Analyzing information requires structured and accessible data for best results. Data transformation enables organizations to alter the structure and format of raw data as needed. Learn how your enterprise can transform its data to perform analytics efficiently.
Businesses run on data that is used to inform decision making in every realm of the organization. But for data to be useful, it has to be changed from its raw data source form into a format that is easy for applications and systems to use — and for people to interpret and understand. To achieve this in the data management process, companies use data transformation to convert the data into the needed format.
One of the major purposes of data transformation is to make data usable for analysis and visualization, key components of business intelligence and data-driven decision making. Businesses generate and collect vast amounts of data, but until it is transformed, its value cannot be leveraged. Raw data is often stored in data warehouses or data lakes, where it waits to be selected and used for analysis.
To obtain the data from its repository, businesses use related data transformation processes called extract/transform/load (ETL) and extract/load/transform (ELT).
For data stored in on-premises data warehouses, ETL extracts the data from the repository, transforms it into the required format, then loads it into an application or system. There it can be used for business intelligence, data analysis, and other purposes.
For cloud-based data warehouses, the ELT process is used. The scalability of the cloud platform lets organizations skip preload transformations and load raw data into the data warehouse, then transform it at query time.
Data transformation may be constructive (adding, copying, and replicating data), destructive (deleting fields and records), aesthetic (standardizing salutations or street names), or structural (renaming, moving, and combining columns in a database).
An enterprise can choose among a variety of ETL tools that automate the process of data transformation. Data analysts, data engineers, and data scientists also transform data using scripting languages such as Python or domain-specific languages like SQL. They may also use tools such as Stitch to get to insights faster using fully automated cloud data pipelines that do not require any coding. This can greatly speed up the process of making data usable and useful.
Transforming data yields several benefits:
However, there are challenges to transforming data effectively:
Data transformation can increase the efficiency of analytic and business processes and enable better data-driven decision-making. The first phase of data transformations should include things like data type conversion and flattening of hierarchical data. These operations shape data to increase compatibility with analytics systems. Data analysts and data scientists can implement further transformations additively as necessary as individual layers of processing. Each layer of processing should be designed to perform a specific set of tasks that meet a known business or technical requirement. The following are techniques for data transformation.
In the modern ELT process, data ingestion begins with extracting information from a data source, followed by copying the data to its destination. Initial transformations are focused on shaping the format and structure of data to ensure its compatibility with both the destination system and the data already there. Parsing fields out of comma-delimited log data for loading to a relational database is an example of this type of data transformation.
Before your enterprise can run analytics, and even before you transform the data, you must replicate it to a data warehouse architected for analytics. Most organizations today choose a cloud data warehouse, allowing them to take full advantage of ELT. Stitch can load all of your data to your preferred data warehouse in a raw state, ready for transformation.
Some of the most basic data transformations involve the mapping and translation of data. For example, a column containing integers representing error codes can be mapped to the relevant error descriptions, making that column easier to understand and more useful for display in a customer-facing application.
Translation converts data from formats used in one system to formats appropriate for a different system. Even after parsing, web data might arrive in the form of hierarchical JSON or XML files, but need to be translated into row and column data for inclusion in a relational database.
Data transformation is often concerned with whittling data down and making it more manageable. Data may be consolidated by filtering out unnecessary fields, columns, and records. Omitted data might include numerical indexes in data intended for graphs and dashboards or records from business regions that aren’t of interest in a particular study.
Data might also be aggregated or summarized by, for instance, transforming a time series of customer transactions to hourly or daily sales counts.
BI tools can do this filtering and aggregation, but it can be more efficient to do the transformations before a reporting tool accesses the data.
Data from different sources can be merged to create denormalized, enriched information. A customer’s transactions can be rolled up into a grand total and added into a customer information table for quicker reference or for use by customer analytics systems. Long or freeform fields may be split into multiple columns, and missing values can be imputed or corrupted data replaced as a result of these kinds of transformations.
Data can be transformed so that it's ordered logically or to suit a data storage schema. In relational database management systems, for example, creating indexes can improve performance or improve the management of relationships between different tables.
Data containing personally identifiable information, or other information that could compromise privacy or security, should be anonymized before propagation. Encryption of private data is a requirement in many industries, and systems can perform encryption at multiple levels, from individual database cells to entire records or fields.
Finally, a whole set of transformations can reshape data without changing content. This includes casting and converting data types for compatibility, adjusting dates and times with offsets and format localization, and renaming schemas, tables, and columns for clarity.
Businesses have multiple options for data transformation tools and technologies, depending on size of organization, budget, and a company’s data management strategy.
There are numerous ETL tools available for data transformation. They are typically categorized into four groups:
Most organizations are already doing data transformation as part of their data management strategy. However, choosing the right ETL tools is often challenging. To help determine the type of ETL tool that is best for your organization, consider the following:
Stitch offers an enterprise-grade cloud ETL platform to help power actionable insights for any analytics environment.