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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Data Mining for Dollars

The more you know, the more you're aware you could be saving. And the deeper you dig, the richer the reward.

That's today's data mining capsulation of your realization: awareness of cost-saving options amid logistical obligations.

According to global trade group Association for Information and Image Management (AIIM), fewer than 25% of organizations in North America and Europe are currently utilizing captured data as part of their business process. With high ease and low cost associated with utilization of their information, this unawareness is shocking. And costly.

Shippers - you're in prime position to benefit the most by data mining and assessing your electronically-captured billing records, by utilizing a freight bill processing provider, to realize and receive significant savings.

Whatever your volume, the more you know about your transportation options, throughout all modes, the easier it is to ship smarter and save. A freight bill processor is able to offer insight capable of saving you 5% - 15% annually on your transportation expenditures.

The University of California - Los Angeles states that data mining is the process of analyzing data from different perspectives and summarizing it into useful information - knowledge that can be used to increase revenue, cuts costs, or both. Data mining software is an analytical tool that allows investigation of data from many different dimensions, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations among dozens of fields in large relational databases. Practically, it leads you to noticeable shipping savings.

Data mining and subsequent reporting of shipping activity will yield discovery of timely, actionable information that empowers you to make the best logistics decisions based on carrier options, along with associated routes, rates and fees. This function also provides a deeper understanding of trends, opportunities, weaknesses and threats. Exploration of pertinent data, in any combination over any time period, enables you the operational and financial view of your functional flow, ultimately providing you significant cost savings.

With data mining, you can create a report based on a radius from a ship point, or identify opportunities for service or modal shifts, providing insight regarding carrier usage by lane, volume, average cost per pound, shipment size and service type. Performance can be measured based on overall shipping expenditures, variances from trends in costs, volumes and accessorial charges.

The easiest way to get into data mining of your transportation information is to form an alliance with a freight bill processor that provides this independent analytical tool, and utilize their unbiased technologies and related abilities to make shipping decisions that'll enable you to ship smarter and save.





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The Benefits of Data Mining

Data mining can truly help a business reach its fullest potential. It is a way to assess how business is being affected by certain characteristics, and can help business owners increase their profits and avoid making business mistakes down the line. Essentially, through this process, a business is analyzing certain data from different perspectives in order to get a full rounded view of how their company is doing. Business owners can get a broad perspective on things such as customer trending, where they are losing money and where they are making money. The information can also reveal ways that can help a business cut unneeded costs and can help them increase their overall income.

Data mining software is one tool that can help a company assess and analyze their data in more efficient terms. It can be extremely user friendly and allow people to delve into their data from a variety of different angles and points of view. In more technical terms, data mining software allows you to see the correlations and patterns of one's own data compared with those across many other regional databases.

People have been using data mining for many years in different formats. Only since the technology has become available has data software been used. But there have been many ways in the past for companies to assess their data and use it to their advantage. By taking polls, or using store scanners, product codes and bar codes, people have been able to gather data, analyze it and use it to their advantage. But it cannot be denied that the availability of greater technology has greatly increased the ability to store or gather data, make predictions about outcomes and use customer trend reports to greater advantages. The ability to store infinite amounts of data has given business owners a great advantage and truly has helped increase sales and lower costs. This data mining has actually led to data being stored in data warehouses. In data warehouses, various organizations will integrate their mined data into one large data warehouse. The information accessible in data warehouses is available to further help companies reduce risk taking and integrate proper selling techniques to improve business.

Data mining also can allow companies to see where their best selling points are and give them the opportunity to take advantage of this information. For example, if a pharmacy places a display of lip balm at the cashier counter, data mining can detect how many people bought lip balm from the cashier counter rather people who bought the lip balm when it was placed at another point in the store. Data mining can determine where the most effective points of sale are throughout a store or if a certain promotion went well one time of the month, but did not go well at another time of the month. Companies can make offers based on the buying habits of their customers as well.

Data mining can truly help businesses reach their highest profitability by paying attention to customer trending.

Improving your overall business performance is never easy. However, new innovations in data mining software can increase your information forecasting capabilities and enhance your profit drivers as well!




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Web Data Extraction Services and Data Collection Form Website Pages

For any business market research and surveys plays crucial role in strategic decision making. Web scrapping and data extraction techniques help you find relevant information and data for your business or personal use. Most of the time professionals manually copy-paste data from web pages or download a whole website resulting in waste of time and efforts.

Instead, consider using web scraping techniques that crawls through thousands of website pages to extract specific information and simultaneously save this information into a database, CSV file, XML file or any other custom format for future reference.

Examples of web data extraction process include:
• Spider a government portal, extracting names of citizens for a survey
• Crawl competitor websites for product pricing and feature data
• Use web scraping to download images from a stock photography site for website design

Automated Data Collection
Web scraping also allows you to monitor website data changes over stipulated period and collect these data on a scheduled basis automatically. Automated data collection helps you discover market trends, determine user behavior and predict how data will change in near future.

Examples of automated data collection include:
• Monitor price information for select stocks on hourly basis
• Collect mortgage rates from various financial firms on daily basis
• Check whether reports on constant basis as and when required

Using web data extraction services you can mine any data related to your business objective, download them into a spreadsheet so that they can be analyzed and compared with ease.

In this way you get accurate and quicker results saving hundreds of man-hours and money!

With web data extraction services you can easily fetch product pricing information, sales leads, mailing database, competitors data, profile data and many more on a consistent basis.




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How Data Mining Can Help in Customer Relationship Management Or CRM?

Customer relationship management (CRM) is critical activity of improvising customer interactions while at the same time making the interactions more amicable through individualization. Data mining utilizes various data analysis and modeling methods to detect specific patterns and relationships in data. This helps in understanding what a customer wants and forecasting what they will do.

Using Data mining you can find out right prospects and offer them right products. This results in improved revenue because you can respond to each customer in best way using fewer resources.

Basic process of CRM data mining includes:
1. Define business objective
2. Construct marketing database
3. Analyze data
4. Visualize a model
5. Explore model
6. Set up model & start monitoring

Let me explain above steps in detail.

Define the business objective:
Every CRM process has one or more business objective for which you need to construct the suitable model. This model varies depending on your specific goal. The more precise your statement for defining the problem is the more successful is your CRM project.

Construct a marketing database:
This step involves creation of constructive marketing database since your operational data often don't contain the information in the form you want it. The first step in building your database is to clean it up so that you can construct clean models with accurate data.

The data you need may be scattered across different databases such as the client database, operational database and sales databases. This means you have to integrate the data into a single marketing database. Inaccurately reconciled data is a major source of quality issues.

Analyze the data:
Prior to building a correct predictive model, you must analyze your data. Collect a variety of numerical summaries (such as averages, standard deviations and so forth). You may want to generate a cross-section of multi-dimensional data such as pivot tables.

Graphing and visualization tools are a vital aid in data analysis. Data visualization most often provides better insight that leads to innovative ideas and success.




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Know What the Truth Behind Data Mining Outsourcing Service

We came to that, what we call the information age where industries are like useful data needed for decision-making, the creation of products - among other essential uses for business. Information mining and converting them to useful information is a part of this trend that allows companies to reach their optimum potential. However, many companies that do not meet even one deal with data mining question because they are simply overwhelmed with other important tasks. This is where data mining outsourcing comes in.

There have been many definitions to introduced, but it can be simply explained as a process that involves sorting through large amounts of raw data to extract valuable information needed by industries and enterprises in various fields. In most cases this is done by professionals, professional organizations and financial analysts. He has seen considerable growth in the number of sectors or groups that enter my self.
There are a number of reasons why there is a rapid growth in data mining outsourcing service subscriptions. Some of them are presented below:

A wide range of services

Many companies are turning to information mining outsourcing, because they cover a wide range of services. These services include, but are not limited to data from web applications congregation database, collect contact information from different sites, extract data from websites using the software, the sort of stories from sources news, information and accumulate commercial competitors.

Many companies fall

Many industries benefit because it is fast and realistic. The information extracted by data mining service providers of outsourcing used in crucial decisions in the field of direct marketing, e-commerce, customer relationship management, health, scientific tests and other experimental work, telecommunications, financial services, and a whole lot more.

A lot of advantages

Subscribe data mining outsourcing services it's offers many benefits, as providers assures customers to render services to world standards. They strive to work with improved technologies, scalability, sophisticated infrastructure, resources, timeliness, cost, the system safer for the security of information and increased market coverage.

Outsourcing allows companies to focus their core business and can improve overall productivity. Not surprisingly, information mining outsourcing has been a first choice of many companies - to propel the business to higher profits.



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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.

Frank Poladi is the author of this article about data mining in the 21st century. In this article he gives his readers insight on the world of data mining and using it with business intelligence solutions. He notes that to make sense of all this data enterprise reporting is a major factor as well.



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