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A Quick Guide to Data Mining

By |2022-10-20T11:10:41+00:00June 11th, 2019|

In a strange stroke of luck, you become the owner of a gold mine. The gold is yours to take, but instead of extracting it and reaping profits out of it, you just sit on it, happy to be its owner. Doesn’t make sense, right? This is what happens when you don’t utilize data to make decisions. Organizations today sit on a treasure trove of data. However, often this gold mine of insights goes to waste because companies fail to extract useful information out of it. Data can help you understand your customers better, increase loyalty and ultimately your revenue, but only if you utilize it. This is where data mining tools come into the picture.

Data mining can give your organization a competitive edge by equipping you with insights that can drive decision-making. Let’s explore what data mining is and how your organization can benefit from it.

What is Data Mining?

Data mining is the process of analyzing large sets of data and deducing useful results from it. 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 data has led to an increase in the popularity of data mining. Thus, resulting in an increase in the use of data mining tools in an attempt to look for hidden patterns in the data. Some common everyday data mining examples would be stock market analysis, online shopping, fraud detection, and financial banking.

The data mining process uses mining algorithms on data assembled in data warehouses or databases to identify hidden patterns and uncover valuable findings. Data mining has become an integral part of businesses, with organizations investing more time and money in the selection and usage of tools used for data mining.

Data Mining vs. Data Integration – The Difference

Data integration is the process of combining, cleaning, and presenting data in a consolidated format. This includes unifying data from different source systems with disparate formats, eliminating duplicates, cleaning data according to business rules, and transforming it into the required format.

On the other hand, the purpose of data mining is to focus on finding patterns and relationships hidden in large data sets using efficient mining tools. The development of data mining projects requires the knowledge of statistics, machine learning algorithms, and database systems. Using advanced analytics and algorithms, makes data usable for faster decision-making.

When is Data Mining Used?

Data mining is used by businesses to gain intelligible insights from data. However, the data mining process is an extensive one, which requires the combination of a number of steps. The data mining process differs with respect to different use cases and companies but this data mining guide will explain the process in a simple and basic manner. The answer to the common question “how many steps are in data mining” is that the process includes the following seven major steps:

  1. Selecting Data

The first step in the data mining analysis process is to select the data sources that can be used to mine and get valuable information.

  1. Extracting Data

The next step involves data collection and extraction. A data scientist identifies and analyzes the data sources, and uses transformations to consolidate data.

  1. Transforming Data

Once collected, data from different sources and formats must be converted to a common format for it to be usable.

  1. Cleansing Data

After data is transformed into a common format, it must be cleansed to ensure that the data is error-free, consistent, and unique. Data cleansing involves minimizing data redundancy, manipulating data, organizing data, and applying governance policies to make the data meet compliance standards.

  1. Storing and Managing Data

The next step is to store and manage data across different destination systems in accordance with the data type. 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 handled separately. Then, the stored data is made available to business analysts using application software.

  1. Analyzing and Mining Data

Then, after data has been collected and loaded into a destination system, a combination of business intelligence and data mining algorithms are used to mine data. Understanding the business makes it easier for data scientists to produce a data mining model for data analysis. The question then arises – what is a data mining model?

A data mining model is created by applying different algorithms to data. Every algorithm involves the process of identifying trends in a data set 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. The model given above can be applied to multiple data mining examples, such as the financial investment industry.

  1. Visualizing Data

Lastly, After obtaining the results from the data mining process, it is necessary to ensure that the data is visually represented in an understandable form. Businesses use data visualization, in the form of charts and infographics, to present the results.

Applications

Data mining has useful applications in different industries, such as:

  • Healthcare: Robust data mining tools can be used in the healthcare industry to reduce costs, detect fraudulent activities, and improve patient outcomes.
  • Education: The use of data mining tools in education can help different aspects of the education industry, such as identifying how to encourage the learning-needs of students, predicting how certain students will perform in examinations, and making efficient operational decisions.
  • Customer-Relationship Management (CRM): Data mining tools can also help analyze the customer data in order to help a business take customer-centric strategies and build successful, loyal, long-lasting relationships with their clients or customers.

Guidelines for Choosing the Best Data Mining Tool

The data mining tool you need depends on your business type, the data mining method or technique that you want to implement, and sample data size. Some data mining tools use visual programming and machine learning to give desirable results while others are drag-and-drop. The ideal one for your business is the one that meets your requirements.

Here are some of the features that you should look into:

Amount of Data

Data mining tools 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 small, a free data mining solution can be a suitable choice to fulfill your requirements.

Human Resources

Using data mining tools also depends greatly on the resources that you have on hand. If you have data analytics and mining experts in your team, it might make sense to ditch the idea of utilizing data mining tools completely. Nonetheless, if your team lacks technical expertise, its’ advisable to invest in a 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.

Support

Choose a data mining tool that offers 24×7 support and adequate, easy to follow documentation.

Graphical UI

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 that has easy-to-use UI and code-free interface.

Ease of Use and Upgrade

Choose a tool that is easy to use, has a short learning curve, and offers regular upgrades. A good data mining software provider upgrades its product regularly with respect to the changing business needs.

Support for Cloud Systems

Depending on your organization’s size, it’s possible that you might have to deal with different cloud sources and destinations to extract and load data. Therefore, make sure that the data mining solution you are selecting offers support for cloud connectivity.

Conclusion

With Astera ReportMiner, you can execute all data mining tasks that are crucial to your business needs. Being a code-free tool, ReportMiner is extremely easy to use. You can use AI to build multiple report models to extract data from PDF reports and other unstructured sources, and automate the entire data mining process the solution can convert the extracted data into structured format and store at any desired location.

Automating your data mining process with ReportMiner saves you crucial time and human resources while multiplying efficiency and productivity.

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