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Beginner’s Guide to Marketing Data Transformation


The ever-growing quantity of data available can provide businesses with new opportunities and potential for growth, but only if it’s managed efficiently. This raises a key question: what could help companies collect and get more out of their data?

One of the answers to this question is data transformation. It’s a process of converting raw data into a format and structure that’s much easier to analyze and interpret. Imagine you’re building a quarterly marketing report and pulling data from over 20 data sources, like Facebook Ads, Amazon Advertising, Mailchimp, and others. Each platform has its own labeling system and unique naming conventions. When combined together, the result fails to present a holistic picture of your marketing efforts. That’s where data transformation comes into play.

In this article, we’ll explore what data transformation is, its benefits, and common data transformation techniques, as well as provide you with some tools to automate this process.

What Is Data Transformation?

Data transformation is the process of converting data from one format or structure into another, typically in order to improve its quality or make it more compatible with other systems. 

What is marketing data transformation?

Transformation can be done manually or through automated processes, and it may involve changing the format, structure, content, or context of the data. Basically, any data manipulation is considered transformation. 

Data transformation can be used for multiple purposes. 

Marketing Data Analysis

Data transformation helps analysts carefully track customer behavior and preferences. Raw insights gathered from different marketing channels often don’t interact with each other. For example, the same metrics across different platforms might be named differently. This  prevents analysts from identifying trends and building a comprehensive picture of marketing performance.

Properly transformed data provides marketing teams with analysis-ready data that can be fed to BI or visualization tools, used in illustrative dashboards, or be used for campaign optimization, A/B testing, and other activities.

Additionally, transformed data can also be used in targeted marketing campaigns. Accurate performance data across channels shows marketers what’s working and what’s not, allowing businesses to create customized messages that are more likely to drive sales.

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Marketing Attribution Modeling

Data aggregation and transformation is the first step in marketing attribution modeling. Once data is gathered and harmonized, analysts can apply complex ML algorithms and test different hypotheses. Otherwise, the attribution tool will fail to correctly attribute each conversion and evaluate the contribution of each channel to the customer journey. 

Reporting

It’s difficult to build a holistic report when performance metrics don’t match. Before compiling a report, the data behind it must be cleansed and normalized. 

By building a report with raw data, you’ll end up with a biased, incomplete analysis with duplicate or redundant data entries. Data transformation is a vital step in analyzing your performance data, deriving insights, and identifying patterns. 

There are many other use cases. Ultimately, the goal of data transformation is to improve the quality and usability of the data, making it more applicable for whatever purpose it’s needed for.

Benefits of Data Transformation

Data transformation is an essential technique that brings numerous benefits to any type of business. Let’s consider the most important ones.

  1. Enables cross-channel reporting. When analyzing top-of-the-funnel activities, you may notice that one platform uses views, while another counts impressions. Ultimately, these two metrics detail the same thing, but to visualize it on one dashboard, you’ll need to unify ​​these disparate naming conventions. When data is stored in different formats, it can be difficult and time-consuming to produce reports that compare data. Data transformation standardizes your data, making it easier to generate insightful reports.
  1. Data compatibility and accessibility. Data transformation makes your data compatible with various systems and applications. Imagine you want to sync data between two CRMs or migrate it to your new system; data transformation, together with tools like reverse ETL, will make that process possible and seamless.
  1. Visualization. The transformation process is particularly important for data visualization. By reducing noise and improving the structure of the data, it becomes easier to create accurate and insightful visualizations. 

Data Transformation Techniques

Data transformation involves a wide range of techniques, including data cleansing, normalization, standardization, enrichment, deduplication, and more. Each of these techniques has its own benefits and drawbacks, so it’s important to select the right approach for the specific needs of your project. When done properly, data transformation can help you achieve success across a range of different data-driven initiatives.

Data Cleansing

Data cleansing, also known as data scrubbing or data cleaning, is the process of identifying and correcting inaccuracies and inconsistencies in data. The main aim of data cleansing is to reduce the redundancy and complexity of the information. It’s a critical step in any data mining or data analysis project, as inaccurate information can lead to incorrect results. 

For example, if a dataset contains multiple rows for the same customer, those rows can be merged into a single row. There are a variety of methods that can be used to cleanse data, including manual correction, standardization, and deduplication.

👉 Read more about the stages, strategies, and tools in our Marketing Data Cleansing guide

Data Standardization

Data standardization involves converting data into a consistent format that can be easily understood and processed by computers. This is often done by converting data into a common format, such as XML or JSON. 

For example, consider a dataset that contains information about customer orders. This dataset may be stored in an Excel spreadsheet with each row representing a different order. To standardize this dataset, we could convert it into JSON format, with each order represented as an object. This would make it much easier for a computer to process the data and would also make the data more readable for a human.

Data Normalization

Data normalization is a data processing technique that is used to organize data in a database. This process is the next logical step after data cleansing. 

Data normalization includes processes, such as creating tables and establishing connections between them. On the grander scale, it’s a method of organizing data to appear similar across all fields and records. 

For example, if dates are listed in different formats, they should be converted to a single standard format. Similarly, if some rows are missing data values, those values can be imputed. 

👉 How to convert raw numbers into revenue with data normalization

Data Deduplication

Data deduplication is a technique used to identify and remove duplicate data. It’s a common but very important process that occurs at every step of the data transformation process. 

Deduplication is often implemented for transformation in data warehouses and databases in order to improve storage efficiency and reduce the amount of data that needs to be processed. Most importantly, duplicate data entries may affect the quality of your reports. Even a single duplicate data row can significantly distort the final form of a dashboard or report.

For example, if a database contains multiple copies of the same customer record, deduplication can remove the duplicate records and consolidate the data into a single record. This can help to improve the quality of the report and query performance, as well as reduce storage costs. 

Data Harmonization

Data harmonization is a data transformation technique that is used to standardize data from multiple sources. This technique is often used when data from different sources need to be combined, such as when merging databases or integrating data from disparate systems. By harmonizing the data, it’s possible to ensure that the data is consistent and can be easily merged or integrated.

Data Enrichment

This strategy helps to improve the quality of data by adding supplementary information. For example, if a dataset only contains information on customer names and contact information, data enrichment can be used to add additional details, such as customer addresses, purchase history, and other demographic information. This additional information can then be used to better understand customer behavior, identify trends, and make better decisions about marketing and selling strategies.

Ways to Perform Data Transformation 

There are a number of ways to perform data transformation; however, the most appropriate approach will depend on the specific needs of your company and project. Some common methods include manual data transformation, on-premise ETL tools, and cloud-based ETL tools.

Manual Data Transformation

Companies that transform data manually usually have an in-house data analyst who manipulates data (cleans, converts from one format to another, performs calculations, etc.) using SQL, scripts, or other tools. 

Manual data transformation is a time-consuming and tedious process. It requires significant effort, including manually coding transforms, testing transforms, and maintaining transform code. In addition, manual data transformation is inherently error-prone due to the many potential sources of inaccuracies, such as incorrect user input, typos, and mistakes in the transform code itself.

Although, in some cases, this type of transformation can be efficient in achieving the desired result. Most commonly, companies that have to analyze large amounts of data opt for automated data transformation. This allows them to automatically generate transform code based on user input, as well as automatically test transforms to ensure accuracy. As a result, the automated transformation can provide significant advantages in terms of speed, accuracy, and efficiency. 

On-Premise ETL Tools

What is ETL?

Before diving deeper into this method of data transformation, let’s clarify exactly what ETL is. 

Extract, transform, load (ETL) is a process in which the data is extracted from one or more sources, transformed into a consistent format, and then loaded into a destination of your choice, which could be a BI, analytics or visualization tool or a data warehouse. 

ETL can be used to migrate data from one database to another, to combine data from multiple sources into a single database, to merge data from multiple sources on a single dashboard, and more.

When it comes to the on-premise ETL process, it’s hosted on an organization’s local servers or computers, as opposed to in the cloud. On-premises ETL tools are software packages that help organizations extract, transform and load data from a variety of sources into a centralized system. The data transformation process can be complex, and this mechanism can automate many of the steps involved. 

One of the benefits of using on-premises ETL tools is that they can help to keep sensitive data within an organization’s internal network. This can be important for compliance with data privacy regulations. Another benefit is that these instruments can provide greater control over the data transformation, as well as the ability to customize the process to meet the specific needs of an organization.

Cloud-Based ETL Tools

Another solution is cloud-based ETL tools. These instruments allow organizations to quickly and easily process large volumes of data from a variety of sources. 

One of the main benefits of cloud-based ETL tools is that they are more cost-effective than traditional on-premise solutions. This is because companies only need to pay for the resources they use, rather than investing in expensive hardware and software upfront. In addition, these instruments tend to be more scalable, which makes them ideal for rapidly growing businesses.

Automate Data Transformation with Improvado

Improvado is a top-notch ETL platform for revenue data management that transforms data in a simple, time-efficient, and cost-effective way. 

ow does Improvado data transformation engine works?

Improvado’s ETL solution is powered by its enterprise-grade transformation engine:  the Marketing Common Data Model (MCDM). This engine automatically transforms data,  standardizes metrics across all channels, runs automated data mapping, and applies out-of-the-box transformation recipes. The MCDM saves customers hours of manual work and IT resources. 

Other features that make Improvado’s data transformation solution stand out:

  1. No code. Being a no-code data transformation platform, Improvado doesn’t require any technical expertise, and thus would be suitable for any team.
  2. Data sources. The platform integrates with 300+ marketing and sales data sources to pull the required data in minutes, not days. If a company requires a unique data source connector, it can request that the Improvado team build a custom connector.
  3. Professional support and customization. Improvado is a highly customizable solution that is flexible in data extraction and scheduling frequency. The company provides Professional Services for situations where a client encounters an analytical or development challenge.
  4. Visualization. Once the data is ready for analysis, Improvado loads it to any visualization, BI, or analytics tool of your choice. Users can also opt for the Improvado dashboard to get an overview of their data.

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Transform Your Raw Data to Get Actionable Insights

As businesses strive to become more data-driven, the need for effective data transformation is becoming increasingly important. However, this process can be rather complex and draining. 

Automated data transformation solutions like Improvado can make this job much easier and more efficient. By automating the data transformation process, businesses can save time and resources, as well as ensure that their data is accurate and consistent.

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