Data Mining Process | 5 mins read

7 Key Steps in the Data Mining Process

7 key steps in the data mining process
Lauren Christiansen

By Lauren Christiansen

Insight Into the Data Mining Process

After the digital revolution, companies had to collect and manage large quantities of data to maintain a competitive edge. While most owners knew that big data was important, they weren't always quite sure how to conduct analysis to answer business questions.

Analytics and business intelligence have now evolved into a science. Teams of engineers, data analysts, and other specialists help businesses sort through and aggregate data to extract insights.

Because companies continue to need data miners, the field grows and evolves. But how does data mining actually work? Read ahead to learn more about how companies use data to develop a greater business understanding of customers, sales, and the bottom line.

  • Data mining first appeared in the 1970's and peaked around 2002
  • Predictive analytics appeared in the 2000's, but has yet to catch on with the general public. It is mostly used by businesses and government agencies.
  • In 2010, experts claimed data science had five purposes. The scientist's job is to obtain data, scrub it, explore it, model it, and interpret it.
  • Social media platforms are now the top locations for data mining and analytics

The 7 Steps in the Data Mining Process

Companies have so much new data available to them in this digital world. It can be complicated to know exactly which data sources to gather to align with business objectives. Businesses use data mining and artificial intelligence to improve data collection efforts and extract useful information.

If internal data specialists use the proper mining processes, an organization learns more about customer needs and purchasing habits. Business leaders use data mining results to learn from past mistakes, customize marketing campaigns, and increase profits. But how does mining data work?

Here are the 7 key steps in the data mining process -

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1. Data Cleaning

Teams need to first clean all process data so it aligns with the industry standard. Dirty or incomplete data leads to poor insights and system failures that cost time and money. Engineers will remove all unclean data from the organization's acquired data.

They use several different data preprocessing and cleaning methods, depending on the resources of the business. For example, they may manually fill in missing values or utilize the mean of other data to fill in a probable value. Teams will also use binning methods to remove noisy data, identify outliers, and resolve any inconsistencies.

2. Data Integration

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When data miners combine different data sets and sources to perform analysis, they refer to it as data integration. This is one of the top mining techniques to streamlines the entire extract, transform, and load process.

Many specialists perform additional data cleaning within different databases during this stage. This further eliminates any inconsistent information and ensures data quality so it meets business requirements. Specialists will use data mining tools such as Microsoft SQL to integrate data.

3. Data Reduction for Data Quality

This standard process extracts relevant information for data analysis and pattern evaluation. Engineers take a small size of the data and still maintain its integrity during data reduction. Teams may use neural networks or other forms of machine learning during this mining process. Strategies may include dimensionality reduction, numerosity reduction, or data compression.

In dimensionality reduction, engineers reduce the quantity of attributes in the analytics data. In numerosity reduction, teams replace the original quantity of data with a smaller quantity of data. In data compression, engineers provide a compressed generalization of the collected data.

  • Sales and marketing departments lose 550 hours per week due to inaccurate data
  • Companies lose up to 20% of revenue due to poor data quality
  • 15% of leads contain duplicate records
  • It costs roughly $1 dollar to prevent a duplicate, $10 to correct a duplicate, and $100 to store a duplicate if it is not eliminated

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4. Data Transformation

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In this industry standard process, engineers transform data into an acceptable form to align with mining goals. They consolidate the preparation data to optimize data mining processes and make it easier to discern patterns in the final data set.

Data transformation encompasses data mapping and other data science techniques. Strategies include smoothing, or eliminating noise from data. Other popular techniques include aggregation, normalization, or discretization.

5. Data Mining

Organizations use data mining applications to extract useful trends and optimize knowledge discovery to generate business intelligence. This is only possible if a company takes full advantage of big data and collects the correct type of information.

Engineers apply intelligent patterns to the available data before they extract it. They then represent all information as models. Specialists use clustering, classification, or other modeling techniques to ensure accuracy.

6. Pattern Evaluation

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This is the stage where engineers stop working behind the scenes and bring insights into the real world. Specialists will pinpoint any useful patterns that can generate business knowledge.

They will use their models, historical data, and real-time information to find out more about customers, employees, and sales. Teams will also summarize information data or use visualization data mining techniques to make it easier to understand.

7. Representing Knowledge in Data Mining

Finally, data analysts use a combination of data visualization, reports, and other mining tools to share the information with others. Before the data mining process even started, business leaders communicated data understanding goals and objectives so engineers knew what to look for.

Now, analysts can share their findings with these leaders in the form of reports. Most companies use dashboards or other business intelligence tools to generate reports and extract insights from internal data miners. Owners use these insights to optimize decision-making, generate new business, eliminate waste, and create better advertising campaigns.

  • Increases communication
  • Improves performance, timeliness, and accuracy
  • Pinpoints potential risks or inefficiencies
  • Insights help to save money
  • Improves the customer experience
  • Optimizes marketing campaigns

Key Takeaways of the Data Mining Process

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In conclusion, here is what to know about the data mining process -

  • First, specialists need to clean the data to remove duplicate or dirty information. They then integrate information, or combine different sources to optimize mining results. Data integration also helps to decrease the amount of noisy or unnecessary data.
  • In data reduction, engineers extract relevant information to identify patterns and answer business questions. They also transform data so it aligns with mining goals. This process is called data transformation.
  • In data mining, engineers assign relevant patterns to each data set before they extract it. They then generate models with clustering or classification techniques.
  • Engineers then bring the information into the real world during the pattern evaluation stage. They extract patterns, identify trends, and make it understandable to users. Finally, they prepare the information to present to any applicable stakeholders. Business owners use data mining insights to optimize decision-making, increase sales, and learn more about customers.

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