Data Transformation in Data Mining – The Basics

Businesses are now leveraging data mining and machine learning to improve everything from their sales processes to interpreting finances for investment purposes. To make predictive analysis work, data transformation in data mining is a crucial step that helps to turn the data into usable and reusable formats and carry on further data mining tasks.

Content

• What is Data Transformation in Data Mining?
• Data Transformation Process
• Why Transform Data?
• How to Transform the Data
• Data Transformation Best Practices
• Benefits of Data Transformation
• Challenges of Data Transformation
• Conclusion

To learn more about data mining, read – What is Data Mining?

What is Data Transformation in Data Mining?

Data transformation involves data conversion from one format to another, one structure to another, or both. This method plays a crucial role in data science tasks, including data integration and data management. The below data describes the increasing usage of data transformation in strategic processes. This data will help you understand the importance of data transformation in data mining and its increasing penetration across various businesses.

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Data Transformation Process

Data transformation can include a range of activities; it can convert data types, clean data by removing null data or duplicate data, and enrich the data or perform aggregations, depending on the needs of your project. Generally, the data transformation process involves two stages.

Stage 1 – Data is discovered from the data sources and types of data are identified. Data scientists then define how individual fields in the obtained data are mapped, modified, joined, filtered, and aggregated.

Stage 2 – Data is extracted from the original source. The range of sources can vary, including structured sources such as databases or streaming sources such as telemetry from connected devices or log files from clients using web applications. Then the transformations are carried out.

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That is, data is transformed by adding sales data or converting date formats, editing text strings, or joining rows and columns. Finally, the data is sent to the destination store. The goal could be a database or a data warehouse that handles structured and unstructured data.

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Commonly used transformation languages:

Perl – A high-level object-oriented and procedural language capable of powerful operations

AWK – One of the oldest languages ​​and a popular TXT transformation language

XSLT – An XML Data Transformation Language

TXL–  A prototyping language used primarily for source code transformation

Template Languages ​​and Processors – These specialize in transforming data into documents

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Why Transform Data?

You may want to transform your data for various reasons. In general, companies want to transform data to make it compatible with other data, move it to another system, combine it with other data, or add information to data you already have in your system.

For example, consider the following scenario – Your company has acquired a smaller firm and needs to combine the Human Resources departments’ information. The purchased company uses a different database than the parent company, so we will need to work to ensure these records match.

Each new hire has received an employee ID, which can serve as a key. However, we will have to change the format of the dates, remove any duplicate rows, and ensure there are no null values ​​for the Employee ID field. All of these critical functions are performed in a staging area before uploading the data to the final destination.

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Other common reasons for transforming data include:

• If you are moving your data to a new data warehouse; For example, if you are moving to a cloud data warehouse and you need to change the data types
• If you want to join unstructured data or streaming data with structured data so that you can analyze the data together
• If you want to add information to your data to enrich it, such as searching, adding geolocation data, or adding timestamps
• If you want to make aggregations, such as comparing sales data from different regions or adding sales from different regions

How to Transform the Data

Data transformation can be achieved through a range of different ways, including –

Scripting

Some companies perform data transformation through scripts that use SQL or Python to write the code to extract and transform the data. The script runs against the given data sample and doesn’t affect the entire data set.

Using ETL Tools on Local Disk

ETL (Extract, Transform, and Load) tools can eliminate the hassle involved in scripting when you want to automate the process. These tools are usually hosted on the company’s server and may require extensive experience and infrastructure costs.

Using Cloud-Based ETL Tools

Cloud-based ETL tools are hosted in the cloud, where you can take advantage of the provider’s expertise and infrastructure.

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Data Transformation Best Practices

Below are some of the best data transformation practices –

Design the Goal

When faced with an ocean of data to process, it’s tempting to jump right into the nuts and bolts of data transformation.

However, before transforming data into information, we must engage business users to understand the business processes we are trying to analyze and design the target format.

Improve Your Data Using Data Profiling

Data profiling examines any issues in your data and ensures that your data is unique. It also checks if you can reuse that data by collecting appropriate statistics.

Once the data source is known, you can extract the raw data into a usable format.

Clean Your Data

Equipped with data profiling insights, you can better understand how much and what kind of data transformation work you need to do with your data in order to use it.

For example, if the date fields of the source data are in the YYYY/MM/DD format, and your destination date fields are in the MM-DD-YYYY format, you will need to transform the source data fields to that match the target format.

Or, if some columns show a high frequency of missing values ​​or unwanted data, you may need to discuss with business stakeholders to determine whether to estimate values ​​for missing data or exclude these records.

Build Dimensions Then Facts

As we mentioned earlier, dimensions put context around data. The facts explain what happened within the dimensional context. For example, customers, products, and dates could be dimensions, and sales results and measurements could be made.

Audit and Data Quality

Data quality assurance is the exclusive step in data transformation. Defining the data quality measures and audit metrics helps transform the data.

Benefits of Data Transformation

The following are the advantages of data transformation – 

• Improved organization – Clean and standardized data can be located easily and can be quickly organized basis its date, size, format, or type.
• Improved data quality – The transformation process ensures that null values, duplicate entries, defects, and incorrect formats are rectified. Therefore, we can improve the overall data quality by correctly formatting and validating the data.
• Enhanced Compatibility – Data can be converted per the defined goal in various ways. A data source can be compatible with different business applications and systems.

Challenges of Data Transformation

The following are the challenges that companies may experience when converting data.

• Expensive processes – Depending on the data infrastructure and the software and application systems, the transformation process can be costly for companies. Companies may also have to budget for licenses, IT and data specialists, and tools.
• Slow down operations – Data transformations require time and resources. For example, staff will need to enter the data into business systems after converting a metric format. This can slow operations as teams focus on updating their data.
• Labor Intensive – The time-consuming data conversion process requires diligence and expertise. Any carelessness will result in inaccuracies and typographical errors in the database. This leads to uninformed business strategies and decision-making.
• Perform multiple transformations – Companies often transform data, only to find out later that it is incompatible with their needs. In addition, they may have multiple systems that require different data formats. Therefore, teams will have to convert their metrics more than one time.

Conclusion

According to a Forbes study, in 95% of companies, unstructured data management is challenging for their operations. Therefore, companies increasingly invest in methods to efficiently transform data sources. Doing so enables them to manage, integrate, and move data. This enriches the basic metric information and highlights vital insights into internal and external functions.

Once the process of data transformation in data mining is completed, data miners and scientists can analyze the information. This first phase ensures the data is cleaned and imported correctly for its subsequent applicability in business intelligence.

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