Guide to Data Transformation: Examples, Types, Benefits

What is Data Transformation?

Your company is likely producing more data than ever, and you know that data is valuable and can provide critical answers for your business — both in understanding your current business and in better and more accurately predicting future business trends. 

But this data, while incredibly valuable for your organization, can cause headaches and frustrations. Because data is created across many tools, platforms, and formats, the different types of data don’t always play well together. How do you know what data is valuable and won’t impact your business decisions? 

This is where data transformation comes in. Data transformation is the first step in any process that involves collecting and analyzing your data — from data mining to data warehousing to data analytics. Data transformation is the process of gathering data from various sources and making adjustments to the data to ensure your team can use the data effectively. It can also be more complex and involve several tools, steps, and features to make it most effective for your business. 

If your company isn’t utilizing data to make business decisions, you’re likely missing out on a massive and cost-effective resource to improve business processes and improve the outlook of your organization. Data is critical for modern businesses. But on the flip side, if you’re gathering and storing all of your data without plans for how to effectively use it or if you don’t have ways to utilize it without a lot of manual effort from people at your company, you won’t get the full benefits of that data. 

Data transformation is a critical tool that will improve your organization’s use of data to meet goals and grow your business. Let’s examine how data transformation works and why it matters for your company. 

Importance of Data Transformation for Businesses

Most of the software tools your company works with can export data points. For example, you can get all sorts of information about customers who visit your website. You can see dates and the time spent on web pages. You can pull sales information, shares, likes, and scroll speeds. You can dive into where people come from, how often they visit, and what browser they use. Suddenly, one person creates thousands of data points. Add in all your visitors, and you have giant spreadsheets that could crash your computer and create a wall of numbers that add little insight into your business. 

You likely could answer important business questions from this data — like what days or times people are more likely to visit your website, so you can ensure website availability, time emails and deals, or optimize buying experiences for visitors. But if it takes days or weeks to dig through the data to analyze this information, your business won’t be able to respond quickly to changes or trends and will devote a lot of resources to your data that may not have a strong ROI. 

Using data transformation, you can begin the process of optimizing your data so you can see the insights and create an agile and responsive business based on your data. Data transformation in data analytics ensures your data can be easily combined with other data, mined for insights, and surface important information your business can act on. 

Here are some of the benefits of data transformation for your business: 

Surface insights that matter to your company. You may not need to look at the time-stamped data of when someone visited your website, such as hours, minutes, and seconds. But your site generates that information whether you need it or not. One step of data transformation could be dropping the seconds and changing the time-stamp data into a format easily readable and understood by stakeholders across your company. 

Create similar data structures. One software tool spits out dates in a month, day, year format. The other does day, month, year. And yet another one inexplicably puts the year first. Even the tools that do have it in the same order differ in whether they use numbers for months, three-letter abbreviations, or spell out the entire month. If you brought in all of the date information from across these tools, it would take a lot of time and work to make sense of the dates. But with data transformation, you can pull in the date information from every tool and transform it into a date format that makes sense for your analysis. Then, you can combine the data from different tools to understand what dates matter and why.

Map data across tools. Another problem with data is that some tools could describe the same thing differently. But you can’t combine data easily if you don’t know that a “dermatological primary skin lesion” is the same as a “rash.” Some tools might have a checkbox users can click to select symptoms, while others might have a text box for users to type open responses. These are two different formats of data that a computer might not always recognize as the same. Data transformation in data mining allows you to map your data into similar formats across tools and combine information from different sources more effectively. 

Develop a comprehensive data strategy. You can’t transform data without knowing how and why you want to use that data. The data transformation process ensures businesses think about why data matters to their company, what data will help drive business processes, and who will benefit from having and using data in their roles. Thinking about how to effectively use data at the beginning of your data processes ensures your company will develop an impactful and strategic strategy. 

How Data Transformation Works

While different companies may approach the data transformation process differently, some common steps occur across all data transformations. 

1. Data Extraction. Before you can transform data, you need to collect it. This is often called extraction and involves either exporting data from a tool, using other software tools to pull data from multiple sources, or setting up things like feeds or establishing APIs that automatically push or pull data into a single location. 
2. Quality checks (QA). Many companies that use extract, transform, lead (ETL) are familiar with the data and what information they want to collect and store. For the transformation process to work effectively, you need to make sure you have the right data in the formats you were expecting. 
3. Data Cleansing. For automatic transformations, you’ll need to analyze what data you want and what you don’t want from each tool. Dropping data you won’t need for future analysis is called “cleansing.” 
4. Data Transform. Once you have the right data and it works, you can transform it into usable formats. Processes in this step can include mapping, standardization, normalization, aggregation, enrichment, and combination (we’ll describe those in more detail below).
5. Data Loading. Once your data is transformed, you need to store it somewhere your team can access and analyze it. Sometimes, this is a business intelligence (BI) tool. Often, it’s a data warehouse that can be accessed through the tools you use to analyze data.

Data Transformation Techniques & Types

Here are some common techniques that are part of the data transformation process. Data transformation can use one or many of these techniques: 

• Data Mapping: Assigning objects from one set of data to another set according to a specific rule or relationship. This helps in organizing and structuring data for easier analysis and combination down the line.
• Data Standardization: Assigning elements from one data set to another according to a specific rule or relationship your organization has established. This helps organize and structure data for easier analysis and integration.
• Data Normalization: Adjusting values to a common scale without adding differences in the ranges of values. This technique is often used in preparing data for statistical analysis.
• Data Aggregation: Summarizing data by combining multiple records into a single summary. For example, total monthly sales can be calculated from daily sales data.
• Data Enrichment: Enhancing data by adding additional information from external sources. This can provide more context and detail, making the data more useful for analysis.
• Data Merging/Combining: Merging data from multiple sources into a single dataset. This process helps create a comprehensive view of the data.

Best practices for data transformation

1. Understand the Data. Before transforming data, it’s essential to understand its source, structure, quality, and how you’re going to use it. This allows you to build manual or automatic streamlined processes for each data source you collect. 
2. Define Clear Objectives. Once you understand the data and how you’ll use it, define your objectives. What is the purpose of this data?
3. Maintain Data Quality. Ensure that the transformation process does not compromise the accuracy and reliability of the data.
4. Document the Process. Keep a record of the steps and techniques used in data transformation for future reference and reproducibility.

Data Transformation & ETL

If you’ve been working in data for a long time, you’ve probably heard the term “ETL.” This stands for extract, transform, load. You might have also heard of ELT: extract, load, transform. These are two similar processes that really only differ in the order and tools in which your company works with the data. Depending on your company’s technical prowess and data needs, ELT or ETL will make more sense for different companies. Both describe a similar process near the beginning of the data lifecycle. 

Data transformation is the “T,” the transform process. It is the step in ETL that changes the data into a usable format. While data transformation is part of the ETL process, ETL encompasses additional steps (and possibly tools) beyond just transforming the data. 

Benefits of Data Transformation

Improved Data Quality

Transformation cleans and standardizes data, making it accurate and reliable. By removing duplicates, finding gaps, correcting errors, and ensuring consistency, your team members can trust their data and base their decisions on solid information. With reliable data, decision-makers can have confidence as they analyze trends, identify opportunities, and mitigate risks.

Enhanced Data Integration

By transforming data, combining information from various sources makes providing a more comprehensive view of your business operations easier. This integrated approach gives you a holistic view of analysis and insights, leading to better planning and coordination toward critical business objectives across departments.

Scalability

Transformed data can be easily scaled to handle larger volumes and more complex processes, supporting your current and future business needs as you grow. As a company expands, its data needs to grow accordingly. Creating scalable data transformation processes ensures that data remains manageable and useful, regardless of volume. And having your data already transformed ensures you can build more complex processes on top of that data in the future,  enabling your organization to stay competitive.

Examples & Use Cases of Data Transformation

Once you’ve looked at the big picture of how you want to use the data, analyzed and organized data from each source, and established the rules and processes for data transformation, you’re ready to apply your transformed data to help reach your critical business objectives. 

Here are some ways companies can apply transformed data: 

• Business Intelligence: You can use data transformation in data analytics to support your company’s business intelligence efforts. Transform data to create reports and dashboards for better, faster, and more accurate insights. Use data transformation to clean, aggregate, and structure data so your business can easily visualize and analyze key metrics. This helps your teams make informed decisions and identify trends.
• Data Warehousing: By using data transformation in data warehousing, your team is preparing your data for future analysis. By transforming data before loading it into a warehouse, businesses ensure that it is consistent, clean, and structured, which enhances the efficiency and accuracy of data retrieval and analysis and sets your team up for building more advanced data processes in the future.
• Machine Learning: To ensure that your artificial intelligence (AI) machine learning (ML) algorithms provide useful information, you need to ensure that they’re working on clean, accurate, and standardized data. You must provide high-quality input to get the most out of your ML models. Transforming your data for AI involves normalizing, standardizing, and cleaning the data to improve the accuracy and performance of the models.

Examples of data transformation

Want some ideas about how you could use your transformed data in your business? Here are examples of how transforming data can benefit a specific role or industry.

Streamlined manufacturing processes. A manufacturing company likely has proprietary software that’s pretty old but critical for its machines to work alongside modern project management and sales software. Traditionally, the data from these tools won’t always play well together. But using data transformations will allow your company to bring data in from legacy tools and make it collaborate with data from more modern software, using the combined data to get an accurate picture of current processes and finding ways to optimize them. 

Customer data integration. Many companies collect customer data from multiple sources, such as online forms, sales transactions, and customer service interactions. Data transformation processes clean and merge this data into a single customer profile, enabling personalized marketing and improved customer service.

Sales data analysis. E-commerce companies may sell hundreds of products across different regions, currencies, and promotions. This produces a massive amount of data that humans could not possibly analyze in real time. Data transformation would aggregate the data, standardize information across sources, and remove duplicate records to ensure sales teams have the most accurate information available for insightful analysis. 

Healthcare data standardization. Healthcare data has a reputation for being difficult to combine. Many healthcare organizations, hospitals, departments, and job roles use different software. Data transformation processes like mapping ensure that data can be standardized across an organization and for a single patient for unified health information. This allows organizations to get better insight into care and results and see where bottlenecks may be occurring or care can be improved. 

Marketing data analysis: Marketers can use data transformation to automatically tag and organize their metrics, performance, and data across campaigns, freeing up team members to focus on creative tasks and reducing errors in manual data collection. This allows teams to easily and quickly get to the information they need for relevant analysis. They can also enrich internal marketing data on customers with external demographic data about target regions to design optimized campaigns.

Incorporating data transformation into your processes not only enhances the quality and usability of your data but also empowers your organization to make informed, strategic decisions. By leveraging transformed data, you can unlock new insights and drive business growth, ensuring you stay competitive in an increasingly data-driven world.