Data mining software helps in extracting useful information from a set of raw data; it will analyze the data and try to identify the hidden patterns. Results of data mining help firms predict outcomes; there can be various techniques that a data mining software can use, such as statistical analysis, machine learning, different kinds of algorithms, artificial intelligence, and database statistics. These techniques help fetch useful information from a set of large datasets and transform that information into a structural format or into useful categories. The data mining market is expected to grow at a CAGR of 25% till 2025.
Factors to consider before choosing the ideal data mining software for one’s business
Type of data
There are numerous types of data, and thus, it must carefully be checked before buying the software whether the format is compatible with the data type one wishes to work with.
There might be a possibility that the data mining software chooses is not compatible with multiple operating systems; hence check for OS compatibility before buying.
Data mining functionality
Some software will provide only one functionality, such as categorizing the data, whereas few will offer more than one functionality such as concept description, OLAP analysis, association analysis, and linkage analysis, so one must check that software suits the best.
There can be two options of scaling: row scalability and column scalability.
There can be two kinds of data mining
Predictive Data Mining Analysis: This kind of analysis is used on datasets that may help project what might happen later. This can further be classified into classification analysis, regression analysis, and prediction analysis.
Descriptive Data Mining Analysis: This method is used to turn data into relevant information; this too can be further classified into clustering analysis, summarization analysis, association rule analysis, and sequence discovery analysis.
Both analysis techniques are used across industries such as telecom & credit card companies, insurance companies, stock markets, retailers, and pharmaceuticals.
Benefits that data mining software tool can offer:
Know more about your customer
The best data mining software will let identify purchasing habits of existing consumers and potential customers.
One can know the pattern those customers usually follow while being on websites; this will benefit in knowing customers better and accordingly set up the websites.
A lot of firms use surveys to get customer responses for certain products; it lets companies understand how to market their product.
The future trends in data mining
Multimedia data mining
The rise in the use of multimedia will soon provide popularity to the latest method of extracting useful data from multimedia sources such as images, videos, audios, and others; the captured data is converted into a numerical representation of various formats. This method can effectively be used in the clustering and classification process.
Ubiquitous Data Mining
Real-time mining of data in a ubiquitous computing environment is referred to as ubiquitous data mining. It involves the mining of data from mobile devices to get information about individuals. It has its risk associated such as privacy, complexity, and cost, but this trend has a lot of potentials, especially in studying human-computer interactions.
Distributed Data Mining
Currently, this method is gaining popularity as it is common to store data at different locations. It is also expected that one must be able to extract information from these large databases, which are geographically stored at different locations. Thus, this method uses some sophisticated algorithm to provide insightful patterns.
Spatial Data Mining
This technique is widely used across the aerospace industry; this method includes extracting data, especially images taken from astronomical data. One can use geographical data as it will provide good results such as distance and topology, which are used in geographic information systems and navigation applications.
Time Series and Sequence Data Mining
This technique is commonly used in studying cyclical and seasonal trends, widely used by retailers to identify customers’ buying behavior. This method is also helpful in analyzing even random events that occur outside the normal event.