What is Data Mining?
As operations grow and businesses become more complex, it becomes difficult for large enterprises to deduce useful information from large data sets. This complexity of dealing with big data has led to the popularity of data mining.
Data mining is the process of analyzing large sets of data and deducing useful results out of it. The data mining process uses mining algorithms on data assembled in data warehouses to identify hidden patterns and uncover valuable findings.
Data mining requires the knowledge of statistics, machine learning algorithms, and database systems. The goal of data mining is to use advanced analytics and data mining algorithms to make data usable. Because of its importance, data mining has become an integral part of data sciences, with businesses investing more time and money on the selection and usage of data mining tools.
How Does Data Mining Work?
Data mining makes it possible for businesses to get intelligible insights out of their data, whether it is open source data or not. However, the data mining process is an extensive one, which requires the combination of a number of steps. The data mining process differs from use case to use case and company to company. However, it basically comprises the following steps:
Selecting data
The first step in the data mining process is to select the data sources that can be used to mine and get valuable information.
Extracting data
The next step in the data mining process is data collection and extraction. A data scientist identifies the data sources, analyzes the sources, and uses integration flow to consolidate useful data.
Transforming data
Once collected, data from different sources and different formats must be converted to a common format for it to be usable.
Cleansing data
After data is transformed into a common format, it must be cleansed in order to ensure that the data is error-free, consistent, and unique. Data cleansing involves minimizing redundancy of data, manipulating data, organizing data, and applying governance policies to make the data meet compliance standards.
Storing and managing data
The next step is to store and manage data across different data warehouses in accordance with the type of data. Data can either be transactional, non-operational, or metadata. Transactional data, which includes day to day operations, is stored in a separate location from non-operational data. Metadata is concerned with logical database design and is also handled separately. The stored data is then made available to business analysts using application software.
Analyzing and mining data
After data has been collected and loaded to a data warehouse, the actual data mining process starts. Mining and analyzing, it requires the combination of business intelligence and data mining algorithms. Understanding the business makes it easier for data scientists to produce a data mining model for data analysis. Every data mining algorithm involves the process of identifying trends in a set of data and using the output obtained to define parameters. These parameters are then used to carry out descriptive analytics, diagnostic analytics, prescriptive analytics, risk management, or predictive analytics.
Visualizing data
After obtaining the results from the data mining process, it is necessary to ensure that the data is visually represented in an understandable form. Data visualization allows businesses to showcase the results generated by using data mining algorithms using charts or infographics.
What are the Techniques of Data Mining?
Depending on the data mining needs of a business, the following data mining techniques are put into use:
Association
Association is one of the most commonly used data mining tasks. It is related to tracking patterns and relationships between dependently linked variables. Association hence looks for specific events or attributes that are correlated with another event or attribute. For example, when customers buy a specific item, businesses might notice that users tend to buy a correlated second or third item.
Classification
Classification is another data mining technique that requires organizations to collect various attributes into discernable categories. For example, this technique of data mining can be done to classify customers in the “low,” “medium,” or “high” credit risks category by analyzing their purchase history and financial background.
Tracking patterns
Tracking patterns is one of the basic techniques of data mining. It generally involves the recognition of some set of data happening at regular intervals. For example, a business can notice that a certain product is sold more before a certain festival.
Outlier detection
Another technique of data mining involves the detection of anomalies. Simply tracking patterns or classifying data cannot always be sufficient to understand your data set. For example, a business can notice a strange spike in female customers in an otherwise male-dominated sale item. The investigation of the spike and the reason behind it is an outlier detection process that makes businesses understand their customers better.
Regression
Regression is a method used to identify the exact relationship between two or more variables of a data set. For example, you can use the regression technique to set the price of a certain commodity, based on customer demand, availability, and competition.
Clustering
Clustering is similar to classification, but it involves chunking of data based on the similarities between the data sets. For example, a business can chunk different demographics of their audience into different packets depending on their income.
Prediction
Prediction is one of the most valuable data mining techniques that allows you to project the types of data you might see in the future through predictive modeling. For example, it allows you to predict the sale of a customer based on their previous purchases, credit histories, and financial status.
What Factors to Consider when Choosing Data Mining Tools?
The data mining software that you need depends on your type of business, the data mining technique that you want to implement, and the amount of data that you want to analyze. Some tools use visual programming mechanisms and machine learning to give desirable results.
There are a number of data mining tools that you can use to meet your data mining needs. However, it is important to take the following considerations in mind when looking for a data mining tool:
Amount of data
The data mining tool you select must be capable of handling the amount of data you manage on a daily basis. If you process a huge amount of transactional data, it makes sense to buy a high-performance data mining tool. If your data set is not huge, a free data mining solution can be a suitable choice to fulfill your data mining requirements.
Human resources
Using a data mining tool also depends greatly on the resources that you have on hand. If you have data analytics and mining experts on your team, it might make sense to ditch the idea of utilizing a data mining tool completely. On the other hand, if your team lacks technical expertise, it makes sense to invest in a good data mining tool that can help automate the entire process.
Results
What results do you need from your data mining activities? Do you want to predict future outcomes, detect anomalies, classify data, or track patterns? The data mining tool that you select also depends on the results that you desire and the kind of organization that you are. Different data analytics tools carry out data mining differently. It is necessary to choose the right tool in accordance with the results that you require.
Price
Price is another important consideration that can help you choose a suitable data mining tool. Choose a data mining tool that you can test for free, before actually having to pay for it. Also, choose a pricing model that meets your organizational needs.
Support
Choose a data mining tool that offers 24*7 support and adequate, easy to follow documentation.
Graphics
A data mining tool that does massive computations but cannot visualize the results is not suitable for any business. Choose a data mining tool with excellent graphical illustrations.
Ease of use and upgrade
Choose a data mining tool that is easy to use, has a natural learning curve, and offers regular upgrades. A good data mining tool company upgrades its product regularly with changing business needs.
Possibility to work on the cloud
Depending on the kind of organization that you have, the possibility to work on the cloud is another added benefit that is inevitably important when it comes to accessing data from online data sources.
In some cases, you might need the combination of more than one data mining tools, one for visualization purposes and one for collecting data and carrying out computations.
Originally published at https://www.astera.com on June 11, 2019.
