large and small have to manage and input their data and data entry outsourcing companies can make this tedious process more efficient and cost effective. Such companies are totally dedicated to keying in all forms of data accurately and quickly. They are experts at meeting the quotas set aside for them and the deadlines.

The more professional data entry outsourcing companies are equipped with the infrastructure and manpower to input all types of data. Location hardly matters as the Internet has shrunk the word considerably. The transference of internal data processes to a third party or outsourcing includes both domestic and foreign contracting. The data entry outsourcing companies function purely on their ability to handle a substantial amount of data. Their driving force is precision, promptness, and fidelity. Data loss or leakage can mean deficit of millions of dollars. Clients can be sure that their data to be entered is in safe hands of professional data entry outsourcing companies. Under no circumstance will it be shared with others.

The bits of information, either in numeric form or text or a combination of the two are valuable for the client company. Inputting data into fields or forms requires a fair bit of skill and an eye for details. Data entry outsourcing companies take responsibility for maximum accurateness and quickness of the data entered by their members of staff who might rope in the regular workforce, freelancers and work-from-home individuals. The choice is theirs but the output submitted is hundred percent correct. Legitimate outsourcing companies involved in such work are methodical, meticulous, and painstaking. They toil round the clock and always deliver on time. These companies accept all types of data jobs and doctor the inputted data before submission.



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The Increasing Significance of Data Entry Services

The instantaneous business environment has become extremely competitive in the new era of globalization. Huge business behemoths that had been benefited from monopolistic luxuries are now being challenged by newer participant in the marketplace, forcing recognized players to reorganize their plans and strategies. These are some of the major reasons that seemed to have forced businesses to opt for outsourcing services such as data entry services that allow them to focus on their core business processes. This in turn makes it simple for them to attain and maintain business competencies, a prerequisite for effectively overcoming the rising competitive challenges.

So, how exactly is data entry helping businesses in achieving their targeted goals and objectives? Well, to be able to know actually that, we will first have to delve deeper into the field of data entry and allied activities. To start with, it would be worth mentioning that every business, big and small, generates voluminous amounts of data and information that is important from a business point of view. This is exactly where the problems start to surface because accessing, analyzing and processing such voluminous amounts of data is too time consuming and obviously a task that can easily be classified as non-productive. And these are exactly the reasons for outsourcing such non-core work processes to third party outsourcing firms.

There is many data entry outsourcing firms and most of them are located in developing countries such as India. There are many reasons for such regional clustering, but the most prominent reason it seems is that India has a vast talent pool, comprising of educated, English-speaking professionals. The best part is that it is relatively less expensive to hire the services of these professionals. The same level of expertise will have been a lot more expensive to hire if it had been in a developed country. Subsequently, more and more businesses worldwide are outsourcing their non-core work processes.

As Globalization intensifies even more in the coming years, businesses will face even greater amounts of competitive pressures and it will just not be possible for them to even think about managing everything on their own, let alone actually going ahead and doing it. However, that should not be a problem, especially for businesses that opt for outsourcing services such as data entry and data conversion. By hiring such high-end and cost-effective services, these businesses will be able to realize the associated benefits that will come mostly as significant cost reductions, optimum accuracy, and increased efficiencies.

So for business executives that think outsourcing data entry related processes can help to achieve your targeted business goals and objectives, it's time you contacted an offshore outsourcing provider and request them precisely how they can ease your business. However just make sure that you opt for the most excellent available data entry services provider, perceptibly because it will be like sharing a part of your business.


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Data Mining Models - Tom's Ten Data Tips

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

6. Models Need To Be Monitored For Performance Degradence

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

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

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

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

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

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

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

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

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

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

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


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

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

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



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Data Mining - Techniques and Process of Data Mining

Data mining as the name suggest is extracting informative data from a huge source of information. It is like segregating a drop from the ocean. Here a drop is the most important information essential for your business, and the ocean is the huge database built up by you.

Recognized in Business

Businesses have become too creative, by coming up with new patterns and trends and of behavior through data mining techniques or automated statistical analysis. Once the desired information is found from the huge database it could be used for various applications. If you want to get involved into other functions of your business you should take help of professional data mining services available in the industry

Data Collection

Data collection is the first step required towards a constructive data-mining program. Almost all businesses require collecting data. It is the process of finding important data essential for your business, filtering and preparing it for a data mining outsourcing process. For those who are already have experience to track customer data in a database management system, have probably achieved their destination.

Algorithm selection

You may select one or more data mining algorithms to resolve your problem. You already have database. You may experiment using several techniques. Your selection of algorithm depends upon the problem that you are want to resolve, the data collected, as well as the tools you possess.

Regression Technique

The most well-know and the oldest statistical technique utilized for data mining is regression. Using a numerical dataset, it then further develops a mathematical formula applicable to the data. Here taking your new data use it into existing mathematical formula developed by you and you will get a prediction of future behavior. Now knowing the use is not enough. You will have to learn about its limitations associated with it. This technique works best with continuous quantitative data as age, speed or weight. While working on categorical data as gender, name or color, where order is not significant it better to use another suitable technique.

Classification Technique

There is another technique, called classification analysis technique which is suitable for both, categorical data as well as a mix of categorical and numeric data. Compared to regression technique, classification technique can process a broader range of data, and therefore is popular. Here one can easily interpret output. Here you will get a decision tree requiring a series of binary decisions.



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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



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

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

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

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

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

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

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

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

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

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

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

    attended previously
    age
    size of company
    industry

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

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

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

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

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

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

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

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

Conclusion

Summarizing, to determine if data mining fits your organization, you must consider:

    your business need
    your available data assets
    the performance improvement required

In the case study, Donna answered yes to each of the questions posed. She is well-positioned to proceed with a data mining project. You, too, can apply the same thought process before you spend a single dollar on DM. If you decide there is a fit, this preparation will serve you well in talking with your staff, vendors, and consultants who can help you move a data mining project forward.


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Basics of Online Web Research, Web Mining & Data Extraction Services

The evolution of the World Wide Web and Search engines has brought the abundant and ever growing pile of data and information on our finger tips. It has now become a popular and important resource for doing information research and analysis.

Today, Web research services are becoming more and more complicated. It involves various factors such as business intelligence and web interaction to deliver desired results.

Web Researchers can retrieve web data using search engines (keyword queries) or browsing specific web resources. However, these methods are not effective. Keyword search gives a large chunk of irrelevant data. Since each webpage contains several outbound links it is difficult to extract data by browsing too.

Web mining is classified into web content mining, web usage mining and web structure mining. Content mining focuses on the search and retrieval of information from web. Usage mining extract and analyzes user behavior. Structure mining deals with the structure of hyperlinks.

Web mining services can be divided into three subtasks:

Information Retrieval (IR): The purpose of this subtask is to automatically find all relevant information and filter out irrelevant ones. It uses various Search engines such as Google, Yahoo, MSN, etc and other resources to find the required information.

Generalization: The goal of this subtask is to explore users' interest using data extraction methods such as clustering and association rules. Since web data are dynamic and inaccurate, it is difficult to apply traditional data mining techniques directly on the raw data.

Data Validation (DV): It tries to uncover knowledge from the data provided by former tasks. Researcher can test various models, simulate them and finally validate given web information for consistency.


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Information About Data Mining

The potential offered by data mining can be included in the category of the processes of the commercial enterprises and looking for information is not a purpose itself, but it is a very useful process if it is transformed into a real action. Thus, enterprises can choose to react to the different situations created by reality, such as the reduction of the number of customers, the loss of certain markets and so on. The next step after making this choice is the proper exploitation of the data, using different algorithms.

Very often, data mining turns out to be a complete failure and not a success, the measures adopted bot always being appropriate for the information obtained. All these elements which are mentioned above lead to the idea that there is a cycle with data mining and that there are four stages when it comes to this process.

First of all, you have to define the commercial possibilities and the data. Then, you have to get information from the existent data collections using data mining techniques, after which you have to make decisions referring to the subsequent actions using the results you obtain. Last but not least, you have to measure your results properly in order to identify other ways of exploiting the data, too. Of course, you should only be looking at the concrete results because the rest of them can meddle with the outcomes and can alter the quality of the ones you should be getting. Therefore, if you take these steps into consideration, you should be properly using data mining in administrating the activity of your company.



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Preference to Offshore Document Data Entry Services

A number or business organizations if different industries are seeking competent and precise document data entry services to maintain their business records safe for future references. Document data entry has advanced as a quickly developing and active industry structure almost accept in all major companies of the world. The companies doing businesses these days are undergoing rapid changes and therefore the need for services is becoming all the more crucial.

To get success you need to accomplish more understanding about the market, your business, clients as well as the prevailing factors that influence your business. A considerable amount of document is in one or the other way included in this entire process. These services is helpful in taking crucial decisions for the organization. It also provides you a standard in understanding the current and future business status of your company.

In this information age data-entry from documents and data conversion have become important elements for most business houses. The requirement for document services has reached zenith since companies work on processes like business merger and acquisitions, as well as new technology developments. In such scenarios having access to the right kind of data at the right time is very crucial and that is why companies opt for reliable services.

These services covers a range of professional business oriented activities such as document plus image processing to image editing as well as catalog processing. A few noteworthy examples of from documents include: PDF document indexing, insurance claim entry, online data capture as well as creating new databases. These services are important in industries like insurance companies, banks, government departments and airlines.

Companies such as Offshore and outsource and others offer an entire gamut of first rate data services. Actually, getting services from documents offshore to developing yet competent countries like India has made the process highly economical plus quality driven too.

Business giants around the world have realized multiple advantages associated in Offshore-Data-Entry. Companies not only prosper because of quality services but are also benefited because of better turn around time, maintaining confidentiality of data as well as economic rates.

Though the company works in all form of documents, there are few below mentioned areas where it specializes:

• Document data entry
• Document data entry conversion
• Document data processing
• Document data capture services
• Web data extraction
• Document scanning indexing

Since reputable companies like Offshore Data-Entry hire only well qualified and trained candidates work satisfaction is guaranteed. There are several steps involved in the quality check (QC) process and therefore accuracy level is maintained to 99.995% ensuring that the end result is delivered to the client far beyond his expectation.



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Tips on Getting Data Entry Freelance Work Online

One of the easiest jobs to get online is data entry work. You can work as a freelancer doing this job, either full-time or part-time. More and more companies around the world are trying to trim their overhead and save money by outsourcing data entry work to various freelance websites. The great thing about working through one of these websites is that you can work at home.

Find Online Freelance Websites

There are tons of freelance websites of employers seeking data entry workers. Some of the websites are free, while other sites cost some money to join, the pay sites are better and give you an opportunity to earn more money than the free sites. The free sites also have more scammers who will try to rip you off and make you work for free.

Work on Your Profile, Resume and Proposal

After you have joined one of these freelance websites you should work on your profile page and resume. The employers who will hire will be looking at these 2 things along with your proposal. The first thing the employer will see is your proposal which is similar to a cover letter. If your proposal is good, they will at your profile page and resume. Make sure your profile page depicts you as a professional hard worker who has experience in the field. Your resume should be written geared towards a position as a data entry worker, so try to only include your past experiences that relate to this job.

Brush Up on Your Data Entry Skills

Most data entry jobs will require you to enter loads of information on to a database in a short amount of time. Make sure that your typing skills are quick and accurate. You want to build up a good reputation, so double check your work and try to send your work a few days early if possible. Try to make sure your work has no errors, employers will usually be able to rate you on your work after they have paid you and you have finished the work. You will receive a bad rating if your work contains any errors and is handed in late. If you receive bad ratings, especially if you are new and just starting out, then it will be extremely difficult for you to land new jobs. New employers will be able to see what your previous employers have rated you and written about you.

See Our Top Recommended Data Entry Work Sites [http://freelanceworkwanted.com/data-entry-work-where-to-find-new-freelance-gigs/] Online.


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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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Accelerating Accumulated Data Mining

We all have heard of Data Mining and we have all seen the abilities it can produce, but we also know how tedious the collection of data can be. It is the same for a little small company with a few customers as it is for a large company with millions of customers. Additionally how do you keep your data safe?

We have all heard of Identity Theft and the importance of secure data. But just because we have spent millions of dollars in IT work does not mean we know it is accurate? Things change fast you see; people get new telephone numbers, change addresses and jobs at least one of the three every three years. The chances of any database having accurate information is simply not possible.

Thus if we are data mining we need a way to verify which data sets are accurate and believe it or not the last set of data may not be the most accurate therefore we cannot simply discard the old data for the new data you see? We need ways to accelerate the accumulated data so we can run through it as fast as possible yet we must insure that our data mining techniques are taking into consideration miss matched data and incorrect data, along with inaccurate data.

Data Mining may have been over hyped a little and those business systems or even government data mining systems at the NSA; if they do not take into consideration these thoughts they are basically worthless and should not be considered you see? Think on this in 2006.


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Data Discovery vs. Data Extraction

Looking at screen-scraping at a simplified level, there are two primary stages involved: data discovery and data extraction. Data discovery deals with navigating a web site to arrive at the pages containing the data you want, and data extraction deals with actually pulling that data off of those pages. Generally when people think of screen-scraping they focus on the data extraction portion of the process, but my experience has been that data discovery is often the more difficult of the two.

The data discovery step in screen-scraping might be as simple as requesting a single URL. For example, you might just need to go to the home page of a site and extract out the latest news headlines. On the other side of the spectrum, data discovery may involve logging in to a web site, traversing a series of pages in order to get needed cookies, submitting a POST request on a search form, traversing through search results pages, and finally following all of the "details" links within the search results pages to get to the data you're actually after. In cases of the former a simple Perl script would often work just fine. For anything much more complex than that, though, a commercial screen-scraping tool can be an incredible time-saver. Especially for sites that require logging in, writing code to handle screen-scraping can be a nightmare when it comes to dealing with cookies and such.

In the data extraction phase you've already arrived at the page containing the data you're interested in, and you now need to pull it out of the HTML. Traditionally this has typically involved creating a series of regular expressions that match the pieces of the page you want (e.g., URL's and link titles). Regular expressions can be a bit complex to deal with, so most screen-scraping applications will hide these details from you, even though they may use regular expressions behind the scenes.

As an addendum, I should probably mention a third phase that is often ignored, and that is, what do you do with the data once you've extracted it? Common examples include writing the data to a CSV or XML file, or saving it to a database. In the case of a live web site you might even scrape the information and display it in the user's web browser in real-time. When shopping around for a screen-scraping tool you should make sure that it gives you the flexibility you need to work with the data once it's been extracted.



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Innovative Online Data Entry Services

Number of companies providing data entry services has increased in the last few years. These companies also provide services on online and offline data-entry and data processing, etc. Data Entry is to enter any form of data into computerized inventory. It could be done by typing at a keyboard plus electronically entering information into the machine.

These companies have updated technologies, unique processes and efficient data processing by integrating skilled professionals. These companies deliver high-quality services with complete accuracy, efficiency plus effectiveness. They provide services through reliable and secure online platform with the help of encrypted FTP upload CD-R or CD-W or E-mail. Adopting this technology customers get an assurance that their information is free from any sort of unauthorized access, copying or downloading. Companies specializing in such services provide a broad spectrum of services fulfilling each customer specific needs.

Few of these services are listed as follows: surveys, online copying, pasting, sorting, editing, and organizing data, questionnaires, online form processing and filing, reports and submissions, online medical and legal data entry, data collection, mailing list / mailing label, email mining, typing the manuscript in MS Word, etc. Outsourcing of the documentation of the work is a workable and a reasonable option.

Such services includes a wide range of back office and BPO - Business Process Outsourcing and ITO - Information Technology Outsourcing enabled data processing services.

Online data input services provided by India have earned a global recognition for its superior quality and timely completion of its work. Saving time is crucial for each organization running its business. Qualitative output is produced in lesser time which is advantageous for using the time at other important places. By availing such services one can save on cost of hiring trained professionals. More services could be availed within the saved cost.

Talking about the role of online data processing services, as the requirements of high quality and accurate data-entry of textual and numeric data processing business needs is most needed. In this way, companies can save valuable time and money by entering information online reduces. You can also consult experts who have vast experience and knowledge about online entry of data.

With the help of these services, mostly many business processing companies are able to focus on their core activates through online services. This kind of services require speed, analytical skills, domain expertise and industry experience. Choosing right outsourcing partner can save you cost and time significantly.


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