You are expected to build kick-ass models with shitty data. We get it. We've been there before, trying to explain why garbage in is garbage out and no amount of fancy tech can change that. Spending hours, days even weeks trying to manually fix data issues is the collective nightmare of data scientists. You are expected to AI-powered data augmentation is here to help with your gap-filled, bias-ridden and limited data that eventually lead to incorrect or even unfair business decisions.
MOSTLY AI’s AI-powered data augmentation capabilities can help you turn the data that you have on hand right now into high-quality data that reflects your real-world customers and the scenarios they operate in. And this is just the cherry on the cake, because synthetic data also provides you automated data privacy on the tap. If you use synthetic data, you'll never have to worry about exposing customer data again.
Who should use data augmentation and why?
If you’re a test engineer, this can help you with creating sufficient test data for your next product release. Business Analysts can use AI-powered data augmentation to complete common gaps in their customer surveys, such as missing income details as customers are often reluctant to report them. And ML/AI engineers can use it to unbias and rebalance their training data, so that your models have a stronger capacity to generalize, adjust to unfamiliar situations, and generate more precise and meaningful insights.
Keep reading to find out how you can use the MOSTLY AI synthetic data generator to supercharge your data assets and make confident, spot-on decisions for your business.
What is AI-powered data augmentation?
Data augmentation is the go-to solution for data scientists when there's not enough data or they need to spice up the diversity and quality of their dataset. It’s a process that generates additional data points to the existing ones in your dataset and is traditionally done using random over and undersampling techniques.
MOSTLY AI's synthetic data generator provides a simpler, smarter, and far more reliable way to achieve high quality results, than traditional data augmentation techniques. It uses machine learning models to learn the underlying patterns and correlations in the data. Once the generator has learned these patterns, it can use them to generate new data points that are statistically similar to the original data.
Smart imputation - no more missing or random data
Smart imputation learns the data’s underlying patterns and correlations and then generates new data points. This AI-powered data imputation allows the MOSTLY AI synthetic data generator to perform some nifty tricks, such as imputing the missing values in your data source. The smart imputation method produces more reliable and realistic results compared to commonly used techniques, such as mean/median imputation and frequent category imputation. Filling in the missing values with meaningful synthetic datapoints is especially important for creating human-readable datasets in downstream analytics tasks.
Rebalancing - no more imbalanced datasets
One of the main advantages of using AI-powered synthetic data for data augmentation is that it can create data points that are more diverse and representative of the underlying population than traditional augmentation techniques. You can use the MOSTLY AI synthetic data generator’s “Rebalance categories” feature to create additional data points for underrepresented business cases in your data. This can rebalance or improve the diversity of the data, leading to better accuracy and robustness of machine learning models, especially when working with small or biased datasets.
Data anonymization automated for privacy
Another benefit of using synthetic data is that it can help preserve the privacy and confidentiality of sensitive data by generating synthetic data that is not directly linked to individual users or customers. This can be particularly important in industries such as insurance, finance or healthcare, where data privacy is a top concern.
Overall, data augmentation using AI-powered synthetic data is a powerful technique that can help improve the quality and diversity of your dataset and, depending on the setup, boost the performance of your machine learning models.
Try data augmentation now
MOSTLY AI's synthetic data platform is free to use for synthesizing up to 100K rows of data daily. Register an account and experiment with rebalancing, smart imputation and experience the easiest and safest tool for synthetic data generation first hand!
Why is data augmentation important?
Data augmentation is essential because it helps overcome the limitations of real-world data. Real-world data can be limited in quantity, quality, and diversity, which can impact the accuracy of models and predictions.
Additionally, when dealing with missing or incomplete data, imputation techniques can be used to fill in the gaps and allow for a more complete analysis. This is especially important in industries like finance, where missing data on customers can hinder accurate risk assessments and financial forecasting.
Synthetic data augmentation can easily solve classification problems–if there are more instances of one class than another, the model may be biased towards the overrepresented class. Using MOSTLY AI’s synthetic data generator, you can help solve this problem by generating new data points for the underrepresented class.
Moreover, data augmentation can improve the robustness of your AI/ML models. It allows models to be trained on a larger, more diverse dataset, which helps them learn to generalize better to new, unseen data points. This results in more accurate predictions.
Data augmentation can also prevent overfitting in machine learning models. Overfitting occurs when the model is too complex and starts learning the noise in the data rather than the underlying patterns. By introducing variability in the data, data augmentation can help models learn to distinguish between relevant and irrelevant information. For instance, if a model is trained on a dataset with only one class label, it may not generalize to new data points with different class labels. However, by introducing synthetic data points with different class labels, the model can learn to identify the underlying patterns in the data, rather than just memorizing the existing data points.
Overall, data augmentation is a powerful tool that can improve the accuracy, robustness, and generalization of AI/ML models.
Benefits of using synthetic data generators for data augmentation
Reduced cost of model development
Generating synthetic data can be a cost-effective alternative to collecting new data, which can be time-consuming and expensive, especially if the data needs to be collected from multiple sources. By generating synthetic data, you can save time and resources, opening up opportunities to focus on data analysis and model development. MOSTLY AI's synthetic data generator can also help to reduce the cost and time required for data collection and preparation, in turn accelerating your research and development projects.
Last but not least, MOSTLY AI's synthetic data generator is free to use, so it doesn’t break the bank either.
Faster time to market
Using synthetic data can reduce the time it takes to train and test models. By augmenting the existing data with synthetic data, you can create a more robust dataset and train the model faster, allowing them to bring products or services to market faster.
More data for training machine learning models
An imbalanced dataset poses a challenge for predictive modelling as most of the machine learning algorithms used for classification were designed around the assumption of an equal number of examples for each class. Using rebalancing as a data augmentation technique, a dataset could be constructed in such a way as to have the same number or a close number of observations of the same class. Depending on the use case, this can be beneficial because it will give the opportunity for a machine learning model to learn from more data.
Let's say you're building a machine learning model to detect credit card fraud. You have a dataset of credit card transactions with labels indicating whether each transaction is fraudulent or not. However, your dataset has a class imbalance, with only a small percentage of transactions being fraudulent.
To address this data imbalance, you could use traditional data augmentation techniques like oversampling or undersampling. However, this may not be enough to create a diverse and representative dataset, especially if the original data is biased or incomplete.
Instead, you could use MOSTLY AI's synthetic data generator to create new synthetic fraudulent transactions. This synthetic data can then be combined with the original dataset to create a larger and more balanced dataset for training your machine learning model. With a more diverse and representative dataset, your machine learning model is more likely to generalize well to new, unseen data, leading to improved accuracy in detecting credit card fraud.
Data bias can occur when the data used to train the model is not representative of the real-world scenarios. By generating synthetic data that reflects the real-world scenarios, bias can be reduced, and the model can make more accurate predictions.
Suppose you are tasked with building a machine learning model to predict loan defaults using historical loan data. However, you suspect that your dataset may be biased due to its limited geographic region and demographic representation.
To address this issue, you could use traditional data augmentation techniques like oversampling or undersampling to try to balance the dataset. However, this may not be enough to remove the underlying bias in the data.
Instead, you could leverage MOSTLY AI's synthetic data generator to create new synthetic loan data that is representative of a more diverse population. By rebalancing your data based on underrepresented different geographic and demographic characteristics, you can create a more diverse and representative dataset.
With the resulting dataset, your machine learning model is less likely to be biased towards any particular group or region, leading to more fair and equitable predictions of loan defaults.
Use cases for AI-powered data augmentation
Whether you’re in finance, insurance, or software development, AI-powered data augmentation is a suitable technique to build better models. Read on to learn how rebalancing can help you have AI models with stronger capacity to generalize, adjust to unfamiliar situations, generate more precise and meaningful insights and have data sources without missing values for exploratory data analysis.
Data augmentation in the wild - how to rebalance for credit scoring
A great example of how rebalancing can be used in everyday machine learning tasks is in the financial industry, particularly in credit scoring and risk categorization processes. These processes rely heavily on data, but often face imbalanced datasets due to a lack of data points for defaulted customers. By using appropriate rebalancing techniques, financial institutions can improve the accuracy of their machine learning models and correctly classify customers into the right risk category, helping to mitigate financial risks and improve overall performance.
Synthetic data augmentation outperforms the original data
In our analysis of credit score datasets, we compared four different versions to determine the impact of rebalancing:
The dataset that augmented the original data with a synthetic version of the minority class outperformed the original data’s accuracy by approximately 4%. In real-world scenarios, this can be beneficial as the resulting model may have an important impact on ROI as well as cost savings.
Synthetic data generated with normal synthetization and those with the rebalancing feature enabled have not increased the accuracy of the model. However, MOSTLY AI’s synthetic data generator manages to come really close to the original data’s accuracy which indicates that the platform can perceive the trends and the predictive power that the original dataset holds.
Using synthetic data augmentation as a simulation tool
Unfortunately, most real-world datasets do not represent real-world situations 100%. Many organizations, especially financial institutions, struggle since the data gathered throughout the years are highly skewed and show bias towards a specific behavior. Many examples of datasets are skewed/biased when exploring gender, age, ethnicity, or even occupation. Therefore, decision-makers find it hard or downright impossible to make the correct decision that helps their organization to grow.
This is where MOSTLY AI's rebalancing feature comes in handy. When it comes to the topic of an imbalanced dataset, the first thing that ML practitioners and Data Scientists are thinking about is how to make a dataset to be equally distributed across the majority vs. the minority class. However, one can use MOSTLY AI's rebalancing feature as a simulation tool. The aim is to provide decision-makers with an effective tool to understand better and exploit new information that might affect their dichotomous decision. Rebalancing can be a critical and effective tool to test numerous hypotheses and 'what-if' scenarios that may affect the whole organization's shift in its strategy.
A rebalancing case study from the insurance industry
Let's take as an example the insurance industry. Two of the main KPIs all insurers around the world have are the total annual premiums and the total claims amount.
Using rebalancing as a simulation tool, one could aim to answer questions such as:
- What if changing our customer business mix will affect our revenues (an increase in total premiums)?
- What if changing our customer business mix will decrease our claim costs?
We have shifted the insurer customer mix distribution toward younger audiences using MOSTLY AI's Rebalancing capability. The remainder of the dataset's features were then generated based on the new information by MOSTLY AI's synthetic data generator. The two aforementioned KPIs have now been recalculated, and a decision-maker may see that revenue has risen and costs have decreased.
Now that they have a thorough study, stakeholders can use it to inform their decisions and possibly change their organizational strategy.
Improve data consistency with smart imputation
Finance is a prime example of an industry that is faced with the challenge of maintaining consistent customer records, often resulting in incomplete datasets. The lack of data and missing values can lead to bias and produce unreliable results for data analysts and data scientists. A common missing value in the finance industry is customer income, which is often self-declared and not updated or never obtained from the customer at all. However, this example is not limited to finance, as many industries are also faced with similar challenges of incomplete and inconsistent datasets, and can benefit from utilizing advanced data imputation techniques to improve the quality and reliability of their data.
Data augmentation empowers data scientists to overcome real-world limitations
MOSTLY AI’s data augmentation capabilities provide you an effective way to overcome the limitations of real-world data. By generating synthetic data that reflects the real-world scenarios, you can improve the accuracy of your AI models, reduce bias, save costs, and bring products or services to market faster. Data augmentation can also be used to make your exploratory data analyses more accurate and reflective of real-world scenarios and to generate test data for software product changes and new features. As data scientists continue to rely on data to gain insights and drive decision-making, AI-powered data augmentation becomes an essential tool to enhance the quality of your analyses and stay ahead of the competition.