Data is being stored in enormous amounts now. With varied sources like credit card transactions, publicly available customer data, data from banks and financial institutions, storing such a huge amount of data is becoming difficult day by day. Many relational database servers are being continuously built to make your work easier. As you already know, data plays a very important role in the growth of a company. It helps in making knowledge-backed decisions that can take a company to a higher level of growth. For this reason, data examination should never happen superficially. We need to analyze the data to enrich ourselves with the knowledge that can help us to make the right calls for the success of our business. But all the data available to us these days is so huge that it becomes humanly impossible for us to process it and make sense of it. Data mining or knowledge discovery can help us solve this problem.
What is data mining?
The process of extraction of useful information from a set of data that will help to identify the trends and patterns with it is called data mining. This technique helps the user in making data-supported decisions from enormous data sets. It recognizes and segregates the patterns in datasets for a particular set of problems that belong to a particular domain.
Predictive analysis, a branch of statistical science that uses complex algorithms designed to work with a special group of problems, works in conjunction with data mining. The predictive analysis helps in the identification of patterns from the huge amounts of data, which the data mining generalizes for predictions and forecasts. It uses a sophisticated algorithm to train a model for a specific problem.
In simple words, data mining is the process of searching large sets of data and look out for patterns that cannot be found using simple analyzing techniques. This helps us in categorizing that data into useful information, which can be then accumulated and assembled to either be stored in database servers, like data warehouses, or used in data mining algorithms and analysis to help in decision making. It is used by businesses to draw out specific information from large volumes of data and find solutions to their business problems.
Data mining process
The various processes used in data mining techniques include:
- Business Research
- Data Quality Checks
- Data Cleaning
- Data Transformation
- Data Modelling
Some important data mining techniques
Data mining can be highly effective if you follow one or more of the following mentioned techniques:
- Tracking patterns: It is one of the most basic techniques for data mining. This technique learns to recognize the patterns in your data sets. For example, recognition of some aberration in your data taking place at regular intervals, or an ebb and flow of a certain variable over time. This can happen like your sales of a certain product seem to spike just before the holidays, etc.
- Classification: A more complex data mining technique involving you to collect various attributes together into discernable categories, which you will further use to draw conclusions, or serve some function. For example, while evaluating data on individual customers’ financial backgrounds, you can classify them as “low,” “medium,” or “high” credit risks. This will help you learn more about those customers.
- Association: Although it is related to tracking patterns, it is more specifically dependent to linked variables. Here any specific events or attributes that are highly correlated with another event or attribute are put together. Like, you might notice, when your customers buy a specific item, they also often buy a second, related item. This populates “people also bought” sections of online stores.
- Outlier detection: Sometimes just recognition of the overarching pattern is not enough for a clear understanding of your data set. You also need to identify anomalies or outliers in your data. For example, if your regular purchasers are almost male, and then suddenly there’s a huge spike in female purchasers, you’ll have to investigate the spike and see what drove it. This will help you to better understand your audience in the process.
- Clustering: Although very similar to classification, but this involves grouping a huge amount of data together based on their similarities. For example, you can choose to cluster different demographics of your audience into different packets to see how often they tend to shop at your store.
- Regression: Although primarily used as a form of planning and modeling, it is also used to identify the likelihood of a certain variable, in the presence of other variables. This will help you understand the relationship between two or more variables, present in a data set. For example, you can use it to project a certain price, based on other factors like availability, consumer demand, and competition
- Prediction: It is considered one of the most valuable data mining techniques, as it’s used to project the types of data that you’ll get to see in the future. Sometimes just recognizing and understanding the historical trends is enough to chart an almost accurate prediction of what will happen in the future. For example, reviewing consumers’ credit histories and past purchases can help you predict whether they’ll be a credit risk in the future or not.
We at Augmento labs can help your business grow and reach the next level with the correct implementation of data mining techniques. We will guide you through the process of data mining. We have a lot of experience in understanding and analyzing data which will benefit your company immensely. With our experience of 18+ years in the IT industry, we are here to share our experience to help you. We will provide cutting-edge solutions across multiple domains and our team of experts will help you move up the maturity chain, guaranteeing consistent growth and productivity.