There’s a lot of value placed on data these days. Small and large scale businesses are currently on the move to improve the information base they have on their target clientele, and plenty of resources are specifically allocated for the gathering of personal data, all in an effort to control and monetize it.
What’s the big deal about data, then?
Data is a term that refers to raw, unprocessed information. This may be information that you give away freely, such as the things you write on a public About section of your Facebook profile. This could also be information that is harvested from your personal accounts, whether you are aware of this or not. Other types of data include operational data (i.e. accounting, sales, inventory, cost, and payroll), non-operational data (i.e. forecast data and macroeconomics), and meta data (i.e. data about the data itself).
Regardless of type, companies are often on the search for data—data mining allows them to create a good picture of what their target demographic looks like, and they subsequently use the gathered information to further strategize on their next marketing move.
On the other hand, data mining is the act of gathering data by the bulk. It can be a pretty in-depth and extensive process—the “mining” part of it should give you a hint. On top of the actual gathering, the data is later analyzed into useful bits of information. It’s amazing how much companies and governments can find out about you from the things they gather online, and in a certain context, this is a frightening fact.
But data mining is not an act that’s limited to large corporations. The way we tend to overshare information on the Internet opens even ordinary individuals to the potential of gathering huge amounts of data. There’s way too much data online than we can possibly digest at one time, and it’s possible that every interaction that takes place on the Internet eventually leaves its own data signature. Still, on this smaller, more personal scale, the patterns in data signatures are not specific, and relationships look too complex after a cursory glance at the data.
There are two general forms of extracting information from large amounts of data. These are description and prediction. The ultimate goal of data mining is to find a simple way to view data, in such a way that is easily understood. Relationships are formed from the information, and it is possible for us to make inferences based on the patterns that eventually emerge.
The inferential power of data mining is its core strength, and many companies are acutely aware of this. If you think data mining is a new thing, it’s not. The technology has been around for ages. In fact, data mining techniques usually have their base in the use of computer programs, and these programs run on algorithms that classify specific cases into categories, based on the data given. More sophisticated programs can estimate wider ranges of information, and this is how a large number of Internet companies gain their profit.