What's Your Excuse For Not Using Data Mining?

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

The most important data to collect for data mining include:

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

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

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

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

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

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

Preparation includes:

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

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

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

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


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Unleash the Hidden Potential of Your Business Data With Data Mining and Extraction Services

Every business, small or large, is continuously amassing data about customers, employees and nearly every process in their business cycle. Although all management staff utilize data collected from their business as a basis for decision making in areas such as marketing, forecasting, planning and trouble-shooting, very often they are just barely scratching the surface. Manual data analysis is time-consuming and error-prone, and its limited functions result in the overlooking of valuable information that improve bottom-lines. Often, the sheer quantity of data prevents accurate and useful analysis by those without the necessary technology and experience. It is an unfortunate reality that much of this data goes to waste and companies often never realize that a valuable resource is being left untapped.

Automated data mining services allow your company to tap into the latent potential of large volumes of raw data and convert it into information that can be used in decision-making. While the use of the latest software makes data mining and data extraction fast and affordable, experienced professional data analysts are a key part of the data mining services offered by our company. Making the most of your data involves more than automatically generated reports from statistical software. It takes analysis and interpretation skills that can only be performed by experienced data analysis experts to ensure that your business databases are translated into information that you can easily comprehend and use in almost every aspect of your business.

Who Can Benefit From Data Mining Services?

If you are wondering what types of companies can benefit from data extraction services, the answer is virtually every type of business. This includes organizations dealing in customer service, sales and marketing, financial products, research and insurance.

How is Raw Data Converted to Useful Information?

There are several steps in data mining and extraction, but the most important thing for you as a business owner is to be assured that, throughout the process, the confidentiality of your data is our primary concern. Upon receiving your data, it is converted into the necessary format so that it can be entered into a data warehouse system. Next, it is compiled into a database, which is then sifted through by data mining experts to identify relevant data. Our trained and experienced staff then scan and analyze your data using a variety of methods to identify association or relationships between variables; clusters and classes, to identify correlations and groups within your data; and patterns, which allow trends to be identified and predictions to be made. Finally, the results are compiled in the form of written reports, visual data and spreadsheets, according to the needs of your business.

Our team of data mining, extraction and analyses experts have already helped a great number of businesses to tap into the potential of their raw data, with our speedy, cost-efficient and confidential services. Contact us today for more information on how our data mining and extraction services can help your business.


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4 Types of Outsourcing Data Entry Services

In present era of globalization, it is required for any type of business to manage all data and information handy and easy accessible. Data entry is a best option with its multitude advantages but it consumes your times. In this competitive business world no one can afford time so outsourcing is become most favorite term. And data entry services are become most popular term for outsourcing.

Internet and batter communication strategies made data entry outsourcing easier. Low pricing, rapid service and accurate result also attract business for outsourcing. There are many types of data entry services available in market depth here we are talking about most important 4 types as defined as below:

Online data entry: It is a process of entering information into online databases or applications. This service includes medical forms, shipping documents, insurance claims, e-books and catalogs data entry. Outsourcing companies have reliable resources like high-speed broadband connection and well configured computer system to accomplish the task rapidly and accurately.

Offline data entry: It includes offline form filling, offline database entry, URL list collection, offline data collection etc. It is most requirements of various types of businesses like telecoms, medical, insurance, social, commercial, financial and others. To complete this task speedily, offshore outsourcing company have skilled experts with good typing speed and latest IT equipments.

Numeric data entry: It is a process of managing digits or numeric information and data into various formats like HTML, XML, EXCEL, WORD and Access. In this service includes medical billing, examination results, identity details, business reports, survey report, estimated budget, numeric information and more... It is very complicated task, outsourcing company make it easier with its expertise. For outsourcing just send requirements in any format and sure get quality output.

Textual data entry: It is mainly used for E-book creation as it is easy to keep and easy to access anywhere. It involves mailing lists, word processing, yellow page listings, manuscript typing, e-books and legal documents. This service offer outputs in various formats like HTML, Frame Maker, XML, PDF, GIF, JPG, TIFF, PageMaker, Excel, Word and QuarkXPress.

All above services is vital for any sized business and organization. With the help of IT outsourcing services you can get effective solution with huge savings of time and cost.



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Advantages of Outsourcing Data Conversion Services

Data conversion is the process of converting data from one format to another. In this era of IT revolution, data conversion services is a vital tool in getting information on finger tips. It has acquired a unique place in this internet driven, fast growing business world.

It gives handiness and security to business organizations in managing, updating and retrieving data. This services help firms to convert their precious data and gather papers into digital format for long-term storage. The data can be stored for the purpose of archiving, easy searching, accessing and sharing.

More and more highly experienced BPO companies are coming into this market providing full range of reliable and trustworthy data conversion services to their clients worldwide. These BPO companies are fully prepared with excellent infrastructure and skilled manpower as per clients' expectations and specifications.

Some of the data conversion services which are available in market are as follows:

    Document conversion
    HTML conversion
    XML conversion
    SGML conversion
    CAD conversion
    Image Conversion
    Book conversion
    PDF conversion
    Catalog conversion
    MS Excel conversion
    Indexing
    OCR / ICR Clean up, OMR

It is a process of changing bits from one format to another in order to get relative interoperability or ability to use new features. By outsourcing  this services companies can minimize the risk, cut down costs and thereby focus on their core issues. Offshore BPO companies are consistent, simple and one stop solution provider.

Advantages of outsourcing data conversion services:

    Focus on core business activities
    Avoids paper work
    Cuts down operating expenses
    Promotes business as effectively as possible
    Eliminates data redundancy
    Easy accessibility of data at any time
    Systemizes company's data in simpler format

If you are planning to outsource this kind of task to an external service provider, better make sure that the provider is consistent in quality, productivity and customer service operations. Automating any business by conversion services definitely increases the productivity of that company.


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What You Should Know About Data Mining

Often called data or knowledge discovery, data mining is the process of analyzing data from various perspectives and summarizing it into useful information to help beef up revenue or cut costs. Data mining software is among the many analytical tools used to analyze data. It allows categorizing of data and shows a summary of the relationships identified. From a technical perspective, it is finding patterns or correlations among fields in large relational databases. Find out how data mining works and its innovations, what technological infrastructures are needed, and what tools like phone number validation can do.

Data mining may be a relatively new term, but it uses old technology. For instance, companies have made use of computers to sift through supermarket scanner data - volumes of them - and analyze years' worth of market research. These kinds of analyses help define the frequency of customer shopping, how many items are usually bought, and other information that will help the establishment increase revenue. These days, however, what makes this easy and more cost-effective are disk storage, statistical software, and computer processing power.

Data mining is mainly used by companies who want to maintain a strong customer focus, whether they're engaged in retail, finance, marketing, or communications. It enables companies to determine the different relationships among varying factors, including staffing, pricing, product positioning, market competition, and social demographics.

Data mining software, for example, vary in types: statistical, machine learning, and neural networks. It seeks any of the four types of relationships: classes (stored data is used for locating data in predetermined groups), clusters (data are grouped according to logical relationships or consumer preferences), associations (data is mined to identify associations), and sequential patterns (data is mined to estimate behavioral trends and patterns). There are different levels of analysis, including artificial neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction, and data visualization.

In today's world, data mining applications are available on all size systems from client/server, mainframe, and PC platforms. When it comes to enterprise-wide applications, the size usually ranges from 10 gigabytes to more than 11 terabytes. The two important technological drivers are the size of the database and query complexity. A more powerful system is required with more data being processed and maintained, and with more complex and greater queries.

Programmable XML web services like phone number validation will assist your company in improving the quality of your data needed for data mining. Used to validate phone numbers, a phone number validation service allows you to improve the quality of your contact database by eliminating invalid telephone numbers at the point of entry. Upon verification, phone number and other customer information can work wonders for your business and its constant improvement.



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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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Enjoy Valuable Advantages of Finding Professional Online Data Entry Services

Outsourcing is eyed as a cost-effective means to make the business cycle run. The market consists of a lot of heartened buyers who have enjoyed the fruits of outsourcing by compensating a trivial sum to online data entry service providers. They have felt that the sum they shelled out to these services is quite insignificant when compared to the work they got completed by doing so. Of late, its effect among corporate people is so huge that even those who did not prefer to outsource their projects have embraced this practice realizing quite a few of the several advantages that it has in store. Online Data Entry Services is subcontracted to a lot of individuals and other smaller business units that take such projects as their prime source of occupation.

Many services are distributed to companies who approach these online data entry service providers. Some of the commonly used services are web research, mortgage research, product entry and lastly data mining and extraction services. Adept professionals are at your service in these service providers as those who run such units strongly believe in deploying a team of skilled professionals to help clients realize results as quick as possible. Moreover, the systems that are up for utilization in these units are technically advanced both in terms of utility and security hence you need not fear for having outsourced some crucial data sheets belonging to your company. These providers value your information as how they treasure you association and hence you need not actually care a lot about the confidentiality of your information.

Business firms can look forward to receiving high-class data entry from the hands of online data entry services that undertake such projects. Some of the below-mentioned points are a short listing of what interests business in subcontracting the work to professionals.

    Keying in the data happens to be the first phase at the end of which the companies get understandable information to make strategic decisions with. What appeared as raw data represented by mere numbers some time ago is a pointer or a guide, at present, to accelerate business progress.
    Systems being used for such processes offer complete protection to the information.
    As chances of obtaining high quality information rises, the company's business executive is expected to arrive at excellent decisions that reflect on the company's better performance in future.
    Turnaround time is considerably shortened.
    Cost-effective approach does hold a lot of substance since it considerably decreases the operational overheads related to data entry services within the business wing of the company itself.

Saving money and time holds a unique advantage and outsourcing of such online data entry services proffers these businesses this distinctive edge. Thriving companies intend to focus on their core operations instead of delving into such non-core activities, which do not weigh as good as other essential industrial operations that they need to look after. Why should one take and put these chores on themselves when some professionals who are capable of delivering effective results can be picked from the outsourcing market.


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Data Mining

Data mining is the retrieving of hidden information from data using algorithms. Data mining helps to extract useful information from great masses of data, which can be used for making practical interpretations for business decision-making. It is basically a technical and mathematical process that involves the use of software and specially designed programs. Data mining is thus also known as Knowledge Discovery in Databases (KDD) since it involves searching for implicit information in large databases. The main kinds of data mining software are: clustering and segmentation software, statistical analysis software, text analysis, mining and information retrieval software and visualization software.

Data mining is gaining a lot of importance because of its vast applicability. It is being used increasingly in business applications for understanding and then predicting valuable information, like customer buying behavior and buying trends, profiles of customers, industry analysis, etc. It is basically an extension of some statistical methods like regression. However, the use of some advanced technologies makes it a decision making tool as well. Some advanced data mining tools can perform database integration, automated model scoring, exporting models to other applications, business templates, incorporating financial information, computing target columns, and more.

Some of the main applications of data mining are in direct marketing, e-commerce, customer relationship management, healthcare, the oil and gas industry, scientific tests, genetics, telecommunications, financial services and utilities. The different kinds of data are: text mining, web mining, social networks data mining, relational databases, pictorial data mining, audio data mining and video data mining.

Some of the most popular data mining tools are: decision trees, information gain, probability, probability density functions, Gaussians, maximum likelihood estimation, Gaussian Baves classification, cross-validation, neural networks, instance-based learning /case-based/ memory-based/non-parametric, regression algorithms, Bayesian networks, Gaussian mixture models, K-Means and hierarchical clustering, Markov models, support vector machines, game tree search and alpha-beta search algorithms, game theory, artificial intelligence, A-star heuristic search, HillClimbing, simulated annealing and genetic algorithms.

Some popular data mining software includes: Connexor Machines, Copernic Summarizer, Corpora, DocMINER, DolphinSearch, dtSearch, DS Dataset, Enkata, Entrieva, Files Search Assistant, FreeText Software Technologies, Intellexer, Insightful InFact, Inxight, ISYS:desktop, Klarity (part of Intology tools), Leximancer, Lextek Onix Toolkit, Lextek Profiling Engine, Megaputer Text Analyst, Monarch, Recommind MindServer, SAS Text Miner, SPSS LexiQuest, SPSS Text Mining for Clementine, Temis-Group, TeSSI®, Textalyser, TextPipe Pro, TextQuest, Readware, Quenza, VantagePoint, VisualText(TM), by TextAI, Wordstat. There is also free software and shareware such as INTEXT, S-EM (Spy-EM), and Vivisimo/Clusty.



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Web Mining

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

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

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

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

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


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Business Intelligence Data Mining

Data mining can be technically defined as the automated extraction of hidden information from large databases for predictive analysis. In other words, it is the retrieval of useful information from large masses of data, which is also presented in an analyzed form for specific decision-making.

Data mining requires the use of mathematical algorithms and statistical techniques integrated with software tools. The final product is an easy-to-use software package that can be used even by non-mathematicians to effectively analyze the data they have. Data Mining is used in several applications like market research, consumer behavior, direct marketing, bioinformatics, genetics, text analysis, fraud detection, web site personalization, e-commerce, healthcare, customer relationship management, financial services and telecommunications.

Business intelligence data mining is used in market research, industry research, and for competitor analysis. It has applications in major industries like direct marketing, e-commerce, customer relationship management, healthcare, the oil and gas industry, scientific tests, genetics, telecommunications, financial services and utilities. BI uses various technologies like data mining, scorecarding, data warehouses, text mining, decision support systems, executive information systems, management information systems and geographic information systems for analyzing useful information for business decision making.

Business intelligence is a broader arena of decision-making that uses data mining as one of the tools. In fact, the use of data mining in BI makes the data more relevant in application. There are several kinds of data mining: text mining, web mining, social networks data mining, relational databases, pictorial data mining, audio data mining and video data mining, that are all used in business intelligence applications.

Some data mining tools used in BI are: decision trees, information gain, probability, probability density functions, Gaussians, maximum likelihood estimation, Gaussian Baves classification, cross-validation, neural networks, instance-based learning /case-based/ memory-based/non-parametric, regression algorithms, Bayesian networks, Gaussian mixture models, K-means and hierarchical clustering, Markov models and so on.


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Should I Really Outsource My Data Entry?

What is Data Entry?

Data entry is the process of entering in various forms of data into an electronic format normally through a computer. As the world continues to advance in technology, more and more businesses are transitioning from the old school way of manual data management to relying on computers to empower these operational needs. Data entry services provide businesses the ability to transfer data from physical paper form into various electronic formats which then can be easily accessed through a computer. Among the different types of data management solutions businesses seek include inventory management, customer database management and sales record management, just to name a few.

Why outsource?

Data entry can be a very tedious and time consuming process. Many businesses today just do not have the time and budget to support such a tedious yet crucial process. Outsourcing your needs will not only save you on the cost of hiring employees internally, it will also help increase productivity. Most outsourcing companies have hundreds of operators and have the capability to run at full production capacity. Most companies just cannot support this if they hired internally. Turn around times can increase by as much as 300% by outsourcing your data entry needs and reduce cost up to 75% of normal domestic salary cost.

What to look for in an outsourcing company?

Probably the biggest concern with outsourcing your internal business processes is the quality of the services provided. When looking for a partner company to outsource to, make sure you choose a company with vast expertise and experience. Ask for previous clients they have worked with and ask for samples. A company that has been in the industry longer will have the resources and experience to provide you with a higher level of quality service.

Another concern of outsourcing is communication. Most outsourcing companies are located abroad in various parts of Asia and in different time zones. Communication problems could arise if you do not choose a company that has a process in place to deal with this. Some things to look out for are the availability of direct phone numbers, instant messaging support, and direct emails.


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Data Mining For Professional Service Firms - The Marketing Mother Lode May Already Be in Your Files

No one needs to tell you about the value of information in today's world--particularly the value of information that could help grow your practice. But has it occurred to you that you probably have more information in your head and your existing files that you realize? Tap into this gold mine of data to develop a powerful and effective marketing plan that will pull clients in the door and push your profitability up.

The way to do this is with data mining, which is the process of using your existing client data and demographics to highlight trends, make predictions and plan strategies.

In other words, do what other kinds of businesses have been doing for years: Analyze your clients by industry and size of business, the type and volume of services used, the amount billed, how quickly they pay and how profitable their business is to you. With this information, you'll be able to spot trends and put together a powerful marketing plan.

To data mine effectively, your marketing department needs access to client demographics and financial information. Your accounting department needs to provide numbers on the services billed, discounts given, the amounts actually collected, and receivables aging statistics. You may identify a specific service being utilized to a greater than average degree by a particular industry group, revealing a market segment worth pursuing. Or you may find an industry group that represents a significant portion of your billed revenue, but the business is only marginally profitable because of write-offs and discounts. In this case, you may want to shift your marketing focus.

You should also look at client revenues and profitability by the age of the clients. If your percentage of new clients is high, it could mean you're not retaining a sufficient number of existing clients. If you see too few new clients, you may be in for problems when natural client attrition is not balanced by new client acquisition.

The first step in effective data mining is to get everyone in the firm using the same information system. This allows everyone in the office who needs the names and addresses of the firm's clients and contacts to have access to that data. Require everyone to record notes on conversations and meetings in the system. Of course, the system should also accommodate information that users don't want to share, such as client's private numbers or the user's personal contacts. This way, everyone can utilize the system for everything, which makes them more likely to use it completely.

Your information system can be either contact information or customer relationship management software (a variety of packages are on the market) or you can have a system custom designed. When considering software to facilitate data mining, look at three key factors:

1. Ease of use. If the program isn't easy to use, it won't get used, and will end up being just a waste of time and money.

2. Accessibility. The system must allow for data to be accessible from anywhere, including laptops, hand-held devices, from the internet or cell phones. The data should also be accessible from a variety of applications so it can be used by everyone in the office all the time, regardless of where they are.

3. Sharability. Everyone needs to be able to access the information, but you also need privacy and editing rights so you can assign or restrict what various users can see and input.

Don't overlook the issue of information security. Beyond allowing people the ability to code certain entries as private, keep in mind that anyone with access to the system as the ability to either steal information or sabotage your operation. Talk to your software vendor about various security measures but don't let too much security make the system unusable. Protect yourself contractually with noncompete and nondisclosure agreements and be sure to back up your data regularly.

Finally, expect some staffers to resist when you ask them to change from the system they've been using. You may have to sell them on the benefits outweighing the pain of making a change and learning the new system--which means you need to be totally sold on it yourself. The managing partner, or the leader of the firm, needs to be driving this initiative for it to succeed. When it does succeed, you'll be able to focus your marketing dollars and efforts in the most profitable areas with the least expense, with a tremendous positive impact on the bottom line.


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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.

TonyRocks.com in Pittsburgh Pennsylvania is one of only a few companies in the region that offer data tools an strategies.

They also keep a nice and updated list of the the latest on new tools in integration strategies for your organization.


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Data Extraction Services For Better Outputs in Your Business

Data Extraction can be defined as the process of retrieving data from an unstructured source in order to process it further or store it. It is very useful for large organizations who deal with large amount of data on a daily basis that need to be processed into meaningful information and stored for later use. The data extraction is a systematic way to extract and structure data from scattered and semi-structured electronic documents, as found on the web and in various data warehouses.

In today's highly competitive business world, vital business information such as customer statistics, competitor's operational figures and inter-company sales figures play an important role in making strategic decisions. By signing on this service provider, you will be get access to critivcal data from various sources like websites, databases, images and documents.

It can help you take strategic business decisions that can shape your business' goals. Whether you need customer information, nuggets into your competitor's operations and figure out your organization's performance, it is highly critical to have data at your fingertips as and when you want it. Your company may be crippled with tons of data and it may prove a headache to control and convert the data into useful information. Data extraction services enable you get data quickly and in the right format.

Few areas where Data Extraction can help you are:

    Capturing financial data
    Generating better sales leads
    Conducting market research, survey and analysis
    Conducting product research and analysis
    Track, extract and harvest product pricing data
    Searching for specific job postings
    Duplicating an online database
    Acquiring real estate data
    Processing auction information
    Searching online newspapers for latest pricing information
    Extracting and summarize news stories from online news sources

Outsourcing companies provide custom made data extraction services to the client's requirements. The different types of data extraction services;

    Web extraction
    Database extraction

Outsourcing is the beneficial option for large organizations seeking to manage large information. Outsourcing this services helps businesses in managing their data effectively, which in turn enables business to experience an increase in profits. By outsourcing, you can certainly increase your competitive edge and save costs too!



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Data Mining - Critical for Businesses to Tap the Unexplored Market

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

What Data Mining is and what it is not?

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

The 'First-mover Advantage'

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

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

The data mining algorithms and challenges

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

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

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


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