An introduction to data transformation
The data transformation process can be complicated. Historically, it’s been an undertaking that requires advanced technical skill, but also one that many data professionals find time-consuming and tedious. Fraught with roadblocks and frustrations, in-house data transformation can take far longer than data analysis, sometimes demanding the bulk of the effort in an analytics project.
Data transformation via ETL is nothing new: Data teams have been doing it for decades. As concepts for extracting and storing data evolved, capabilities for data transformation also improved. Spurred on by the rise of data warehousing solutions in the mid-nineties, tech providers began developing more advanced tools to support the entire extract, transform, load (ETL) process.
Businesses initially embraced the new ETL tools, but eventually started creating their own in-house frameworks to better meet specific data transformation requirements. While these frameworks helped organizations accelerate their data transformation processes for standard workloads, large datasets still presented challenges, taxing on-premises machines, data team resources, and budgets, as enterprises attempted to quench the relentless thirst for as much data as possible, as fast as possible..
The advancement of supercomputing techniques like massively parallel processing (MPP) provided more power for ETL processes, but as the need to analyze more and more data increased, so did the demand for platforms with even greater scalability and performance.
The good news? By leveraging cloud technology, enterprises can now transform data at the pace the current digital business landscape requires. But before looking more closely at the uses and benefits of the process today, let’s explore data transformation in further detail.
What is data transformation?
Data transformation is the process of changing or converting data to make it valuable—or usable—for an organization’s purposes. Depending on the changes applied to the source data, a transformation can be considered simple or complex. These changes can include aggregating, deduplicating, enriching, filtering, joining, merging, or splitting data.
One of the core components in an ELT/ETL undertaking, data transformation typically converts data from its original format within a source system to a different format required by a destination system. The process can be automated, performed manually, or conducted using a combination of the two approaches.
Unlike data extraction, which retrieves data from various sources, or data ingestion, which transports the data to a target destination, the data transformation process adds to, removes from, or changes a given dataset. In other words, data transformation can be:
- Aesthetic: Standardizing fields or records into the same format
- Constructive: Adding to the source dataset
- Destructive: Removing fields from the source dataset
- Structural: Combining or moving components within the source data
Not all raw data requires transformation for use. Source data that is already in a usable format is often referred to as “direct move” or “pass-through” data.
How data transformation works
For some enterprise data transformation efforts, source data will first require cleansing, a process for identifying and modifying any incomplete, incorrect, or inaccurate data. After data cleansing, a company will typically perform five basic steps to transform data:
Data discovery and profiling
In the first phase of the data transformation process, an organization will need to interpret and understand the raw data involved. What are its attributes? How is it structured? What needs to be transformed?
Next, the company creates a plan for how the transformation will happen. If it needs to transform data for compatibility purposes, it will identify which components will change and what can remain as is. The data team also needs to consider whether there’s a risk of data loss during the process and how to mitigate that loss.
Workflow planning and creation
In this phase, the business determines whether it will perform the data transformation with a tool or by writing a script. Considerations include the data team’s expertise, whether data structure will change over time, and whether others in the organization will be using the transformation workflow. During this phase, a company will also identify the input and output data file formats within the workflow and clearly delineate the transformation required.
Workflow testing and execution
Next, the organization runs tests by connecting input data to the transformation workflow. Ideally, running the workflow will result in data that matches the company’s target format.
Finally, the business reviews the quality and accuracy of its output data, and based on its findings, makes any necessary changes to the workflow.
When the transformation process is complete, the data is ready for loading in a target destination.
Data transformation examples and benefits
Whether it’s simply changing data from one format to another or a more complex transformation, organizations of all sizes and across industries need to modify raw data for various purposes.
Common use cases for data transformation include:
- Stripping personal identifying information (PII) from data that needs to be anonymized for storage in a data warehouse environment
- Deduplicating data and removing inconsistencies to provide teams with more reliable data for analytics
- Enriching business data with third-party data sets to create 360-degree customer views
- Merging data from separate database management systems into a target database following a merger or acquisition
- Cleaning, preparing, and dividing data for use in machine learning models
- Integrating data from multiple CRM platforms in a single unified system
- Streamlining data access for all users across global business operations
- Combining data from various social media platforms to gauge the effectiveness of online ad campaigns
- Collecting and integrating data from across business units to improve operational efficiency and automate processes
- Creating data lakes that combine both structured and unstructured data for mining and use in real-time applications
The benefits of data transformation
Companies benefit from modifying and manipulating their raw data because these processes:
- Make data valuable: By transforming data, companies make it usable for their employees and applications. Without transformation, businesses can’t tap into the full value of their data.
- Enhance data quality: Bad data can damage relationships with customers, negatively impact a company’s reputation, and result in poor decision making. During the data transformation process, organizations can address inconsistencies and missing values to enhance the quality of their data.
- Improve data organization and management: Working with data from today’s many sources—systems, devices, and apps—can also mean working with metadata that is inconsistent, which makes organization difficult. Refining metadata during the data transformation process helps businesses better understand and manage their data.
- Accelerate queries and data retrieval: When organizations standardize their data through transformation and store it in a centralized location, users can more quickly and easily retrieve it.
Matillion and data transformation
Purpose-built for the cloud, Matillion ETL software can help your enterprise quickly transform its data to unlock valuable insight. Leveraging an extensive list of pre-built data source connectors, our ETL platform easily loads data from systems and sources, rapidly performing the transformations necessary to make it consumable by your analytics tools.
With our ETL platform, your business can:
- Streamline data preparation by automatically generating ETL documentation that includes all necessary job details
- Maintain control and ownership of your data at all times to comply with regulations and company security requirements
- Make data easily available to all authorized users, allowing them to mine it for business intelligence as needed
- Manage costs by choosing the model that fits your needs, from pay-as-you-go and hourly pricing to enterprise contract models
Want to know how our customers are putting the Matillion ETL platform to work? Read our case study to find out how customer management leader Clutch is leveraging Matillion ETL technology to transform massive amounts of the retail data its clients rely on for business-critical insight.
Read our ebook, The Business Benefits of Data Transformation, to learn more about reducing costs, centralizing data for easier reporting, and enhancing flexibility with cloud-native data transformation.
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