Do you like searching for a needle in a haystack? Do you like to put together a thousand piece puzzle of the Eiffel Tower non-stop? Do you like to dabble in statistics? Well then, all you need to know is this one career option called data mining.
When I first heard the term ‘data mining’, I imagined miners with hard-hats and pickaxes, hacking away at rocks made of binary code. As amusing and illogical as it was, data mining is pretty much just that – scouring huge expanses of data to turn up with the most convincing patterns and statistics. Data mining is an analytical tool for anyone who needs it. And boy, do people need it!
While calling it ‘researching a warehouse filled with data’ would still be putting it lightly, the intense demand for data mining has led to faster searching software, better scanners and stronger, more secure storage methods. We are far past the stage where we can just say that we live in an age of information. We can now say that we live in an age where information is so vital, so vast and so sensitive to change that it would take us millions of years over again to read all of it.
So you can imagine if you manage to get the job done in months, days or even hours, you might as well be mining gold rather than information for a payroll! It would, on the other hand, be the same as mining an entire mountain for a single vein of gold. In short, data mining needs more people and those who become successful data miners, earn pretty well.
How to Perform Data Mining
There are four steps to it. They seem simple, but not when you actually ‘dig in’.
- The first step is to start the process of extraction. This not only means gathering statistics of single events, but the statistics of the transitions between any two events as well.
- The second step is relatively simple – you need to reorganize whatever statistical data you’ve gathered and store it someplace where it will be safe and incorruptible.
- In the next step, the researcher, following the protocol specified to him/her, reveals the total relevant data that they have deduced for the research, to those concerned. They will then analyze the data and if approved, it falls into the final stage.
- You use whatever procedure or software that has been given to you (or built by you) and start to make sense of the data you’ve stored.
The Scope of Data Mining
A quick look at some places where this is used and what it means to analysts to understand its need in modern fields. Most of the algorithms are iterative and require most details on the data sections between which you need to find the link.
Artificial Neural Networks: Simply put, they resemble the human neural network, only they aren’t. They are meant to follow the human neural pattern – they learn, practice, adapt and conclude, according to what the programmer maps it as. They are used for data mining where there are large non-linear systems that possess some similarities, but seemingly conclude as totally unrelated. You basically find out this unknown factor that relates all the segments of data and conclude it as a new discovery.
Genetic Algorithm: This field uses all the evolutionary tools – inheritance, mutation, crossover and selection to find the missing links between data, both modern and prehistoric. What you do here, in a system with too many unknown factors, is to find the closest possible result using the sharpest predictive tools in your arsenal. You come up with the most probable and optimized answer to the questions.
Data Visualization: Here, what you end up mining is generally too complicated to read through a single software. The data intersects and ends at multiple points through a time-line, giving a somewhat multidimensional chunk of data. In a way, it’s like saying, ‘if the structure is too big, take a step back to get a better look’. You pretty much display all data through graphical means.
Career Opportunities in Data Mining
You’ll find the use of data mining mostly in business fields under the following branches:
• Advertising: Getting the right kind of commercials using customer relations and feedback. Data mining relies heavily on customer relations because that’s where all transitional data comes from, whether it’s changing trends or satisfaction with products.
An example of direct visual advertising is a certain tactic employed by a supermarket. Doesn’t really sound like the right place for data mining, does it? Well, this actually has been done once. The researchers, through a string of grocery bills, working-class statistics and surveillance footage, showed a definitive relation between the days of the week, the presence of a toddler in a house and the sex of the parent who’s shopping. The conclusion – men who have a child will go shopping mostly on Saturdays and buy a lot of diapers and beer. The store owners then simply shifted the diaper section next to the beer section and just sat back and watched the beer sales rise!
• Healthcare: Evaluating the recent health trends to put forth the next health or diet product. Data mining has proved to be more and more essential when it comes to making cost-cutting medicine, optimizing surgical procedures and keeping a check over pharmaceutical sales. It can be used to find out the success rate and side-effects of a medicine in the market.
The result will gather enormous chunks of data from pharmacies around the country that sell the drug, organize the data and conclude the effectiveness of the medicine. You can also use it to evaluate a medical or surgical procedure and calculate its effectiveness on a patient. This involves sifting a lot of data from multiple patients and multiple aspects (like weight, diet, changes, long-term effects).
• Sports: Televising a particular sport more, according to localities that are interested in that sport. On the field, data mining is used to evaluate the players, to check their on-field statistics and training practices to find out the best ways to make players better.
• Fraud detection: Archives can be researched through to find out the patterns in many fraud cases, whether it is forging money or major bank frauds.
• Investments: A very lucrative method for firms or individuals to pursue increased gains over their investments. Data mining helps reduce the risk rate and capital to give the best returns possible. Another angle is in the actuarial practices. For someone who needs nothing but statistical data that is concrete, in order to assess the market to find out all insurance and investment risks, data mining is an excellent tool.
• Manufacturing: The assembly line is another place that can benefit from this. You can use it to increase performance of automated tools as well as employee efficiency along with their welfare.
The US Government takes up one of the largest projects of data mining. They use it to increase the effectiveness of law enforcement and more recently (and in surprising measures), in anti-terrorism.
Science uses data mining in places where links can be established in fields that gather large amounts of data, like astronomy and bioinformatics.
Perhaps the biggest data mining concept that is in use has been the one that everyone uses – search engines on the Internet. Ever wondered how Google comes up with the most relevant search result before you even finish typing the entire search request?
For a field that’s growing in demand by the day, professionals in the field are earning something between the $65,000 to $70,000 bracket. Not bad, is it? So what are you waiting for? Put your hard hat on and get working!