7 Fundamental Steps of Data Mining

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What is Data Mining?

Data mining is a brilliant method of  extracting and scrutinising huge quantities of data and searching for the repetitive relationships among them. It helps businesses in spam detection or fraud detection in their database. As a result, businesses can come up with more-improved decisions by avoiding prior mistakes in their future endeavours. Data mining is different from data analysis and follows a separate method.

Why Data Mining?

From providing better customer service to increasing product uptime and strengthening risk management, data mining can benefit businesses in various ways.

·      Risk management plays a significant role in improving the brand value of companies. It’s also important for maximising their opportunities and minimising losses. Data mining can provide optimum risk management.

·      Data mining helps companies to assess their legal, financial and cybersecurity risks and control them quickly.

·      Understanding the preferences and behavioural patterns of customers is important for marketers. Accordingly, they can target their advertising and marketing campaigns. Data mining plays an important role in marketing.

·      For the sales teams, it’s crucial to enhance the conversion rates by encouraging existing customers to choose new services or products.Data mining can help sales teams in achieving their specific goals.

·      Data mining plays an important role in retail businesses as it can help companies identify the customer preferences.

 

7 Fundamental Steps of Data Mining

If you’re interested to know the basic steps of data mining, follow the list below.

1.    Cleaning of Incomplete Data:

The first step to data mining is cleaning incomplete or dirty data in order to maintain the industry standard.Otherwise, there will be endless system failures and poor insights, which can take more time and effort. As per the requirements of specific industries, the specialists use multiple methods or tools to accomplish this task.

2.    Integration of Data:

In the second step, the specialists perform data integration, which refers to analysing data by combining the sources and sets of multiple data. It’s a crucial step that requires different databases to do the second layer of data cleaning. The main purpose here is to improve data quality by eliminating inconsistent information.

3.    Reduction of Data:

Now that the cleaning process is complete, it’s time for the reduction of data so that the quality enhances further.Hence, specialists take small data and reduce the structure, to sum up, its main message. Machine learning is a very important process that is used along with several data mining tools for smooth performance in this third step.

4.    Transformation of Data:

Every data mining task has its own mining goals, which gets clarified in the fourth step. It’s the phase when the specialists combine all the preparation data through different methods such as data mapping, normalisation, aggregation and others. As a result, the quality of data gets improved further and the specialists move one step forward to create a final report.

5.    Data Mining:

Though the entire process is known as data mining, this step specifically includes the mining tasks. Some modelling techniques used in this step are classification, clustering etc. The specialists use multiple tools for data mining and other intelligent methods to come up with models, which are basically the extracted information.

6.    Pattern Analysis:

Data mining is a process that finds out the pattern of relationships between multiple data. In the sixth step, the specialists finally come up with their insights and discuss them with business owners so that new decisions can be taken. Starting from sales to employee behaviour and customer needs, all things are discussed in this step.

7.    Sharing Final Report:

Right after the discussion, the specialists usually present their final report that includes every relevant information of the process including their intelligent insight on the overall business performance and its pattern of problems. Companies get the report and realise the pattern of their behaviour so that they can improve it in the future.

Data Mining vs Data Analysis

Data mining and data analysis are two crucial elements of business intelligence.But, data mining and data analysis are totally different in terms of their action. Here are some major differences between these two services:

·      Purpose:

Data mining means extracting data and finding out their patterns and trends so that spam and fraud data are removed. But, data analysis is the further process that involves evaluating data and finding out useful information from it.

·      Manpower:

Data mining is possible with a single specialist whereas data analysis often requires an entire team of specialists.

·      Process:

Data mining is a process of collecting data and finding out the structures of data whereas data analysis translates the structured data into useful information.

·      Tools:

Data mining and data analysis require different tools. Data mining tools follow algorithms whereas data analysis tools focus more on interpretation.

·      Expertise:

Data mining experts need to learn statistics, machine learning, programming languages and operating systems. On the other hand, data analysts need to acquire knowledge of industry trends and develop thinking skills.

Now that you know the 7 fundamental steps of data mining, go ahead and get your company data analysed by a specialist. If you are looking for a data mining company that provides accurate and insightful data mining services, you can feel free to get in touch with us. We at Sprinkle Data provide data mining, analysis, and other relevant tools to help companies ace the task and create better value in the industry.

Our list of reputed clients includes Swiggy, Byju’s, Tata Cliq and others who are highly satisfied with our data mining tools. So, just start your free trial today and contact us for further details.

 

Written by
Sarika Jain

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7 Fundamental Steps of Data Mining