What Is the Difference Between Data Capture and Data Entry?

In business, surveys and feedback forms are excellent ways to get to know more about your customers and even your own staff. But what happens when you gather all of this data together? Sometimes it can become too much information for one or even a group of people to wade through in order to extract the vital pieces of information needed to know how to be better for your clients and what they really want.

So you might have contemplated outsourcing the service instead. Outsourcing data capture and data entry saves a lot of time, hassle and usually gives you back truly excellent results that are presentable and easily understandable. But there are differences between data entry and data capture that you need to know, whether you already have your forms back with you or you are thinking about how to set out the form itself.

Thankfully you don't need to be too specific about how to set out the form, but from a monetary and time perspective you should probably consider the differences between capture and entry.

It might seem straight forward but often these things can overlap and then you're not truly sure what you're getting but here are the fundamental differences between data capture and data entry.

Data capture is a service in which data is captured via tick or check boxes and other items where areas are filled in with simple lines or shapes in order to get the right answer. These are usually multiple choice answers or the may be yes or no questions. Essentially that is what a data capture service would offer, the ability to extract data from particular text boxes in order to gather the most and least popular responses. Once this is done it can be extracted into documents such as Excel files and can be displayed as graphs or pie charts. Data capture is also known to be fairly cheap in comparison with data entry as most of the capturing process can be automated via intelligent software usually developed by the company themselves. In turn, it is also acknowledged to be quicker to get responses back because of the nature of how the data is extracted for you.

Data entry on the other hand is almost always manually entered text, copying exactly what the person who filled out the feedback form has written. Unfortunately at this point, it is not possible for computers to completely automate handwriting and this can be a somewhat more costly procedure. A lot of companies that do this kind of work tend to outsource the work to other countries such as India and China where it is cheaper to get the work done, thus passing on the savings, but as a manual job it is always going to cost more than an automated computer system. However data entry can give a much higher insight into what it is you're looking to find out. The written word is always considerably more useful than someone who checks a box as you get a vital look into what the returner is thinking and feeling about your product or service. And because there is manual work involved the time difference can often be quite a while.

These two services are worth considering when you look into setting out your form when it comes to how much it will cost you.

If you're interested in going ahead with of the two or are looking to find out more about data capture or data entry and would like to speak to a knowledgeable company who also offer excellent rates within the UK, please visit our website. There more information about how having your data converted can help you and your organisation.



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Data Mining Software - Discover Software Modernization

Data mining software is usually an application that one uses and covers mostly with one's knowledge in the discovery of software modernization. Mining data software involves the understanding of the software artifacts that exist and the mining data tools. This process has very close relations with reverse engineering. The knowledge that one gains from studying data software that exists is usually presented in forms of models and by doing these queries one can be in a position to make his personal data mining software. With the knowledge that someone gains it must be applicable and one must also know the mining data tools that are suppose to be used apart from the soft wares. One can be able to know very widely about the mining data tools that are there in mining data software by doing computer science as a course. Computer science covers widely on what are the procedures, steps of mining data software and how can use the mining data tools.

This software is mostly used in making of databases schemes. Making of databases is not as easy as many would think it requires one to have some knowledge about computer engineering and the basic concepts of computers.;This software is mostly used in data crawling because it can be in a position to store data and one can be able to retrieve the data when needed.

The softwares are not that cheap they come in different varieties and it will depend on which information or the database on which one is coming up with.

Data mining software are usually in different levels there is the data level, design level, application level, architectural level, call graph level and program level it will depend on which level one is covering and this come together with mining data tools.

Data software's have increased rapidly through the introduction of computers and ERP definition. Computers hackers have been able to get the softwares at a very low price and this has made data mining to become very easy and quick to use in the shops and supermarkets and also government institutions. One cannot do data crawling without having the basic knowledge about data mining soft wares because soft wares are the programmes that are usually installed into the computer and without the programmes then no data can be processed.

There are a lot of challenges that come with the use of the mining soft ware. One can easily crush the software he is using or the softwares can easily break they are normally sold on CDS one can easily break it or loose it.

High chances of losing the data that someone is coming up with is very high because computers easily crash due to some difficulties that they experience or a virus can easily crush the computer.

Mining software take a very large space and in most of the computers. The reason behind this is because, data crawling use graphics. Graphics usually occupy a lot of space in terms of the size of the local disk. One is suppose to look for a computer that has very good memory. Data crawling is something that needs to be updated each and every time something appears along the way.



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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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Why Outsource Data Entry Work to India? Here are the Reasons

India, the third largest English speaking nation in the world, is purportedly the hub for outsource service providers in Asia. With more and more people investing billions of dollars into websites and other online advertising campaigns, India stands as a united front to answering the demands of various business clients and industries that span the entire globe.

Search engine optimization companies, especially those involved with database or website management projects from outsource clients would agree, that data entry is one of the more basic, yet more complicated procedures in the early stages of directory development. The procedure for data entry work is fairly systematic. The primary data entry level task is to gather a list of data from specific industries or categories. For example, hotels can be classified under one category, or business industry.

The main task of the data entry personnel is to identify possible records which could help in supplementing the base data on the hotel or hotel chains being discussed in the directory. Common items like hotel locations, hotel management contact details, reservation information and dining services are the most fundamental facts which need to be entered for a particular hotel index on the directory.

It is not a coincidence that India should be one of the more deserving nations who could easily acquire data entry jobs from outsource clients, as the whole country itself is rapidly mobilizing its technological prowess to facilitate the outburst of outsource service demands from around the world. In a sense, India is qualified to brag that it can certainly do the job. In terms of technology and experience on the "Web Business", India has greater lead than the rest of the Asian countries.

Even the Philippines, which ranks as the fourth largest English speaking nation in Asia, is curbed to follow a very far distance, already paced by India.

It's no wonder then, that outsourcing data entry level work in India could be an excellent choice for most businessmen or web investors. Nothing really beats the quality and reliability honed by years of dealing with optimization and outsourcing services, which India has been exposed to.

India nonetheless remains as the dominating force in Asia when it comes to outsource services with its proven track record.

In totality, Outsourcing data entry work in India is a decision that should be made with quality in mind. Nothing beats India when it comes to this, as they are bantering on a more acknowledged field, to which they have been first made privy, and have done reasonably well to maintain. To get the best deals for Outsource service with Data Entry work, do not forget to level out with India on this aspect.

India stands to gain on the following services which are commonly outsourced by major clients from both U.S. and Europe: PHP Programmers, Bulk Linkers, Data Entry work, Content development, Web Design and Web Development services and many others.


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Outsourcing Data Entry - A Right Move

Competition that is what it is all about. Every business on the planet has a competitor and the writing on the wall is simple, stay a step ahead, which is easier said than done. Customer is the key to all business and keeping the customer happy means repeat business and proper growth. That would require companies to do research, plan marketing strategies, and always have quick access to data to process customer request. This article is not to talk about what is data entry? Or what is data processing? Rather it is to give you enough reasons to find an outsourcing partner who would get the job done for you.

Why Consider Outsourcing

Ever felt overwhelmed with work, too many things to take care of and you have a lot of catching up to do? Well that's where the idea of outsourcing comes in. Finding an outsourcing partner who could share that load and give you what you need, the way you need it and to the time that you set, is something worth looking into. Outsourcing eases your financial load; you could get the job done at a lesser cost externally than in-house. This would mean less pressure on the financial side of things.

Ever wondered how much of your time is consumed in areas that do not require it, when it could be well spent on some core parts of the process. Outsourcing your work will help take care of that. You could give your full to parts of your business that requires attention, confidently knowing that work is still getting completed half way around the globe by people who are knowledgeable about your business and understands your business process.

The workforce is always a concern. Finding the right person for the job is a task in itself and is also expensive. So without the right person on the job, you could be getting less quality and slower turn-around-time than what you bargained for. Business is money and low quality and slow work is only going to let you end up losing it than making any. With an outsourcing partner in place, the workforce, the quality, the infrastructure is a non-issue. The work is done faster at half the cost.

Now every business has some sort of risks involved and outsourcing is no different. Outsourcing would mean the transfer of key information of process and compromising on what needs to be kept hush. Also, you might not have complete control of the business activity. Fortunately, there are ways to arrest these sorts of risks. As far as keeping things under wraps is concerned one could sign in an agreement that would bind both parties in keeping information from leaking out. Making sure that your outsourcing partner is as transparent as glass to you when it comes to the people and budget should cover the problem of having control.

When choosing your outsourcing partner choose one with an accomplished track record. Investigate if the company you choose to partner with has some good accounts to show. Feedback for those accounts should help you decide if you are making a good investment or not.


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

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

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

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

Business Definition of Data Mining

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

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

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

Mapping Business Needs to Solutions and Profits

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

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

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

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

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

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

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

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

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

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

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

Getting Started

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

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

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

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

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

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


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Data Extraction Services - A Helpful Hand For Large Organization

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

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

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

The different types of Data Extraction Services:

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

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

Advantages of Outsourcing Data Extraction Services:

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

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


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