Just like it sounds complex “Data mining” has been a popular method for a time now to extract useful information from large sets of data used by many of the top-notch corporate companies. These extensive data are mainly raw in form so it demands the use of technical software that could be installed in your office computer to tote up mathematical algorithms.
Literal Meaning of Data Mining
Most people take data mining as a jargon as they find it that hard to understand. However, it’s the most common and simple method in the field of science and research. Data mining is the most crucial step in discovering useful information from large databases and is likely to be referred in terms such as “Knowledge Discovery in Data” (KDD) that leads to the logical inference of the calculated statistics.
Apart from its defined context, it has a more optimal center proven in business intelligence case studies. Data mining programs are favorable for companies so that time and again they can check on their data insights about their customers to evaluate the company’s performance at their best.
What is Data Mining Process?
If you need to understand the whole process, keep in mind these basic five steps to calculate the meaningful data from raw data.
• Collects data in the first place and stockpile it somewhere in a closed folder
• Manage it further to get handy results
• Let your business analyst collect it from where you’ve stored and let it organize it some more.
• The data mining software sorts the data on their own based on user results.
• The end user makes data in a perfect easy sharable format, and you’re ready on the point.
A Useful Example of Data Mining Process
Presume that you’re running a clothing store in Michigan and you want certain business results. At current, you want to know which area of your business is performing well and which one needs to be improved. Obviously, you can’t do clean up on your fingers as your company’s data is far-reaching to your estimations. As to know the exact results, you would barely spend the whole day to know the details and keep your business on hold. It is what Data mining tool saves you from. It tracks record of your high selling products that are sold on the particular time and gives you the quantified results to recover your ROI.
6 Stages of Data Mining to Evaluate Your Business Performance
Don’t forget that at the end of the day your ultimate goal is to make money on what you invest. It must be going off your ears but here let we provide you some result-driven data mining techniques involved in the stages of the data mining process.
It would be pretty much stress-free for your long day if you divide the whole process into two main stages:
a. Data Preparation
b. Data Mining
A. Data Preparation (The Initial Stage)
Data preparation stage has 4 major steps which include data purification, data integration, data selection, and data transformation.
So, let’s go through by these.
1. Data Purification
It is the foremost state in the data mining process as you first need to get your large data cleaned. Data purification asks you to complete the missing values of your data, maintains a consistency towards it and then combines the overall data for a complete picture.
2. Data Integration
Here comes a second step in the data mining process. From various zones, your data is incorporated into a single zone. Your data in your computer system is stored in different formats under different locations. These are your saved spreadsheets, text files, images, documents, etc. Data integration can give you a real tough time if you are previously messed up with your organization. Data integration sets free data from repetition without affecting the reliability of the data.
It is time taking and often can be a little frustrating so don’t lose on it.
3. Data Selection
In this third phase, the relevant data is filtered from the database. The database has a history of large volume of your data that surely comprehensive analysis. Data selection allows you to select and store data as per your interest from the available data.
4. Data Transformation
It is the last phase of data preparation stage as by now your data is now transformed into distinct forms. E.g., the figures like “-4, 101 and 65” is auxiliary altered to “-0.04, 1.01 and 0.65”. The available data is now ready for data mining stage.
B. Data Mining (The Final Stage)
Landing at the final stage of the data mining process, there are specific methods used to extract final data from the database. The mining is composite and a challenge for intellectuals. These are pattern evaluation, knowledge representation and a conclusion retrained from all these stages.
5. Pattern Evaluation
Patterns are designs which are recognized by a human if finds interesting. Pattern evaluation is a method which identifies and embraces interesting patterns which mean representing knowledge from data based on different measures. Your brains work like a pattern interpreter when receives the rightful designs of information. It is also a new form of data with a degree of certainty.
6. Knowledge Representation
Call this a closure step of the data mining process; the final data is designed in an engaging way which is later presented to your customer. Based on this information, it is totally extracted from your data. To come up with variant data mining output, these techniques are essential to be applied right away for the potential results.
Since every day new hypothesis is formed from the data mining process, it is turning to be fruitful for businesses who develop business applications at their chief operations. Giving the data mining process a summarized version of what we’ve learned so far above, data mining process cuts down to three stages.
Stage: 1: Initial Exploration- Same as the word data preparation that does a cleanup of the large volume of data to bring these values to an amendable state. It is done through the use of statistical methods to generate simple profits from the complex ones.
Stage: 2: Model Building — It is related to choosing a particular model to calculate the performance. It involves a number of techniques to generate the most effective result out of the data mining process.
Stage3: Composition- This final stage lets you choose the previous model to apply it to your new data to generate desired outcomes.
Though, the framework of the data mining process is quite simple if you pick up a step-by-step knack of it and so you are required to be patient and focused on getting the definite results. The process is explorative for a cause that any company’s data have lengthy customer insights but have adequate uses to a business.