Data Mining: From Moore's Law to One Sale a Day

Today the internet is more customized than it ever has been before. This is largely because of data mining, which involves using patterns and records of how you use the internet, to anticipate how you will continue to use the internet. This is an application of data mining, however; more broadly, the term refers to how to analyze data to cut costs or increase revenue.

While the term data mining is new, the practice is not. Due to Moore's Law, which states that processing power and data storage double every 18 months, over the past five years, it has become significantly easier to access vast stores of data. People are also continuing to use the internet and explore the web at an exponential rate so that the effect of data mining by 2020 will mean that roughly five billion of the world's seven and a half billion people will be affected. After about 2020, integrate circuits will be so advanced and tiny, that many predict Moore's law will be inapplicable to circuitry, but will continue to dictate the conventions of nanotechnology and biochips.

Data mining has more practical examples, too. The products you've bought off Amazon, for example, are analyzed by data miners at that company, to show you similar products that you may be interested in. Applied more widely, a restaurant chain could determine what customers buy and when they visit in order to tailor their menu to fit the tastes of the public at large, as well as to invent and supply new dishes and offer specials. This is called class data mining. A deal of the day site could target its giveaway of the day to a certain segment of the population that visits its site. If it knows that most people visit its site searching for technology-related items, chances are it will offer more of those items instead of a clothing or travel deal of the day. This is called cluster data mining. Association mining is a logical rule followed by supermarkets such that if a customer buys bread and butter, he will is likely to also buy milk.

Data mining involves statistics which determine what customers will buy over the course of thousands and millions of interactions. In effect, this is what makes technology seem smarter. The logical and statistical formulae humans implement make these rules widely applicable and largely sensible. The applications of data mining are various and exciting. In the future, the internet will be that much closer to reading your mind.


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Data Extraction Software Explained in Plain English

Data Extraction Software is designed to automatically collect data from web pages. A lot of money can be made with Data Extraction Software, but there are two types of program - custom made and typical.

Custom made solutions are designed by developers to extract from one particular source and they can't automatically adjust to another. So for example, if we are to create a custom-build data extraction program for website A, it won't work for website B, because they have different structures. Such custom made solutions cost more money than the standard ones, but they are designed for more complicated and unique situations.

Every data extraction program is based on an algorithm that has to be programmed in such a way that it will collect all the needed data from a given website. The reason why data extraction is so popular is that it saves on manual labor which can become expensive when outsourced. Data extraction software automates repetitive operations. For example, if you want to extract just the emails of every user in a given website, then you will have to pay a person to repeat the same steps over and over again like a robot. Those steps will most likely include clicking on the same place, then copying to the clipboard a piece of data that always resides on the same place on the screen.

Data extraction software is based on certain constants. By constants, I mean certain facts about a given program that do not change, no matter what. This is perhaps the only drawback of this type of software. But for the time being, it's the only way. The other way is to use artificial intelligence and make software programs think and make decisions like humans. They would have to adapt to new systems and it's almost unthinkable to consider such complicated solutions unless you are working on a very large scale.

The Bottom line here is that data extraction software is able to automate cycled operations that are otherwise expensive if handled by humans. Although the initial investment of money and time might seem expensive, it's definitely worth it in the long run, because your customized software will do the job in much less time, without the need for any human intervention.

So, if you are working on any task on the Internet, and you feel that task could be automated, then data extraction software maybe the solution you have been looking for.



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Online Data Entry Services

Online data entry services are now commonly used by businesses and these services are generally offered by outsourcing companies with the required standards and specifications. As everything is becoming global, business entities need to manage their valuable and critical data in an accurate and organized manner in order to maintain their competitiveness in the global marketplace. They usually entrust their non core, repetitive and other support tasks to BPO firms who can offer affordable, reliable and trustworthy documentation services online.

Online data entry services have become immensely helpful in all fields where the data needs to be stored, maintained and used for future applications. Today, many firms have partnered with business process outsourcing companies to have an excellent data management system in their facilities. By integrating state-of-the-art technologies, unique processes and skilled data entry specialists, these firms deliver data entry services with accuracy, efficiency and effectiveness. They offer their services through safe and secure online platform. They deliver the final outputs in encrypted FTP upload, CD-R or CD-W or email. Thus, clients are assured that their data or information is free from unauthorized access, copying or downloading.

Business process outsourcing companies specializing in online data entry services offer a wide spectrum of services, tailored to the particular needs of each client. Some of them are listed below:

o Text, numeric or alphanumeric, image or hardcopy date entry
o Data entry from handwritten or printed materials such as books, newspapers, magazines
o Catalog and business card documentation
o E-books and e-magazines
o Data entry from insurance claims and property tax records
o Online listing of yellow pages
o For website content
o Documentation of surveys, questionnaires, company reports and airway bill entries
o Data capture/collection
o Online form processing and submission
o For mailing list/mailing label
o Email mining
o Typing manuscript into MS Word
o Online copying, pasting, editing, sorting, and indexing data
o Online medical and legal data entry
o Data entry of historical data

Outsourcing your documentation task to a BPO firm is a viable and economical choice. You can eliminate tedious and time consuming tasks from your regular routine. As data entry services are developing in tune with the giant leaps in technology, your firm can also utilize these services and stay competitive in the field. Moreover, you can reduce costs, improve productivity and give more importance to core and revenue generating functions.



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Can You Use Data Mining to Determine What Internet Marketing Tactic Works the Best?

Data mining in general is a sequence that analyzes and collects data from people about something that they are already doing. In essence it collects information from people about things that they normally do, for instance you can do a survey on how many people enjoy making money on the internet. In depth you can collect the demographics on how great a marketing method is preforming for other marketers that are using that same method fir there businesses and see if you should get involved with it as well.

You can also use data mining to collect intel on products that are out there in the market, and you can use the results to compare to your products, if you have any. Data mining can be used as a tool that can help you analyze information that you are already accessing, but with this tool you can funnel it in the right direction. This direction is to help depict information that you are going to need to strategically market your business on the internet or maybe target a specific group in which you can generate sales from.

How can a aspiring entrepreneur in the network marketing industry apply data mining in there small business?

First off, for any new marketer entering the industry there is a series of questions that the marketer have to ask in order for he or she can do a synopsis for his niche. Questions marketers can go out and ask in a survey is, What are your interests? Or what do you do for a living? Do you like what you do for a living? He or she can go as far as, Have you ever thought of owning your business? or How many people are interested being financially independent? These question are to basically give the marketer feel for what there niche is looking for, and how he can position the business, in a strategic way.

Many top marketers use data mining to compare tactics to see what would be a smarter way to promote there businesses. I'm sure you've those annoying pop up promotions that are all over the internet, that say " Get paid to do this survey " Well those are marketers out there that are using data mining to get information for there business. Now I'm not saying go out there and begin spamming everyone that comes on your website your products, because at the end of the day, spam is spam, I do not recommend spamming. But I do recommend using data mining to your advantage.

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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.


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