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.

Steve Bogdon is the Advertising director for Dashboard Insight, one of the fastest growing business intelligence (BI) sites on the web. Dashboard Insight is an authoritative and trusted online resource for the business intelligence, data visualization and dashboard software communities.




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Outsourcing And Archiving Your Data

Whether a company relies heavily on database activity for critical everyday business operations or only for select requirements, the loss of data due to technological failure can have far reaching negative implications. The loss of valuable information and records can cause productivity setbacks, lost profits, lost customers, and headaches for everyone involved. Aside from the obvious business challenges associated with the loss of data, legislation such as the Sarbanes-Oxley Act (SOX) places requirements on the retention and provision of certain types of financial data. Companies assume the risk of non-compliance if they are unable to produce information within the specified time constraint required by Sarbanes-Oxley (SOX) or other information-focused legislation. Database and mainframe disaster recovery is more important in today¹s technology dependent business world than ever before.

When it comes to archiving your company¹s data, the advantages of archiving your information with an outside source include:

o Fast and straightforward deployment with no large out-of-pocket initial expenses.

o If customers don't like the service, they can simply decline renewing their contract (which usually runs for one to three years), rather than worry about the unwanted hardware and software sitting on their premises.

o Outsourcing is great for companies with no IT department, or a small or overstretched IT department. The service provider handles all heavy-duty aspects of administration, while the customer is left with relatively few tasks.

o By and large, outsourcers are always up-to-date with the latest releases and versions of hardware and software. The upgrade process is more painful and expensive in-house.

o Scalability and dispersed geographic locations can be more easily accommodated by outsourcers than through in-house installations.

Ever increasing data retention requirements have placed monumental pressure on companies, as the software for archiving must be extremely advanced with tremendous capacities and prolonged reliability.

Outsourcing your archival data saves time and money and reduces the risk and complexity of keeping up with such demands. Are there cons to having to outsource your archives? Possibly, but certainly not compared to the value.


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Advantages of Online Data Entry Services

People all over the world are enthusiastic to buy online data entry services as they find it cost effective. Most of them have an impression that they get quality services against the prices they have to pay. Entering data online is of a great help to business units of all sizes as they consider them as their main basis of profession.

Online data entering and typing services providers have skilled resources at their service who deliver quality work timely. These service providers have modernized technology, assuring cent percent security of data. Online data entry services include the following:

    Data entry
    Data Processing
    Product entry
    Data typing
    Data mining, Data capture/collection
    Business Process Outsourcing
    Data Conversion
    Form Filling
    Web and mortgage research
    Extraction services
    Online copying, pasting, editing, sorting, as well as indexing data
    E-books and e-magazines data entry

Get companies world wide quality services to business units of all sizes, some of the common input formats are:

    PDF
    TIFF
    GIF
    XBM
    JPG
    PNG
    BMP
    TGA
    XML
    HTML
    SGML
    Printed documents
    Hard copies, etc

Benefits of outsourcing online data entering services:

Major benefits of data entry for business units is that they get the facts and figures which helps in taking strategic decisions for the organization. The data projected by numbers turns to be a factor of evaluation that accelerates the progress of the business. Online data typing services maintain high level of security by using systems that are highly protected.

The business organization progresses because of right decisions taken with the help of superior quality data available.

    Save operational overhead expense.
    Saves time and space.
    Accurate services can be accessed.
    Eliminating the paper documents.
    Cost effective.
    Data accessible from anywhere in the world.
    100% work satisfaction.
    Access to professional and experienced data typing services.
    Adequate knowledge of wide range industrial needs.
    Use of highly advance technologies for quality results.

Business organizations find themselves blessed because of the benefits they receive out of outsourcing their projects on online data entering and typing services, because it not only saves their time but also saves a huge amount of money.

Upcoming business companies can focus on their key business functions instead of dealing with non-key business activities. They find it sensible to outsource their confidential and crucial projects to trustworthy online data entry services and remain free for their key business activities. These companies have several layers of quality control which assures 99.9% quality on projects on online data entry.



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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.



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Data Mining Introduction

Introduction

We have been "manually" extracting data in relation to the patterns they form for many years but as the volume of data and the varied sources from which we obtain it grow a more automatic approach is required.

The cause and solution to this increase in data to be processed has been because the increasing power of computer technology has increased data collection and storage.

Direct hands-on data analysis has increasingly been supplemented, or even replaced entirely, by indirect, automatic data processing.

Data mining is the process uncovering hidden data patterns and has been used by businesses, scientists and governments for years to produce market research reports. A primary use for data mining is to analyse patterns of behaviour.

It can be easily be divided into stages

Pre-processing

Once the objective for the data that has been deemed to be useful and able to be interpreted is known, a target data set has to be assembled. Logically data mining can only discover data patterns that already exist in the collected data, therefore the target dataset must be able to contain these patterns but small enough to be able to succeed in its objective within an acceptable time frame.

The target set then has to be cleansed. This removes sources that have noise and missing data.

The clean data is then reduced into feature vectors,(a summarized version of the raw data source) at a rate of one vector per source. The feature vectors are then split into two sets, a "training set" and a "test set". The training set is used to "train" the data mining algorithm(s), while the test set is used to verify the accuracy of any patterns found.

Data mining

Data mining commonly involves four classes of task:

    Classification - Arranges the data into predefined groups. For example email could be classified as legitimate or spam.
    Clustering - Arranges data in groups defined by algorithms that attempt to group similar items together
    Regression - Attempts to find a function which models the data with the least error.
    Association rule learning - Searches for relationships between variables. Often used in supermarkets to work out what products are frequently bought together. This information can then be used for marketing purposes.

Validation of Results

The final stage is to verify that the patterns produced by the data mining algorithms occur in the wider data set as not all patterns found by the data mining algorithms are necessarily valid.

If the patterns do not meet the required standards, then the preprocessing and data mining stages have to be re-evaluated. When the patterns meet the required standards then these patterns can be turned into knowledge.

Mike has more than 15 years of experience designing and implementing data warehouses based on Oracle, MS SQL Server, MySql, PostgreSQL and more.



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Outsource Your Work To Data Entry Services To Convert Your Paperwork To An Electronic Format

Among the many services that are outsourced, data entry services are much in demand. While the job profile might seem simple it does in fact require a certain degree of exactness and an eye for detail. Maintaining and handling the client confidentiality is also very important. Data needs to be processed and the first step is always entering the information in the system. An operator needs to be careful while entering information in the system as often this data is used to collate data and for statistical reports and is also the foundation for all the information on the company. These services include much more than just basic information in this technology driven age. An operator today has projects that require Image entry, card Entry, legal document's entry, medical claim entry, entry for online survey forms, online indexing, copying, pasting and sorting of data etc.

A Data entry operator is competent at handling online as well as offline data and even to excel. Specialized services like Image editing, image clipping and cropping services are also available with this service. BPO companies offer these services at very cost effective rates and the work is processed 24x7 ensuring that the work is constantly auctioned. Many data sensitive projects are also completed even in a 24 hour. There are many online services to choose from and each specializes in various features with ample industry experience. These services use the latest technology to ensure that paperwork is processed in the shorted possible time and is converted into electronic data that is easier to store.

A professional service must be able to offer the following features like data conversion and even storage, effective management of databases and an adherence to turnaround times, 100% accuracy of the data entered, 24x7 webs and phone support, a secure and accurate data capture, data extraction and data processing and importantly a cost effective solution for quality data services. A professional company will also ensure that there is a Quality Assurance department monitoring the quality of the work being handled with relevant feedback to both the client and to the operator.

Before deciding on outsourcing your work to a data entry service ensures that the company is known for its reliability and quality. A company that offers data backup is also a good option as it will take care of all the paperwork while forwarding the converted electronic data back. This paperwork could be extracted in the case of a claim or any legal requirement. There are many BPO companies online advertising their services, browse through their features and find one that suits your requirements.

The writer is a Data entry service provider who specializes as data entry operator. Inquire for a free quote for data entry services. If you want services as data entry operators or data entry for your organizations. We are able to provide data entry services at affordable low cost.




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Advantageous Data Entry Services in Era of Globalization

Data generally represent the information and can be defined with numbers or alphabetical symbols. Data entry can be determined as process that converts data from one form to another one. Such solutions usually includes almost all business fields and professional services, such as data conversion, offline data entry work, data processing, image processing, data entry outsourcing, data mining etc. One has to collect data on various topics and have to represent them in some meaningful manner.

There are several tasks for data entry services. It may includes data-entry into websites, tracking debit or credit card transactions, entry into electronic books, image formatting, keeping hard copy of office applications for scanning or printing, database for mails, use of data entry software as well as management of all these activities. In addition some time consuming tasks such as entering data in offline mode to track websites, gathering effective websites, which may need for consultation and to fill online forms. One of the good examples of data entry tasks is writing the image. You have to enter the images to incorporate pictures and attachments in magazines, e Books and white papers. Scanned images also needed to enter the details on the file. Another example of data-entry work is insurance claim. Insurance firms file a claim for insurance in process to get the cost of services. All systems for payment, form processing and insurance claims are followed by data entry services.

Data processing is also very useful tasks needed to be managed, regardless of company size or complexity. You have to follow some methods in order to accomplish your data processing tasks accurately. Such services help firms in terms of clear analysis of activities, policies, strategies and actions. Data processing and other services like data cleaning, image processing, OCR clean up, survey processing are related to provide a well-processed and complete data which can be used to get simple explanation of data.

There are plenty of advantages such services. For example data conversion is process which is very significant for any firm to drive their business powerfully. Data conversion can be considered as transfer of data from one format to another. There are also some other useful services like data transformation and many other which directly or indirectly essential for smooth functionality of any business.

Be advantageous in this competitive environment by choosing the right business services for benefits of yours and your organization.



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