What is data transformation?

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.

Defining data transformation and its role in data management

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.

The importance of transforming raw data for analysis and visualization

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.

How data transformation fits into the ETL/ELT process

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.

Processes such as data integration, data migration, data warehousing, and data wrangling all may involve data transformation.

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.

The benefits and challenges of data transformation

Transforming data yields several benefits:

  • Data is transformed to make it better organized. Transformed data may be easier for both humans and computers to use.
  • Properly formatted and validated data improves data quality and protects applications from potential landmines such as null values, unexpected duplicates, incorrect indexing, and incompatible formats.
  • Data transformation facilitates compatibility between applications, systems, and types of data. Data used for multiple purposes may need to be transformed in different ways.

However, there are challenges to transforming data effectively:

  • Data transformation can be expensive. The cost is dependent on the specific infrastructure, software, and tools used to process data. Expenses may include software licensing, computing resources, and the time spent on task by the needed personnel.
  • Data transformation processes can be resource-intensive. Performing transformations before loading into a data warehouse, or transforming data before feeding it into applications can create a computational burden that slows down other operations. If you use a cloud-based data warehouse, you can do the transformations after loading because the platform can scale up to meet demand.
  • Lack of expertise and carelessness can introduce problems during transformation. Data analysts without appropriate subject matter expertise are less likely to notice typos or incorrect data because they are less familiar with the range of accurate and permissible values. For example, someone working on medical data who is unfamiliar with relevant terms might fail to flag different names for a disease that should be mapped to a singular value or notice and correct misspellings.
  • Enterprises can perform transformations that don't suit their needs. A business might change information to a specific format for one application only to then need to revert the information back to its prior format for a different application.

Techniques for data transformation

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.

Extraction and parsing: Accessing data from different sources

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.

Translation and mapping: Converting data formats and structures

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.

Filtering, aggregation, and summarization: Reducing and generalizing data

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.

Enrichment and imputation: Handling missing values and enhancing the dataset

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.

Indexing and ordering: Organizing data for optimal retrieval

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.

Anonymization and encryption: Protecting sensitive data

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.

Modeling, typecasting, formatting, and renaming: Preparing data for analysis

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.

Data transformation tools and technologies

Businesses have multiple options for data transformation tools and technologies, depending on size of organization, budget, and a company’s data management strategy.

ETL tools for data transformation

There are numerous ETL tools available for data transformation. They are typically categorized into four groups:

  • Enterprise-grade. These tools are sold by commercial organizations and often deliver the most mature solutions. They are geared for businesses that do not have the time or resources to devote to staffing an in-house team to build their own solutions. Enterprise-grade solutions come with pre-defined pipelines, easy to use interfaces, and are built with the IT and line of business user in mind. Stitch provides an enterprise-grade solution.
  • Open-source. Teams that can develop, build, and maintain their own ETL process often use open-source ETL tools to do so. Many are free and allow businesses to access the tool’s source code to study its technical infrastructure and extensibility.
  • Cloud-based platform tools. Integration platform-as-a-service providers often bake ETL tools into their offerings. Platform-based tools offer high latency, availability, and elasticity, enabling organizations to scale their data transformation to the volume and speed the business needs.
  • Custom ETL tools. Some businesses prefer to develop their own custom ETL tools so they can tailor a solution to their organization’s unique infrastructure or priorities. ETL tools are often built with SQL, Python, and Java programming languages. This approach requires intensive internal resources to build, test, maintain, and update the tool. It also requires in-house documentation and training to enable new users.

Getting started with ETL tools for data transformation

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:

  • Determine your use case. How much data do you need to transform and how will it be used?
  • Align with your budget. Consider the cost of each option, including the “hidden” costs of open-source and custom ETL, when evaluating options.
  • Consider who will use it. Will you need your ETL tools to be available to various teams? If so, how easy it to make a tool available to others and to train new users?
  • Be mindful of data quality. Automated features in ETL tools can help enforce data quality.
  • Be sure it can access data wherever it lives. ETL tools need to be able connect to data sources in all different formats.
  • Know what level of technical skills are required. Will you have only developers working with the tool, or do you want to make it available to business users who can then build their own data pipelines and use the tool to inform business processes and analytics?

Stitch offers an enterprise-grade cloud ETL platform to help power actionable insights for any analytics environment.

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