Data Entry Service - What You Should Know About It

Providing data entry services is one of the most in-demand jobs for home-based professionals these days. Businesses find such service very important and helpful to their daily operations.

Data entry is an activity wherein data are transcribed into another form that could be better and more effectively utilized for specific purposes. Such data could be in the form of handwritten documents, spreadsheet information, letters and symbols, sound recordings, sequences of numbers, names, and addresses. Most of the time, such information could not be easily and effectively conveyed in their original form. That is why data entry service is required to have them converted into useable forms.

The advent of computers brought about the need to gather and present documents in a neat way. Data should be encoded through accurate typing. Thus, good typists are said to be most qualified and most appropriate for data entry service jobs. Many professionals now perform jobs that exclusively involve data entry. On the other hand, several computer programmers need to occasionally input data. That is why they should also be exposed to such skills.

People who opt to exclusively work in such field are more likely to be fast typists. They should be able to read off typewritten and longhand documents. In some cases, they should know how to carefully and accurately transcribe sound materials. Accuracy is always a major issue. Data entry service jobs require outputs that are accurate and free from any typographical error. At the same time, speed of typing is a crucial ability that could be a great help.

Every job requires concentration and focus. You may find the tasks physically exhausting even if you would just need to sit down in front of the computer for many hours. You may need to spend long hours dealing with the keyboard and reading the computer screen.

Data entry service need not take place in a workplace or office. These days, many people generate income from taking such jobs at home. You may opt to enter data, transcribe medical information, and update websites without leaving the comfort of your home. There are telecommuting positions that are offered at flexible schedules for home-based workers.

Usually, such professionals earn about $10 to $15 per hour of doing their job. Those with specialties like medical transcription could earn more. You may find such jobs online. There are reputable freelance online sites that offer and provide such jobs to people without any unnecessary fee or commission.

These days, many businesses find it more practical and advantageous to outsource such jobs. You surely would find such a home-based job if you need a steady source of income while you are at the comfort of your house. You should start searching for and trying out one now.


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The Benefits of Data Mining

Data mining can truly help a business reach its fullest potential. It is a way to assess how business is being affected by certain characteristics, and can help business owners increase their profits and avoid making business mistakes down the line. Essentially, through this process, a business is analyzing certain data from different perspectives in order to get a full rounded view of how their company is doing. Business owners can get a broad perspective on things such as customer trending, where they are losing money and where they are making money. The information can also reveal ways that can help a business cut unneeded costs and can help them increase their overall income.

Data mining software is one tool that can help a company assess and analyze their data in more efficient terms. It can be extremely user friendly and allow people to delve into their data from a variety of different angles and points of view. In more technical terms, data mining software allows you to see the correlations and patterns of one's own data compared with those across many other regional databases.

People have been using data mining for many years in different formats. Only since the technology has become available has data software been used. But there have been many ways in the past for companies to assess their data and use it to their advantage. By taking polls, or using store scanners, product codes and bar codes, people have been able to gather data, analyze it and use it to their advantage. But it cannot be denied that the availability of greater technology has greatly increased the ability to store or gather data, make predictions about outcomes and use customer trend reports to greater advantages. The ability to store infinite amounts of data has given business owners a great advantage and truly has helped increase sales and lower costs. This data mining has actually led to data being stored in data warehouses. In data warehouses, various organizations will integrate their mined data into one large data warehouse. The information accessible in data warehouses is available to further help companies reduce risk taking and integrate proper selling techniques to improve business.

Data mining also can allow companies to see where their best selling points are and give them the opportunity to take advantage of this information. For example, if a pharmacy places a display of lip balm at the cashier counter, data mining can detect how many people bought lip balm from the cashier counter rather people who bought the lip balm when it was placed at another point in the store. Data mining can determine where the most effective points of sale are throughout a store or if a certain promotion went well one time of the month, but did not go well at another time of the month. Companies can make offers based on the buying habits of their customers as well.

Data mining can truly help businesses reach their highest profitability by paying attention to customer trending.


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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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Outsource Data Entry Services to Reduce Labour Cost

India has become a hub for outsourcing services. One of the services being outsourced highly to India is data entry. Managing data is a tough task, especially for growing organizations. So, many of these organizations outsource their services to India.

Outsourcing data entry services to India helps companies in maintaining everyday records and details in proper order. India has many reputed firms dealing with our services. These companies have highly qualified and experienced experts that assist you in managing business related data aptly. Experts keep themselves updated with the latest technologies to live up to your expectations.

These can be availed both offline and online. Online services include managing data from e books, image files, and web browsers. Offline services include managing data from documents, papers and directories. Outsourcing data entry services to India will help you get an appropriate solution for data processing. You can also ask for a free trial of the data entering services before outsourcing your work to them. Other services that you can avail are word and data conversion, OCR clean up and PDF conversion.

By outsourcing your services to India, you can enjoy the benefit of saving up to 70% cost incurred on business operations. Thus, outsourcing will help you drive down the cost that can be further invested in the expansion of the business.

Data entry services can be outsourced regardless of the industry type. Be it retail, lodging, finance or real estate, this type of companies cover every field you can think of. By outsourcing tasks you can minimize the workload of your employees, thereby improving their efficiency and competence. This too puts you on the forefront as it reduces the labor cost up to a great extent without any effect on quality.


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Advantages of Outsourcing Product Data Entry

Offshore outsourcing has proved itself to be the most effective business solution over the years and data entry is one business service that is outsourced today. Data is the lifeline of any company and it plays a huge role when it comes to planing for the future. Maintaining huge chunks of data is easier said than done. It requires a lot of time and money. So many top companies prefer to outsource data entry services. Online store owners outsource product data entry services because it is a cost effective solution to its customized needs. Outsourcing product data entry provides e-business owners with the opportunity to cut costs, deliver customer value, speed up their turn around time and maintain data security. It is one of the best business moves which has provided tangible results consistently.

COSTS
The one thought that spins the wheels on outsourcing is its cost cutting feature. Outsourcing product data entry brings down the expenditure on manpower and allows access to specialized skills which saves time and effort. Not to mention expenditure on infrastructure. Investments made need to be worthwhile and one made to outsource data entry is worth every penny. Outsourcing also cuts costs in terms of training employees and ensuring continuous learning processes so as to be updated with the latest technology.

TIME ZONE
An underlying advantage of this business practice is its willingness to be flexible according to client needs and expectations. The time zone difference is a big advantage and one that could be exploited. Offshore outsourcing provides the luxury of getting work done around the clock and that is an advantage worth being made use of. The time zone advantage gives the opportunity to increase the volume of work and its expected level of quality.

QUALITY CONTROL
Quality is usually a great concern which is overcome by a team of quality experts who adopt stringent process to generate accurate results. It simplifies processes and increases the client efficiency in achieving their targets. Outsourcing product data entry builds a competitive edge to the clients; it reduces stress and enables them to be more creative at their core processes. A good quality control process promises better customer satisfaction.

BETTER CUSTOMER SATISFACTION
Customer is the king; the market place survives on his satisfaction and loyalty. Outsourcing helps its clients build its brand image, grade up its value and focus on important areas in reaching its set promises to the customer. Employees can generate more revenues as data entry tasks are fulfilled by reliable resources. On the other hand, outsourcing companies also grow in providing prompt delivery of projects, low turn around time without any excuses on quality.

DATA SECURITY
Security plays a major role in outsourcing product data entry. Businesses have an edge over their competitors because of their ability to maintain data security. Most Outsourcing companies have Non Disclosure agreements with their clients. These agreements put in place policies and procedures to ensure no loss of critical data. Security concerns are promptly addressed with proficient support teams who maintain confidential details.

CONCENTRATION ON CORE BUSINESS AREAS
Outsourcing product data entry also gives businesses the chance to spend more time and effort on their core competencies. This would enhance innovative business models which generates expected profits, thus expanding e business to new heights of growth and development.


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Data Entry Services by a Virtual Assistant

Data Entry is a basic requirement for any business and it may appear to be simple to supervise and handle, this engage a lot of procedures that require a proper handling. Enormous modifications have taken place in the field of data entry and because of this data processing work has become really easier then before. So if you are looking to make data entry services useful to maintain the information and data of your company, you need a skilled virtual assistant. These days it is almost impossible to say Data Entry Services are costly; however, the fact is this by outsourcing a data process to country like India will be a good option for an organization to find a quality services with cost-effective solutions. All you need to choose you will hire a VA for the job you wanted to complete within a particular time frame, with quality and a cost-effective solution or to hire an in house employee for which you have to pay employee benefits such as sick pay, employee insurance, vacation pay, worker's compensation and much more. You are the best person to decide, you want to outsource the job to a virtual assistant who only charge for the job they work for after all this is your business.

Data Entry is one of the important features for your business and as a result you must make sure that this is dealt in a right direction. Outsourcing Data Entry service to a virtual assistant is not only a part of a business. With the enormous flow on the ground of Information Technology Data Conversion service is evenly significant. Data Conversion is the process to renovate the data in which data is converted from file source to another file type such as extracting the data from PDF file to excel spreadsheet and business world need these conversion for efficiency in performance. Virtual Assistant's are skilled enough to convert almost any file type to another for a business owner to access the data in any format.

By outsourcing your data entry jobs to a virtual assistant in India has been found very cost-effective solutions with quality of the job. Outsourcing Data Entry Services is one of the rise these days and the reason behind this is business owners has enjoyed the success of outsourcing the job to a virtual assistant. The major benefit of getting data entry services complete by a virtual assistant in India is they work really cheap and the work done by them is of top quality job. So if the data entry services provided by a virtual assistant are cheap and of top quality there is completely no possibility why someone would not take the benefits of a VA services.

Amit Ganotra is a skilled virtual assistant providing services like Data Entry, Data Processing, Data Conversion, Data Mining, Data cleaning, OCR Cleanup, Article Submission, Directory Submissions, Web Development. For more information about the services we provide please visit the website.


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Cutting Down the Cost of Data Mining

For most industries that maintain databases, from patient history in the healthcare industry to account information for the financial and banking sectors, data entry costs are a significant expense for maintaining good records. After data enters a system, performing operations and data mining extractions on the information is a long process that becomes more time consuming as a database grows.

Data automation is essential for reducing operational expenses on any type of stored data. Having data entrants performing every necessary task becomes cost prohibitive quickly. Utilizing software solutions to automate database operations is the ultimate answer to leveraging information without the associated high cost.

Data Mining Simplified

Data management software will greatly enhance the productivity of any data entrant or end user. In fact, effective programs offer macro recording that can turn any user into a data entry expert. For example, a user can perform an operation on a single piece of data and "record" all the actions, keystrokes, and mouse clicks into a program. Then, the computer software can repeat that task on every database entry automatically and at incredible speeds.

Data mining often requires a decision making process; a recorded macro is only going to perform tasks and not think about what it is doing. Software suites are able to analyze data, decide what action needs to be performed based on user specified criteria, and then iterate that process on an entire database. This function nearly eliminates the need for a human to have to manually look at data to determine its content and the necessary operation.

Case Study: Bank Data Migration

To understand how effective data mining and automation can be, let us take a look at an actual example.

Bank data migration and manipulation is a large undertaking and an integral part of any bank's operations. Account data is constantly being updated and utilized in the decision making process. Even a mid-sized bank can have upwards of a quarter million accounts to maintain. In order to update every account to utilize new waive fee codes, data automation can save approximately 19,000 hours that it would have taken to open every account, decide what codes applies, and update that account's status.

Recurring operations on a database, even if small in scale, that can be automated will reap cost saving benefits over the lifetime of a business. The credit department within a bank would process payment plans for new home, car, and personal loans monthly, saving thousands of operations performed every month. Retirement and 401k accounts that shift investments every year based on expected retirement dates also benefit from automatic account updates, ensuring timely and accurate account changes.

Cost savings for data mining or bank data migration are an excellent profit driver. Cutting down on expenses on a per-client or per-account basis increases margins directly without having to secure more customers, reduce prices, or remove services. Efficient data operations will save time and money, allowing personnel to better direct their energy and efforts towards key business tasks.


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The Benefits of Investing in a Web Copywriting Service

Web copywriting is just one of the tasks Internet marketers must do. Writing effective web content can take a large chunk of your time. Online entrepreneurs have numerous tasks to attend to daily.

Hiring professionals to write copy will free you for other projects. Spend more time on product development, promotion, or other tasks. Delegating some tasks will keep you free for project management.

Professional copywriters are skilled in search engine optimization. They understand how search engines work and deliver copy that ranks. Don't trust this task to amateur or unskilled content writers.

Skilled web content writers tailor their services to your needs. Professional copywriters will assess your unique needs and meet them. Their objective is to write copy that makes your website stand out.

Semantically Related Words and Structural Keyword Proximity

Semantically related words are words similar to your keywords. They describe your products or services without useless repetition. These words drive higher search engine rankings based on relevance.

Web copywriting experts understand this search engine algorithm. They also understand how the right words affect readability. They write engaging content that attracts visitors and search engines.

Structural keyword proximity illustrates keyword relationship. The distance between keywords affects search engine placement. Professional SEO copywriters understand and implement this metric.

Good writers may be proficient at spelling, grammar, and structure. However, without training, keyword proximity may be foreign to them. Don't let these crucial techniques be ignored in your web copy.

Professionals Implement Key SEO Metric Strategies

Writing for the web is a unique science that requires certain skills. Incorporating phrase-based keywords is not enough to rank well. Each search engine optimization metric must be considered in order to attract visitors.

Writers skilled in web copy execute each element of the process. They can turn idle traffic into targeted traffic and conversions. While you refine your business model, your web copy is working too.

Skilled web copywriters understand typical web surfing habits. They know how to draw visitors in and keep them on your site. Leaving this task to the inexperienced may cost you business.

Even if you write your own web copy, you may miss essential elements. Unless you are skilled in web metrics and SEO, your copy may suffer. For optimum site performance, engage professional web copywriters.

Professional Copywriters Avoid Scraped Content

Some unscrupulous freelance writers sell marketers duplicate content. They may use web-scraping software, or they may plagiarize content. Most search engines will eliminate such content from results.

Document near-duplicate detection will ferret out scraped content. This means visitors won't find your website if it contains plagiarism. While not all writers do this, it is important to use professionals.

Professional web copywriters write original and useful copy. Not only is it written to rank high, but it is written to be helpful. Unless site visitors find answers to their problems, they leave.


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Web Data Extraction Services and Data Collection Form Website Pages

For any business market research and surveys plays crucial role in strategic decision making. Web scrapping and data extraction techniques help you find relevant information and data for your business or personal use. Most of the time professionals manually copy-paste data from web pages or download a whole website resulting in waste of time and efforts.

Instead, consider using web scraping techniques that crawls through thousands of website pages to extract specific information and simultaneously save this information into a database, CSV file, XML file or any other custom format for future reference.

Examples of web data extraction process include:
• Spider a government portal, extracting names of citizens for a survey
• Crawl competitor websites for product pricing and feature data
• Use web scraping to download images from a stock photography site for website design

Automated Data Collection
Web scraping also allows you to monitor website data changes over stipulated period and collect these data on a scheduled basis automatically. Automated data collection helps you discover market trends, determine user behavior and predict how data will change in near future.

Examples of automated data collection include:
• Monitor price information for select stocks on hourly basis
• Collect mortgage rates from various financial firms on daily basis
• Check whether reports on constant basis as and when required

Using web data extraction services you can mine any data related to your business objective, download them into a spreadsheet so that they can be analyzed and compared with ease.

In this way you get accurate and quicker results saving hundreds of man-hours and money!

With web data extraction services you can easily fetch product pricing information, sales leads, mailing database, competitors data, profile data and many more on a consistent basis.


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Data Mining Is Useful for Business Application and Market Research Services

One day of data mining is an important tool in a market for modern business and market research to transform data into an information system advantage. Most companies in India that offers a complete solution and services for these services. The extraction or to provide companies with important information for analysis and research.

These services are primarily today by companies because the firm body search of all trade associations, retail, financial or market, the institute and the government needs a large amount of information for their development of market research. This service allows you to receive all types of information when needed. With this method, you simply remove your name and information filter.

This service is of great importance, because their applications to help businesses understand that it can perform actions and consumer buying trends and industry analysis, etc. There are business applications use these services:
1) Research Services
2) consumption behavior
3) E-commerce
4) Direct marketing
5) financial services and
6) customer relationship management, etc.

Benefits of Data mining services in Business

• Understand the customer need for better decision
• Generate more business
• Target the Relevant Market.
• Risk free outsourcing experience
• Provide data access to business analysts
• Help to minimize risk and improve ROI.
• Improve profitability by detect unusual pattern in sales, claims, transactions
• Major decrease in Direct Marketing expenses

Understanding the customer's need for a better fit to generate more business target market.To provide risk-free outsourcing experience data access for business analysts to minimize risk and improve return on investment.

The use of these services in the area to help ensure that the data more relevant to business applications. The different types of text mining such as mining, web mining, relational databases, data mining, graphics, audio and video industry, which all used in enterprise applications.


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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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Startling Benefits Of Outsourcing Data Entry Services

It is essential for running the business successfully. Business organizations into insurance, medical, financial, banking, educational, commercial, and social are the most which require help of professional service providers. For proper management of information it is better to take help of professional outsourcing service.

In the current business world there are many companies providing outsourcing service at affordable rates. These companies providing customized solutions provide a wide range of services such as:

• Online/offline data outsourcing
• Image entry
• Copy typing
• Book typing
• Report copy typing
• Document and image processing
• Insurance claim entry
• Medical record entry, etc.

Few benefits of availing outsourcing are as follows:

Competent Services
Companies providing outsourcing services have well trained and experienced work force with updated technology to deliver accurate output in bare minimum time. Companies providing data outsourcing services invest on advanced infrastructure with upgraded technological instruments as well as secured systems, etc. to meet requirements of the clients.

Cutting down cost
Outsourcing your services saves up to 60% cost on total operations of the business. By outsourcing, you may cut down cost of capital incurred during in-house process. Additional benefit of outsourcing is saving cost on resources which could be invested in widening the business activity.

High Return on Investment
Outsourcing fetches standard agreement with the companies to provide maximum return on investment. Thus it is easier for companies to lower down expenditures on resources and improve the competence as well as output. Obviously the company will be yearning great profits on their investments.

Multiple Services
Outsourcing is the collection of connected areas of services which comprises: data processing, data conversion, word conversion, PDF conversion, PDF to DOC conversion, OCR clean up, etc.


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Backtesting & Data Mining

In this article we'll take a look at two related practices that are widely used by traders called Backtesting and Data Mining. These are techniques that are powerful and valuable if we use them correctly, however traders often misuse them. Therefore, we'll also explore two common pitfalls of these techniques, known as the multiple hypothesis problem and overfitting and how to overcome these pitfalls.

Backtesting

Backtesting is just the process of using historical data to test the performance of some trading strategy. Backtesting generally starts with a strategy that we would like to test, for instance buying GBP/USD when it crosses above the 20-day moving average and selling when it crosses below that average. Now we could test that strategy by watching what the market does going forward, but that would take a long time. This is why we use historical data that is already available.

"But wait, wait!" I hear you say. "Couldn't you cheat or at least be biased because you already know what happened in the past?" That's definitely a concern, so a valid backtest will be one in which we aren't familiar with the historical data. We can accomplish this by choosing random time periods or by choosing many different time periods in which to conduct the test.

Now I can hear another group of you saying, "But all that historical data just sitting there waiting to be analyzed is tempting isn't it? Maybe there are profound secrets in that data just waiting for geeks like us to discover it. Would it be so wrong for us to examine that historical data first, to analyze it and see if we can find patterns hidden within it?" This argument is also valid, but it leads us into an area fraught with danger...the world of Data Mining

Data Mining

Data Mining involves searching through data in order to locate patterns and find possible correlations between variables. In the example above involving the 20-day moving average strategy, we just came up with that particular indicator out of the blue, but suppose we had no idea what type of strategy we wanted to test? That's when data mining comes in handy. We could search through our historical data on GBP/USD to see how the price behaved after it crossed many different moving averages. We could check price movements against many other types of indicators as well and see which ones correspond to large price movements.

The subject of data mining can be controversial because as I discussed above it seems a bit like cheating or "looking ahead" in the data. Is data mining a valid scientific technique? On the one hand the scientific method says that we're supposed to make a hypothesis first and then test it against our data, but on the other hand it seems appropriate to do some "exploration" of the data first in order to suggest a hypothesis. So which is right? We can look at the steps in the Scientific Method for a clue to the source of the confusion. The process in general looks like this:

Observation (data) >>> Hypothesis >>> Prediction >>> Experiment (data)

Notice that we can deal with data during both the Observation and Experiment stages. So both views are right. We must use data in order to create a sensible hypothesis, but we also test that hypothesis using data. The trick is simply to make sure that the two sets of data are not the same! We must never test our hypothesis using the same set of data that we used to suggest our hypothesis. In other words, if you use data mining in order to come up with strategy ideas, make sure you use a different set of data to backtest those ideas.

Now we'll turn our attention to the main pitfalls of using data mining and backtesting incorrectly. The general problem is known as "over-optimization" and I prefer to break that problem down into two distinct types. These are the multiple hypothesis problem and overfitting. In a sense they are opposite ways of making the same error. The multiple hypothesis problem involves choosing many simple hypotheses while overfitting involves the creation of one very complex hypothesis.

The Multiple Hypothesis Problem

To see how this problem arises, let's go back to our example where we backtested the 20-day moving average strategy. Let's suppose that we backtest the strategy against ten years of historical market data and lo and behold guess what? The results are not very encouraging. However, being rough and tumble traders as we are, we decide not to give up so easily. What about a ten day moving average? That might work out a little better, so let's backtest it! We run another backtest and we find that the results still aren't stellar, but they're a bit better than the 20-day results. We decide to explore a little and run similar tests with 5-day and 30-day moving averages. Finally it occurs to us that we could actually just test every single moving average up to some point and see how they all perform. So we test the 2-day, 3-day, 4-day, and so on, all the way up to the 50-day moving average.

Now certainly some of these averages will perform poorly and others will perform fairly well, but there will have to be one of them which is the absolute best. For instance we may find that the 32-day moving average turned out to be the best performer during this particular ten year period. Does this mean that there is something special about the 32-day average and that we should be confident that it will perform well in the future? Unfortunately many traders assume this to be the case, and they just stop their analysis at this point, thinking that they've discovered something profound. They have fallen into the "Multiple Hypothesis Problem" pitfall.

The problem is that there is nothing at all unusual or significant about the fact that some average turned out to be the best. After all, we tested almost fifty of them against the same data, so we'd expect to find a few good performers, just by chance. It doesn't mean there's anything special about the particular moving average that "won" in this case. The problem arises because we tested multiple hypotheses until we found one that worked, instead of choosing a single hypothesis and testing it.

Here's a good classic analogy. We could come up with a single hypothesis such as "Scott is great at flipping heads on a coin." From that, we could create a prediction that says, "If the hypothesis is true, Scott will be able to flip 10 heads in a row." Then we can perform a simple experiment to test that hypothesis. If I can flip 10 heads in a row it actually doesn't prove the hypothesis. However if I can't accomplish this feat it definitely disproves the hypothesis. As we do repeated experiments which fail to disprove the hypothesis, then our confidence in its truth grows.

That's the right way to do it. However, what if we had come up with 1,000 hypotheses instead of just the one about me being a good coin flipper? We could make the same hypothesis about 1,000 different people...me, Ed, Cindy, Bill, Sam, etc. Ok, now let's test our multiple hypotheses. We ask all 1000 people to flip a coin. There will probably be about 500 who flip heads. Everyone else can go home. Now we ask those 500 people to flip again, and this time about 250 will flip heads. On the third flip about 125 people flip heads, on the fourth about 63 people are left, and on the fifth flip there are about 32. These 32 people are all pretty amazing aren't they? They've all flipped five heads in a row! If we flip five more times and eliminate half the people each time on average, we will end up with 16, then 8, then 4, then 2 and finally one person left who has flipped ten heads in a row. It's Bill! Bill is a "fantabulous" flipper of coins! Or is he?

Well we really don't know, and that's the point. Bill may have won our contest out of pure chance, or he may very well be the best flipper of heads this side of the Andromeda galaxy. By the same token, we don't know if the 32-day moving average from our example above just performed well in our test by pure chance, or if there is really something special about it. But all we've done so far is to find a hypothesis, namely that the 32-day moving average strategy is profitable (or that Bill is a great coin flipper). We haven't actually tested that hypothesis yet.

So now that we understand that we haven't really discovered anything significant yet about the 32-day moving average or about Bill's ability to flip coins, the natural question to ask is what should we do next? As I mentioned above, many traders never realize that there is a next step required at all. Well, in the case of Bill you'd probably ask, "Aha, but can he flip ten heads in a row again?" In the case of the 32-day moving average, we'd want to test it again, but certainly not against the same data sample that we used to choose that hypothesis. We would choose another ten-year period and see if the strategy worked just as well. We could continue to do this experiment as many times as we wanted until our supply of new ten-year periods ran out. We refer to this as "out of sample testing", and it's the way to avoid this pitfall. There are various methods of such testing, one of which is "cross validation", but we won't get into that much detail here.

Overfitting

Overfitting is really a kind of reversal of the above problem. In the multiple hypothesis example above, we looked at many simple hypotheses and picked the one that performed best in the past. In overfitting we first look at the past and then construct a single complex hypothesis that fits well with what happened. For example if I look at the USD/JPY rate over the past 10 days, I might see that the daily closes did this:

up, up, down, up, up, up, down, down, down, up.

Got it? See the pattern? Yeah, neither do I actually. But if I wanted to use this data to suggest a hypothesis, I might come up with...

My amazing hypothesis:

If the closing price goes up twice in a row then down for one day, or if it goes down for three days in a row we should buy,

but if the closing price goes up three days in a row we should sell,

but if it goes up three days in a row and then down three days in a row we should buy.

Huh? Sounds like a whacky hypothesis right? But if we had used this strategy over the past 10 days, we would have been right on every single trade we made! The "overfitter" uses backtesting and data mining differently than the "multiple hypothesis makers" do. The "overfitter" doesn't come up with 400 different strategies to backtest. No way! The "overfitter" uses data mining tools to figure out just one strategy, no matter how complex, that would have had the best performance over the backtesting period. Will it work in the future?

Not likely, but we could always keep tweaking the model and testing the strategy in different samples (out of sample testing again) to see if our performance improves. When we stop getting performance improvements and the only thing that's rising is the complexity of our model, then we know we've crossed the line into overfitting.


Source: http://ezinearticles.com/?Backtesting-and-Data-Mining&id=341468

Remuneration of Outsourcing Data Entry

Outsource Data entry is a fast growing industry. The world of business is dynamic, fast paced, and in constant change. In such an environment the accessibility of accurate, detailed information is a necessity. Entry is the main component of any business firm. Online data entry is a very lengthy and tiresome work, so the best option for companies to take care of this is through data entry outsourcing services.

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Source: http://ezinearticles.com/?Remuneration-of-Outsourcing-Data-Entry&id=2122790

How to Reduce Data Entry Operating Cost

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Source: http://ezinearticles.com/?How-to-Reduce-Data-Entry-Operating-Cost&id=4169043