What is Data Transformation? The Ultimate Guide for Businesses in 2023
Analyzing business data is one of the keystones of modern data-driven commerce. Through data transformation, organizations can alter the structure and format of raw data to run more efficient data analytics operations. Let’s explore the role of data transformation for digital businesses in 2023.
Table of Contents
What is data transformation?
Data transformation is the process of changing the format, structure, or values of data into a usable format to support decision-making processes in an organization.
Businesses usually transform data when different data formats need to be converted to match the target destination system, which can occur at two stages of the data pipeline:
1. Before loading the data (ETL)
2. After loading the data (ELT)
However, today, most organizations use cloud-based data warehouses and data transformation tools that can scale compute and storage with latency measured in seconds in minutes, achieving near real-time insights.
Thanks to the scalability of the cloud, teams can skip preload transformations and load raw data into the cloud data warehouse, such as Google BigQuery, and transform it at query time.
This model is called ELT (extract, load, transform).
Businesses can choose among a variety of ETL tools that automate the data transformation process. Data analysts, data engineers, and data scientists, on the other hand, often prefer the ELT model as it allows them to prepare raw data for data analysis using scripting languages like Python or domain-specific languages like SQL.
We designed Whatagraph as a one-stop data transfer service for two types of users:
- Small non-tech teams
- Marketing agencies that handle hundreds of clients, each with their own data stacks
In both cases, Whatagraph saves the time needed to load data from multiple sources to Google BigQuery.
So we asked ourselves - What is the simplest way to run a data transfer?
The answer: point-and-click.
Our developers got busy and created the data transfer workflow that everyone can run.
Benefits of data transformation
- Organization: Data is transformed to make it more organized. Transformed data is easier to work with for both humans and machines. After transformation, data can be used for machine learning which helps businesses with profit and revenue projections and supports decision-making with the automation of multiple business processes
- Quality: Formatted and validated data improves the data quality and protects applications from potential errors, such as missing values, unexpected duplicates, incorrect indexing, and incompatible formats.
- Accuracy: Transformation generates datasets that are converted to metrics, dashboards, and reports which help organizations achieve specific goals.
To create accurate reports and visualize your transformed data, you can use Whatagraph’s data visualization service. Now you can create interactive dashboards using any data available in your BigQuery account.
Creating a new report with Whatagraph has never been easier — just select your BigQuery account and connect your sources.
When your report is created, you can use the filter function to show only a specific subset as well as drag-and-drop individual fields with metrics and dimensions to customize your report.
- Compatibility: Data transformation enables compatibility between applications, source systems, and different data types. Data used for multiple purposes may need to be transformed in different ways.
Downsides of data transformation
- Expensive: The cost of data transformation can accumulate because it depends on the specific infrastructure, software, and tools used to process data. Expenses also include licensing, computing resources, and hiring necessary experts.
A solution? Use a data transformation tool with transparent pricing and a cloud-based data warehouse, such as Google BigQuery.
- Resource-heavy: Data transformation processes can lock substantial resources. Performing transformations in an on-premises data warehouse after loading data often create a computational bottleneck that slows down other operations.
However, if you use a cloud-based warehouse, you can do the transformations after loading because the platform can scale up to meet demand.
- Lack of expertise: Data analysts who lack appropriate subject matter are less likely to notice incorrect data because they are less familiar with the range of accurate and permissible values.
You can avoid this using a fully-managed data pipeline where no data is lost or misplaced.
- Redundancy: Businesses often invest in transformations that don’t suit their needs. A team might change information to a specific format for one application and then revert the information to its original format for different applications.
To avoid this, you can use a data transformation tool that supports data virtualization (DV). In this system, data analysts get a virtualized view of different data sets on demand without having to enlist an IT team to load the data into a data warehouse.
The data transformation process explained in 9 steps
The first phase of data transformation includes the conversion of different data types and the flattening of hierarchical data. The aim of these operations is to increase data compatibility with analytics systems while implementing further transformations through added layers of processing.
1. Extraction and parsing
In the modern ELT processes, data ingestion starts with extracting information from a data source, followed by copying the data to its destination. Initial transformations focus on shaping the format and structure of data to make sure it’s compatible with both the destination systems and data already stored there.
An example of this type of data transformation is parsing fields out of comma-delimited log data for loading into a relational database.
2. Mapping and translation
Data mapping and translation of data are among the most basic data transformations. For example, a column containing integers that represent error codes can be mapped to the relevant error descriptions. This way, the column becomes easier to understand and more useful in customer-facing frontends.
Translation converts data from formats used in one system to formats suitable for a different system. Even when parsing is done, web data might arrive as JSON or XML files and needs translating into row-and-column data to be included in a relational database.
3. Aggregation, filtering, and summarization
In many aspects, data transformation relates to whittling down data and making it more manageable. You can consolidate data by filtering out unnecessary fields, columns, and records.
These might include numerical indexes in data intended for graphs and dashboards or records from business regions that are irrelevant to a particular report.
Business data might also be aggregated or summarized by transforming a time series of customer transactions into hourly or daily sales counts.
Business intelligence tools can also help with filtering and aggregation, but it’s better to complete the transformations before a reporting tool can access the data.
4. Enrichment and imputation
Data from different sources is often merged to create denormalized enriched information. For example, a customer’s transaction can be bundled up into a grand total and added to a customer information table for a quicker reference or the customer’s analytics systems.
These transformations split long or freeform fields into multiple columns, input missing values, and replace corrupted data.
This technique creates interval labels in continuous data aiming to enhance its efficiency and make it easier to analyze. This process uses decision tree algorithms that transform large datasets into categorical data.
Low-level data attributes are transformed into high-level attributes by applying the concept of hierarchies and creating layers of successive summary data.
7. Indexing and ordering
Data is sometimes transformed to be ordered logically or to suit a data storage scheme. In the case of relational database management systems, creating indexes can improve performance or the management of relationships between separate tables.
8. Anonymization and encryption
Data containing personally identifiable information or other privacy-sensitive information should be anonymized before propagation. Encryption of private data is already a requirement in many industries, where systems perform encryption at multiple levels, from personal database cells to entire records.
9. Modeling, formatting, renaming and typecasting
Last but not least, there’s a set of transformations that reshapes data without changing content. This includes casting and converting data types for compatibility, adjusting dates and times, and renaming schemas, tables, and columns for clarity.
Before you can run analytics on your business data, you must transform the data, and to do that, you must load it to a data warehouse that supports big structured and unstructured data analytics. Most organizations today chose a cloud data warehouse to load their data.
Whatagraph allows you to load data from multiple platforms in just four steps.
If you’re looking for an easy-to-use solution to connect data from various marketing sources, keep your marketing data safe, or want to visualize your BigQuery data, book a demo call and find out how Whatagraph can help.
Published on Apr 09 2021
WRITTEN BYNikola Gemes
Nikola is a content marketer at Whatagraph with extensive writing experience in SaaS and tech niches. With a background in content management apps and composable architectures, it's his job to educate readers about the latest developments in the world of marketing data, data warehousing, headless architectures, and federated content platforms.
Get marketing insights direct to your inbox