💡 Download the complete guide to AI-generated synthetic data!
Go to the ebook

This article explains what data drift is, how it affects machine learning models in production, what is the difference between data drift and concept drift, and what you can do to tackle data drift using synthetic data.

What is data drift?

“Data drift” is a term in machine learning that refers to the phenomenon in which a machine learning model’s performance slowly decreases over time. This happens because machine learning models are trained on historical data (i.e. “the past”) but then use current data (i.e. “the present”) when they are being used in production. In reality, the historical data and the current data may have different statistical characteristics and this is what we call “data drift”: the data used for predictions starts to drift from the data used for training. This means the machine learning model is no longer fully optimized for the data it is seeing. 

Figure 1 - Data drift refers to the phenomenon where the data used for predictions starts to drift from the data used for training. This can have a negative impact on a machine learning model’s performance.

How does data drift affect ML models?

Drift can be a big problem when using machine learning models in the real world, causing a decrease in predictive power. For example, let’s say we have trained a machine learning model to accurately predict the quarterly sales of a particular fashion brand. We then put this model into production.

At first it operates well: the actual data it is receiving (from the present) resembles the data that was used to train the model (from the past). But then something unexpected happens. A popular influencer spontaneously posts about the fashion brand and the post goes viral. Sales sky-rocket in a way that the machine learning model could never have foreseen because nothing like the unexpected viral post event was present in the training data.

This causes a significant change in the statistical distribution of the input data (i.e. “data drift”) and the machine learning model no longer performs at optimum performance. The model loses accuracy and may even produce unreliable predictions if the data distributions vary significantly.

Figure 2 - Data drift can occur due to many different reasons. One common cause is unexpected influential events that were not present in the training data.

Data drift vs concept drift

There are different kinds of drift that can be observed in machine learning projects. Data drift refers specifically to the phenomenon in which the distribution of the real-world data used when the model is in production drifts from the data that was used for training.

Concept drift refers to the situation in which the relationship between features in the data changes over time. In this case, the pattern (or “concept”) that the machine learning model is trying to learn is evolving. In short, data drift deals with changes in the data that the model uses to make predictions, whereas concept drift refers to changes in the patterns between features in the data.

How can I deal with data drift?

Data drift is a complex phenomenon that generally requires a multidimensional approach to solve. Some of the most effective things you can do to deal with data drift include:

  1. Retrain your machine learning model on fresh data that includes the drifted distribution so that the model is performing at peak performance again.
  2. Perform robust feature engineering so that features are less sensitive to changes in the underlying data.
  3. Use ensemble methods like model blending and stacking or building a fully-online machine learning pipeline that can continuously update and retrain itself as new data comes in.

In practice, retraining a machine learning model with fresh data is one of the most common methods used to deal with data drift. However, this approach comes with some drawbacks. Acquiring new data that is ready for training a machine learning model is often:

Tackle data drift with synthetic data

Synthetic data generation can help you tackle data drift by providing a high-quality, low-friction source of data on which you can retrain your machine learning models. Synthetic data generators enable you to produce virtually limitless data and often give you fine-grained control over the distributions of this new data. By accurately modeling new synthetic datasets, you can then update your machine learning model to incorporate the drifted data distribution.

We’ve broken it down into 5 steps for clarity:

  1. Detect your data drift
  2. Understand your data drift
  3. Generate synthetic data
  4. Retrain your model
  5. Monitor and repeat
Figure 3 - Tackling data drift is a complex process that requires a multidimensional approach. By continuously monitoring, analyzing and modeling your data drift you can generate the right kind of synthetic data to tackle the problem.

1. Detect your data drift

Detecting data drift should be a fundamental part of any machine learning life cycle. There are many ways to perform data drift detection and many resources to learn about it. This article focuses on solutions that will help you fix data drift once it has been detected.

2. Understand your data drift

Before tackling data drift, it’s important that you have a good understanding of its nature and potential causes. Analyze your model and the incoming data to identify points where the data is drifting and analyze its statistical characteristics. This will help you understand how to incorporate the data drift into your updated model.

For example, in the case of the quarterly fashion sales predictions mentioned above, the fact that we can reliably trace the data drift to the viral influencer post helps us know how to deal with the data drift. It’s reasonable to expect the influencer post to have lasting effects on the fashion brand’s perception and future sales: we should therefore adjust our data projections to include some of the ripple effects of this unexpected sales boost.

On the contrary, if we had instead seen a massive but temporary drop in sales due to a failure in the webshop’s main server, we may want to choose not to incorporate this data at all in the projections for next quarter, the assumption here being that the webshop will not experience another failure.

Figure 4 - Understanding the cause of your data drift is crucial in order to know how to deal with it. The data drift may be an anomaly you want to ignore or an important change in trends you want to incorporate into your machine learning model.

3. Generate synthetic data

Once you have a good understanding of the statistical nature and potential sources of your data drift, you can then proceed to use synthetic data generation to supplement your training dataset with cases that might occur due to data drift.

We’ll walk through how to generate the right kind of synthetic data to tackle your data drift with MOSTLY AI's synthetic data platform, using a technique called conditional generation.

  1. Split your dataset into two separate tables: one table containing the ID column and the columns containing your desired target features, and a second table containing the ID column along with all of the other predictor columns.
  2. Log in to your MOSTLY AI account.
  3. Launch a new job using the “Create Synthetic Data” button in the “Synthetic Datasets” tab. Upload the first table (containing the ID column and the target feature(s) to start a new job and then add the second table.
Figure 5 - On the “Synthetic Datasets page, click on “Create Synthetic Data” to start a new job.
Figure 6 - Upload your table with the ID and target feature columns first and.
Figure 7 - Click “Add Table” and upload your table with the ID and predictor columns.

4. Define the relationship between the two tables using the Data Settings tab and navigating to the settings for the table containing the predictor columns. Click on the gear icon to the right of the ID column and set the following settings:

Generation Method:    Foreign Key
Foreign Key:    Type:   Context
Parent Table:    <your-table-with-target-column>
Parent Primary column:   <id-column-of-target-table>

Save the settings. Under the “Tables” tab you should now see that the predictor table has changed into a Linked Table (lime green color coding).

Figures 8-10 - Assign the appropriate primary and foreign keys to define the relationships between your subject and linked tables.

5. Once the job has been completed, select the “Generate more data” action on the right-hand side of the newly-generated dataset row and select “Generate with seed” to perform conditional generation.

Figure 11 - Once the job has been completed, select “Generate more data”.
Figure 12 - Select the “Generate with seed” option

6. Now upload a subject table with a different kind of distribution. 

This subject table can be generated manually or programmatically and should contain the drifted distribution. The simulated subject table (containing the drifted target feature distribution) will be used to generate a synthetic dataset (i.e. the predictor columns) that would produce the new, drifted distribution.

In our viral fashion post example, we would create a simulation of the target feature (sales) that follows the “new training distribution” depicted in Figure 4 above and use this to generate a synthetic dataset.

Open-source Python packages like NumPy or SciPy enable you to perform fine-grained data simulation. You can use MOSTLY AI’s rebalancing feature to programmatically simulate drifted target feature distributions for categorical columns.

Figure 13 - Use MOSTLY AI’s rebalancing feature to create customized data simulations.

7. Repeat for all the different scenarios you want to model.

To properly accommodate all of the possible future scenarios, you may want to create multiple simulated datasets, each with a different assumption and associated distribution. In the case of our viral fashion post, we may want to create three simulations: one in which sales continue to skyrocket at the same rate as we saw this quarter, one in which sales just go back to ‘normal’ (i.e. the influencer post has no lasting effect), and a third scenario that takes the average of these two extremes. With these 3 synthetic datasets we can then train different models to predict 3 kinds of possible future scenarios.

4. Re-train your model

With your freshly generated synthetic data ready, you can now proceed to re-train your machine learning model. You can use just the synthetic data or a mix of real and synthetic data, depending on the privacy requirements of your model.

5. Monitor model performance and repeat

Finally, make sure to put precise monitoring tools in place to continue to detect data drift. For example, you could use open-source Python libraries like Evidently or NannyML to keep track of your model performance throughout the machine learning lifecycle. When your model metrics indicate a recurrence of data drift, update your synthetic data to reflect the new distributions and re-train your model.

Tackling data drift with MOSTLY AI

Synthetic data generation can help you tackle data drift by making it easy to simulate potential future scenarios based on new statistical distributions of the data. By providing a high-quality, low-friction source of data on which you can retrain your machine learning models, synthetic data generators enable you to produce virtually limitless data to model changes in the underlying data. MOSTLY AI gives you fine-grained control over the distributions of this new data so you can accurately model new synthetic datasets that take into consideration the drifted data distributions.

Try it out today – the first 100K rows of synthetic data are on us!


Generating synthetic data from original data

Understanding Model Collapse

Mitigating Model Collapse

Balance between privacy, generalization and accuracy

Synthetic data generation & validation on original data

Up-to-date original data for reliable synthetic data generators

Validation using a holdout data set

Data augmentation



The phrase synthetic data generation has gained a lot of traction in the world of data. At its core, synthetic data refers to artificially generated data that mimics the statistical properties and patterns of real-world data. This approach has shown to be an effective tool in a variety of domains, including machine learning and data analysis, as well as privacy protection and risk assessment.

The increased demand for high-quality data to train and test models and rising concerns about data privacy and security have highlighted the importance of synthetic data generation. This strategy handles data shortage while protecting sensitive information and private knowledge. What happens, though, when the synthetic data is created directly from the original data at a specific point?

Generating synthetic data from original data

Consider the following scenario: an organization has a valuable dataset collecting consumer behaviors, market trends, or medical diagnoses at a certain point in time. This dataset has enormous potential for research and model development, but sharing it risks disclosing sensitive information or violating privacy restrictions.

This is where the concept of generating synthetic data from the original dataset comes into play. Instead of releasing the real data, which may raise privacy issues, companies and researchers can employ advanced techniques to produce synthetic data that closely resembles the statistical properties of the original dataset. The created data serves as a snapshot of the original at that point in time, keeping its essence while disclosing no individual-level information.

The primary advantage is the ability to exploit the insights contained within the original data while adhering to privacy standards and reducing security threats. This strategy encourages innovation, creativity, and collaboration by allowing data professionals and researchers to experiment, construct models, and conduct analysis without jeopardizing data integrity or infringing on privacy rights. Furthermore, by producing synthetic data from the original dataset, organizations may overcome data shortage challenges. When obtaining more data is difficult or expensive, synthetic data can efficiently magnify the given dataset, improving model training and validation.

Understanding model collapse

In machine learning, "model collapse" often refers to a situation in which the model fails to provide varied or relevant outputs and instead produces a narrow collection of repeated or low-quality outputs. This can happen for a variety of reasons and in a variety of models, but it most commonly happens while training generative adversarial networks (GANs) or other complicated models. 

Recent advances in generative AI, notably for images and text, have piqued the attention of researchers interested in using synthetic data to train new models. There is, however, a concept known as 'Model Autophagy Disorder' (MAD), which compares the process of employing synthetic data in a self-consuming loop. This indicates that unless there is enough injection of new real-world data at each generation, the quality and variety of subsequent generative models would ultimately deteriorate.

Model collapse

The MAD notion emphasizes the vital necessity for a careful mix of synthetic and real data to prevent model quality and variety from deteriorating during subsequent training sessions. Understanding the complexities of how to use synthetic data successfully while preventing model collapse is a continuous endeavor in the evolution of generative AI and synthetic data consumption. In this blog, we present some of our suggestions on how to mitigate model collapse in the context of tabular synthetic data and hence deflating the MAD concept.

Jumping specifically into tabular synthetic data, model collapse can also be a concern. Tabular synthetic data generation involves creating new data samples that resemble the original dataset in terms of its structure, statistical properties, and relationships between variables. In this context, model collapse refers to the situation where the generative model produces synthetic data that lacks diversity and fails to capture the complexity of the original data. As a result, new models become excessively reliant on patterns present in the generated data, leading to a degradation in the model's capacity to produce novel and meaningful results.

Therefore, data professionals and researchers need to be very careful and aware of the situation where any new models become too dependent on patterns in the synthetically generated data, leading to their inability to adapt to unseen scenarios effectively. The root of the problem is frequently the complex relationship between data distribution and model learning dynamics. It's imperative to recognize that model collapse often emerges due to a combination of factors, primarily stemming from the quality of synthetic data and how it's employed. While synthetic data generation can be a powerful ally, its misuse or generation of low-quality data can indeed trigger the collapse phenomenon.

Low-quality synthetic data significantly contributes to model collapse. Models trained on such data are sure to overfit on limited patterns if the generated data lacks diversity, and accuracy, or fails to accurately represent the underlying distributions. This limits their ability to adjust to unknown conditions and compromises their overall effectiveness.

Similarly, the misuse of synthetic data increases the likelihood of model collapse. Models might accidentally depend on erroneous patterns if synthetic data is utilised arbitrarily, without sufficient validation or consideration of its alignment with the original data distribution.

Mitigating Model Collapse

Balance between privacy, generalization and accuracy

In data science, there is a balance between protecting privacy, achieving robust generalization, and reaching high accuracy. This balance is frequently difficult to achieve, but it is critical to the success of data-driven models. Let's explore how synthetic data generation with MOSTLY AI emerges as a potential solution.

Many models try for extreme accuracy by methodically collecting small details in the data. However, this effort frequently comes at the expense of privacy. To safeguard individual privacy, traditional methods include introducing excessive noise or changing data distribution. Differential privacy, for example, adds noise to data to avoid reidentification. However, this might reduce the model's accuracy and generalization capacities.

Models that prioritize privacy, on the other hand, may face a trade-off. While they take great care to protect individual information, they may oversample majority classes or compromise their ability to model outliers and unusual events. As a result, the model's ability for rigorous generalization and complete comprehension is hampered.

This is where MOSTLY AI’s synthetic data generator comes in as a solution for navigating the complex combination of accuracy, privacy protection, and generalization. MOSTLY AI’s synthetic data avoids the need for excessive noise or alteration of data by creating data points that perfectly mimic the statistical features of the original data. It protects privacy while maintaining data integrity.

Furthermore, it is adaptable enough to overcome the constraints of previous methodologies. Models trained on well-crafted synthetic data generated by MOSTLY AI are well-positioned to recognize both majority trends and outlier occurrences. This equilibrium promotes the model's capacity to generalize well, even in new territory, without sacrificing privacy or accuracy.

Utilizing synthetic data, though, might introduce the notion of model collapse in models built for a downstream task. The complex patterns within synthetic data require careful handling to maintain equilibrium. As we are discussing in the next sections, through a combination of validation against actual data and a thorough understanding of data distributions, we could strengthen the models against collapse while upholding their ability to grasp the statistical properties and characteristics of the original data.

Synthetic data generation & validation on original data

It is crucial to understand that training your model on original data provided by users can indeed help mitigate the risk of synthetic data poisoning the downstream machine learning models. However, ensuring the safety and reliability of the generated data is a multi-faceted process that involves more than just using original data. Let's dive deeper into two critical aspects: the importance of using up-to-date original data for generating synthetic data and the significance of validating your models using a holdout set.

Up-to-date original data for reliable synthetic data generators

To build reliable and effective synthetic data generators, the original data used for generating synthetic samples should ideally be up to date. The underlying patterns and distributions within your dataset may change over time due to evolving user behaviors, market dynamics, or other factors. If your generators are based on outdated data, they might fail to capture these changes accurately, leading to discrepancies between the synthetic and real data distributions. This, in turn, could negatively impact downstream tasks where the synthetic data is used.

By continuously updating your generators with fresh, current original data, you enhance their ability to create ML models that faithfully represent the most recent data patterns. This ensures that any model development based on the SD remains relevant and applicable to the context in which it will be used.

Validation using a holdout data set

In the context of model validation, the concept of a "holdout set" plays a crucial role in assessing the performance and reliability of your models, including those that utilize synthetic data. A holdout set is a portion of your original data that is set aside during the training process and not used for model training. Instead, it serves as an independent dataset for evaluating the model's performance.

An important point here is that the holdout set should not be used to create the generator as well. It should be a portion of the dataset that will only be used to validate a downstream task's outcome. Incorporating a holdout set into your validation process adds a layer of assurance that the models trained using synthetic data are reliable, effective, and aligned with the behavior of real-world data.

When working with synthetic data, the utilization of a holdout set takes on heightened significance due to several compelling reasons.

Upsampling training data with MOSTLY AI's synthetic data generator

Data augmentation

If the primary purpose is to augment the original dataset, you may use a variety of data augmentation techniques to generate variants of the existing data points. This can assist in increasing the dataset's variety and boost model generalization, mitigating the risk of model collapse.

MOSTLY AI's AI-powered data augmentation capabilities may assist you in transforming the data you already have into high-quality data that represents your real-world customers and the settings in which they operate. Features like Smart Imputation and Rebalancing could assist you in achieving your goal of generating different versions of your original data points.


We've explored strategies such as training a synthetic data generator on up-to-date real data and using the data augmentation feature, all of which contribute to the generation of reliable and adaptable synthetic data. We've strengthened the trustworthiness of the synthetic data that fuels our models by combining up-to-date real data with rigorous validation.

When utilized properly, synthetic data generation can act as a solution to protect machine learning growth against model collapse, reducing the danger of models becoming excessively reliant on specific patterns.

There is something different about Merkur Versicherung AG. It’s the oldest insurance company in Austria, but it doesn’t feel like it.

For starters, there’s the Campus HQ in Graz. An Illuminous race track lines the floor of the open plan space. The vibrant lobby is filled with eclectic artwork and unconventional furniture. And there’s a beautiful coffee dock beside the fully functioning gym in the lobby.

Then, there’s the people. One team in particular stands out amongst the crowd: the Merkur Innovation Lab. A group of self professed “geeks, data wizards, future makers” with some “insurance guys” thrown in for good measure. Insurance innovation is born right here. Daniela Pak-Graf, the managing director of Merkur Innovation Lab — the innovation arm of Merkur Insurance, told us in the Data Democratization Podcast:

“Merkur Innovation Lab is the small daughter, the small startup of a very old company. Our CEO had the idea, we have so much data, and we're using the data only for calculating insurance products, calculating our costs, and in the era of big data of Google, of Amazon, Netflix, there have to be more possibilities for health insurance data too. He said, "Yes,  a new project, a new business, what can we do with our data?" Since 2020, we are doing a lot.”

Oh, and then there’s synthetic health data.

The Merkur Innovation Lab has fast become a blueprint for other organizations looking to develop insurance innovations by adopting synthetic data. In the following, we’ll introduce three insurance innovations powered by synthetic data adoption.

Insurance innovation no. 1: data democratization


Like many other data-driven teams, the Merkur Innovation Lab team faced the challenge of ensuring data privacy while still benefiting from valuable insights. The team experimented with data anonymization and aggregation but realized that it fell short of providing complete protection. The search for a more comprehensive solution led them to the world of synthetic data.


According to Daniela Pak-Graf, the solution to the problem is synthetic data:

"We found our way around it, and we are innovating with the most sensitive data there is, health data. Thanks to MOSTLY."

Merkur didn’t waste time in leveraging the power of synthetic data to quickly unlock the insights contained within their sensitive customer data. The team has created a beautifully integrated and automated data pipeline that enables systematic synthetic data generation on a daily basis, fueling insurance innovations across the organization. Here’s how they crafted their synthetic data pipeline:


The end-to-end automated workflow has cut Merkur’s time-to-data from 1-month, to 1-day. The resulting synthetic granular health data is read into a dynamic dashboard to showcase a tailored ‘Monetary Analysis’ of Merkur’s customer population. And the data is available for consumption by anyone at any time. True data democratization and insurance innovation on the tap.

Insurance innovation no. 2: external data sharing


As we know, traditional data sharing approaches, particularly in sensitive industries like health and finance, often faced complexity due to regulatory constraints and privacy concerns. Synthetic data offered a quick and secure solution to facilitate data collaboration, without which scaling insurance innovations would be impossible.


According to Daniela:

“...one of the biggest opportunities is working with third parties. When speaking to other companies, not only insurance companies, but companies working with health data or customer data, there's always the problem, "How can we work together?" There are quite complex algorithms. I don't know, homomorphic encryption. No one understands homomorphic encryption, and it's not something which can be done quickly. Using synthetic data, it's a quick fix if you have a dedicated team who can work with synthetic data.”


One exciting collaboration enabled by synthetic data is Merkur Innovation Lab’s work with Stryker Labs. Stryker Labs is a startup focused on providing training management tools for professional athletes. The collaboration aims to extend the benefits of proactive healthcare and injury prevention to all enthusiasts and hobby athletes by merging diverse datasets from the adjacent worlds of sport and health. Daniela explained the concept:

“The idea is to use their expertise and our knowledge about injuries, the results, the medication, how long with which injury you have to stay in hospital, what's the prescribed rehabilitation, and so on. The idea is to use their business idea, our business idea, and develop a new one where the prevention of injuries is not only for professional sports, but also for you, me, the occasional runner, the occasional tennis player, the occasional, I don't know.”

This exciting venture has the potential to improve the well-being of a broader and more diverse population, beyond the privileged few who make it into the professional sporting ranks.

Insurance innovation no. 3: empowering women in healthcare

Another promising aspect of synthetic data lies in its potential to address gender bias and promote fairness in healthcare. By including a more diverse dataset, synthetic data can pave the way for personalized, fairer health services for women. In the future, Merkur Innovation Lab plans to leverage synthetic data to develop predictive models and medication tailored for women; it marks a step towards achieving better healthcare equality. According to Daniela:

“...it could be a solution to doing machine learning, developing machine learning algorithms with less bias. I don't know, minorities, gender equality. We are now trying to do a few POCs. How to use synthetic data for more ethical algorithms and less biased algorithms.”

The insurance industry and innovation

Insurance companies have always been amongst the most data-savvy innovators. Looking ahead, we predict that the insurance sector will continue to lead the way in adopting sophisticated AI and analytics. The list of AI use cases in insurance continues to grow and with it, the need for fast and privacy safe data access. Synthetic data in insurance unlocks the vast amount of intelligence locked up in customer data in a safe and privacy-compliant way. Synthetic healthcare data platforms are becoming a focal point for companies looking to accelerate insurance innovations.

The Merkur Innovation Lab team of “geeks, data wizards, future makers” are only getting started on their synthetic data journey. However, they can already add “synthetic data trailblazers” to that list. They join a short (but growing) list of innovators in the Insurance space, like our friends, Humana, who are creating winning data-centric products with their synthetic data sandbox.

Machine learning and AI applications are becoming more and more common across industries and organizations. This makes it essential for more and more developers to understand not only how machine learning models work, but how they are developed, deployed, and maintained. In other words, it becomes crucial to understand the machine learning process in its entirety. This process is often referred to as “the machine learning life cycle”. Maintaining and improving the quality of a machine learning life cycle enables you to develop models that consistently perform well, operate efficiently and mitigate risks.

This article will walk you through the main challenges involved in ensuring your machine learning life cycle is performing at its best. The most important factor is the data that is used for training. Machine learning models are only as good as the data that goes into them; a classic example of “garbage in, garbage out”.

Synthetic data can play a crucial role here. Injecting synthetic data into your machine learning life cycle at key stages will improve the performance, reliability, and security of your models.

What is a machine learning life cycle?

A machine learning life cycle is the process of developing, implementing and maintaining a machine learning project. It includes both the collection of data as well as the making of predictions based on that data. A machine learning life cycle typically consists of the following steps:

  1. Data Collection
  2. Data Preparation
  3. Model Selection
  4. Model Training and Evaluation
  5. Model Tuning
  6. Model Deployment and Monitoring
  7. Model Explanation
  8. Model Retraining
Machine learning life cycle

In reality, the process of a machine learning life cycle is almost never linear. The order of steps may shift and some steps may be repeated as changes to the data, the context, or the business goal occur.

There are plenty of resources out there that describe the traditional machine learning life cycle. Each resource may have a slightly different way of defining the process but the basic building blocks of a machine learning life cycle are commonly agreed upon. There’s not much new to add there.

This article will focus on how you can improve your machine learning life cycle using synthetic data. The article will discuss common challenges that any machine learning life cycle faces and show you how synthetic data can help you overcome these common problems. By the end of this article, you will have a clear understanding of how you can leverage synthetic data to boost the performance of your machine learning models.

The short version: synthetic data can boost your machine learning life cycle because it is:

Read on to learn more 🧐

The role of synthetic data in your ML life cycle

Every machine learning life cycle encounters some, if not all, of the problems listed here: 

  1. A lack of available training data, often due to privacy regulations 
  2. Missing or imbalanced data, which will impact the quality of the downstream machine learning model
  3. Biased data, which will impact the fairness of the downstream machine learning model
  4. Data drift, which will impact model performance over time
  5. Inability to share models or results due to privacy concerns

Let’s take a look at each one of these problems and see how synthetic data can support the quality of your machine learning life cycle in each case.

Machine learning life cycle without synthetic data
Common problems of any machine learning life cycle that does not make use of synthetic data

Data collection

The problem starts right at the first step of any machine learning project: data collection. Any machine learning model is only as good as the data that goes into it and collecting high-quality, usable data is becoming more and more difficult. While the overall volume of data available to analysts may well be exploding, only a small fraction of this can actually be used for machine learning applications. Privacy regulations and concerns obstruct many organizations from using available data as part of their machine learning project. It is estimated that only 15-20% of customers consent to their data being used for analytics, which includes training machine learning models. 

Synthetic data is infinitely available. Use synthetic data to generate enough data to train your models without running into privacy concerns.

Once your generator has been trained on the original dataset it is able to generate as many rows of high-quality, synthetic data as you need for your machine learning application. This is a game-changer as you no longer have to scrape together enough high-quality rows to make your machine learning project work. Make sure to use a synthetic data generator that performs well on privacy-preservation benchmarks.

Data preparation

Real-world data is messy. Whether it’s due to human error, sensor failure or another kind of anomaly, real-world datasets almost always contain incorrect or missing values. These values need to be identified and either corrected or removed from the dataset. The first option (correction) is time-intensive and painstaking work. The second option (removal) is less demanding, but can lead to a decrease in the performance of the downstream machine learning model as it means removing valuable training data.

Even if the data sourcing is somehow perfect – what a world that would be! – your machine learning lifecycle may still be negatively impacted by an imbalanced dataset. This is especially relevant for classification problems with a majority and a minority class in which the minority class needs to be identified.

Fraud detection in credit card transactions is a good example of this: the vast majority of credit card transactions are perfectly acceptable and only a very small portion of transactions are fraudulent. It is crucial to credit card companies that this very small portion of transactions is properly identified and dealt with. The problem is that a machine learning model trained on the original dataset will not have enough examples of fraudulent behavior to properly learn how to identify them because the dataset is imbalanced.

Synthetic data can be better than real. Use synthetic data to improve the quality of your original dataset through smart imputation and synthetic rebalancing.

Model training and evaluation

Many machine learning models suffer from embedded biases in the training data which negatively impact the model’s fairness. This can have negative effects on both societal issues as well as on companies’ reputation and profit. In one infamous case investigated by ProPublica, a machine learning model used by the U.S. Judicial system was shown to make biased decisions according to defendants’ ethnicity. This led to incorrect predictions on the likelihood of defendants to re-offend which in turn affected their access to early probation or treatment programs.

While there is no single cause for biased training data, one of the major problems is a lack of sufficient training data, leading to certain demographic groups being underrepresented. As we have seen, synthetic data can overcome this problem both because it is infinitely available and because imbalances in the data can be fixed using synthetic upsampling.

But biases in AI machine learning models are not always due to insufficient training data. Many of the biases are simply present in the data because we as humans are all biased to some degree and these human biases find their way into training data. This is how Amazon’s recruiting algorithm learned that – because historically the majority of technical roles were filled by males – it should penalize resumes for including the word “woman”.

This is precisely where Fair Synthetic Data comes in. Fair Synthetic Data is data whose biases  have been corrected through statistical tools such as demographic parity. By adding fairness constraints to their models, synthetic data generators are able to ensure these statistical measures of fairness.

Synthetic data can increase data fairness. Use synthetic data to deal with embedded biases in your dataset by increasing the size and diversity of the training data and ensuring demographic parity.

Model tuning and maintenance

Once a machine learning model has been trained, it needs to be tuned in order to boost its performance. This is generally done through hyperparameter optimization in order to find the model parameter values that yield the best results. While this is a useful tool to enhance model performance, the improvements made tend to be marginal. The improvements are ultimately limited by the quality and quantity of your training data.

If you are working with a flexible capacity machine learning model (like XGBoost, LightGBM, or Random Forest), you may be able to use synthetic data to boost your machine learning model’s performance. While traditional machine learning models like logistic regression and decision trees have a low and fixed model capacity (meaning they can’t get any smarter by feeding them more training data), modern ensemble methods saturate at a much later benefit and can benefit from more training data samples.

In some cases, machine learning model accuracy can improve up to 15% by supplementing the original training data with additional synthetic samples.

Once your model performance has been fine-tuned, it will need to be maintained. Data drift is a common issue affecting machine learning models. As time passes, the distributions in the dataset change and the model is no longer operating at maximum performance. This generally requires a re-training of the model on updated data so that it can learn to recognize the new patterns. 

Synthetic data can increase model accuracy. Use synthetic data to boost flexible-capacity model performance by providing additional training samples and to combat data drift by generating fresh data samples on demand.

Upsampling minority groups with synthetic data
Use synthetic data to improve your machine learning model’s accuracy

Model explanation and sharing

The final step of any machine learning life cycle is the explanation and sharing of the model and its results.

Firstly, the project stakeholders are naturally interested in seeing and understanding the results of the machine learning project. This means presenting the results as well as explaining how the machine learning model arrived at these results. While this may seem straightforward at first, this may become complicated due to privacy concerns. 

Secondly, many countries have AI governance regulations in place that require access to both the training data and the model itself. This may pose a problem if the training data is sensitive and cannot be shared further. In this case, high-quality, representative synthetic data can serve as a drop-in replacement. This synthetic data can then be used to perform model documentation, model validation and model certification. These are key components of establishing trust in AI.

Synthetic data safeguards privacy protection. Use synthetic data to support Explainable AI efforts by providing highly-representative versions of sensitive training datasets.

The role of synthetic data in your ML life cycle

Synthetic data can address key pain points in any machine learning life cycle. This is because synthetic data can overcome limitations of the original, raw data collected from ‘the real world’. Specifically, synthetic data is highly available, balanced, and unbiased.

You can improve the quality of your machine learning life cycle by using synthetic data to:

Machine learning life cycle with synthetic data
The machine learning life cycle can be improved at multiple stages by using synthetic data

If you’re looking for a synthetic data generator that is able to consistently deliver optimal privacy and utility performance, give synthetic data generation a try today and let us know what you think – the first 100K rows of synthetic data are on us!

Acquiring real-world data can be challenging. Limited availability, privacy concerns, and cost constraints are the usual suspects making life difficult for the average data consumer. Generative AI synthetic data has emerged as a powerful solution to overcome these limitations. However, it’s not enough to add yet another tool to the tech stack. In order to serve the data consumer better, the data architecture also needs to change. 

While traditional approaches involve synthesizing data from centralized storage or data warehouses, a more effective and efficient strategy is to bring generative AI synthetic data closer to the data consumer. In this blog post, we explore the importance of this approach and how it can unlock new possibilities in data-driven applications.

Data consumer limitations: traditional data synthesis

Traditional data synthesis approaches usually rely on centralized storage, creating bottlenecks and delays in data access. The centralized governance model hinders the agility and autonomy of data consumers, limiting their ability to respond to evolving needs quickly. Moreover, traditional synthesis methods need help to scale and accommodate diverse data requirements, making it challenging to meet the specific needs of individual data consumers. The one size fits all approach doesn’t work.

In many organizations, data owners focus on replacing legacy data anonymization processes with generative AI synthetic data to populate lower environments, mistaking data availability for data usability. Generating full versions of their production databases resolves the data accessibility problem but locks the power of generative AI synthetic data. It's crucial to move beyond the mindset of merely replacing original data with synthetic data and instead focus on bringing generative AI synthetic data closer to the data consumer. Not only in terms of proximity but also in terms of usability.

The 3 benefits of generating synthetic data closer to the data consumer

1. Enhanced agility and autonomy

Organizations empower their teams with increased autonomy and agility. Data consumers gain greater control and flexibility in generating synthetic data tailored to their requirements, enabling faster decision-making, experimentation, and innovation.

Generative AI models can upsample minority classes for better representation, downsample high quantities of data for smaller but still representative datasets, and augment the data by filling the gaps in the original data. This level of customization and control allows data consumers to improve overall data quality and diversity and address the following data challenges:

2. Reduced latency and improved efficiency

Proximity to the data consumer minimizes delays in accessing and synthesizing data. Rather than relying on centralized storage, generative AI models can be deployed closer to the data consumer, ensuring faster generation and synthesis of synthetic data. This reduced latency results in more efficient workflows and quicker insights for data consumers.

3. Data collaboration and innovation

The proximity between generative AI synthetic data generators and data consumers fosters data collaboration and innovation. Data consumers can work closely with the generative AI model creators, providing feedback and insights to improve the quality and relevance of the synthetic data. This collaborative approach facilitates faster innovation, experimentation, and prototyping, unlocking new possibilities in various domains.

Data consumers: real-world examples

Healthcare diagnosis and treatment

In healthcare, generating synthetic data from patient data closer to the data consumer can revolutionize diagnostic and treatment research. Researchers and data scientists can utilize generative AI models to create synthetic patient data that captures a wide range of medical conditions, demographics, and treatment histories embedded in the real data. This synthetic data can be used to train and validate predictive models, enabling more accurate diagnosis, personalized treatment plans, and drug development without compromising patient privacy or waiting for access to original patient data. A healthcare data platform populated with synthetic health data can empower data consumers even outside the organization, like in the case of Humana's synthetic data exchange, accelerating innovation, research and development.

Financial fraud detection

In the financial industry, synthetic data generated from privacy sensitive financial transaction data brought closer to the data consumer can significantly improve fraud detection capabilities. Financial institutions can train machine learning models to identify and prevent fraudulent transactions by generating synthetic data representing various fraudulent activities, including upsampling fraud patterns. Using this upsampled synthetic data, organizations can stay ahead of evolving fraud techniques without compromising the privacy and security of original customer data.

Data consumers & synthetic data

There needs to be more than the traditional approach of data synthesis from centralized storage or data warehouses to meet the evolving needs of organizations. Bringing generative AI synthetic data closer to the data consumer offers a paradigm shift in data synthesis. More autonomy, less latency, improved privacy, and higher levels of customization are all among the benefits. Organizations must embrace this approach to promote collaboration, experimentation, and innovation, empowering organizations to unlock new possibilities and leverage the full potential of synthetic data.

By bringing generative AI synthetic data closer to the data consumer, we can embark on a transformative journey that empowers data consumers and accelerates the development of intelligent applications in various industries.

Table of Contents

Data simulation: what is it?

The process of creating synthetic or fake data that resembles real-world data is referred to as data simulation. It is widely used in a variety of domains, including statistics, machine learning, and computer science, for a variety of reasons, including testing algorithms, assessing models, and performing experiments.

Data simulation is the process of producing a dataset with specified traits and qualities that imitate the patterns, distributions, and correlations seen in real data. This generated data may be used to conduct studies, evaluate the efficacy of statistical methods or machine learning algorithms, and investigate various situations without the limits or limitations associated with real data collecting.

How do data simulations work with synthetic data?

Synthetic data and data simulation are closely related concepts, as synthetic data is often generated through data simulation techniques. MOSTLY AI is considered a leader in the synthetic data field and can provide artificially generated data that mimics real-world data's statistical properties and characteristics.

Data professionals can employ MOSTLY AI’s capabilities as a data simulation technique to create data points based on known distributions, mathematical models, or observed patterns. Data simulation aims to replicate the statistical properties and relationships present in the target actual data.

Why organizations can't afford to miss out on data simulation

Data simulation is an invaluable tool for businesses of all sizes. It has several advantages that help with decision-making, risk assessment, performance evaluation, and model creation.

One of the key benefits of data simulation is its capacity to assist in making informed decisions. Organizations can explore numerous alternatives and analyze potential results by simulating different situations and creating synthetic data that closely reflect real-world settings. This enables individuals to make data-driven decisions, reducing uncertainty and increasing the possibility of obtaining desired outcomes.

Risk assessment and management are also greatly enhanced through data simulation. Organizations may simulate various risk scenarios, assess their likelihood and impact, and design risk-mitigation strategies. They may implement proper risk management strategies and defend themselves against possible threats by proactively identifying vulnerabilities and analyzing the potential repercussions of various risk variables.

When it comes to model development and testing, synthetic data generated through simulation is highly valuable. Organizations can train and test statistical or machine learning models in controlled environments by developing synthetic datasets that closely imitate the properties of real data. This allows them to uncover flaws, enhance model accuracy, and reduce error risk before deploying the models in real-world settings.

Decoding of model development & testing with synthetic data simulation

The data simulation toolbox: exploring techniques & tools

Data simulation comprises a wide range of methodologies and technologies that businesses may use to produce simulated data. These techniques and tools cater to different data characteristics and requirements, providing flexibility and versatility in data simulation. Let's take a closer look at some regularly used strategies and tools!

Random sampling is a key tool for data simulation. This method entails picking data points at random from an existing dataset or creating new data points based on random distributions. When the data has a known distribution or a representative sample is required, random sampling is valuable.

Another extensively used approach in data simulation is Monte Carlo simulation. It makes use of random sampling to describe and simulate complex systems that include inherent uncertainty. Monte Carlo simulation models a variety of possible outcomes by producing many random samples based on probability distributions. This approach is used in a variety of industries, including finance, physics, and engineering.

For data simulation, statistical modeling techniques such as regression analysis, time series analysis, and Bayesian modeling can be employed. Fitting statistical models to existing data and then utilizing these models to produce simulated data that closely mimics the original dataset are examples of these approaches.

To facilitate data simulation, various software packages and tools are available. AnyLogic is a sophisticated simulation program that allows for the modeling of agent-based, discrete events, and system dynamics. Simul8 is a well-known program for discrete event simulation and process modeling. Arena is a popular modeling and simulation tool for complex systems, processes, and supply chains. R and Python programming languages, along with packages like NumPy and SciPy, provide substantial capabilities for data simulation and modeling.

data simulation

How to ensure simulated data mirrors the real world

For enterprises seeking reliable insights and informed decision-making, ensuring the accuracy of simulated data in contrast to real-world data is critical. Several factors and practices can aid in achieving this precision, allowing for more relevant analysis.

Obtaining a thorough grasp of the data generation process is a critical first step toward realistic data modeling. Collaboration with subject matter experts and domain specialists gives important insights into essential aspects, connections, and distributions that must be included in the simulation. Organizations may build the framework for correct representation by understanding the complexities of the data generation process.

Validation and calibration play a vital role in ensuring the fidelity of simulated data. Comparing statistical properties, such as means, variances, and distributions, between the real and simulated datasets allows for an assessment of accuracy. Calibration involves adjusting simulation parameters and models to achieve a closer match between the simulated and real data, enhancing the quality of the simulation.

A feedback loop involving stakeholders and subject experts is essential. Gathering input and thoughts from folks who are familiar with the real data on a regular basis improves the simulation's accuracy. By incorporating their experience into the simulation process, tweaks and enhancements may be made, better matching the simulated data with the real-world environment. Validation against real data on a regular basis maintains the simulation's continuous fidelity.

Data simulation risks & limitations

While simulated data can closely resemble real-world data and offer numerous benefits, it is essential to acknowledge the inherent limitations and assumptions involved in the simulation process. Organizations should recognize the uncertainties and limitations associated with simulated data, using it as a complementary tool alongside real data for analysis and decision-making.

The assumptions and simplifications necessary to mimic real-world settings are one of the fundamental limits of data simulation. Simulated data may not fully reflect the complexities and nuances of the actual data generation process, resulting in possible disparities between simulated and real data. Organizations should be cautious of the assumptions they make as well as the amount of authenticity attained in the simulation.

The accuracy of simulated data is strongly dependent on the quality of the underlying simulation models. Models that are inaccurate or inadequate may fail to convey the complexities and interdependencies seen in real data, resulting in erroneous simulated data. It is vital to ensure the validity and accuracy of simulation models in order to provide relevant insights and dependable forecasts.

The quality and representativeness of the training data used to develop the simulation models are intrinsically dependent on simulated data. If the training data is biased or does not represent the target population successfully, the simulated data may inherit those biases. To reduce the possibility of biased simulations, organizations must carefully curate and choose representative training data.

Another danger in data simulation is overfitting, which occurs when models become highly fitted to the training data, resulting in poor generalization to unknown data. Organizations should take caution and not depend too much on simulated data that has not been thoroughly validated against real-world data. Real-world data should continue to be the gold standard for evaluating the performance and dependability of simulation models.

Data simulation in finance

Data simulation is used in various use cases by banks and financial institutions. Here are the most important examples:

Empowering data simulation with MOSTLY AI's synthetic data generator

Simulated data for machine learning

Training machine learning models on synthetic data rather than actual data can potentially increase their performance. This is achievable because synthetic data assists these models in learning and understanding patterns. In the realm of data simulation, there are two essential ways in which it can significantly enhance the representation of data: by supplying a greater number of samples than what may be available in the original dataset and, more notably, by providing additional examples of minority classes that would otherwise be under-represented. These two aspects of data simulation play a crucial role in addressing the challenges associated with imbalanced datasets and expanding the diversity of data for more robust analysis.

Firstly, the MOSTLY AI synthetic data generator allows organizations to generate a larger volume of synthetic data points beyond the existing dataset. This serves as a valuable advantage, particularly in situations where the original data is limited in size or scope. By artificially expanding the dataset through simulation, organizations gain access to a richer and more comprehensive pool of samples, enabling more accurate and reliable analysis. The additional samples offer increased coverage of the data space, capturing a wider range of patterns, trends, and potential outcomes.

Secondly, and perhaps more significantly, data simulation offers the opportunity to address the issue of under-representation of minority classes. In many real-world datasets, certain classes or categories may be significantly under-represented, leading to imbalanced distributions. This can pose challenges in accurately modeling and analyzing the data, as the minority classes may not receive adequate attention or consideration. MOSTLY AI provides a solution by generating synthetic examples specifically targeted towards these under-represented classes. By creating additional instances of the minority classes, the simulated data helps to balance the distribution and ensure a more equitable representation of all classes. This is particularly important in various domains, such as fraud detection, where the minority class (e.g., fraudulent cases) is often of particular interest.

Synthetic data for machine learning

As discussed above, it is important to note that data simulation is not without its own challenges. The process of generating synthetic data requires careful consideration and validation to ensure that the simulated samples accurately capture the characteristics and patterns of the real-world data. The quality of the simulation techniques and the fidelity of the generated data are critical factors that need to be addressed to maintain the integrity of the simulation process.

Rebalancing as a data simulation tool

Recently, MOSTLY AI introduced us to its data augmentation features. Rebalancing is one of them and it can be used as a data simulation tool. Most real-world datasets do not accurately represent real-world circumstances. Many businesses, particularly financial institutions, suffer because the data they have accumulated over time is significantly skewed and biased towards a specific behavior. There are several examples of skewed/biased data sets investigating gender, age, ethnicity, or even occupation. As a result, decision-makers are sometimes unable, if not impossible, to make the optimal decision that will help their firm flourish.  

MOSTLY AI's rebalancing capability may be used as a simulation tool. The goal is to offer decision-makers an effective tool for better understanding and exploitation of newly acquired information that may influence their contradictory decision to make. Rebalancing may be a key and useful technique for testing multiple hypotheses and 'what-if' situations that may impact the entire organization's strategy change.

Take the insurance business as an example. Total yearly premiums and total claims amount are two of the most important KPIs for all insurers worldwide. One might use rebalancing as a simulation tool to answer questions like:

Using MOSTLY AI's rebalancing feature, we changed the insurance customer mix distribution toward younger audiences. MOSTLY AI's synthetic data generator then created the rest of the dataset's characteristics based on the new information. The two previously mentioned KPIs have been adjusted, and a decision-maker may notice that income has increased while costs have fallen.

Stakeholders can utilize the above comprehensive research to guide their judgments and perhaps adjust their organizational strategy.

Synthetic data rebalancing


In the dynamic and data-intensive landscape, data simulation has emerged as a powerful tool for organizations seeking to enhance decision-making, manage risks, and optimize operations. We've seen how data simulation helps organizations get useful insights and create informed strategies through a variety of effective use cases.

Data simulation has become an indispensable tool for organizations, providing them with the means to make evidence-based decisions, optimize strategies, and navigate complex landscapes. As organizations embrace the power of simulated data, they can unlock new insights, enhance their competitive advantage, and deliver superior services in an ever-changing world.

In recent years, the healthcare industry has seen a surge in the importance of data granularity. With vast amounts of data being generated by patients and healthcare providers, it has become increasingly necessary to understand and utilize data at the granular level.

The dire state of health data granularity

Old ways of anonymizing and sharing sensitive health data often involve aggregation and generalization. Both of these data anonymization techniques reduce data granularity by summarizing or grouping data. Combining multiple data points into a single summary results in a loss of detail and data granularity. As a result, important variations within the data can be overlooked, making it challenging to identify important patterns and trends.

Aggregating and generalizing data can smooth out variations and fluctuation within the original dataset, obscuring anomalies that may be valuable for research and analysis. Subtle changes are almost certainly missed, while a misrepresentation of minority groups is a very tangible risk. Aggregated data is a sure-fire way to arrive at incorrect conclusions. 

Summary statistics also strip away the contextual nuances associated with individual data points. Accuracy also suffers greatly and can lead modeling and data-driven processes astray. For example, predictive analytics in healthcare couldn’t thrive on aggregated or even heavily masked health data. Temporal distributions might be lost or greatly reduced as a result of legacy data anonymization techniques.

Privacy is also an issue. While old data anonymization tools like pseudonymization, generalization and randomization destroy important statistical information, a high risk of privacy violation remains. Especially in cases of time-series health data, like patient journeys, the task of de-identification is notoriously difficult. Healthcare data platforms offering synthetic data generation and assets provide the best alternative to old data sharing practices, as evidenced by the European Commission’s JRC Report on synthetic data and as practiced by Humana, one of the largest North American health insurance providers, who published a synthetic health data exchange.  

Understanding data granularity in healthcare

Data granularity is a mission-critical aspect of health data management practices. Choosing data anonymization tools and a health data platform that keeps patient data privacy-safe, yet granular, readable, accessible and shareable is the first step towards data-driven healthcare.

The richness and precision of data directly contribute to better healthcare outcomes, patient safety, and the overall quality of care provided. Moreover, healthcare organizations increasingly focus on population health management, which involves monitoring and improving the health outcomes of specific groups or communities.

Granular data helps in identifying population health trends, understanding risk factors, and designing targeted interventions. Fine-grained data allows for stratification of populations based on various characteristics, enabling proactive and tailored healthcare strategies.

In order to ensure data granularity, we must first understand what it means in the context of healthcare.

What is data granularity in a healthcare context? 

Data granularity in healthcare refers to the level of detail or specificity of clinical data. It is the measure of how fine or coarse the data points are that make up each patient's medical record. Greater granularity means more granular data points, which provides a more detailed picture of a patient's health status, medical history, treatment plans, and health outcomes. In contrast, less granularity implies fewer data points or less detailed data, which can limit the quality of insights that can be gained from the data.

Definition of data granularity

In healthcare, data granularity refers to the level of medical detail in a patient's electronic health record (EHR). Data granularity is a measure of the degree to which data in a system can be broken down into smaller data elements. This means that for data to be considered granular, the information must be detailed enough to reveal deeper insights and trends.

Importance of granular data in healthcare

Granular data is critical in healthcare because ultimately it improves the quality of care that patients receive. It also aids in the identification of health risks, the development of effective treatment plans, and facilitating critical decisions about care provision. More granular data can also enable healthcare providers to better track patient progress, improve outcomes, and develop more targeted interventions to mitigate specific health risks. 

The different levels of data granularity

Data granularity in healthcare commonly exists at three levels: administrative, clinical, and patient-specific data.

Administrative data includes patient data that identifies them within healthcare systems, like their name, address, social security number, and insurance information. This type of data is important for healthcare providers to have in order to properly identify and bill patients for services rendered. 

Clinical data pertains to data that healthcare providers collect during patient interactions. It includes information such as symptoms, diagnosis, medical history, laboratory values, medications, vital signs, and other clinical observations about an individual's health status. This type of data is crucial for healthcare providers to make informed decisions about patient care and treatment plans.

Patient-specific data includes any patient-generated data, such as health behavior, lifestyle habits, and social determinants of health. More granular patient-specific data is necessary for developing personalized treatment plans that facilitate optimal health outcomes. This type of data can be collected through patient surveys, wearable technology, and other patient-generated sources.

High data granularity is critical for healthcare providers and researchers to understand the intricacies of correlations and patterns. While the benefits of granular data are numerous, there are several challenges that stand in the way.

Challenges and limitations of data granularity

Although a high level of data granularity is always desired, some argue that too much data granularity is difficult to handle and too much details can make data useless. Although human-readability of highly granular data could indeed be an issue, when it comes to training AI and machine learning models or deriving insights on a population level, the more granular the data, the better for algorithms.

Storage requirements might also increase with granularity, however, the cost of data storage has been on a steady decline and is less of a concern today.

Data privacy and security concerns

The more granular the data, the easier to re-identify someone. This is especially true for behavioral data often encountered in healthcare settings. Until recently, striking the balance between data utility and privacy was attempted using subpar data anonymization tools, such as aggregation on a linear scale. The result: both data granularity and privacy suffered. Synthetic data generation offers a cost-effective and privacy-safe way of preserving maximum data granularity, unlocking a new dimension of data granularity for healthcare providers, researchers and policy makers.

What is a data catalog?

Data catalog tools enable centralized metadata management, providing a comprehensive inventory of all the data assets within an organization. A data catalog is a searchable, curated, and organized inventory of all the data sources, datasets, and data flows, along with information about their lineage, quality, and other attributes.

Data catalogs are the single source of truth for all data assets. Data catalogs make it easier for data users and stakeholders to discover, understand, and trust the data that is available to them. They provide detailed information about the structure, content, and context of data assets, including their data definitions, data types, and relationships to other data assets.

By providing a centralized view of data assets, data catalogs can help organizations to better manage and govern their data. Data catalog tools also facilitate compliance by providing visibility into the data lineage and usage, as well as access controls and permissions.

Why do we need data catalog tools?

Data catalog tools are software applications that allow you to create and manage the collection of your data assets described above. These data catalog tools typically store, and share metadata about data assets, including data definitions, data types, data lineage, and data quality.

Developing and maintaining these extensive data assets without a dedicated tool is near-impossible once tables number in the thousands, which is often the case in larger organizations.

Data catalog tools have been around as long as computing, but with the evolution of large scale data and distributed data architectures, they became mission critical. The data catalog tools of today often incorporate machine learning and artificial intelligence capabilities, enabling them sometimes to even automatically classify, tag, and analyze data assets.

What are the challenges of using data catalog tools?

While data catalogs can make it easier to manage data assets, there are also several challenges associated with using data catalog tools.

Data quality

Data catalogs rely on accurate and up-to-date metadata to be effective, and poor data quality can undermine the usefulness of the catalog. If the metadata is incomplete, inconsistent, or inaccurate, it can lead to confusion and misinterpretation of the data. 

Data governance 

Data catalogs can be an important tool for enforcing data governance policies and managing data assets, but this requires careful planning and implementation to ensure that the right policies and procedures are in place. You can’t govern what you can’t see. An effective data catalog tool allows governance people to track governance initiatives and co-operate with other stakeholders across organizations. Confining governance to IT departments is a mistake that should be avoided. However, sharing data assets downstream comes with its own privacy issues. AI-generated, realistic, yet privacy-protective synthetic data can serve as a drop-in placement for production data samples.


Data catalogs need to integrate with other data management tools, such as data warehouses, data lakes, and ETL tools, in order to provide a comprehensive view of an organization's data assets. This can be challenging, particularly when dealing with legacy systems or complex data architectures.


Data catalogs require ongoing maintenance and updates to ensure that the metadata is accurate and up-to-date. This can be time-consuming and resource-intensive, particularly for larger organizations or those with complex data architectures.

Data catalogs can provide significant benefits, however, they require careful planning, implementation, and ongoing curation to be effective. In our experience, it pays to have a dedicated team of data stewards who truly care about data democratization.

How to use data catalog tools

A data consumer uses a data catalog to find data. They may use full text search across the entire data catalog content, or navigate in a more structured manner and use filters to search for very specific tables, for example. In most cases, the user ends up on the catalog page of a table. A table is the most relevant entity for a data consumer. On the table page, they can inspect the title, description, and any other custom fields at the table level, and go into the details of each column, as well.

Chief Data Officers can effectively improve the analytical capabilities, scale governance and increase data literacy using a reliable data catalog tool. Enabling people across the organization to self-serve data on the fly should be the ultimate goal, while keeping data privacy and governance policies top of mind too. An essential tool in the journey towards full data democratization is to develop, curate and catalog synthetic data products. These readily available, statistically near-identical datasets can accelerate all data-intensive processes from third party POCs to the development of accurate machine learning models. 

Why do data catalog tools need synthetic data?

Since data catalog tools typically display sample data on the table page, visible to every catalog user, there is a danger of accidentally revealing sensitive information, such as names, ages, salaries, health status and other privacy violations. The usual answer to the problem: just mask the sensitive columns. However, data masking renders the sample less useful by destroying readability and still failing to protect privacy in meaningful ways.

A Table page with Sample Content (Values)

Synthetic data alternatives are needed to provide high readability and privacy protection to sample data displayed within data catalog tools. Furthermore, AI-powered synthetic data generation can also improve data quality by filling in gaps in the existing dataset or providing additional examples of rare or hard-to-find data points.

Some data catalog tools also include built-in SQL editors. If a user has a username and password or other credentials for the database in question, they can start querying the database from within the data catalog tool. They can reuse queries other users have published, and publish their own queries. Here, as well, it may be useful to direct the user (by default) to synthetic data rather than production data.

Synthetic data generation itself can be managed through data catalog tools. Datasets in need of full synthesization or data augmentation can be marked by data consumers directly in the data catalog tool, allowing seamless access to high quality, curated or even augmented synthetic datasets. In short, combining data catalogs with synthetic data can be an excellent way of accelerating time-to-value for any data project.

How to replace real data with synthetic data in a data catalog?

In this tutorial, we'll show you how to add synthetic data to Alation, a data catalog tool.

Get in touch with us if you need any help with integrating data catalog tools with synthetic data assets!

Historically, synthetic data has been predominantly used to anonymize data and protect user privacy. This approach has been particularly valuable for organizations that handle vast amounts of sensitive data, such as financial institutions, telecommunications companies, healthcare providers, and government agencies. Synthetic data offers a solution to privacy concerns by generating artificial data points that maintain the same patterns and relationships as the original data but do not contain any personally identifiable information (PII).

There are several reasons why synthetic data is an effective tool for privacy use cases:

  1. Privacy by design: Synthetic data is generated in a way that ensures privacy is built into the process from the beginning. By creating data that closely resembles real-world data but without any PII, synthetic data allows organizations to share information without the risk of exposing sensitive information or violating privacy regulations.
  2. Compliance with data protection regulations: Synthetic data helps organizations adhere to data protection laws, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). Since synthetic data does not contain PII, organizations can share and analyze data without compromising user privacy or breaching regulations.
  3. Collaboration and data sharing: Synthetic data enables organizations to collaborate and share data more easily and securely. By using synthetic data, researchers and analysts can work together on projects without exposing sensitive information or violating privacy rules.

However, recent advancements in technology and machine learning have illuminated the vast potential of synthetic data, extending far beyond the privacy use case. A recent paper from Boris van Breugel and Michaela van der Schaar describes how AI-generated synthetic data moved beyond the data privacy use case. In this blog post, we will explore the potential of synthetic data beyond data privacy applications and the direction in which MOSTLY AI's synthetic data platform has been developing, including new features beyond privacy, such as data augmentation and data balancing, domain adaptation, simulations, and bias and fairness.

Data augmentation and data balancing

Synthetic data can be used to augment existing datasets, particularly when there is just not enough data or there is an imbalance in data representation. Already back in 2020 we showed that by simply generating more synthetic data than was there in the first place, it’s possible to improve the performance of a downstream task.

Since then, we have seen more and more interest in utilizing synthetic data to boost the performance of machine learning models. And there are two distinct approaches that one can take to achieve this: either amplifying existing data by creating more synthetic data (as we did in our research) and only working with the synthetic data or mixing real and synthetic data.

But synthetic data can also help with highly imbalanced datasets. In the realm of machine learning, imbalanced datasets can lead to biased models that perform poorly on underrepresented data points. Synthetic data generation can create additional data points for underrepresented categories, effectively balancing the dataset and improving the performance of the resulting models. We recently published a blog post on data augmentation with details about how our platform can be used to augment existing datasets.

Domain adaptation

In many cases, machine learning models are trained on data from one domain but need to be applied to a different domain where no or not enough training data exists, or where it would be costly to obtain that data. Synthetic data can bridge this gap by simulating the target domain's data, allowing models to adapt and perform better in the new environment. One of the advantages of this approach is that the standard downstream models don’t need to be changed and can be compared easily.

This has applications in various industries. We currently see the most applications of this use case in the unstructured data space. For example, when generating training material for autonomous vehicles, where synthetic data can be generated to simulate different driving conditions and scenarios. Or, similarly, in medical imaging, synthetic data can be generated to mimic different patient populations or medical conditions, allowing healthcare professionals to test and validate machine learning algorithms without the need for vast amounts of real-world data, which can be challenging and expensive to obtain.

However, the same approach and benefits hold true for structured, tabular data as well and it’s an area where we see great potential for structured synthetic data in the future.

Data simulations

But what happens if there is no real-world data at all to work with? Synthetic data can help in this scenario too. Synthetic data can be used to create realistic simulations for various purposes, such as testing, training, and decision-making. Companies can develop synthetic business scenarios and simulate customer behavior.

One example is the development of new marketing strategies for product launches. Companies can generate synthetic customer profiles that represent a diverse range of demographics, preferences, and purchasing habits. By simulating the behavior of these synthetic customers in response to different marketing campaigns, businesses can gain insights into the potential effectiveness of various strategies and make data-driven decisions to optimize their campaigns. This approach allows companies to test and refine their marketing efforts without the need for expensive and time-consuming real-world data collection.

In essence simulated synthetic data holds the potential of being the realistic data that every organization wishes to have: data that is relatively low-effort to create, cost-efficient, and highly customizable. This flexibility will allow organizations to innovate, adapt, and improve their products and services more effectively and efficiently.

Bias and fairness

Bias in datasets can lead to unfair and discriminatory outcomes in machine learning models. These biases often stem from historical data that reflects societal inequalities and prejudices, which can inadvertently be learned and perpetuated by the algorithms. For example, a facial recognition system trained on a dataset predominantly consisting of light-skinned individuals may have difficulty accurately identifying people with darker skin tones, leading to misclassifications and perpetuating racial bias. Similarly, a hiring algorithm trained on a dataset with a higher proportion of male applicants may inadvertently favor male candidates over equally qualified female candidates, perpetuating gender discrimination in the workplace.

Therefore, addressing bias in datasets is crucial for developing equitable and fair machine learning systems that provide equal opportunities and benefits for all individuals, regardless of their background or characteristics.

Synthetic data can help address these issues by generating data that better represents diverse populations, leading to more equitable and fair models. In short: one can generate fair synthetic data based on unfair real data. Already 3 years ago we showed this in our 5-part Fairness Blogpost Series that you can re-read to learn why bias in AI is a problem and how bias correction is one of the main potentials of synthetic data. There we also show the complexity and challenges of the topic including first and foremost how to define what is fair. We see an increasing interest in the market for leveraging synthetic data to address biases and fairness.

There is no question about it: the potential of synthetic data extends far beyond privacy and anonymization. As we showed, synthetic data offers a range of powerful applications that can transform industries, enhance decision-making, and ultimately change the way we work with data. By harnessing the power of synthetic data, we can unlock new possibilities, create more equitable models, and drive innovation in the data-driven world.