data transformation

Data Transformation: What Is It? (Definition, Tools, and Use Cases)

Modern businesses rely on data to make informed decisions. But very rarely does a dataset meet all the needs of those using it. Even so, that doesn’t mean that there isn’t a way to work around it. Many businesses are taking advantage of data transformation to convert data into formats that can be used across various systems. This post will walk you through everything you need to know about data transformation.

What Is Data Transformation? A Simple Definition

Data transformation is the process of changing data from one format to another to make it more valuable. The most common forms of data transformation involve the conversion of raw data into a clean and usable form, removing duplicates, converting data types, and enriching data to benefit an organization.

Data may be transformed at two stages in the data pipeline. Businesses that use on-premise data warehouses typically leverage an ETL (extract, transform, load) process, in which data transformation is the middle step. On the other hand, businesses that use cloud-based data warehouses usually skip preload transformations (given the high scalability of cloud platforms) and load raw data into the data warehouse, then transform it at query time. This data transformation model is referred to as ELT (extract, load, transform).

Processes such as data migration, data integration, data wrangling, and data warehousing may all involve data transformation. The data transformation can be:

  • Aesthetic: The transformation process standardizes data to meet parameters or requirements.
  • Constructive: The transformation process adds, replicates, or copies data.
  • Destructive: The transformation process deletes records or fields.
  • Structural: The database is reorganized by moving, renaming, or combining columns.

An organization can select among a variety of ETL tools that automate the data transformation process.

How Does Data Transformation Work?

The data transformation process aims to extract data from a source, convert it into a usable and clean format, and then deliver it to a specific destination. This entire process is referred to as ETL.

Data transformation concept
Data Transformation

During the extraction stage, data is identified and retrieved from many different sources or locations into a central repository. This data is often raw and not usable in its original format–the data is transformed to eliminate this obstacle. This is the most important step in the ETL process, given that it adds more value to your data. During the transformation process, various steps are taken to convert the data into a usable format. In some instances, data is first cleansed before it can be transformed. The cleansing of the data prepares it for transformation by fixing inconsistencies. Once the data has been cleansed, it passes through the following five steps:

  1. Data Discovery: Data discovery entails the identification and understanding of data in its source format. It does this by leveraging data profiling solutions. Data discovery basically assists you to determine what actions you should take, and when to take them to convert data into the desired format.
  2. Data Mapping: This stage involves establishing a sound plan that determines the elements of data that will be transformed and how the transformation will be conducted. You should ensure that your plan takes into account whether or not data will be lost during the transformation and how to mitigate the losses.
  3. Code Generation: To complete the transformation process, you need to create a code that runs the transformation task. These codes are usually generated with the help of a data generation platform or tool.
  4. Code Execution: Once the transformation process has been mapped out and the transformation code created, the code is run to convert the data into the desired format.
  5. Review: After the data has been transformed, you need to check it to ascertain that everything has been formatted properly.

Apart from these basic steps, other customized operations may also occur. These include:

  • Filtering, e.g., selecting only specific columns to load
  • Combining multiple columns into a single column, and vice versa
  • Combining data from multiple sources
  • Eliminating duplicate data

Once the data has been transformed, it is ready to be sent to its target destination (data analytics platform, data warehouse, or database) so that it can be put to work.

Data Transformation: Different Methods

There are numerous methods of data transformation that can help structure and clean up data before it’s analyzed and stored in a data warehouse. Here’s an outline of some of the common techniques:

  • Attribute Construction: This data transformation method allows the organization of a dataset by creating new attributes from an existing set of attributes. The new attributes usually have greater predictive power than the original attributes.
  • Data Aggregation: Data aggregation compiles raw data from several sources with the intent of preparing combined datasets for data processing. This technique is ideal for businesses that collect large volumes of data.
  • Data Smoothing: This data transformation technique removes meaningless or distorted data from a dataset. Data smoothing can identify specific trends or patterns in a dataset by detecting any minor modifications to the data.
  • Discretization: Discretization creates interval levels in continuous data to enhance the ease and efficiency of its analysis. By using decision tree algorithms, discretization converts large datasets into compact categorical data.
  • Normalization: This technique converts source data into another form to minimize the occurrence of data duplication. The data is usually transformed such that it stays within a particular range for efficient data extraction and mining.
  • Generalization: This technique uses concept hierarchies to transform low-level data attributes into high-level data attributes, thereby creating a clear data snapshot.

Benefits of Data Transformation

Organizations across the board need data for several reasons. Whether it’s information about internal processes, customer behavior, supply chain management, or any other reason, data has the potential to improve efficiencies and increase revenue generation.

Male employee looking at data charts

To gain insight into and enhance these operations, organizations require high-quality data transformation that is compatible with the systems consuming the data. By using data transformation, organizations can reap several benefits from their data, including:

1. Better Data Quality

While data is an important tool in the decision-making process, bad data quality can be costly to your organization. Given the high risks that are involved when dealing with data, data quality has become a major concern in recent years. Data transformation can minimize or eliminate quality issues such as missing values and inconsistencies, thereby making it more valuable.

2. Better Data Management

Businesses are continuously generating large volumes of data from multiple sources. Given the large volumes of data, inconsistencies in metadata can make it challenging to organize and understand data. Such data can do more harm than good to organizations. Data transformation refines metadata, thereby making it easier to organize and understand when it’s in your dataset. Also, transformed data is usually standardized and stored in a source location. As such, you can perform queries faster and retrieve information more easily.

3. Enhances the Ability to Become More Data-Driven

Data transformation is usually a part of business intelligence or analytics. The ultimate goal is to use the data to understand your business.

While businesses collect large volumes of data constantly, most of that isn’t analyzed. Data transformation makes it easier to get the most out of your data by making it more understandable and usable.

Note: Whereas data transformation comes with a host of benefits, it also has a few drawbacks.

They include:

  • Acquiring transformation tools and hiring transformation experts can be costly.
  • It can be challenging to properly align data transformation activities to an organization’s data-related priorities and needs.

How ClicData Can Help You

ClicData is a cloud-based application that is easy to use and maintain. Its data transformation feature enables you to transform data whichever way you want. With ClicData, you can use familiar functions and formulas to filter data, add new columns to it, group it, or pivot it to change its structure.

Once you have mastered the art of data transformation, you won’t need to create formulas on your raw data. You can create all your key ratios and calculations with ClicData.

Here are a few examples of how our solution helped customers:

First Example

A client whose marketing team was using HubSpot marketing and CRM tools to manage new business pipelines and digital marketing was having some reporting challenges. The issue with HubSpot is that it fell short in data consistency and segmentation. The team had to perform this particular manipulation manually, which was very time-consuming: They had to export data in Excel spreadsheets from HubSpot, cleanse the data, and cross-reference it each time they were trying to find answers.

They engaged ClicData for help pulling and aggregating their data from HubSpot together with time-sliced data. ClicData helped them build one consistent view of this data across the deal object, the contact object, and the company object within HubSpot. They now have a single platform that the leadership and sales team can use to determine lead attribution and lead contribution.

Read more: Using ClicData and Hubspot for Custom Sales & Marketing Reporting

Second Example

The second client sells B2B software and tracks customer deals and contracts across multiple countries and various industries. Its marketing team deals with hundreds of contacts every month, and they often have different contacts for the same company. Even so, they were struggling to understand who was the very first touch point for every company, and they needed that information to calculate the ROI of each marketing campaign and determine which funnel converts better.

With their native HubSpot connectors, the client extracts data from HubSpot into ClicData. They can now define who is the very first marketing touchpoint. That data is imported back to HubSpot into a custom field so that the CRM users can access that information.

Conclusion

If your business isn’t leveraging data transformation, you’ll constantly fall behind your competition. Data transformation enables organizations to convert data from different locations into actionable insights – it does this by streamlining the process of refining, standardizing, and consolidating these data types.

Are you looking to make the most of data transformation? Try ClicData for free.


You may also like:

Share this on