Business Uses For Data Mining

When used wisely within Customer Relationship Management applications data mining can significantly improve the bottom line. It will end the process of randomly contacting a prospective or current customer through a call centre or by mailshot. With the effective use of data mining a company can concentrate its efforts on targeting prospects that have a high likelihood of being open to an offer. This in turn gives the ability for more sophisticated methods to be used such as campaigns being optimised to individuals.

Businesses that employ data mining techniques will usually see a high return on investment, but will also find that the number of predictive models can quickly increase. Rather than just implementing one model to predict which customers will respond positively, a business could build a different models for each region and customer type. Then instead of sending an offer to all prospects it may only want to send to prospects that have a high chance of taking up the offer. It may also want to determine which customers are going to be profitable during a certain time frame and direct their efforts towards them. To be able to maintain this quantity and quality of models, these model versions have to be well managed and automated data mining implemented.

Human Resources departments can also make a valid case for using data mining. It will allow them to in identifying the characteristics of their most successful employees. Information gained from such as resource can help HR focus their recruiting efforts accordingly.

Another example of data mining, is that used in retail. Often called market basket analysis, it is, for example, when a store records the purchases of customers, it could identify those customers who favour silk shirts over cotton ones; or customers who bought certain grocery items would also also buy the same specific item as well. This is often highlighted in on-line stores when you are told that so many people who bought a certain book or CD also bought XX as well.

Although some explanations of relationships may be difficult, taking advantage of it is easier. The example deals with association rules within transaction-based data. Not all data are transaction based and logical or inexact rules may also be present within a database. In a manufacturing application, an inexact rule may state that 73% of products which have a specific defect or problem will develop a secondary problem within the next six months.


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Top Data Mining Tools

Data mining is important because it means pulling out critical information from vast amounts of data. The key is to find the right tools used for the expressed purposes of examining data from any number of viewpoints and effectively summarize it into a useful data set.

Many of the tools used to organize this data have become computer based and are typically referred to as knowledge discovery tools.

Listed below are the top data mining tools in the industry:

    Insightful Miner - This tool has the best selection of ETL functions of any data mining tool on the market. This allows the merging, appending, sorting and filtering of data.
    SQL Server 2005 Data Mining Add-ins for Office 2007 - These are great add-ins for taking advantage of SQL Server 2005 predictive analytics in Office Excel 2007 and Office Visio 2007. The add-ins Allow you to go through the entire development lifecycle within Excel 2007 by using either a spreadsheet or external data accessible through your SQL Server 2005 Analysis Services instance.
    Rapidminder - Also known as YALE is a pretty comprehensive and arguably world-leading when it comes to an open-source data mining solution. it is widely used from a large number of companies an organizations. Even though it is open-source, this tool, out of the box provides a secure environment and provides enterprise capable support and services so you will not be left out in the cold.

The list is short but ever changing in order to meet the increasing demands of companies to provide useful information from years of data.


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Data Entry in Outsourcing Businesses

The process in, which a business house engages another company to do a particular type of work instead of using its own employees to do the same work, is called outsourcing. This is basically practiced so that the company can concentrate more on the core function. The cheap cost of outsourcing work is also another reason.

Outsourcing companies are often referred as "business to business" companies. Their business is dependent on the service provided by them to other business houses. Nowadays, every company is engaged in outsourcing. When a sole proprietor gives responsibility to another to buy supplies for the office, then automatically this process becomes outsourcing. In a real sense, it is almost impossible to do everything by yourself. You have to become dependent on those who are skilled in certain fields.

Data entry is one of the oldest and well known as the most common outsourcing activities that have been widely accepted across the globe for a long period of time. Still today, the demand is sky rocketing and the scope of data entry companies are just expanding.

All companies value their data very much. In order to generate good business, you need to deal with your data efficiently. Thus, companies related to BTB activities take care of the data handling very seriously. The employees are trained and prepared for all sorts of detailed oriented work. The services vary from back office support for a banking institute, calculation of medical bills, maintaining payroll functions etc. Banks generally outsource the work of the business class customers. Lock box payment is one of such example.

There are plenty of companies in the market of outsourcing who are engaged in providing in different kinds of services to the clients all across the globe. Many companies, which are earlier engaged into hard core data entry operations, are now exploring the area of medical billing, research work, project work for various universities, marketing job, news agencies, trade and several types of insurance organizations.

You can help your company to grow and reach a tremendous height once you get accustomed to take the advantages from various available data entry work. The service providers take an extra step to make sure that the work those are being delivered are of high quality and fulfill all the requirements as asked by the clients. Accuracy and punctually are the keywords to survive in the outsourcing market. Companies prefer outsourcing as the cost is always lower than the company would require spending on salaries if the same work was done by their own employees. Outsourcing is a very lucrative option for many business houses as it gives you the freedom to concentrate on your core business process and even you end up saving a good sum of money by outsourcing data entry work.



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Show the Potential of Your Business With Data Entry Services

When you are into a business, every day is like a new challenge for you. You have to do your tasks in a more improved way keeping your business activities maintained. Managing your core activities will help you in building up your business and will also help you to get the best results. Non-core activities will support your core activities in turn enhancing your business. One of the most important non-core activities that get neglected in most of the cases is Data Entry Services. This is one such offshore service that almost all the organizations use. With the increase in popularity, these Services are in high demand these days.

Significance of Data Entry Services

The question is how your data entry services are helping you in achieving your targeted goals and goals? You have to move deeper into the field of these Services to know how exactly these services are helping you. Before we start it is better to know that every business, be it a large-scale business or small-scale, every business produce voluminous amount of data which is very important from business point of view. This is the point where actually the problem starts. Accessing this large volume of data and then analyzing and processing of such a huge amount of data is a difficult task. This work is too much hectic and time-consuming. This is why the organizations are looking for good Data Entry Service to help them organize their data.

Hiring Data Entry Services is beneficial - some points to prove it

1. Low cost: The main benefit of hiring these services is that they will cut their total cost. They offer you the services at lower prices. It is one of the best ways to cut the cost.

2. Professional help: You get direct help from Data Entry Services. You will be given the professionals who have experience from years and will give you correct and proper solutions.

3. Accurate and Fast Services: You are given with fast and correct services as professionals work on your project. These professionals will offer you quick solutions to any of your problems.

4. Security of the data: Organizations are more concerned about their data security because there is an equal competition among the different services. Almost all the service providers offer high security to their customers.

5. More focus on the core activities: With the help of these services, you can concentrate more on your core business. You don't have to worry about managing your data but you can give more attention to your core activities of the business.

6. Comprehensive Advantage: You can enjoy a competitive advantage by concentrating more on your business and spending less on the data management.


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The Need for Specialised Data Mining Techniques for Web 2.0

Web 2.0 is not exactly a new version of the Web, but rather a way to describe a new generation of interactive websites centred on the user. These are websites that offer

interactive information sharing, as well as collaboration - a case in point being wikis and blogs - and is now expanding to other areas as well. These new sites are the result of new technologies and new ideas and are on the cutting edge of Web development. Due to their novelty, they create a rather interesting challenge for data mining.

Data mining is simply a process of finding patterns in masses of data. There is such a vast plethora of information out there on the Web that it is necessary to use data mining tools to make sense of it. Traditional data mining techniques are not very effective when used on these new Web 2.0 sites because the user interface is so varied. Since Web 2.0 sites are created largely by user-supplied content, there is even more data to mine for valuable information. Having said that, the additional freedom in the format ensures that it is much more difficult to sift through the content to find what is usable.The data available is very valuable, so where there is a new platform, there must be new techniques developed for mining the data. The trick is that the data mining methods must themselves be flexible as the sites they are targeting are flexible. In the initial days of the World Wide Web, which was referred to as Web 1.0, data mining programs knew where to look for the desired information. Web 2.0 sites lack structure, meaning there is no single spot for the mining program to target. It must be able to scan and sift through all of the user-generated content to find what is needed. The upside is that there is a lot more data out there, which means more and more accurate results if the data can be properly utilized. The downside is that with all that data, if the selection criteria are not specific enough, the results will be meaningless. Too much of a good thing is definitely a bad thing. Wikis and blogs have been around long enough now that enough research has been carried out to understand them better. This research can now be used, in turn, to devise the best possible data mining methods. New algorithms are being developed that will allow data mining applications to analyse this data and return useful. Another problem is that there are many cul-de-sacs on the internet now, where groups of people share information freely, but only behind walls/barriers that keep it away from the genera results.

The main challenge in developing these algorithms does not lie with finding the data, because there is too much of it. The challenge is filtering out irrelevant data to get to the meaningful one. At this point none of the techniques are perfected. This makes Web 2.0 data mining an exciting and frustrating field, and yet another challenge in the never ending series of technological hurdles that have stemmed from the internet. There are numerous problems to overcome. One is the inability to rely on keywords, which used to be the best method to search. This does not allow for an understanding of context or sentiment associated with the keywords which can drastically vary the meaning of the keyword population. Social networking sites are a good example of this, where you can share information with everyone you know, but it is more difficult for that information to proliferate outside of those circles. This is good in terms of protecting privacy, but it does not add to the collective knowledge base and it can lead to a skewed understanding of public sentiment based on what social structures you have entry into. Attempts to use artificial intelligence have been less than successful because it is not adequately focused in its methodology. Data mining depends on the collection of data and sorting the results to create reports on the individual metrics that are the focus of interest. The size of the data sets are simply too large for traditional computational techniques to be able to tackle them. That is why a new answer needs to be found. Data mining is an important necessity for managing the backhaul of the internet. As Web 2.0 grows exponentially, it is increasingly hard to keep track of everything that is out there and summarize and synthesize it in a useful way. Data mining is necessary for companies to be able to really understand what customers like and want so that they can create products to meet these needs. In the increasingly aggressive global market, companies also need the reports resulting from data mining to remain competitive. If they are unable to keep track of the market and stay abreast of popular trends, they will not survive. The solution has to come from open source with options to scale databases depending on needs. There are companies that are now working on these ideas and are sharing the results with others to further improve them. So, just as open source and collective information sharing of Web 2.0 created these new data mining challenges, it will be the collective effort that solves the problems as well.

It is important to view this as a process of constant improvement, not one where an answer will be absolute for all time. Since its advent, the internet has changed quite significantly as well as the way users interact with it. Data mining will always be a critical part of corporate internet usage and its methods will continue to evolve just as the Web and its content does.

There is a huge incentive for creating better data mining solutions to tackle the complexities of Web 2.0. For this reason, several companies exist just for the purpose of analysing and creating solutions to the data mining problem. They find eager buyers for their applications in companies which are desperate for information on markets and potential customers. The companies in question do not simply want more data, they want better data. This requires a system that can classify and group data, and then make sense of the results.While the data mining process is expensive to start with, it is well worth for a retail company because it provides insight into the market and thus enables quick decisions.The speed at which a company which has insightful information on the marketplace can react to changes, gives it a huge advantage over the competition. Not only can the company react quickly, it is likely to steer itself in the right direction if its information is based on updated data.Advanced data mining will allow companies not only to make snap decisions, but also to plan long range strategies, based on the direction the marketplace is heading. Data mining brings the company closer to its customers. The real winners here, are the companies that have now discovered that they can make a living by improving the existing data mining techniques. They have filled a niche that was only created recently, which no one could have foreseen and have done quite a, good job at it.


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One of the Main Differences Between Statistical Analysis and Data Mining

Two methods of analyzing data that are common in both academic and commercial fields are statistical analysis and data mining. While statistical analysis has a long scientific history, data mining is a more recent method of data analysis that has arisen from Computer Science. In this article I want to give an introduction to these methods and outline what I believe is one of the main differences between the two fields of analysis.

Statistical analysis commonly involves an analyst formulating a hypothesis and then testing the validity of this hypothesis by running statistical tests on data that may have been collected for the purpose. For example, if an analyst was studying the relationship between income level and the ability to get a loan, the analyst may hypothesis that there will be a correlation between income level and the amount of credit someone may qualify for.

The analyst could then test this hypothesis with the use of a data set that contains a number of people along with their income levels and the credit available to them. A test could be run that indicates for example that there may be a high degree of confidence that there is indeed a correlation between income and available credit. The main point here is that the analyst has formulated a hypothesis and then used a statistical test along with a data set to provide evidence in support or against that hypothesis.

Data mining is another area of data analysis that has arisen more recently from computer science that has a number of differences to traditional statistical analysis. Firstly, many data mining techniques are designed to be applied to very large data sets, while statistical analysis techniques are often designed to form evidence in support or against a hypothesis from a more limited set of data.

Probably the mist significant difference here, however, is that data mining techniques are not used so much to form confidence in a hypothesis, but rather extract unknown relationships may be present in the data set. This is probably best illustrated with an example. Rather than in the above case where a statistician may form a hypothesis between income levels and an applicants ability to get a loan, in data mining, there is not typically an initial hypothesis. A data mining analyst may have a large data set on loans that have been given to people along with demographic information of these people such as their income level, their age, any existing debts they have and if they have ever defaulted on a loan before.

A data mining technique may then search through this large data set and extract a previously unknown relationship between income levels, peoples existing debt and their ability to get a loan.

While there are quite a few differences between statistical analysis and data mining, I believe this difference is at the heart of the issue. A lot of statistical analysis is about analyzing data to either form confidence for or against a stated hypothesis while data mining is often more about applying an algorithm to a data set to extract previously unforeseen relationships.


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Text Analysis, Text Mining and Information Retrieval

For years, big companies, business houses, government institutions, banks, stock and brokerage firms have been on the lookout to accumulate knowledge about the behavior of their customers and clients. This knowledge can be derived by analyzing in details the behavior of the clients and customers. This has become possible with the implementation of the text mining software. Text mining software is software that helps to extract information from a large amount of data sources. The software does a detailed text analysis of the data sources and retrieves such information that would not have been visible or accessible by the average, normal human beings.

Use of text mining software

The text mining software works hand in hand with the data mining software. There is no difference between these two kinds of software because of the fact that text mining software is an offshoot of the data mining software. Both these software have been found to be of immense importance in fields like banking sector, bio medicine, programming and developing of software, stock markets and in various government institutions.

There are numerous advantages of using these kinds of software. These advantages are

    The use of text mining software is widely dominant in case of retail industry. With the help of the two kinds of software, the marketers can get to know detailed information regarding the behavioral pattern of the consumers. As a result, the marketers can also get the prediction as to whether their products would fare well in the market or not. This prediction will allow the manufacturers to develop better products according to the needs and requirements of the consumers.
    Crime investigation is another field that is widely benefited due to the use of both the software. These softwares provide the police and the detectives will vital information regarding the criminals by analyzing the patterns of their criminal habits, modus operandi and location.
    In the banking sector, the use of these softwares help the bank personnel and the credit card holders to keep a track of credit card forgery and all the transactions related with the false credit card.
    Text mining software increases the accuracy to a great extent. This is precisely the reason why this software has been used alongside the stock picking software in order to predict accurately the value of the stocks, when to buy them and also when to sell them. All these information are provided with the help of sentiment analysis.

There are various kinds of tools available in the market that can be used for effecting retrieval of the information. These include Arrowsmith Software, Copernic Summarizer, and Crossminder etc.

Along with this software, these tools perform various functions like summarizing the documents or texts, comparing of keywords, doing grammatical as well as analyze large amount of data that is not organized properly. The software and the various tools have indeed made doing business a lot easier task.



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Data Mining, Not Just a Method But a Technique

Web data mining is segregating probable clients out of huge information available on the Internet by performing various searches. It could be well organized and structured, or raw, depending on the use of the data. Web data mining could be done using a simple database program or investing money in a costly program.

Start collecting basic contact information of probable clients, such as: names, addresses, landline and cell phone numbers, email addresses and education or occupation if required.

CART and CHAID data mining

While collecting data you will find that tree-shaped structures that represent decisions. These derived decisions give rules for the classification of data collected. Precise decision tree methods include Classification and Regression Trees also know as CART data mining and Chi Square Automatic Interaction Detection also known as CHAID data mining. CART and CHAID data mining are decision tree techniques used for classification of data collected. They provide a set of rules that could be applied to unclassified data collected in prediction. CART segments a dataset creating two-way splits whereas CHAID segments using chi square tests creating multi-way splits. CART requires less data preparation compared to CHAID.

Understanding customer's actions

Keep a track of customer's actions like: what does he buy, when does he buy, why does he buy, what is the use of his buying, etc. Knowing such simple things about your customer will help you to understand needs of your customer better and thus process of data mining services will be easier and quality data would be mined. This will increase your personal relations with your customer which would finally result in a better professional relationship.

Following demography

Mine the data as per demography, dependent on geography as well as socio economic background of business location. You can use government statistics as the source of your data collection. Keeping it in mind you can go ahead with the understanding of the community existing and thus the data required.

Use your informal conversation in serving your clients better

Use minute details of your conversation and understanding with your customers to serve them. If essential, conduct surveys, send a professional gift or use some other object that helps you understand better in fulfilling customer needs. This will increase the bonding between you and your customer and you will be able to serve your customer better in providing data mining services.

Insert the collect information in a desktop database. More the information is collected you will find that you can prepare specific templates in feeding information. Using a desktop database, it is easier to make changes later on as and when required.

Maintaining privacy

While performing, it is essential to ensure that you or your team members are not violating privacy laws in gathering or providing the data information. Once trust is lost, you may also loose the customer, because trust is the base of any relationship, let it be a business relation.


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Data Mining in the 21st Century: Business Intelligence Solutions Extract and Visualize

When you think of the term data mining, what comes to mind? If an image of a mine shaft and miners digging for diamonds or gold comes to mind, you're on the right track. Data mining involves digging for gems or nuggets of information buried deep within data. While the miners of yesteryear used manual labor, modern data minors use business intelligence solutions to extract and make sense of data.

As businesses have become more complex and more reliant on data, the sheer volume of data has exploded. The term "big data" is used to describe the massive amounts of data enterprises must dig through in order to find those golden nuggets. For example, imagine a large retailer with numerous sales promotions, inventory, point of sale systems, and a gift registry. Each of these systems contains useful data that could be mined to make smarter decisions. However, these systems may not be interlinked, making it more difficult to glean any meaningful insights.

Data warehouses are used to extract information from various legacy systems, transform the data into a common format, and load it into a data warehouse. This process is known as ETL (Extract, Transform, and Load). Once the information is standardized and merged, it becomes possible to work with that data.

Originally, all of this behind-the-scenes consolidation took place at predetermined intervals such as once a day, once a week, or even once a month. Intervals were often needed because the databases needed to be offline during these processes. A business running 24/7 simply couldn't afford the down time required to keep the data warehouse stocked with the freshest data. Depending on how often this process took place, the data could be old and no longer relevant. While this may have been fine in the 1980s or 1990s, it's not sufficient in today's fast-paced, interconnected world.

Real-time EFL has since been developed, allowing for continuous, non-invasive data warehousing. While most business intelligence solutions today are capable of mining, extracting, transforming, and loading data continuously without service disruptions, that's not the end of the story. In fact, data mining is just the beginning.

After mining data, what are you going to do with it? You need some form of enterprise reporting in order to make sense of the massive amounts of data coming in. In the past, enterprise reporting required extensive expertise to set up and maintain. Users were typically given a selection of pre-designed reports detailing various data points or functions. While some reports may have had some customization built in, such as user-defined date ranges, customization was limited. If a user needed a special report, it required getting someone from the IT department skilled in reporting to create or modify a report based on the user's needs. This could take weeks - and it often never happened due to the hassles and politics involved.

Fortunately, modern business intelligence solutions have taken enterprise reporting down to the user level. Intuitive controls and dashboards make creating a custom report a simple matter of drag and drop while data visualization tools make the data easy to comprehend. Best of all, these tools can be used on demand, allowing for true, real-time ad hoc enterprise reporting.


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Data Mining As a Process

The data mining process is also known as knowledge discovery. It can be defined as the process of analyzing data from different perspectives and then summarizing the data into useful information in order to improve the revenue and cut the costs. The process enables categorization of data and the summary of the relationships is identified. When viewed in technical terms, the process can be defined as finding correlations or patterns in large relational databases. In this article, we look at how data mining works its innovations, the needed technological infrastructures and the tools such as phone validation.

Data mining is a relatively new term used in the data collection field. The process is very old but has evolved over the time. Companies have been able to use computers to shift over the large amounts of data for many years. The process has been used widely by the marketing firms in conducting market research. Through analysis, it is possible to define the regularity of customers shopping. How the items are bought. It is also possible to collect information needed for the establishment of revenue increase platform. Nowadays, what aides the process is the affordable and easy disk storage, computer processing power and applications developed.

Data extraction is commonly used by the companies that are after maintaining a stronger customer focus no matter where they are engaged. Most companies are engaged in retail, marketing, finance or communication. Through this process, it is possible to determine the different relationships between the varying factors. The varying factors include staffing, product positioning, pricing, social demographics, and market competition.

A data-mining program can be used. It is important note that the data mining applications vary in types. Some of the types include machine learning, statistical, and neural networks. The program is interested in any of the following four types of relationships: clusters (in this case the data is grouped in relation to the consumer preferences or logical relationships), classes (in this the data is stored and finds its use in the location of data in the per-determined groups), sequential patterns (in this case the data is used to estimate the behavioral patterns and patterns), and associations (data is used to identify associations).

In knowledge discovery, there are different levels of data analysis and they include genetic algorithms, artificial neural networks, nearest neighbor method, data visualization, decision trees, and rule induction. The level of analysis used depends on the data that is visualized and the output needed.

Nowadays, data extraction programs are readily available in different sizes from PC platforms, mainframe, and client/server. In the enterprise-wide uses, size ranges from the 10 GB to more than 11 TB. It is important to note that two crucial technological drivers are needed and are query complexity and, database size. When more data is needed to be processed and maintained, then a more powerful system is needed that can handle complex and greater queries.

With the emergence of professional data mining companies, the costs associated with process such as web data extraction, web scraping, web crawling and web data mining have greatly being made affordable.


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Data Mining and Financial Data Analysis

Most marketers understand the value of collecting financial data, but also realize the challenges of leveraging this knowledge to create intelligent, proactive pathways back to the customer. Data mining - technologies and techniques for recognizing and tracking patterns within data - helps businesses sift through layers of seemingly unrelated data for meaningful relationships, where they can anticipate, rather than simply react to, customer needs as well as financial need. In this accessible introduction, we provides a business and technological overview of data mining and outlines how, along with sound business processes and complementary technologies, data mining can reinforce and redefine for financial analysis.

Objective:

1. The main objective of mining techniques is to discuss how customized data mining tools should be developed for financial data analysis.

2. Usage pattern, in terms of the purpose can be categories as per the need for financial analysis.

3. Develop a tool for financial analysis through data mining techniques.

Data mining:

Data mining is the procedure for extracting or mining knowledge for the large quantity of data or we can say data mining is "knowledge mining for data" or also we can say Knowledge Discovery in Database (KDD). Means data mining is : data collection , database creation, data management, data analysis and understanding.

There are some steps in the process of knowledge discovery in database, such as

1. Data cleaning. (To remove nose and inconsistent data)

2. Data integration. (Where multiple data source may be combined.)

3. Data selection. (Where data relevant to the analysis task are retrieved from the database.)

4. Data transformation. (Where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance)

5. Data mining. (An essential process where intelligent methods are applied in order to extract data patterns.)

6. Pattern evaluation. (To identify the truly interesting patterns representing knowledge based on some interesting measures.)

7. Knowledge presentation.(Where visualization and knowledge representation techniques are used to present the mined knowledge to the user.)

Data Warehouse:

A data warehouse is a repository of information collected from multiple sources, stored under a unified schema and which usually resides at a single site.

Text:

Most of the banks and financial institutions offer a wide verity of banking services such as checking, savings, business and individual customer transactions, credit and investment services like mutual funds etc. Some also offer insurance services and stock investment services.

There are different types of analysis available, but in this case we want to give one analysis known as "Evolution Analysis".

Data evolution analysis is used for the object whose behavior changes over time. Although this may include characterization, discrimination, association, classification, or clustering of time related data, means we can say this evolution analysis is done through the time series data analysis, sequence or periodicity pattern matching and similarity based data analysis.

Data collect from banking and financial sectors are often relatively complete, reliable and high quality, which gives the facility for analysis and data mining. Here we discuss few cases such as,

Eg, 1. Suppose we have stock market data of the last few years available. And we would like to invest in shares of best companies. A data mining study of stock exchange data may identify stock evolution regularities for overall stocks and for the stocks of particular companies. Such regularities may help predict future trends in stock market prices, contributing our decision making regarding stock investments.

Eg, 2. One may like to view the debt and revenue change by month, by region and by other factors along with minimum, maximum, total, average, and other statistical information. Data ware houses, give the facility for comparative analysis and outlier analysis all are play important roles in financial data analysis and mining.

Eg, 3. Loan payment prediction and customer credit analysis are critical to the business of the bank. There are many factors can strongly influence loan payment performance and customer credit rating. Data mining may help identify important factors and eliminate irrelevant one.

Factors related to the risk of loan payments like term of the loan, debt ratio, payment to income ratio, credit history and many more. The banks than decide whose profile shows relatively low risks according to the critical factor analysis.

We can perform the task faster and create a more sophisticated presentation with financial analysis software. These products condense complex data analyses into easy-to-understand graphic presentations. And there's a bonus: Such software can vault our practice to a more advanced business consulting level and help we attract new clients.

To help us find a program that best fits our needs-and our budget-we examined some of the leading packages that represent, by vendors' estimates, more than 90% of the market. Although all the packages are marketed as financial analysis software, they don't all perform every function needed for full-spectrum analyses. It should allow us to provide a unique service to clients.

The Products:

ACCPAC CFO (Comprehensive Financial Optimizer) is designed for small and medium-size enterprises and can help make business-planning decisions by modeling the impact of various options. This is accomplished by demonstrating the what-if outcomes of small changes. A roll forward feature prepares budgets or forecast reports in minutes. The program also generates a financial scorecard of key financial information and indicators.

Customized Financial Analysis by BizBench provides financial benchmarking to determine how a company compares to others in its industry by using the Risk Management Association (RMA) database. It also highlights key ratios that need improvement and year-to-year trend analysis. A unique function, Back Calculation, calculates the profit targets or the appropriate asset base to support existing sales and profitability. Its DuPont Model Analysis demonstrates how each ratio affects return on equity.

Financial Analysis CS reviews and compares a client's financial position with business peers or industry standards. It also can compare multiple locations of a single business to determine which are most profitable. Users who subscribe to the RMA option can integrate with Financial Analysis CS, which then lets them provide aggregated financial indicators of peers or industry standards, showing clients how their businesses compare.

iLumen regularly collects a client's financial information to provide ongoing analysis. It also provides benchmarking information, comparing the client's financial performance with industry peers. The system is Web-based and can monitor a client's performance on a monthly, quarterly and annual basis. The network can upload a trial balance file directly from any accounting software program and provide charts, graphs and ratios that demonstrate a company's performance for the period. Analysis tools are viewed through customized dashboards.

PlanGuru by New Horizon Technologies can generate client-ready integrated balance sheets, income statements and cash-flow statements. The program includes tools for analyzing data, making projections, forecasting and budgeting. It also supports multiple resulting scenarios. The system can calculate up to 21 financial ratios as well as the breakeven point. PlanGuru uses a spreadsheet-style interface and wizards that guide users through data entry. It can import from Excel, QuickBooks, Peachtree and plain text files. It comes in professional and consultant editions. An add-on, called the Business Analyzer, calculates benchmarks.

ProfitCents by Sageworks is Web-based, so it requires no software or updates. It integrates with QuickBooks, CCH, Caseware, Creative Solutions and Best Software applications. It also provides a wide variety of businesses analyses for nonprofits and sole proprietorships. The company offers free consulting, training and customer support. It's also available in Spanish.

ProfitSystem fx Profit Driver by CCH Tax and Accounting provides a wide range of financial diagnostics and analytics. It provides data in spreadsheet form and can calculate benchmarking against industry standards. The program can track up to 40 periods.



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Why Data Entry Outsourcing?

Data entry is the core of any business and though it may appear to be easy to manage and handle, this involves many processes that need to be dealt systematically. Huge changes have taken place in the field of data entry and due to this, handling work has become much easier then before. So if you want to make use of the best data entry services to maintain the data and other information about your company, then you need to have a professional company which provides data entry services with lowest possible rates and also within deadline.

Nowadays, it's becoming trend to outsource your Work to reliable service provider who provides excellent output out of their work. Many Companies or Organization prefer to outsource their data entry work to an offshore location. One of the key reasons why it has become so popular is the fact that the services they are providing from highly qualified professionals is cost effective and time bound.

Following are benefits of data entry outsourcing

o It helps you to focus on core business

o It reduces capital cost of infrastructure

o Competitive pricing which are as low as 40-60% of the prevailing US cost

o Remove management headaches

o Improves employee satisfaction with higher value addition jobs

o Use latest standard and new technology

o Quick turn around time and strong quality

o Make best use of competitive resources available worldwide

o High speed and low cost communication

o Line data processing possible from any location

Boost up your business by outsourcing data entry work.



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Data Mining Basics

Definition and Purpose of Data Mining:

Data mining is a relatively new term that refers to the process by which predictive patterns are extracted from information.

Data is often stored in large, relational databases and the amount of information stored can be substantial. But what does this data mean? How can a company or organization figure out patterns that are critical to its performance and then take action based on these patterns? To manually wade through the information stored in a large database and then figure out what is important to your organization can be next to impossible.

This is where data mining techniques come to the rescue! Data mining software analyzes huge quantities of data and then determines predictive patterns by examining relationships.

Data Mining Techniques:

There are numerous data mining (DM) techniques and the type of data being examined strongly influences the type of data mining technique used.

Note that the nature of data mining is constantly evolving and new DM techniques are being implemented all the time.

Generally speaking, there are several main techniques used by data mining software: clustering, classification, regression and association methods.

Clustering:

Clustering refers to the formation of data clusters that are grouped together by some sort of relationship that identifies that data as being similar. An example of this would be sales data that is clustered into specific markets.

Classification:

Data is grouped together by applying known structure to the data warehouse being examined. This method is great for categorical information and uses one or more algorithms such as decision tree learning, neural networks and "nearest neighbor" methods.

Regression:

Regression utilizes mathematical formulas and is superb for numerical information. It basically looks at the numerical data and then attempts to apply a formula that fits that data.

New data can then be plugged into the formula, which results in predictive analysis.

Association:

Often referred to as "association rule learning," this method is popular and entails the discovery of interesting relationships between variables in the data warehouse (where the data is stored for analysis). Once an association "rule" has been established, predictions can then be made and acted upon. An example of this is shopping: if people buy a particular item then there may be a high chance that they also buy another specific item (the store manager could then make sure these items are located near each other).

Data Mining and the Business Intelligence Stack:

Business intelligence refers to the gathering, storing and analyzing of data for the purpose of making intelligent business decisions. Business intelligence is commonly divided into several layers, all of which constitute the business intelligence "stack."

The BI (business intelligence) stack consists of: a data layer, analytics layer and presentation layer.

The analytics layer is responsible for data analysis and it is this layer where data mining occurs within the stack. Other elements that are part of the analytics layer are predictive analysis and KPI (key performance indicator) formation.

Data mining is a critical part of business intelligence, providing key relationships between groups of data that is then displayed to end users via data visualization (part of the BI stack's presentation layer). Individuals can then quickly view these relationships in a graphical manner and take some sort of action based on the data being displayed.


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What is Data Management?

The Definition

Data Management is the comprehensive series of procedures to be followed and have developed and maintained the quality data, using the technology and available resources. It can also be defined that it is the execution of architectures under certain predefined policies and procedures to manage the full data life cycle of a company or organization. It is comprised of all the disciplines related to data management resources.

Following are the key stages or procedures or disciplines of data management:

1. Database Management system

2. Database Administration

3. Data warehousing

4. Data modeling

5. Data quality assurance

6. Data Security

7. Data movement

8. Data Architectures

9. Data analysis

10. Data Mining

1. Database Management system:

It is one of the computer software from various types and brands available these days. These software are designed for specifically for the purpose of data management. These are few of these; Ms Access, MsSQL, Oracle, My Sql, etc. The selection of any one of these depends upon the company policy, expertise and administration.

2. Database Administration:

Data administration is group of experts who are responsible for all aspects of data management. The roles and responsibilities of this team depends upon the company's over all policy towards the database management. They implement the systems using protocols of software and procedures, to maintain following properties:

a. Development and testing database,

b. Security of database,

c. Backups of database,

d. Integrity of database, and its software,

e. Performance of database,

f. Ensuring maximum availability of database

3. Data warehousing

Data warehousing, in other words is the system of organization of historical data, its storage capability etc. Actually this system contains the raw material for the management of query support systems. That raw material is such that the analysts can retrieve any type of historical data in any form, like trends, time stamped data, complex queries and analysis. These reports are essential for any company to review their investments, or business trends which in turn will be used for future planning.

The data warehousing are based on following terms:

a. The databases are organized so that all the data elements relating to the same events are linked together,

b. All changes to the databases are recorded, for future reports,

c. Any data in databases is not deleted or over written, the data is static, readable only,

d. The data is consistent and contains all organizational information.

4. Data modeling

Data modeling is the process of creating a data model by applying and model theory to create data model instance. The data modeling is actually, defining, structuring and organizing the data using predefined protocol. Then the theses structures are implemented in data management system. In addition, it also will impose certain limitation on the database with in the structure.

5. Data quality assurance

Data quality assurance is the procedure to be implemented in data management systems, to remove anomalies and inconsistencies in the databases. This also performs cleansing of databases to improve the quality of databases.

6. Data Security

It is also called as data protection, this is system or protocol which is implemented with in the system to ensuring that the databases are kept fully safe and no one can corrupt by access controlling. The data security, on other hand, also provides the privacy and protection to the personal data. Many companies and governments of the world have created law to protect the personal data.

7. Data movement

It is one term broadly related to the data warehousing that is ETL (Extract, Transform and Load). ETL is process involved in data warehousing and is very important as it is the way data is loaded into the warehouse.

8. Data Architectures

This is most important part of the data management system; it is the procedure of planning and defining the target states of the data. It is, realizing the target state, describing that how the data is processed, stored and utilized in any given system. It created criterion to processes the operation to make it possible to design data flows and controls the flow of data in any given system.

Basically, data architecture is responsible for defining the target states and alignment during the initial development and then maintained by implementations of minor follow-ups. During the defining of the states, data architecture breaks into minor sub levels and parts and then brought up to the desired form. Those levels can be created under the three traditional data architectural processes:

a. Conceptual, which represents all business entities

b. Logical means the how these business entities are related.

c. Physical, is the realization of the data mechanism for specific function of database.

From above statements, we can define that the data architecture includes complete analysis of the relationship between functions, data types and the technology.

9. Data analysis

Data analysis is the series of procedures which is used to extract required information and produce conclusion reports. Depending upon the type of the data and the query, this might include application of statistical methods, trending, selecting or discarding certain subsets of data based on specific criteria. Actually, data analysis is the verification or disapproval of an existing data model, or to the extract the necessary parameters to achieve theoretical model over realty.

10. Data Mining

Data mining is the procedure to extract unknown but useful parameters of data. It also can be defined that it is the series of procedures to extract the useful and desired information from large databases. Data mining is the principle of sorting the large through the large amount of data and selected the relevant and required information for any specific purposes.


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Data Entry Outsourcing Companies

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

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

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

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



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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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