What is data transformation?

The question of “what is data transformation” can have a wide array of answers that range from surface level overviews to heavy, deep explorations that require a cohesive understanding of programming languages and best practices. 

But what about a nice happy medium in between those two polar extremes? Many digital marketers don’t want to be bogged down in the minutiae of data warehousing architectures, but they do want to understand this increasingly important concept. 

So let’s explore the world of data transformation together. We’ll dive past the surface explanations. But don’t worry — you won’t need a metaphorical submarine, maybe just your favorite imaginary SCUBA gear. 

Here we go!

What is data transformation?

Data transformation is the process of modifying data to fit a particular purpose or context. This can involve tasks such as cleaning, standardization, or aggregation, all aimed at enhancing data quality and usability for more accurate analysis.

Transformed data, then, is the result of this process. It's data that's been reshaped from its original form into a more structured and usable format. By transforming raw data into a more analyzable form, it paves the way for data-driven decision making in fields like business intelligence.

Let’s explore that a bit further.

A first data transformation example

Think of your ad performance data from social media platform “InstaTok.” Perhaps you’d like to visualize the key performance metrics from the last 6 months. When quickly viewing the raw data, though, you notice that there are some broken cells due to missing values or incorrect data. 

If you were to send these raw data values on to your visualization software, your final dashboard would likely be broken in one or more places. In order to avoid this, you need to transform data in order to remove those broken values from the raw data, or to ensure that the data is accurately sent through. 

Simple enough, right? 

We’re still at surface level in our data transformation exploration, though. To continue our ocean metaphor, here, we are enjoying a nice relaxing snorkel around a reef. Time to plunge a bit deeper. 

Why data transformation is so valuable

So, why all the fuss about this data transformation idea? Well, for starters, it’s a critical step in broader, more detailed data analysis and maintenance. However, it’s also common sense for modern digital marketers who are running comprehensive campaigns across many different platforms. 

To effectively carry out the data transformation process, a robust data transformation tool is often required. These tools can automate many of the tasks involved in improving data quality like cleaning and standardizing data, making the process more efficient and less prone to error.

Even Facebook and Google, two of the most popular digital advertising platforms, treat data differently. Whether it’s cross-device conversions, clicks, UTM parameters, attribution, and more, it can be extremely difficult to directly compare performance results between the two platforms using their standard raw data sets. 

Instead, marketers need to integrate and transform this unstructured data so that the various parameters and metrics align. This is a process also known as data integration or data mapping. Data integration allows you to make easy comparisons between platforms as well as using the combined data to gain higher-level insights from the broader campaign ecosystem.

Beyond ensuring that all of your advertising sources are “speaking the same data language,” it is often worth thinking about the types of data transformations that are useful on a global scale, literally. 

Data transformation makes global campaigns easier to analyze

If your campaign is running across international boundaries, or serves customers in different countries, you will almost certainly need to employ some currency transformations. 

It’s important to be strategic and careful here, though. This is one of those points where you could stay nice and comfy by our metaphorical reef, or you could drift off into the big dark ocean depths. What we mean is that, with currency, you need to factor in current versus historical conversion rates. 

Are you looking at real values? Are your historical values adjusted for inflation? The questions go on, but let’s swim back toward the safety of our reef for a second to talk about a simpler global transformation: time zones. 

Imagine you drop a new sneaker or software product to your markets globally. You may want to launch the products at the same moment, regardless of the time zone in each market. That could be noon in the UK, which would be 7 a.m. in New York. When looking back on your performance numbers, it may be valuable to adjust time zone data to reflect the coordinated global launch. 

Sending transformed data to your target destination

Let’s also not forget one of the most common reasons we see data transformation being implemented. As we covered previously, Looker Studio is one of the most popular visualization services on the market. It’s robust, solid, and free. 

One of its main limitations, however, is that it can only pull data from a single source. Because of this, we see many of our clients start out their data journey (before joining forces with Funnel) by pulling everything into a massive Google Sheet where they apply a series of data transformations. After all, Looker Studio is great at making visualizations, but it's simply not built for transforming and cleaning your data. 

For larger organizations, a cloud data warehouse can be a more scalable solution for storing and transforming data. These platforms offer the advantage of high capacity storage, computing power, and distributed processing capabilities. The data warehouse makes it possible to transform data on large datasets in real-time, ensuring that your transformed data is always up-to-date and ready for analysis. 

Go deeper: what is a data warehouse?

Is data transformation difficult?

There is a reason we picked a deep sea diving metaphor to speak about data transformation. If you are experienced (or have an expert guide) and you are ready to dive deep, there is plenty of room to explore the topic of data transformation. 

Ready to take the plunge and start transforming data?

For beginners, it’s best to start with simple transformations, and build from there. Feel free to dip your toe in the shallow end, then begin a short “snorkeling adventure.” As your skills and knowledge improve, you can feel confident in exploring transformation further and deeper while creating a refined data transformation process. 

However, one of the biggest challenges with data transformation is that, at the more complex level, it often requires custom code and even BI teams. Especially when converting and integrating custom data fields, specialized programming languages and logic need to be employed to ensure that every inconsistency is picked up and addressed. 

A common way to achieve this is through regular expressions, or Regex. Think of it as a sort of “find and replace” for your mismatched data. In fact, Alex addressed the topic in one of his Funnel Tips episodes. 

The data transformation process

Let's have a quick look at the impact transformation can have on raw data.

What changes when we transform data? 

Aesthetic Transformation

This focuses on improving the presentation and consistency of the data without changing its actual meaning. Examples include:

• Standardizing abbreviations (e.g., converting "St." to "Street")
• Converting dates to a consistent format (e.g., YYYY-MM-DD)
• Ensuring units are consistent (e.g., converting all measurements to meters)

Constructive Transformation

This adds information in the data to improve its completeness and usefulness for analysis. This can involve:

• Adding calculated fields (e.g., calculating total sales from quantity and price)
• Imputing missing values with estimated data (following proper statistical methods)
• Merging data from different sources to enrich existing records

Destructive Transformation

This removes unwanted or irrelevant data to streamline analysis and improve data quality. This includes:

• Deleting duplicate records
• Removing rows or columns with a high percentage of missing values
• Filtering data based on specific criteria (e.g., excluding outliers)

Structural Transformation

This re-organizes the data's internal structure to improve efficiency and usability. Examples include:

• Renaming columns for clarity
• Moving columns to improve data flow
• Combining or splitting columns based on their content

How is data transformation used?

In addition to some of the types we listed above, here are a few common data transformation techniques and methods:

Translation and data mapping
Splitting
Generalization
Integration
Discretization
Manipulation

Translation and data mapping

With this specific data transformation, you are simply matching fields from one database to another – converting and aligning data fields between different databases. For instance, if one database uses abbreviated country codes like USA, MEX, and GRE, while another spells out country names, the data mapping process ensures that these fields are matched accordingly. This data mapping (read more about data mapping here) ensures seamless communication and consistency between databases, allowing for accurate data transfer and analysis.

Splitting

If we think of our data represented in a spreadsheet, splitting is the process of  converting a single column of data points into multiple columns. For instance, if a column consists of full URLs, splitting during the data transformation process separates the URLs into distinct components, such as domain names and slugs. Data splitting allows for more efficient data handling and clearer presentation of information, making it easier for you to dig deeper into your data.

Generalization

It’s typically bad to generalize in polite conversation, but it can be useful in data transformation. It can help to simplify low-level data sets into higher-level categories that are easier to understand. One example may be simplifying the timing of customer activities throughout the day into two categories: morning and afternoon.

Integration

Integration, also known as aggregation, is the process of combining data from different sources into one united view. For example, you might want to integrate your sales metrics from a brick-and-mortar store with those from your e-commerce platform. By integrating this data, you can get a better understanding of your operations as a whole. 

Discretization

While it may be a bit of a tongue twister, discretization (also known as “binning”) is simply the act of grouping continuous data values into discrete variables. It’s commonly used by data scientists and training AI models. Discretization organizes your numbers into tidy little groups, which helps you see the bigger picture without getting lost in the details. 

Manipulation 

This one is pretty straightforward. Manipulation is the act of changing data to make it more legible and better organized. It can even be as simple as changing the length of decimal values. 

For more data transformation examples, see the dedicated article.

Looking for more?

Data transformation is a big part of why Funnel exists. We are pretty obsessed with it. Well… maybe not obsessed, but we’re really into it. Check out some of the videos and links below to explore the topic further. 

The basics of REGEX:

Data aggregation in 2 minutes:

Events in Google Analytics 4, explained:

Are you trying to figure out how to apply data transformations within Funnel? Great! We have plenty of resources available. Click one of the explainers below, or head to our Knowledge Base for the full listing. 

• How to rename fields
• How does currency conversion work in Funnel?
• Custom dimensions explained
• How to use text splitting
• How to use regex matches
• Why can’t I calculate CPC in a rule?

Frequently Asked Questions

What are data transformation tools?

Data transformation tools allow you to process and change data to ensure it is ready for visualization or analysis. These tools can play an important role in the data transformation process. While there are a lot of data transformation tools on the market, we recommend products that are closer to a data hub, which can transform your data, collect it, store it, and send it anywhere it needs to go. 

An example of data transformation

As we mentioned above, some examples of data transformation include mapping, integration, discretization, and more. Read more here: Data transformation examples.

Why is data transformation important?

Data transformation ensures that all of your metrics are uniform, which allows for better analysis and stronger insights. Data transformation tools have thus become very important in BI, but are also increasingly important in other areas, such as marketing, sales, and even HR.