Data Transformation in Machine Learning: Best Methods and Challenges

The fields of data processing and collection have gone through a major resurgence in the past 10 years. Analysts have access to more data than ever before.

However, this means that the issue of poor data quality has never been more problematic.

As per a survey conducted by Statista, 46% of Chief Procurement Officers interviewed said they felt that the biggest obstacle to implementing digital technology was having to work with low-quality data.

So, the question is, how do you fix the issue? While your first instinct would be to increase the total amount of data so that you could reduce the impact of each bad data point, this only exacerbates the issue because it adds costly processing and filtering time to your process.

Instead, the solution is not gathering extra data but rather changing how you decide which data to handle and process. This is where data transformation becomes important.

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What is Meant by Data Transformation?

Data transformation refers to the process of converting and restructuring data from one format into another.

This can vary from simply removing duplicate records, to modifying data records to only include relevant fields, to even adding more fields and dimensions of derived information to make handling large amounts of data easier.

Data transformation facilitates converting disorganized, difficult-to-use data into a process enhancement tool, which is a critical phase in data mining.

What is the Use of Data Transformation in Machine Learning?

Within the field of machine learning, there are a variety of types of algorithms. One such algorithm, called a classifier, can identify whether a given data point falls into a given class of desirable outcomes or not.

It does this by taking in raw data as a training set, determining how much weight each feature of a data record plays on what class the data point falls into, and then applying these weights to other data sets of the same format, called test sets, to deliver accurate classifications.

If performed perfectly, machine learning algorithms can increase the growth and profitability of a company’s processes by reducing the amount of calculation time needed.

However, the reliability of these learning algorithms is heavily affected by the quality of the data; if the data set used to train the algorithm is of low quality, then the algorithm is prone to making bad predictions. So, the data transformation steps in here.

Suppose you worked for a bank that wanted an ML algorithm that helped determine whether a given housing loan should or should not be approved.

You might ask applicants to submit data on their income, their current credit score, the valuation of the house they wish to purchase, the amount of money they wish to borrow, and so on.

You would also prepare the training data set using records of already approved or rejected loans the bank had on file to help the algorithm learn what factors to consider when labeling applications as approved or rejected.

Before the training data is fed to the algorithm, an ETL specialist can ensure the data quality by applying the following fixes:

  1. filtering out data points that are missing fields, such as loans that were accepted or rejected before credit checks being part of the bank’s approval process
  2. remove duplicate records 
  3. apply normalization techniques so that fields that are on different scales will not be given wildly inaccurate weights. For example, FICO scores are always between 300 and 850. On the other hand, the house’s price will likely be hundreds of thousands of dollars; you should normalize the price value so that the relatively small changes in house price are not given more weight than small changes in the FICO score.

By applying these fixes, you would have an algorithm that would more accurately provide insights.

What are the Factors to be Considered in Data Transformation?

When performing data transformation, there are three key factors to consider:

Time: The data transformation step is time-consuming, but the right decisions can only be drawn from the right data, so performing this step should still be done at the end. 

Cost: Data transformation tends to be a cost-intensive process, so you need to set the scope of how thorough your transformation process should be based on what your budgetary restrictions are.

Process performance: As data transformation adds an entire layer of processing, your overall ETL process slows down.

You should take care to ensure that adding this step does not make delivering your insights an arduous process.

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What are the Benefits of Data Transformation in Business?

The following are the major benefits of using data transformation in your business:

Quick data retrieval: Querying and parsing through data that has been organized and standardized is significantly faster than attempting to do the same with disorganized data.

Data quality: Data that has gone through the transformation layer will be of higher quality and accuracy, which will reduce the cost and risk of incorrect insights that incomplete or junk data might cause.

Added value: Data transformation makes data easier to utilize, thus reducing the likelihood that meaningful insights that could direct business decisions would be left unrealized.  

Efficient data management: There are several sources of data, and consistently gathering, storing, organizing, staging, modifying, and handling data can make it simple to understand and handle.  

Furthermore, data transformation minimizes noisy data, anomalies, and variability to ensure quality analysis.

What are the Methods for Data Transformation?

It is recommended to conduct a data transformation process after data cleansing. You have to include or resolve blank (null) values, eliminate contradictions, and erase duplicate entries.  

Effective methods for data transformation in ML include:

Data exploration

The initial step in data transformation is knowing the origins of the data. Find the origins from which your data is flowing.

Know the structure of the data streaming into your database, the probable missing data points, and the variables in the incoming data.

Now, list out all the data points that have to be transformed.    

Data mapping and profiling

Data mapping acts as a ground plan for data relocation. At this stage, you determine which points should stay as they are and which data should change. 

Data execution

Decide how you might update your information at this stage.

Will you use a technique for a manual script or data transformation? Data extraction from several origins and various data processing techniques consists of:

Consolidating: integrating or linking data from several origins. 

Filtering: carefully segregating particular columns from rows. Hence, you can store a few entries in a database and erase others. 

Enriching: Streamlining asset structures is enriching. For example, modifying a name’s typography from lowercase letters to uppercase would be wang yeo to Wang Yeo. 

Split: Turn rows into different columns by splitting them.

Summarization: Generating a summary will save the data as major metrics. For example, geography, socioeconomic position, or overall installations were broken down by race. 

Derivation: Generating fresh data pieces from present ones by implementing rules or algebraic alterations. 

Binning: Reduces the outcomes of slight observational faultness. We substitute data indicating large spans for the original values represented by a small bin.  

Erasing unnecessary data: When turning over the content, consider if the format of the information will change over time and if you can change it instantly to meet changing needs.

Make it easy for others to grasp, so they can use it without your support.

Move processed data 

After transforming data, you can move the data to the necessary location (Drive, Google Sheets, Salesforce). 
Verify the processed data to authenticate its reliability and correctness. List any problems and take action as required.

How can You Transform Data Automatically?

As an organization that specializes in data analytics, Express Analytics has subject-matter expertise in the ETL field, and we have successfully found ways to automate the data transformation process.

This includes an automated workflow that uploads raw data and categorizes, validates, and wrangles data per predefined rules to then be cleansed, organized, and run through a merge/purge process to deduplicate it.

This workflow’s efficacy is enhanced by Express Analytics connecting to various business applications so that your data is pulled from and synchronized across multiple platforms and business needs.

Data Transformation for Businesses

Businesses obtain data from business documents, sales, markets, customers, and so on. Every data source consists of various elements of the customer experience.

Consolidating them all together requires converting data points for improved data integration. 

Data transformation plays a crucial role in this phase. Suitable transformation techniques will generate better outcomes. 

Listed below are several reasons why businesses must proceed after data transformation:

Enhanced data quality: Incomplete or garbage data is misleading and costly. So, data accuracy can be expected from data transformation. 

Enhanced efficiency: Data transformation can minimize errors and decrease manual data entry. 

Cost reduction: Operational costs for businesses will be saved with this by removing data silos and irregularities.

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Data Transformation Best Practices 

If you are intent on implementing data transformation into your business processes, it is advisable to implement the following best practices:

Set a goal

Set a defined target before you start the data transformation process. Include the consumers to help them understand the processes you will be inspecting.

Data profiling

Inspect your information to determine the fundamental information’s condition before transforming.

Metrics that should be considered when building your data profile are:

  1. The amount of information you would be working with
  2. row headings
  3. erased and unessential data
  4. attribute values
  5. section associates
  6. the number of columns
  7. consistency of garbage and section associations
Data cleaning

Purify your data before moving it to another location to make it more useful. To make the necessary adjustments, you have to understand what kinds of formats your proposed target supports. 

Deduplication and structuring of data at the initial level make sure that your outcomes are of the highest quality and support suitable choices.

In addition, make sure the team members who will work with the data regularly are consulted about how to gap-fill or exclude records. 

Manage dimension tables and facts

When structuring your data, you should consider organizing your data into a snowflake design, where you have a core fact table and various dimension tables that are focused on a specific aspect of the overall data record.

For example, if you have a database for sales, you might organize your data into a fact table consisting of records of each line item sold and dimension tables for product information, customer information, and store location information.

Analyzing data consolidation

With the help of a monitoring audit, you can monitor the data you upload at every stage, as it happens.

Adding this step to your data loading process ensures that there are no irrelevant or blank data points and that the information is perfectly structured.  

In addition, adding a monitoring audit means that whenever a customer raises an issue, you have the means to explain the origin of every piece of data, which establishes your customer’s trust in your processes.

What are the Limitations of Data Transformation?

Even though data transformation techniques are useful for businesses, there are still some challenges to be aware of:

Necessary Tools: Without clear and specialized tools, data transformation processes will not be possible.

A report by Forbes notes that 23% of companies still rely on spreadsheets for their work related to data, with an additional 17% relying on dashboards.

Only 41% use advanced analytics along with predictive models.

Subject Matter Expertise: Data analysts without proper subject matter experts are unlikely to notice inaccurate data because they are less conventional in their range of valid and acceptable values.

Meaningful transformation: Many businesses conduct transformations that don’t match their requirements.

An organization can convert data to a particular structure for one application only and later revert the data to its previous structure for a different application.

Cost Considerations: Data transformation can be highly expensive.

The expense is completely based on the particular software, infrastructure, and tools required for data processing, as well as the personnel needed to perform these transformations.

Human errors, incompatible data formats, data migration issues, human errors, or data entry mistakes can result in irregular data.


While it is a time-intensive and expensive prospect, the value added to your business processes by adding a data transformation Layer to your data workflow can translate to high-quality data that is easily parsed for usage across your business needs.


Data Transformation and Its Benefits

Data Transformation in Machine Learning: Why You Need It and How to Do It With AI

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