Raw data—like unrefined gold buried deep in a mine—is a precious resource for modern businesses. However, before you can benefit from raw data, the process of data transformation is a necessity.

Data transformation is the process where you extract data, sift through data, understand the data, and then transform it into something you can analyze. That’s where ETL (extract, transform, load) pipelines come into play. As a vital stage of the ETL process, data transformation is necessary to change the information into a format or schema that a business intelligence platform can interact with to derive actionable insights.

Good data can effectively transform a struggling business into a successful one. In the global marketplace, good data powers dynamic business analysis, which in turn promotes business agility. Essentially, good data boost supply chain efficiency and customer satisfaction.

The opposite, bad data, is what keeps CTOs, BI professionals, and developers up at night. Bad data is data that is suspect, missing, or flawed. It's also very dangerous. In fact, bad data can lead to misguided decision-making and lost profits. According to Gartner, poor data quality costs companies millions in revenues annually - dollars and cents proof of the importance of data transformation.

The insights value chain is only as strong as its weakest data link, however. According to KPMG, 71% of CEOs say they have disregarded insights from unreliable data. It's no surprise that BI professionals spend 80% of their time preparing (cleaning and transforming) data before they can engage in actual analysis.

However, as essential as data transformation is, only data engineers and data scientists tend to understand it. Read on to learn more about data transformation, different types, and common challenges that come along with data transformation. 

Table of Contents

What is Data Transformation?

Raw or source data is often:

  • Inconsistent: It contains both relevant and irrelevant data.
  • Imprecise: It contains incorrectly entered information or missing values.
  • Repetitive: It contains duplicate data.

Data transformation is the process of extracting good, reliable data from these sources. This involves converting data from one structure (or no structure) to another so you can integrate it with a data warehouse or with different applications. It allows you to expose the information to advanced business intelligence tools to create valuable performance reports and forecast future trends.

Data transformation includes two primary stages: understanding and mapping the data; and transforming the data.

Related Reading: Data Engineering: What is a Data Engineer and How Do I Become One?

Stage 1: Understanding and Mapping the Data

During the first stage of data transformation, you’ll identify and study your data sources. Examples of data sources include:

  • Databases attached to different business systems
  • Accounting software
  • CRM platforms
  • Streaming sources
  • Customer log files
  • Web application data
  • Information related to mobile app usage statistics

Once you identify your data sources, you’ll then determine what kind of data structures each one has, and the types of transformations required to connect them. Some questions you might want to ask are:

  • What do the columns and rows look like?
  • How are they labeled?
  • What kind of information do they contain?
  • How does the information in one data source relate to another source? 

Next, you'll perform data mapping to define how the fields in different data sources connect together, and what types of data transformations they require. 

Stage 2: Transforming the Data

During the second stage of data transformation, you will carry out the different data transformations that you mapped in the first stage. There are several strategies for doing this: 

  • Hand-Coding ETL Solutions: Traditionally, you would set up your ETL process through scripting, by hand-writing code in SQL or Python. This was a task you'd give to offsite developers, and it was a time-consuming process. Also, because offsite developers had to interpret your requirements, the process often resulted in misunderstandings and unintentional errors.
  • Onsite Server-Based ETL Solutions: Onsite ETL solutions work through your onsite servers to extract, transform, and load information into an onsite data warehouse. Although most companies are now moving to cloud-based data warehousing and ETL solutions, onsite ETL still has its place. Compared to offsite scripting solutions, onsite ETL offers the benefit of more oversight by the end-user. However, you may need to hire expert staff to manage it. 
  • Cloud-Based ETL Solutions: Cloud-based ETL solutions like Integrate.io are the future of data transformation. They work through the cloud rather than on an onsite server. These solutions are particularly useful when you're linking cloud-based software as a service (SaaS) platforms like Salesforce to a cloud-based data warehouse like Amazon Redshift. They can also help you integrate an onsite business system to a cloud-based data warehouse. Many feature drag-and-drop graphical interfaces that make it easy for end-users to manipulate and control their data transformations (with no expertise in data science). Furthermore, they offer automatic integrations that automatically perform the transformations your data requires.

Types of Data Transformations

Below we've listed the types of transformations that you, your ETL platform, or your data team may need to perform during the ETL process. Although the majority of these tasks can happen automatically with a data transformation platform, sometimes you may need to set up and code ETL processes yourself. 

Related Reading: ETL & Data Warehousing Explained: ETL Tool Basics


Bucketing or binning gets used to change a numeric series into fixed, categorical ranges, say, from {2,5,8…} to {2-5, 6-9, 10-13…}. Take, for example, the seasonal fluctuations in consumer prices. Bucketing/binning lets us isolate noisy data and look at long-term averages. The focus away from short-term volatility provides a truer picture of price trends over time.

Data Aggregation

Data aggregation is a process that searches, gathers, summarizes, and presents data in different reports. For example, you have a list of male and female employees and their salaries, and you want to know the total male salaries compared to the total female salaries. You can aggregate the list by male and female, then sum up the total salaries for each group.

You can use a business intelligence platform to perform data aggregations based on the insights decision-makers need, or you can perform manual aggregations by coding in SQL.

Data Cleansing

Data cleansing involves deleting out-of-date, inaccurate, or incomplete information to increase the accuracy of data. Also referred to as data scrubbing and data cleaning, data cleansing relies on the careful analysis of datasets and data storage protocols to support the most accurate data possible. 

The process might include parsing data to remove syntax errors, deleting record fragments, and correcting typos. It could also involve fixing duplication problems that result from merging multiple datasets. The ultimate goal of data cleansing is to ensure that any data you work with is as accurate as possible and meets the highest quality standard.

Data Deduplication

Data deduplication is a data compression process where you identify and remove duplicate or repeated copies of information. Also referred to as single-instance storage, intelligent compression, commonality factoring, or data reduction, deduplication allows you to store one unique copy of data in your data warehouse or database.

The deduplication process analyzes incoming data and compares it to data that's already stored in the system. If the data is already there, deduplication algorithms delete the duplicate information while creating a reference to it. If you upload a changed version of a previous file, the system will back up a said file while adding the changes to the data segment. Deduplication algorithms also keep track of outgoing data to delete duplicates, which speeds up the information transfer process.

Data Derivation

Data derivation involves the creation of special rules to “derive” the specific information you want from the data source. For example, you might have a database that includes total revenue data from sales, but you’re only interested in loading the profit figures after subtracting costs and tax liabilities. Data derivation allows you to create a set of transformation rules that subtract costs and taxes from the total revenue information.

Data Filtering 

Data filtering includes techniques used to refine datasets. The goal of data filtering is to distill a data source to only what the user needs by eliminating repeated, irrelevant, or overly sensitive data. Data filters can get used like this to amend query results and data reports.

In its most practical form, data filtering simply involves the selection of specific rows, columns, or fields to display from the dataset. For example, if the end-user doesn’t need to see the addresses or Social Security numbers of each client in the report, data filtering will scrub them from the report.

Data Integration

Data integration is the process of taking different data types (like different databases and datasets relating to sales, marketing, and operations) and merging them into the same structure or schema. As a primary goal of ETL for data warehousing purposes, data integration supports the analysis of massive data sets by merging multiple data sources into an easy-to-analyze whole.

At its core, data integration reconciles differing values and names that refer to the same data elements within the data warehouse. By giving each element a standard name and definition, data integration makes it possible to analyze the information with a business intelligence platform.

Data Joining

Joining data is one of the most important functions of data transformation. A “join” is an operation in the SQL database language that allows you to connect two or more database tables by their matching columns. This allows you to establish a relationship between multiple tables, which merges table data together so you can query correlating data on the tables.

Data Splitting

Data splitting refers to dividing a single column into multiple columns. This is critical for analyzing the available data; splitting the single column into multiple columns can be useful to develop "training" and "testing" sets, for example. The "training" gets used for experimental analysis and making models, while the "testing" set is the untouched, "control" element. Data splitting can be helpful with a large amount of data gathered over a significant amount of time.

Data Summarization

Data summarization is similar to data aggregation. It refers to the creation of different business metrics through the calculation of value totals. You could sum up the total revenue of all the sales made by the individual salespeople on your staff, then create sales metrics that reveal total sales for individual time periods.

Data Validation

Data validation is the process of creating automated rules or algorithms that engage when the system encounters different data issues. Data validation helps ensure the accuracy and quality of the data you transform. For example, a rule could go into effect when the system finds that the first three fields in a row are empty (or NULL value). The rule might flag the row for the end-user to investigate later or stop the system from processing the row altogether.

Format Revision

Format revisions fix problems that stem from fields having different data types. Some fields might be numeric, and others might be text. One data system could treat text versus numeric information differently, so you might have to standardize the formats to integrate source data with the target data schema. This could involve the conversion of male/female, date/time, measurements, and other information into a consistent format.

Field lengths can also be an issue—especially if the target schema has smaller character limits. In these cases, it may be necessary to standardize the length of fields by breaking up long serial numbers into smaller parts and putting them into separate columns.

Additionally, format revision could involve splitting up a comma-separated list of words or numbers into multiple columns. 

Key Restructuring

When the tables in a data warehouse have keys with built-in meanings, serious problems can develop. For example, if a client phone number serves as a primary key, changing the phone number in the original data source means that the number would have to change everywhere it appears in the data system. That would cause a cascade of updates that over-burden or slow down the system.

Through key restructuring, you can transform any keys with built-in meanings to generic keys—i.e., random numbers that reference back to the source database with the actual information. By drawing key connections from one table to another, key restructuring optimizes the data warehouse for speed and efficiency.

Z-Score Normalization and Max-Min Scaling

Scaling changes our data ranges, but in z-score normalization, individual data features have zero-min and unit variance. So, all values will be between 0 to 1. Scaling is especially important because datasets often contain elements in varying units and ranges. This is incompatible with many machine learning algorithms that use Euclidean metric measurements.

Related Reading: What is a Data Warehouse and Why Are They Important?

The Top 4 Data Transformation Challenges

According to a recent survey, companies are falling behind in their data-driven goals: 72% of survey participants have yet to forge an internal data culture, while 52% say they have not leveraged data and analytics to remain competitive.

Why are companies failing to meet their goals? There are a few possibilities:

  1. The talent gap may be insurmountable. Depending on your infrastructure, transforming your data may require a team of experts and substantial investment in on-premise infrastructure. New tools have evolved to optimize the process of data transformation. However, the ability to wield big data technologies successfully requires both knowledge and talent.
  2. The process of preparing and migrating data is complex and time-consuming. Data scientists and BI professionals maintain that the process of data preparation (prior to transformation) takes up more than two-thirds of their time. According to a 2017 Crowdflower report, data scientists spend 51% of their time compiling, cleaning, and organizing data. They also spend 30% of their time collecting datasets and mining data to identify patterns.
  3. Without the proper tools, data transformation is a daunting process for the uninitiated. Ideally, data discovery and mapping must occur before transformations can commence. Without a proper roadmap, the already daunting task of data transformation is made more challenging. However, roadmap and workflow creation may be impossible without the proper tools and expertise.
  4. Developing a sustainable, fault-tolerant data pipeline often requires consensus building. For many organizations, building an efficient data pipeline involves extensive buy-in from key stakeholders. Consensus on the data collection and transformation process must often precede the building of a pipeline. This is easier said than done.

Additionally, the pipeline must easily accommodate changes to support scalability and functionality. The path to ETL hell is broad and wide, especially if there isn't an efficient mechanism in place to support schema evolution.

Related Reading: Top Challenges of Data Migration

The Best Tools to Help With Data Transformation Needs

With data being such a valuable resource to businesses of today, it’s essential that you are able to seamlessly transform data to meet all of your business needs and expectations. Data transformation tools play the role of “T” in ETL. Of course, ETL stands for Extract, Transform, Load. The overall process of ETL looks something like this: 

    • Extract: Data gets extracted from one or more specified locations. 
  • Transform: The extracted data is transformed and prepared in such a manner that it is readable for its next intended destination. 
  • Load: After the data is transformed, it is sent and loaded into its new data warehouse. Once in the warehouse, the data can be used for all types of analytic and reporting purposes.

Related Reading: In-House ETL vs Integrate.io: Comparison & Overview

Some of the top platforms offering data transformation tools in 2021, include:

  • Integrate.io
  • Airflow
  • EasyMorph 
  • Dataform 
  • Matillion
  • RudderStack
  • Trifacta

How Integrate.io Can Help 

To code or not to code, that is the question. With Integrate.io, you don't need to grapple with Pig, SQL, or Java code to fix bugs. Our cloud-based ETL platform allows you to execute basic and advanced transformations with ease.

Integrate.io facilitates agility; with the platform, you can integrate multiple data sources and retrieve insights from your data in real-time. This means you can use reliable data to optimize your algorithms and achieve business agility. Another benefit? The right ETL platform can save you money on OpEx and CapEx costs. Integrate.io's solution is cloud-based, so you don't need to rely on IT talent to maintain expensive infrastructure.

Finally, Integrate.io provides network, system, and physical security and is SOC2 compliant. Our physical infrastructure utilizes AWS technology and has accreditations for ISO 27001, Sarbanes-Oxley, PCI Level 1, and SOC 1 and SOC 2/SSAE 16/ISAE 3402. The platform also complies with the dictates of international privacy laws.

Are you curious about how Integrate.io can help with all your data transformation needs? Contact our team today to schedule a 14-day demo or pilot and see how we can help you reach your goals.