Data comes in all shapes and sizes: from images to text to time series data. A simple Excel spreadsheet might have data in a few columns, while a more complex BigQuery dataset could have millions of rows and thousands of columns. No matter the format, though, all data has to be transformed before it can be used in a machine learning (ML) project.
Data transformation is the process of taking raw data from the real world and turning it into something that can be used by a computer. It’s an important step in any ML project, but it can be confusing and complicated.
In this article, we’ll look at data transformation, why it’s important, and how you can do it with no code or data science skills.
Humans can look at photos, listen to audio, and read text without needing to consciously think about individual pixels, waveforms, or words. Computers, on the other hand, are not so good at understanding this type of data. In order to work with data on a computer, it must be transformed into a format that the machine can understand. This process is known as data transformation.
For instance, suppose a data scientist at a competitor to Zillow is building a deep learning model (or a neural network trained on big data) to predict the prices of houses based on a variety of features. In order to work with this training data, the data scientist will first need to process and clean the data, including dealing with missing data, merging different data sources, encoding categorical data, handling outliers, and so on.
An outlier might be found by looking for data several standard deviations away from the mean, or by looking for anomalies in the minimum or maximum value in the data.
Simply put, a business needs to transform data to increase data quality, which will then lead to better results from their machine learning models. This is often done using tools like SQL and Python Pandas, but can now be done in a fully no-code way.
Data transformation is also known as data preparation or data preprocessing. There are lots of different names for the same thing. It makes sure that your data is clean and ready to be used by your machine learning algorithm. Without data transformation, your AI won’t be able to make accurate predictions.
There are many different types of data transformation, depending on what kind of data you have and what you want to do with it. Some common types include:
We’ll look at each of these in more detail below.
Data cleaning is the process of removing incorrect or incomplete information from your dataset, adding or fixing missing values, dealing with outliers, and so on. It’s an important step in any data transformation process, and it’s often the most time-consuming.
This type of data transformation is necessary because real-world data is often messy and imperfect. Bad data can come in many forms. It might be incorrect (for instance, a birth date that doesn’t match the age of the person), or it might be incomplete (for instance, a postcode that’s missing the last few digits). It can also be noisy (for instance, data that’s been entered by hand and is full of typos).
These errors can happen for a number of reasons, including human error, software bugs, or simply because data is missing in the original source. No matter the cause, though, it’s important to clean up your data before using it for machine learning.
Feature extraction is the process of reducing a large amount of information down to a smaller set of more useful variables. It’s a common data transformation technique, and it’s often used when working with images or videos.
Feature extraction is a type of data reduction, and it’s a common technique in machine learning. It’s used to make working with data easier and to improve the accuracy of predictions.
Feature creation is the process of adding extra information to your dataset where none existed previously. It’s a useful data transformation technique when you want to make use of data that’s not in a standard format.
For instance, you might have a dataset of photos, but there’s no information about when each photo was taken. Feature creation can be used to add this information to the dataset. This might be done by looking at the EXIF data of each photo (this is the data that’s automatically added by the camera when a photo is taken), or by cross-referencing with scraped web data.
Feature creation is a type of data augmentation, and it’s a common technique in machine learning. It’s used to make use of data that would otherwise be ignored, and it can improve the accuracy of predictions.
Data normalization is the process of making sure all values in your dataset are on the same scale. It’s a common data transformation technique, and it’s often used when working with numerical data.
For instance, you might have a dataset with values that are measured in inches and values that are measured in centimeters. Another column might have a metric that ranges from 0 to 100, and another column might have a metric that ranges from 0 to 1.
If you were to build a machine learning model using this data, you would need to normalize the data first so that all the features are on the same scale.
Data aggregation is the process of combining multiple datasets into one. It’s a common data transformation technique, and it’s often used when working with data from different sources.
For instance, you might have data from two different surveys, each with different questions. Data aggregation can be used to combine the two datasets into one. This way, you can analyze the data from both surveys together.
Data disaggregation is the opposite of data aggregation. It’s the process of splitting one large dataset into several smaller ones.
For instance, you might have data that’s been aggregated by country. Data disaggregation can be used to split this dataset into smaller datasets, one for each country. This way, you can analyze the data for each country separately.
Sampling is the process of using only part of your dataset rather than all of it. It’s a common data transformation technique, and it’s often used when working with very large datasets which cannot be stored fully on the computer being used.
For instance, you might have a dataset with millions of rows. Sampling can be used to select a smaller subset of this data, such as 10,000 rows.
Once your data is transformed with techniques like these, you can then start to build your machine learning model. This might involve training a regression or logistic model, or building a decision tree or artificial neural network. The choice of model will depend on the specific problem you’re trying to solve.
Once you’ve built your model, you’ll need to evaluate it using some sort of metric. This might be accuracy, precision, recall, or something else. You’ll also need to compare your model to other models in order to choose the best one.
Visualization is also often used at this stage, in order to help understand the data and the results of the model.
With all the complexity involved in data transformation, a business might be wondering why it should bother going through the process. After all, data transformation can be time-consuming and expensive.
However, there are several good reasons to undergo data transformation, including understanding your customers better, making better decisions, and having better data organization and management.
One of the most important benefits of data transformation is that it can help you understand your customers better. For instance, data transformation can help you cluster and classify your customer data.
Clustering is the process of grouping data points together that are similar. This can be helpful because it can give you insights into customer behavior. For example, if you have a dataset of customer purchase history, you could cluster the data to see which customers tend to buy similar items.
Classification is the process of assigning labels to data points. This can be helpful because it can help you understand customer preferences. For example, if you have a dataset of customer purchase history, you could classify the data to see which customers are more likely to buy certain items.
Another important benefit of data transformation is that it can help you make better decisions. This is because data transformation can help you get more value out of your data.
If you have a dataset that is not clean or is missing values, your predictions will be inaccurate. This is because your predictions will be based on incomplete or incorrect data. However, if you undergo data transformation and clean up your data, you will be able to make more accurate predictions.
Another benefit of data transformation is that it can help you have better data organization and management. This is because data transformation can help you reduce the number of mistakes in your data.
If you have a dataset that is full of errors, it will be difficult to understand and manipulate. However, if you transform your data and remove the errors, you will have a more accurate representation of your data. Additionally, organized data is easier to understand and manipulate.
Overall, data transformation is important because it can help you understand your customers better, make better decisions, and have better data organization and management.
There are many online tools that allow you to apply simple transformations to data sets, such as removing duplicates.
While things like Excel macros and Python scripts can be used to automate these data transformation tasks, they require coding knowledge, can be difficult to use and are often not specific enough for the task at hand.
Akkio is a no-code platform that makes it easy for anyone to build powerful custom AI solutions for their business using existing data from spreadsheets, databases, and any other data source. With Akkio, you don’t need any code or technical knowledge—just point-and-click.
Are you tired of dealing with messy, unorganized data? If you're looking for a way to make your data analysis more precise and your results more reliable, data transformation is the answer.
Data transformation ensures that your data is formatted correctly and is easy to read and analyze. With Akkio, there's no need for any coding skills or technical knowledge – it's easy to use and comes at an affordable price.
If you're struggling with manually cleaning data, categorizing and filtering data, or dealing with errors, data transformation can help. Akkio makes data preparation easy, so you can focus on your analysis and getting insights from your data. Stop struggling with messy data – sign up for Akkio today.