What’s the difference between data mining and data warehousing?
Data mining is the process of finding patterns in a given data set. These patterns can often provide meaningful and insightful data to whoever is interested in that data. Data mining is used today in a wide variety of contexts – in fraud detection, as an aid in marketing campaigns, and even supermarkets use it to study their consumers.
Data warehousing can be said to be the process of centralizing or aggregating data from multiple sources into one common repository.
Example of data mining
If you’ve ever used a credit card, then you may know that credit card companies will alert you when they think that your credit card is being fraudulently used by someone other than you. This is a perfect example of data mining – credit card companies have a history of your purchases from the past and know geographically where those purchases have been made. If all of a sudden some purchases are made in a city far from where you live, the credit card companies are put on alert to a possible fraud since their data mining shows that you don’t normally make purchases in that city. Then, the credit card company can disable your card for that transaction or just put a flag on your card for suspicious activity.
Another interesting example of data mining is how one grocery store in the USA used the data it collected on it’s shoppers to find patterns in their shopping habits.
The grocery store could have used this valuable information to increase their profits. One thing they could have done – odd as it sounds – is move the beer display closer to the diapers. Or, they could have simply made sure not to give any discounts on beer on Thursdays and Saturdays. This is data mining in action – extracting meaningful data from a huge data set.
Example of data warehousing – Facebook
A great example of data warehousing that everyone can relate to is what Facebook does. Facebook basically gathers all of your data – your friends, your likes, who you stalk, etc – and then stores that data into one central repository. Even though Facebook most likely stores your friends, your likes, etc, in separate databases, they do want to take the most relevant and important information and put it into one central aggregated database. Why would they want to do this? For many reasons – they want to make sure that you see the most relevant ads that you’re most likely to click on, they want to make sure that the friends that they suggest are the most relevant to you, etc – keep in mind that this is the data mining phase, in which meaningful data and patterns are extracted from the aggregated data. But, underlying all these motives is the main motive: to make more money – after all, Facebook is a business.
We can say that data warehousing is basically a process in which data from multiple sources/databases is combined into one comprehensive and easily accessible database. Then this data is readily available to any business professionals, managers, etc. who need to use the data to create forecasts – and who basically use the data for data mining.
Datawarehousing vs Datamining
Remember that data warehousing is a process that must occur before any data mining can take place. In other words, data warehousing is the process of compiling and organizing data into one common database, and data mining is the process of extracting meaningful data from that database. The data mining process relies on the data compiled in the datawarehousing phase in order to detect meaningful patterns.
In the Facebook example that we gave, the data mining will typically be done by business users who are not engineers, but who will most likely receive assistance from engineers when they are trying to manipulate their data. The data warehousing phase is a strictly engineering phase, where no business users are involved. And this gives us another way of defining the 2 terms: data mining is typically done by business users with the assistance of engineers, and data warehousing is typically a process done exclusively by engineers.