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Data mining otherwise known as Knowledge discovery/extraction, Data/pattern analysis, Information harvesting, etc. is an automatic or semi-automatic technical process that looks for hidden, valid, and useful patterns within scattered information to make sense of it, use it or turn it into knowledge. It’s all about discovering unsuspected/ previously unknown relationships amongst the data and also looks for anomalies, patterns or correlations among millions of records to predict results.
According to Forbes, data mining is a strategic practice considered important by almost 80% of organizations that apply business intelligence.
Not only that, the insights derived via Data Mining can be applied in marketing, fraud detection, and scientific discovery, etc.
So, it’s no surprise that data mining became a crucial component for businesses especially after internet became mainstream. For many businesses data mining involves comparing millions of isolated pieces of data which can then be used by companies to detect and predict consumer behavior and generate new market opportunities.
Some of the basic functions of data mining include:
To clean data of noise and repetitions
Extract the relevant information and use it to evaluate possible results
Make better and faster business decisions
Data Mining Process
The 4 main processes involved in data mining are as shown above, each of which are explained below:
1. Data Collection
It is the process of gathering and measuring data, information or any variables of interest in a standardized and established manner. The primary goal of any data collection is to capture quality data or evidence that easily translates to rich data analysis that may lead to credible and conclusive answers to questions that have been posed.
2. Data Cleaning
It is the process of “cleaning” the data i.e. preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted, smoothing noisy data and filling in missing values. Any data which tend to be incorrect, incomplete, noisy and inconsistent can affect your result.
3. Data Analysis
It is the process of inspecting, cleaning, transforming and modelling data with the objective of revealing significant and valuable insights, arriving at conclusions and supporting the decision-making process.
4. Data Interpretation
It is the process of attaching meaning to the data and refers to the implementation of processes through which data is reviewed for the purpose of arriving at an informed conclusion about generalization, correlation, causation etc. The interpretation of data assigns a meaning to the information analyzed and determines its signification and implications.
Steps in Data Mining
The steps involved in Data Mining can vary depending on the practitioner, scope of the problem and how they aggregate the steps and name them.
1. Defining the problem
Identifying business goals: You need to understand business and client objectives. You need to define what your client wants. What business problem are you trying to solve? Customer Acquisition? Retention? Reduce maintenance costs or operational costs?
Identifying required data and data mining goals: In this phase, data is gathered from multiple data sources available in the organization and evaluated. A data check is performed to see what the data quality of those records and attributes are like? How the selected business goals translate into specific data mining project goals? The answer to this question will lead to discovering what data sets may be needed and what is in those data sets etc. A visual inspection of data and spot checks will give an idea of how much data preparation and pre-processing may be required.
2. Data Preparation and Pre-processing
In this phase, data is made production ready. The data preparation process consumes about 90% of the time of the project. The required data from different sources should be selected from the overall collection and cleaned, transformed, formatted, anonymized, and constructed (if necessary). There may be a need for integration of multiple data sources to prepare the final data. Some of these data sources may even be external to complete some attributes of the data.
3. Modelling
In this phase, mathematical models are used to determine data patterns. Actual mining part of data mining will start with this step. Select appropriate algorithms for the required task and necessary parameters. Select data mining tools (2 popular Data Mining Tools widely used in Industry are R-language and Oracle Data Mining) to build the model and assess initial results. Based on the business objectives, suitable modeling techniques should be selected for the prepared dataset. Create a scenario to test check the quality and validity of the model. Run the model on the prepared dataset. Given that the end goal of data mining is about predicting, the results sometimes may invalidate prior assumptions if the predictions are outside prior hypothesis.
4. Testing
This phase involves evaluating preliminary results and testing the model on different sample data sets and reviewing the results. A check is done to see whether these results across different samples correlate, whether there are any inconsistencies, etc. The process is repeated until a satisfactory consistency of results is obtained.
The final phase is Deployment.
Data Mining Applications
There are a number of applications for Data Mining. A few are mentioned below:
Marketing
Data mining is used to explore increasingly large databases and to improve market segmentation. By analyzing the relationships between parameters such as customer age, gender, tastes, etc., it is possible to guess their behavior in order to direct personalized loyalty campaigns. Data mining in marketing also predicts which users are likely to unsubscribe from a service, what interests them based on their searches, or what a mailing list should include to achieve a higher response rate.
Retail
Data Mining techniques help retail malls and grocery stores identify and arrange most sellable items in the most attentive positions. Supermarkets, for example, use joint purchasing patterns to identify product associations and decide how to place them in the aisles and on the shelves. Data mining also helps store owners assess offers most valued by customers and can help them come up with offers which encourages customers to increase their spending or increase sales at the checkout queue.
Banking
Banks use data mining to get a better understanding of market risks and manage regulatory compliance. Data mining is commonly applied to credit ratings and to intelligent anti-fraud systems to analyze transactions, card transactions, purchasing patterns and customer financial data, to identify probable defaulters to decide whether to issue credit cards, loans, etc. It also allows banks to learn more about customers’ online preferences or habits to optimize the return on their marketing campaigns and study the performance of sales channels.
Healthcare
Data mining enables more accurate diagnostics. Having all of the patient's information, such as medical records, physical examinations, and treatment patterns, allows more effective treatments to be prescribed. It also enables more effective, efficient and cost-effective management of health resources by identifying risks, predicting illnesses in certain segments of the population or forecasting the length of hospital admission. Detecting fraud and irregularities, and strengthening ties with patients with an enhanced knowledge of their needs are also advantages of using data mining in medicine.
Education
Data mining benefits educators to access student data, predict achievement levels and find students or groups of students which need extra attention. For example, students who are weak in math subject.
E-commerce sites and Fast-food chains
Amazon is the best example of an E-commerce site uses Data mining techniques to get more customers into their eCommerce store. They use DM to offer cross-sells and up-sells through their websites. Fast-food chains like Arby’s use Data Mining to determine the best targets for their advertisements. One of the most famous names is Amazon, who use Data mining.
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