Businesses have massive amounts of data moving back and forth through data pipelines, data warehouses, cloud apps, and SaaS tools. In order to make sure that data is useful and accurate at every step, you need to get the data transformation process right. With a clear idea of how to transform your data and the tools to do it you can improve data analysis and reporting, sharpen decision-making, and make it easier to build new digital products and experiences.
Data transformation can happen inside your cloud data warehouse and other times the data will need to be transformed before it’s moved. The number of points where you need to transform your data and the techniques you employ will depend on your business goals and on your data infrastructure. You’ll likely use ETL (extract, transform, load) and ELT (extract, load, transform) tools that data transformation built in at different stages — but you may need other data transformation tools.
This guide will walk you through all the ins and outs of data transformation so you can start working with your own data more efficiently.
What is data transformation?
Data transformation is the process of converting data from one format or structure to another. It involves changing your data values (and even foundational schema) to match the requirements of the data warehouse or tool your data is moving into next.
Cloud data warehouses typically have enough computing resources to transform data once it’s loaded inside—that’s why ELT tools like Fivetran (where transformation happens last) are popular. On the other hand, on-premise data warehouses tend to require data transformation before the data is loaded from the data pipeline—your traditional ETL tools and processes handle this.
Where the transformation steps happen in your DataOps workflows depends on your unique set of data tools and technology. A modern data stack will likely have data transformation happening in multiple locations and in different directions, using ETL, ELT, or even reverse ETL. Some of your data will need to be transformed for storage in a cloud-based data warehouse and some will need to be transformed for use in customer-facing business apps.
You’ll have to outline a basic strategy for data transformation to be sure you have the right transformation points located. Data transformation processes follow some basic steps.
What are the steps of data transformation?
The actual process of data transformation follows some common steps—no matter where, when, or how it happens. The right data transformation tools will walk you through these steps or perform them in an automated way.
- Data discovery: First, the data has to be identified and understood in the context of its source and original format. Data profiling tools like Atlan are extremely useful, if not required, at this step. Once you know the source you can map what needs to happen to transform the data.
- Data mapping: This step maps what the source data is like and how it needs to be transformed in a clear and simple way. Once you know this your data engineers can write the code they need to run the process or give your data transformation tool (such as dbt) the parameters it needs.
- Generating/executing code: It’s likely you’ll use data transformation software to perform this step. But there is a chance you’ll need to generate code if you have custom jobs or don’t have a tool like dbt.
- Data transformation: This is where the data changes shape. Format, structure, and values of data can all be transformed to match the destination requirements.
- Data validation/quality check: Once the data has been transformed it’s vital to check for errors or inconsistencies. This is yet another reason to have a trustworthy data transformation tool. If something goes wrong in the data transformation process or an unexpected output occurs, you want to know about it before it causes data downtime.
- Sending data: After the data is transformed and validated, it is sent to its target destination.
Seems easy enough, but those five steps can also contain a lot of complexity—especially if you have multiple data transformation events between a certain data source and your data warehouse. This might happen because your target destinations require different types of data transformation along the way.
What are the types of data transformation?
There are many different ways to work with pools of big data and change its structure and format for different uses. It’s critical to know what you want to use your data for before you decide on your method of data transformation. Your data analysts and data scientists can identify which types of data transformation are useful to your business and workflows.
For example, a data pipeline running from Salesforce to your data warehouse might require one type of transformation to use that data in your lifetime value data models. Then to move enriched customer data back to Salesforce so it’s usable by your sales team, it might need to be transformed back to a different schema or format that’s compatible with the Salesforce database.
Here are a few of the main types of data transformation:
- Constructive: Adds, copies, or replicates data
- Destructive: Removes data, fields, values, schema, or records
- Aesthetic: Fine-tunes the data for specific uses
- Structural: Changes the column structure and reorganizes the database or data set at its foundation
From here, you can employ different data transformation techniques to get more specific results. The data can go through multiple layers of transformation, with each layer clearly defined to produce desired business or technical requirements. Rigorous data management will require many different combinations of these techniques.
What are the different data transformation techniques?
A level down in hierarchy from data transformation types, data transformation techniques are the tactics your data analysts, scientists, and engineers will employ to make data more useful in different scenarios.
Here’s a list of the most common data transformation techniques:
- Data smoothing: Removes corrupted, degraded, or meaningless data from your data sets
- Data aggregation: Collects raw data from many sources and combines it into a single format for storage, analysis, reporting, and use in business applications
- Discretization: Labels data in intervals in continuous data pipelines to make data lineage easier to track and observe
- Normalization: Transforms all data within a data set into specific ranges or parameters so that it can be used consistently by data teams, algorithms, workflows, and customer experience tools
- Attribute construction: Groups together data that shares common attributes under a newly created attribute
- Indexing: Orders or organizes data logically to match a data storage schema
- Enriching: Combines data sets from multiple sources to add missing, incomplete, or strategically useful data
All of these different data transformation techniques can stack with each other to lead to a wide array of benefits to your data teams and business.
What are the main benefits of data transformation?
Data transformation makes your data more accessible for all your systems, cleaner for analysis and reporting, and more reliable for business processes and decisions. To put it simply, transforming your data makes it easier for both humans and computers to use.
It speeds up your business processes and makes it simpler to scale and grow your organization when digital behaviors change and emergent technologies come onto the market.
Data transformation has many benefits:
- Improved data quality
- Faster analysis, reporting, and data science workflows
- Increased organizational agility and workflow efficiencies
- Better decision-making
- Less data downtime
- Faster product launches and feature testing
What if you wanted to build a new app or product discovery feature and all your product data was organized and ready to use? Having the data transformation piece done would take care of what is likely the most difficult and expensive part of the puzzle.
You could also use data transformation to personalize the content experience across all your channels. For example, you could use Reverse ETL to move customer data models out from your data warehouse and transform them to use in a digital experience tool like Adobe Experience Cloud. From there, you can orchestrate personalized content at every step of the customer journey.
It will also make it easier to build and maintain more accurate machine learning (ML) and AI processes that deliver higher value.
Just imagine what you could do if your data sets were normalized and you could use them in all your workflows, digital platforms, and business intelligence processes. So why doesn’t everyone have this figured out already? Turns out, there are some big challenges to getting data transformation right.
Challenges of data transformation
The challenges of data transformation include everything from expensive infrastructure to lack of expertise. When you’re dealing with huge amounts of data that need to move in real time from an SaaS app to a cloud data warehouse and back out to the SaaS app, it’s a complicated process.
Your team needs the right technology to deal with complex data structures and get data to the level of accuracy and completeness you need—and then maintain it.
Here are the three most common challenges blocking companies from using data transformation successfully:
Cost: Your data stack might need to be updated to make data transformation possible, and that can get expensive if you’re using custom or legacy systems. You’ll also need to hire and maintain staff who understand your data stack, which can quickly exceed your payroll budget.
Computational resources: Data transformation processes take up a lot of computing power and can put a burden on your operations. This is one of the reasons cloud-based data warehouses are growing more and more popular. But if you’re using an on-premise data warehouse, the challenge of replatforming your data might be overwhelming.
Lack of data expertise: It can be difficult to find the right data analytics team to make data transformation happen. For example, your data team could be great at DataOps but they might not understand the context of the data well enough (for example, if your data comes from the medical field, sporting goods industry, Internet of Things, insurance industry, etc.) to build data transformation processes that really help your business.
While you can’t solve all of these challenges with tools, you can make data transformation less challenging with the right ones.
Choosing your data transformation tools
The following tools can help you overcome many data transformation challenges and get the benefits you’re looking for. These range from dedicated cloud transformation platforms like dbt to the simple and extensible ETL processes of Stitch. You’ll likely use a combination of these powerful tools to build your modern data stack and make data transformation possible.
dbt enables data teams to work directly within data warehouses to produce accurate and trusted data sets for reporting, machine learning (ML) modeling, and operational workflows. It’s a developmental framework that combines modular SQL with software engineering best practices to make data transformation reliable, fast, and easy.
Snowflake is a powerful solution for data warehouses, data lakes, data application development, and securely sharing and consuming data. It’s a fully managed cloud data service that’s simple to use but powers a near-unlimited number of concurrent workloads. Snowflake gives your data analytics team the performance, flexibility, and near-infinite scalability to easily load, integrate, analyze, and share data.
Fivetran helps you securely access and send all data to one location. It instantly connects hundreds of your most demanding databases and data sources. Fivetran helps data engineers effortlessly centralize data so your team can deliver better insights faster.
Shipyard integrates with dbt Cloud, Snowflake, Fivetran, and many more to build error-proof data workflows in 10 minutes without relying on DevOps. Data engineers can use these tools to quickly launch, monitor, and share resilient data workflows and drive value from your data at record speeds, without the headaches. Hundreds of high-growth data teams are using Shipyard to modernize their data workflows and build solid data infrastructures that connect their data stacks from end to end.
Get started with data transformation
Data transformation makes your data usable at every step of the data science pipeline, increases reporting and analysis accuracy, and helps you build digital experiences faster. With the right approach to data transformation you can reduce data downtime, speed up your business workflows, and equip your company to scale and grow with changing digital landscapes.
Shipyard integrates with powerful data transformation tools to build data pipelines that help you run and grow your business. Schedule a demo of the Shipyard app or sign up for our Developer plan that’s free forever—no credit card required. Start to build data workflows in 10 minutes or less, automate them, and see how Shipyard fits your business.