Son_of_Perdition
Senior Member
I use Firefox for my browser, I installed the ABP app to block advertising. This morning I added 'Ghostery' on a lark. I'm learning the app and all it can do. It's amazing what it catches as far as the websites that track your browsing. I'm in the process of disabling just the advertising data miners. My point being that ABP doesn't get down deep enough for my paranoia. The journey continues.....
Data Mining
Over the last decade, advances in processing power and speed have enabled companies to move beyond manual, tedious and time-consuming practices to quick, easy and automated data analysis. The more complex the data sets collected, the more potential there is to uncover relevant insights. Retailers, banks, manufacturers, telecommunications providers and insurers, among others, are using data mining to discover relationships among everything from pricing, promotions and demographics to how the economy, risk, competition and social media are affecting their business models, revenues, operations and customer relationships.
There have been studies done that show the general public has resigned themselves to fact that giving your personal information is almost unavoidable and will hand over their identification more readily if they deem it is necessary to make a purchase of sign up for a service. You have rewards cards for many stores that mine data when it's swiped. I've found that most of the rewards are not worth their effort. You shop at warehouse box stores handing over you card mindlessly so they can extract and analyze your buying habits and what days you are apt buy a given product.
Banking
The tremendous increase in the power of information technology will enable banks to tap existing information systems, also known as legacy systems, and mine useful management information and insights from the data stored in them. This process can be done without the need to change the current systems and the data they generate. But before data mining can proceed, a data warehouse will have to be created first. Data warehousing is the process of extracting, cleaning, transforming, and standardizing incompatible data from the bank's current systems so that these data can be mined and analyzed for useful patterns, relationships, and associations. The data warehouse need not be updated as regularly or daily as the transaction based systems. Data warehouses can be updated and mined as infrequently as the need for management reports and decisions dictate, i.e., monthly, quarterly, or on a ad hoc basis. Data warehousing and mining can run parallel with banking transaction information systems, without intrusion and interruptions.
Retail Shopping
One of the earliest application of data mining was in retail supermarket. Mining the volumes of point of sale (POS) data generated daily by cash registers, the store management analyzed the housewife's shopping basket, and discovered which items were often bought together. This knowledge led to changes in store layout the brought the related items physically closer and better promotions that packaged and sold the related items together. The knowledge discovered also led to better stocking and inventory management. Retailers like WalMart have experienced sales increase as much as 20% after extensively applying data mining. Some frequently bought item pairs discovered by data mining may be obvious, like toothbrush and toothpaste, wine and cheese, chips and soda. Some were unexpected and bizarre like disposable diapers and beer on Friday nights. Using credit cards enables information to used for mailings, offers based upon your purchases and gathering information about your spending habits. Cash is hard to track! Giving out too much information helps retailers decide what is best for you.
Insurance
The potential of data mining can be of immense importance to insurance companies. A car insurance company wants to create a prediction model to predict the probability of a car accident happening within a certain period of time on the basis of customer data which is available at the time of signing the insurance policy (e.g. personal data, attributes of the car to be insured, history of accidents.). Looking at the past it is known whether past customers had an accident within a certain period of time or not. Past customers are split into different classes with respect to the costs of their claims. Therefore, a data table is available with each data record representing the data of a past customer at the beginning of a year and the customer’s claim class in that year. The prediction model is created using this data table. The prediction model also reveals interesting customer segments with a high risk of belonging to a bad claim class.
Data Mining
Over the last decade, advances in processing power and speed have enabled companies to move beyond manual, tedious and time-consuming practices to quick, easy and automated data analysis. The more complex the data sets collected, the more potential there is to uncover relevant insights. Retailers, banks, manufacturers, telecommunications providers and insurers, among others, are using data mining to discover relationships among everything from pricing, promotions and demographics to how the economy, risk, competition and social media are affecting their business models, revenues, operations and customer relationships.
There have been studies done that show the general public has resigned themselves to fact that giving your personal information is almost unavoidable and will hand over their identification more readily if they deem it is necessary to make a purchase of sign up for a service. You have rewards cards for many stores that mine data when it's swiped. I've found that most of the rewards are not worth their effort. You shop at warehouse box stores handing over you card mindlessly so they can extract and analyze your buying habits and what days you are apt buy a given product.
Banking
The tremendous increase in the power of information technology will enable banks to tap existing information systems, also known as legacy systems, and mine useful management information and insights from the data stored in them. This process can be done without the need to change the current systems and the data they generate. But before data mining can proceed, a data warehouse will have to be created first. Data warehousing is the process of extracting, cleaning, transforming, and standardizing incompatible data from the bank's current systems so that these data can be mined and analyzed for useful patterns, relationships, and associations. The data warehouse need not be updated as regularly or daily as the transaction based systems. Data warehouses can be updated and mined as infrequently as the need for management reports and decisions dictate, i.e., monthly, quarterly, or on a ad hoc basis. Data warehousing and mining can run parallel with banking transaction information systems, without intrusion and interruptions.
Retail Shopping
One of the earliest application of data mining was in retail supermarket. Mining the volumes of point of sale (POS) data generated daily by cash registers, the store management analyzed the housewife's shopping basket, and discovered which items were often bought together. This knowledge led to changes in store layout the brought the related items physically closer and better promotions that packaged and sold the related items together. The knowledge discovered also led to better stocking and inventory management. Retailers like WalMart have experienced sales increase as much as 20% after extensively applying data mining. Some frequently bought item pairs discovered by data mining may be obvious, like toothbrush and toothpaste, wine and cheese, chips and soda. Some were unexpected and bizarre like disposable diapers and beer on Friday nights. Using credit cards enables information to used for mailings, offers based upon your purchases and gathering information about your spending habits. Cash is hard to track! Giving out too much information helps retailers decide what is best for you.
Insurance
The potential of data mining can be of immense importance to insurance companies. A car insurance company wants to create a prediction model to predict the probability of a car accident happening within a certain period of time on the basis of customer data which is available at the time of signing the insurance policy (e.g. personal data, attributes of the car to be insured, history of accidents.). Looking at the past it is known whether past customers had an accident within a certain period of time or not. Past customers are split into different classes with respect to the costs of their claims. Therefore, a data table is available with each data record representing the data of a past customer at the beginning of a year and the customer’s claim class in that year. The prediction model is created using this data table. The prediction model also reveals interesting customer segments with a high risk of belonging to a bad claim class.
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Yeppers, I love it, even more for use with youtube, I never have those ads appear prior or in between viewing videos.