Data Transformation Techniques, Types, and Methods
Getting data right for your company will be transformational. Your company goes from hoping it has the right answers to building on trends and experiences already available in its data. Team members will be able to multiply their impact by spending less time searching for insights and more time surfacing answers.
But if the data you’re working with doesn’t work for you, your company won’t be able to realize these benefits. Building a foundation of clean, accurate, actionable, and trustworthy data ensures your organization can be ready to benefit from the insights available in that data. Getting data in the right format so everyone can benefit means transforming your data. Data transformation is the process of taking data as it is produced by the systems and tools you use (in its “raw” format) and cleaning, updating, adapting, validating, linking, or otherwise manipulating the data so it can be easily available for your team to combine and analyze data in a way that best meets your needs.
While raw data can provide valuable information for your company, it’s often in a format that requires far too much time and resources to surface those insights accurately. Data transformation allows you to build on insights now while preparing your data to scale with your future needs. It also ensures that data from one system can be combined with other systems for broader insight across your teams.
Here are some ways companies typically use transformed data:
- Preparing for future insights. Data is messy when it’s raw with errors, missing information, and slight differences in calculations. Taking the time early in your data process to clean and transform the data for future use will save time and resources when your company needs to make data-based decisions.
- Enhancing and enriching data. Organizations sometimes combine multiple datasets to add context and value to the data. This data transformation technique ensures your company can get deeper insights.
- Compliance with regulatory requirements. Some data, like personal health information (PHI), personally identifiable information (PII), or financial information, is strictly regulated and cannot be viewed by most people in its raw state without an individual’s consent. Using data transformation to remove PHI, PII, or other regulated information ensures companies remain compliant while benefiting from data insights.
- Utilizing artificial intelligence (AI). AI results are only as good as the data they’re ingesting and training on. Data transformation techniques in machine learning help ensure you’re providing clean, accurate, and reliable data so that no mismatched datasets could disproportionately influence a model’s outcomes.
- Preparing for visualization and insights. When people think of data, they often think of charts and graphs. This type of reporting makes your data valuable, but it can be incredibly time-consuming to collect and visualize manually. Utilizing data transformation techniques in data science allows your team to start with data that supports your end goal so you can get insights and answers far more quickly.
Types of data transformation techniques
If data transformation is taking raw data and transforming it into a format that supports your company’s goals, how do you get there? Some broad categories of data transformation provide a framework for understanding the different ways your data can be changed or altered to best meet your needs. Let’s take a look at some of the data transformation techniques your company could use to achieve your goals with your data:
Constructive Transformation. In this category, you’re adding data (or creating new data) to the existing dataset. For example, you could be creating new attributes or adding external datasets. The idea is that you are building upon the existing data to create a more comprehensive and deeper dataset.
Destructive Transformation. As you may be able to tell from the name, this is the opposite of constructive transformation. This method involves removing data elements that aren’t necessary. That can be pulling irrelevant data, eliminating outliers, or grouping the data into smaller and more manageable subsets. The idea here is that you are honing in on the most useful information, making the data easier to work with, and removing distracting noise.
Aesthetic Transformation. This data transformation technique modifies data without changing its underlying content. It could mean normalizing the data so it fits within a consistent scale or standardizing formats across the same data types from different sources. You’re working to make the data more visually and structurally consistent without affecting its values.
Structural Transformation. This method is about reorganizing data to make it easier to analyze. That might involve combining data from different sources into one unified set or blending data together during analysis. The goal of this data transformation method is to make sure your data is organized in a way that best meets your future analysis or data usage.
Methods of data transformation
Data transformation is about making the data work for you and your needs. So whatever you need to do to get the data ready can fall under the broad umbrella of data transformation. Now that we’ve talked about the framework and broad categories of data transformation, let’s dive into the specific methods people and tools use to transform data.
Aggregation
Data aggregation refers to combining multiple pieces of data into a summary form. For example, instead of analyzing daily sales, you could aggregate the data to look at monthly or yearly sales figures. Using this data transformation technique allows you to focus on broader trends.
Normalization
Data normalization is the process of organizing and standardizing data to ensure consistency across all records. If you have a dataset with measurements in different units (like centimeters, inches, and yards), using normalization as a data transformation method would convert all the measurements to a common unit, making it easier to compare and analyze the data. This practice helps eliminate inconsistencies and redundancy, ensuring that the data is clean, structured, and ready for meaningful analysis.
Smoothing
Data smoothing is used to remove noise from a dataset to make the patterns in the data more apparent. If a couple of events cause abnormal website traffic (like a single viral image or a technology issue making the site temporarily unavailable), you don’t want those to skew your trends. Smoothing techniques, like moving averages, help even out these fluctuations, making it easier to identify the actual trends.
Attribution Construction
Attribution construction involves creating new attributes (or features) from existing data. For example, if you have a dataset with a “Date of Birth” field, you could construct a new attribute called “Age” to better understand the data.
Discretization
Data discretization is the process of converting data into buckets or intervals. For example, instead of using exact income values, you could group incomes into ranges like “$0-$50k” and “$50k-$100k”. Discretization is often used in classification tasks where categorical data is easier to work with than continuous data.
Other ways of transforming data
While the list above covers many common ways to transform your data, there are other ways to approach it. These include:
- Data Integration — combining data from different sources to create a unified view.
- Data Enrichment — enhancing data by adding additional relevant information from another source.
- Data Anonymization — protecting sensitive or regulated data by removing or encrypting PII.
- Data Cleansing — identifying and correcting (or removing) inaccurate or incomplete records from a dataset.
What technique to use for your data transformation needs
Knowing the data transformation methods and techniques can’t help unless you know how to use the right methods to achieve your data goals. The good news is that you can tailor your data transformation techniques to best meet your needs. Which method you use, or which combination of methods you deploy, will depend on the type of data you need to transform, your goals for your data now and in the future, and what tools you have available.
Here are a few things to think about when choosing your data transformation methods:
- Data type
- Data volume
- Quality
- Goals for the data
- Compliance requirements
- Data tools available
- Automation
Each of these considerations needs to be looked at individually and as a whole to help you decide how and which data transformation techniques will work with your data. For example, if you’re dealing with large datasets that you want to analyze and visualize but have a business intelligence (BI) tool that performs slowly when analyzing a lot of data, you will want to use data transformation methods that focus on reducing the amount of data. The data transformation technique that best suits your needs is data discretization, where you’re grouping and classifying data. This method still allows you to see patterns and track analysis but significantly reduces the data your BI tool needs to analyze.
Or say you’re a healthcare insurer trying to forecast costs in the future year. To do this, you need to be able to combine data across enrollees for medical encounters, treatments, prescriptions, and more. However, since this is PHI, the data is regulated. The data transformation types used in this scenario will have to include anonymizing the data and aggregating it so individuals cannot be identified. You could also enrich the data with outside datasets, like social determinants of health, to better understand economic and social factors that may affect the insured population’s health.
Another example of using data that will require data transformation is if you want to combine cross-departmental information. Say your team wants to get a good idea of customer behavior. This will require data from your customer relationship management (CRM) tool. This data can be very structured and easy to work with. However, you could also want to combine marketing, sales, social media, and even customer review data. Suddenly, you’re dealing with multiple data sources, both structured and unstructured data, and even real-time information. If your team wants to use any sort of AI model to quickly surface insights and sentiments, then you’ll need to keep that in mind as well. In this instance, the data transformation techniques in AI would involve normalization, ensuring all the data sources can contribute to your model. You could also incorporate data integration into the transformation process, creating a unified data set for each customer from these multiple sources, making tracking a customer across their lifecycle easier.
Best practices for implementing data transformation techniques
Regardless of how you’re going to utilize your data and which data transformation methods will work best for you, there are some best practices to keep in mind. They can include:
- Define your goals for the data.
- Become very familiar with the data you want to include.
- Prioritize building trust in the data throughout your organization.
- Have a clear path of data transformations and governance.
- Start with a small data test and then grow your data transformations.
These steps may look familiar. They’re very similar to any change management process your company can undertake. The key to deploying your data transformation goals is to make sure you’re also building a culture that will support understanding and using data so the work you do to transform your data to help meet your goals can support using data to transform your business.
Embracing data transformation is essential for unlocking your company’s potential and fostering a data-driven culture. With Domo, you can streamline your data processes, ensuring that insights are readily accessible and actionable for every team member. Ready to take the next step in transforming your data landscape? Explore how Domo can empower your organization today.