Web Mining

With the bang of the era of information technology, we have entered into an ocean of information. This information blast is strongly based on the internet; which has become one of the universal infrastructures of information. We can not deny the fact that, with every passing day, the web based information contents are increasing by leaps and bounds and as such, it is becoming more and more difficult to get the desired information which we are actually looking for. Web mining is a tool, which can be used in customizing the websites on the basis of its contents and also on the basis of the user interface. Web mining normally comprises of usage mining, content mining and structure mining.

Data mining, text mining and web mining, engages various techniques and procedures to take out appropriate information from the huge database; so that companies can take better business decisions with precision, hence, data mining, text mining and web mining helps a lot in the promotion of the 'customer relationship management' goals; whose primary objective is to kick off, expand, and personalize a customer relationship by profiling and categorizing customers.

However, there are numbers of matters that must be addressed while dealing with the process of web mining. Data privacy can be said to be the trigger-button issue. Recently, privacy violation complaints and concerns have escalated significantly, as traders, companies, and governments continue to gather and warehouse huge amount of private information. There are concerns, not only about the collection and compilation of private information, but also the analysis and use of such data. Fueled by the public's concern about the increasing volume of composed statistics and effective technologies; conflict between data privacy and mining is likely to root higher levels of inspection in the coming years. Legal conflicts are also pretty likely in this regard.

There are also other issues facing data mining. 'Erroneousness of Information' can lead us to vague analysis and incorrect results and recommendations. Customers' submission of incorrect data or false information during the data importation procedure creates a real hazard for the web mining's efficiency and effectiveness. Another risk in data mining is that the mining might get confused with data warehousing. Companies developing information warehouses without employing the proper mining software are less likely to reach to the level of accuracy and efficiency and also they are less likely to receive the full benefit from there. Likewise, cross-selling may pose a difficulty if it breaks the customers' privacy, breach their faith or annoys them with unnecessary solicitations. Web mining can be of great help to improve and line-up the marketing programs, which targets customers' interests and needs.

In spite of potential hurdles and impediments, the market for web mining is predicted to grow by several billion dollars in the coming years. Mining helps to identify and target the potential customers, whose information are "buried" in massive databases and to strengthen the customer relationships. Data mining tools can predict the future market trends and consumer behaviors, which can potentially help businesses to take proactive and knowledge-based resolutions. This is one of the causes why data mining is also termed as 'Knowledge Discovery'. It can be said to be the process of analyzing data from different points of view and sorting and grouping the identified data and finally to set up a useful information database, which can further be analyzed and exploited by companies to increase and generate revenue and cut costs. With the use of data mining, business organizations are finding it easier to answer queries relating to business aptitude and intelligence, which were very much complicated and intricate to analyze and determine earlier.


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What is Data Mining?

Data mining is the process in which there is analysis of data forming different angles and perspectives and summarizing the same data into the relevant information. This kind of information could be utilized to increase the revenue, cutting the costs or both.

Software is mainly used for analyzing data and also assists in accumulation of data for the different sources and categorize and summarize the given data into some useful form.

Though the data mining is new term, the software used for mining the data was previously used. With the constant upgradations of the software and the processing power, the market tools, data mining software has increased in its accuracy. Formerly, this data mining was widely used by the businessmen for the market research and the analysis. There were few companies that used the computers to examine through the column of the supermarket data.

The data mining is the technique of running the data through the sophisticated algorithms for discovering the meaningful correlations and patterns that would have otherwise remained hidden. It is very helpful, since it aids in understanding the techniques and methods of business and you can accordingly apply your own intelligence fitting in the current market trend. Even the future performances get enhanced by the predictive analysis.

Business Intelligence operations occur in the background. Users of the mining operation can just see the end result. The users are in apposition to get the results through the mails and can also go through the recommendation through web pages and emails.

The data mining process indicates the invention of trends and tactics. The moment you discover and understand the market trends, you have the knowledge of which article is sold more and which article is sold with the other one. This kind of tend has an enormous impact on business organization. In this manner, the business gets enhanced as the market gets analyzed in a perfect manner. Due to these correlations, the performance of business organization increases to a lot of extent.

Mining gives a chance or opportunity to enhance the future performance of the business organization. There is a common philosophical phrase that, 'he who does not learn from the history is destined to repeat the same'. Therefore, if these predictions are done with the help and assistance of the historical information (data), then you can get sufficient data for improvising the products of the business organization.

Mining enables the embedding of the recommendations in the applications. Simple summary statements and the proposals can be displayed within the operational applications. Data mining also needs powerful machines. The algorithms might be applied to a Java or a Dataset code for using the same. Data mining is very useful for knowing the trends and making future predictions based on the predictive analysis. It also helps in cost cutting and increase in the revenue of the business organization



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Data Entry Services - Using At Home Workers

Is there any question that the competition in the data entry market place is fierce? Just like in all other industries data entry service providers are always looking for ways to reduce cost, increase margins and look more attractive to the consumer. One way many companies have gone about this task is through the use of "at home workers". Is this a good idea? Does it impact the quality? Both are questions that should be investigated closely by both the provider and consumer.

Is this a good idea? First, let me state I am not against people trying to make a living by working at home. Honestly, I love the idea. Who doesn't dream of working for themselves, setting their own hours and working in their pajamas. My writing is focused more on the industry and quality of work, not the worker.

Let us get a feel for how companies utilize the at home worker. The use of this work force is broad, some companies actually outsource the majority of their work to this pool of workers, while some simply use the at home worker to fill in gaps (i.e. when the in house workloads become too great to handle). you might be thinking, "who cares?" as a consumer it should be a factor in selecting the right vendor for your company. Here are just a few pros and cons that should be considered:

Pros:

1. Cost savings to the service provider, reducing internal cost. vendors save money by reducing payroll, equipment cost, benefits, training, etc. One would hope these savings would be passed along to the client.

2. Increase to the current staff at hand. With more workers able to assist on a project, the time required to complete a data entry project should decreased. Most data entry service providers who choose to use at home workers utilize them either for ongoing projects, thus freeing up in house workers for new projects, or they use their in house staff for large ongoing projects and have the at home crew waiting in the wings for the new projects.

Cons:

1. Potential for security risk. I am not saying at home workers are evil and plotting to take your data, but let's be honest when there is a lack of direct supervision the occurrences of improper use of data increases. Keep in mind, not all at home workers are local to the vendor they work with. Depending on the type and sensitivity of the data you are outsourcing, data security should be a top priority.

2. Quality of work. The quality of the work, being of high importance, is many times much lower when completed by at home workers, when compared to the in house full time employee. In a conversation I recently had with a data entry vendor I learned that when they employed at home workers the accuracy level ranged anywhere from 78% to 92%, while the accuracy level of their in house employees ranged from 94% - 98%. This results because of lack of proper ongoing training and supervision.

It is necessary to note that in large, the at home worker is a dedicated, trustworthy and hard working group and if managed properly by the vendor can be a wonderful resource used to offer quality services. I simply recommend that during your due diligence in selecting the right data entry vendor, you ask how and where your work is being done. Should the company use at home workers ask about their training and quality controls.

I hope you find this information useful. Please feel free to contact me anytime, I am happy to answer all questions and help in any way I can.



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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.



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Data Mining - Critical for Businesses to Tap the Unexplored Market

Knowledge discovery in databases (KDD) is an emerging field and is increasingly gaining importance in today's business. The knowledge discovery process, however, is vast, involving understanding of the business and its requirements, data selection, processing, mining and evaluation or interpretation; it does not have any pre-defined set of rules to go about solving a problem. Among the other stages, the data mining process holds high importance as the task involves identification of new patterns that have not been detected earlier from the dataset. This is relatively a broad concept involving web mining, text mining, online mining etc.

What Data Mining is and what it is not?

The data mining is the process of extracting information, which has been collected, analyzed and prepared, from the dataset and identifying new patterns from that information. At this juncture, it is also important to understand what it is not. The concept is often misunderstood for knowledge gathering, processing, analysis and interpretation/ inference derivation. While these processes are absolutely not data mining, they are very much necessary for its successful implementation.

The 'First-mover Advantage'

One of the major goals of the data mining process is to identify an unknown or rather unexplored segment that had always existed in the business or industry, but was overlooked. The process, when done meticulously using appropriate techniques, could even make way for niche segments providing companies the first-mover advantage. In any industry, the first-mover would bag the maximum benefits and exploit resources besides setting standards for other players to follow. The whole process is thus considered to be a worthy approach to identify unknown segments.

The online knowledge collection and research is the concept involving many complications and, therefore, outsourcing the data mining services often proves viable for large companies that cannot devote time for the task. Outsourcing the web mining services or text mining services would save an organization's productive time which would otherwise be spent in researching.

The data mining algorithms and challenges

Every data mining task follows certain algorithms using statistical methods, cluster analysis or decision tree techniques. However, there is no single universally accepted technique that can be adopted for all. Rather, the process completely depends on the nature of the business, industry and its requirements. Thus, appropriate methods have to be chosen depending upon the business operations.

The whole process is a subset of knowledge discovery process and as such involves different challenges. Analysis and preparation of dataset is very crucial as the well-researched material could assist in extracting only the relevant yet unidentified information useful for the business. Hence, the analysis of the gathered material and preparation of dataset, which also considers industrial standards during the process, would consume more time and labor. Investment is another major challenge in the process as it involves huge cost on deploying professionals with adequate domain knowledge plus knowledge on statistical and technological aspects.

The importance of maintaining a comprehensive database prompted the need for data mining which, in turn, paved way for niche concepts. Though the concept has been present for years now, companies faced with ever growing competition have realized its importance only in the recent years. Besides being relevant, the dataset from where the information is actually extracted also has to be sufficient enough so as to pull out and identify a new dimension. Yet, a standardized approach would result in better understanding and implementation of the newly identified patterns.



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Basics of Web Data Mining and Challenges in Web Data Mining Process

Today World Wide Web is flooded with billions of static and dynamic web pages created with programming languages such as HTML, PHP and ASP. Web is great source of information offering a lush playground for data mining. Since the data stored on web is in various formats and are dynamic in nature, it's a significant challenge to search, process and present the unstructured information available on the web.

Complexity of a Web page far exceeds the complexity of any conventional text document. Web pages on the internet lack uniformity and standardization while traditional books and text documents are much simpler in their consistency. Further, search engines with their limited capacity can not index all the web pages which makes data mining extremely inefficient.

Moreover, Internet is a highly dynamic knowledge resource and grows at a rapid pace. Sports, News, Finance and Corporate sites update their websites on hourly or daily basis. Today Web reaches to millions of users having different profiles, interests and usage purposes. Every one of these requires good information but don't know how to retrieve relevant data efficiently and with least efforts.

It is important to note that only a small section of the web possesses really useful information. There are three usual methods that a user adopts when accessing information stored on the internet:

• Random surfing i.e. following large numbers of hyperlinks available on the web page.
• Query based search on Search Engines - use Google or Yahoo to find relevant documents (entering specific keywords queries of interest in search box)
• Deep query searches i.e. fetching searchable database from eBay.com's product search engines or Business.com's service directory, etc.

To use the web as an effective resource and knowledge discovery researchers have developed efficient data mining techniques to extract relevant data easily, smoothly and cost-effectively.


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What's Your Excuse For Not Using Data Mining?

In an earlier article I briefly described how data mining and RFM analysis can help marketers be more efficient (read... increased marketing ROI!). These marketing analytics tools can significantly help with all direct marketing efforts (multichannel campaign management efforts using direct mail, email and call center) and some interactive marketing efforts as well. So, why aren't all companies using it today? Well, typically it comes down to a lack of data and/or statistical expertise. Even if you don't have data mining expertise, YOU can benefit from data mining by using a consultant. With that in mind, let's tackle the first problem -- collecting and developing the data that is useful for data mining.

The most important data to collect for data mining include:

oTransaction data - For every sale, you at least need to know the product and the amount and date of the purchase.

oPast campaign response data - For every campaign you've run, you need to identify who responded and who didn't. You may need to use direct and indirect response attribution.

oGeo-demographic data - This is optional, but you may want to append your customer file/database with consumer overlay data from companies like Acxiom.

oLifestyle data - This is also an optional append of indicators of socio-economic lifestyle that are developed by companies like Claritas. All of the above data may or may not exist in the same data source. Some companies have a single holistic view of the customer in a database and some don't. If you don't, you'll have to make sure all data sources that contain customer data have the same customer ID/key. That way, all of the needed data can be brought together for data mining.

How much data do you need for data mining? You'll hear many different answers, but I like to have at least 15,000 customer records to have confidence in my results.

Once you have the data, you need to massage it to get it ready to be "baked" by your data mining application. Some data mining applications will automatically do this for you. It's like a bread machine where you put in all the ingredients -- they automatically get mixed, the bread rises, bakes, and is ready for consumption! Some notable companies that do this include KXEN, SAS, and SPSS. Even if you take the automated approach, it's helpful to understand what kinds of things are done to the data prior to model building.

Preparation includes:

oMissing data analysis. What fields have missing values? Should you fill in the missing values? If so, what values do you use? Should the field be used at all?

oOutlier detection. Is "33 children in a household" extreme? Probably - and consequently this value should be adjusted to perhaps the average or maximum number of children in your customer's households.

oTransformations and standardizations. When various fields have vastly different ranges (e.g., number of children per household and income), it's often helpful to standardize or normalize your data to get better results. It's also useful to transform data to get better predictive relationships. For instance, it's common to transform monetary variables by using their natural logs.

oBinning Data. Binning continuous variables is an approach that can help with noisy data. It is also required by some data mining algorithms.


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