Unless you are quite gifted in juggling numbers, you may still remember the dread of having to use your calculator to compute the values using the same formula several times, only to arrive at a single number as a final answer. Worse, you may even have to interpret that value using the clues in the question given to you! Now imagine having to do that hundreds of times, within a second! Well, that’s what computers are for, right?
That’s precisely the rationale behind the development of the process of data mining. Today, we can easily become flooded with huge amounts of data (the so-called big data), and processing it manually would take an unreasonably long time. We need to automate such data processing so that we can get a meaningful big picture of the state of our business in real-time. At this point, we can refer to the definition of data mining by SAS:
Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes.
Data mining is about quickly processing and analyzing large data sets to find patterns, correlations, and anomalies. This useful information can be used to quickly make important decisions for your business.
Data mining is also a synergy of statistics, artificial intelligence (AI), and machine learning (ML). The acceleration in the development of AI and ML in the last few years has enabled the development of data mining as the current pinnacle of data analysis.
Why is data mining important?
In the age of big data, data mining serves as your main solution to the problem of big data: the large amounts of data that your business generates everyday. The immense amount of data that you collect from your business will look, at first, quite chaotic and repetitive. If done by hand, it will take you months to process it for meaningful information! Even a weeklong delay to analyze a day’s worth of data is too much in this fast-paced world! Data mining rises to this challenge of analyzing data in real-time as well as taking on months’ worth of historical data and extracting patterns from it.
Specifically, data mining is designed to tackle the following conditions about the data that you have:
Increased amounts of data - data mining can be used to handle increasing amounts of data that enter your databases over time.
Incomplete data - typical data analysis methods will require you to fill all the blanks in your databases because most data analysis software will either run into errors when trying to analyze incomplete data
Complicated data structure - besides incomplete data, you can also generate a more complicated data structure that would change depending on the circumstances when the data is generated. This adds a layer of complexity to the data that typical methods cannot address without requiring more data munging.
How can your business benefit from data mining?
Due to how data mining can be used to sift through a wide variety of voluminous data that is being generated by various platforms and sources today, data mining is welcomed by industries today. There are two main benefits from data mining: market analysis and management and corporate analysis and risk management.
Market analysis and management
Markets are complex in nature. Thus, a wide range of methods are used to collect data from your target market. This can range from surveys to one-on-one interviews. It can still take time to analyze the data from these methods, especially if you want to combine the data together to find patterns present in your target market. Data mining is designed to handle the diversity of the data available about your target market. The following data can be extracted through data mining:
Market and customer profiling
Enhancing brand loyalty and customer satisfaction
Determining customer purchasing pattern
Future market trends
Corporate analysis and risk management
Businesses will also find data mining useful in improving their own management and processes. Here are some of the applications:
What are the precautions when doing data mining?
There are some precautions that you should take note when doing data mining. All of these are not insurmountable problems and you can still maximize the benefits of data mining while being mindful of these things.
As data mining requires collecting large amounts of data from the market in order to analyze it, there are concerns about violating the privacy of the involved users. Important information such as name, location, and credit card information are common targets of hackers.
Several techniques can be used to preserve data privacy while doing data mining. Besides securing the databases storing the data, you should also check the data mining software you use for its privacy features.
Accuracy of information
More data may not result in better analysis if your datasets include inaccurate data. In fact, you also need to validate the accuracy of the results of data mining methods that you use! To ensure the accuracy of the input data, you can compare it with data from existing open databases. Additionally, you can also check the output of your data mining methods by comparing it with other results. Getting different results is usually, but not always, a sign of inaccuracy of the input data and/or the method used. It will ultimately depend on the market conditions at hand.
What are the steps in data mining?
Data mining is not just about loading datasets into a data mining software and hoping for the best results. Data mining software takes time to process data and requires large datasets, so data mining has steps outside the data mining software that you need to follow so that you can achieve the best results.
Define the business question that needs an answer
As data mining takes time to run depending on the volume of data that you have, you should properly define the business problem or objective that needs a solution. The business problem or objective dictates what metrics or variables you should calculate, determine, or measure using data mining methods. If you improperly define the question, then you will be measuring the incorrect metrics or variables.
Here is a list of questions that will help you define the business question:
Establish the need for a solution. What is the basic need? What is the desired outcome? Who stands to benefit and why?
Justify the need. Is the effort aligned with our strategy? What are the desired benefits for the company, and how will we measure them? How will we ensure that a solution is implemented?
Contextualize the problem. What approaches have we tried? What have others tried? What are the internal and external constraints on implementing a solution?
Write the problem statement. Is the problem actually a combination of problems? What requirements must a solution meet? Which problem solvers should we engage? Can the in-house experts tackle the problem? Or might it need the help of external consultants? What information and language should the problem statement include? What do solvers need to submit? What incentives do solvers need? How will solutions be evaluated and success measured?
Most of the time, the data that you gather for data mining will come from internal databases and may sometimes be combined with external data that complement your internal data.
After collecting the relevant data, you still need to check it for consistency in its content and format. The process is called data munging. It has six steps:
Discovering: this process involves understanding the data that you are about to process. To help you understand the data, you look at its source and the context in which they are created.
Structuring: this process organizes the data to prepare them for easier analysis.
Cleaning: this process irons out possible errors and outliers. The format of the data is standardized.
Enriching: this process considers whether new data or information can already be derived from the existing data set and identifies them.
Validating: this process cross-checks the dataset for data consistency, quality, and security. This is important in order to recheck the data for missed inconsistencies.
Publishing: this process prepares the data for use in analysis. The requirements of the analysis software that you will use should guide this process.
Data preparation and data munging will depend on the data mining software and method you use; some may require CSV files while others can work well with Excel files. Unlike in typical data analysis, data mining software might already have built-in data munging and cleaning functions which can adequately process the data with little required input from the user.
While data munging can be automated, the dataset may sometimes require you to manually check the entries for possible issues that can escape the data munging software. Learn more about data munging here.
After preparing the data, you can now load it to your data mining software. There is a wide range of methods in data mining, but the most popular ones are listed in the next section below.
Interpretation of the results
After data mining, it’s now your turn to analyze and interpret the results. To do so, you should go back to the business question you defined at the start. It specifies not only the immediate problem at hand but also the variables and metrics you need to measure. Finally, it also includes a guide to how you should interpret the results that will arise from data mining. The last point will help you analyze and interpret the results and convert it into solutions that can be implemented in your business.
What are the methods used in data mining?
The powerful capabilities of data mining are backed up by its impressive arsenal of methods that can be used to mine a wide range of input data for important patterns. Some of these methods are listed below:
Clustering. Analyzes the characteristics of the objects in the dataset and puts them into clusters according to these characteristics.
Anomaly Detection. Scans through datasets to find highlight deviations from the regular patterns as established by existing precedent behaviors.
Association. Identifies relationships between variables and objects in a given dataset.
Classification. Classifies the objects in a dataset into externally predefined groups or classes. Externally means the definitions of these groups and classes are defined before the analysis.
Prediction. Analyzes the existing time-based datasets for patterns to extrapolate it to the future.
Regression. Measures the strength of the relationship between a set of independent variables and a dependent variable in a dataset.
Neural networks. An advanced algorithm that can learn to make predictions by detecting patterns from datasets.
Decision trees. Predicts possible outcomes and identifies the actions that can lead to them.
Marketing optimization. Identifies the best mix of marketing channels to be used in a marketing campaign for highest ROI.
Visualization. Not exactly a data analysis method, but the right method of visualization enhances the patterns found in the datasets by the data mining algorithms.