Comment pouvez-vous générer des données synthétiques pour l’analyse des données?

There clearly is a risk with synthetic data generation in creating artificial realities. However, similar risks exist also when creating “real” datasets because certain groups are over represented in the data. I think synthetic data generation is unavoidable sometimes (particularly where data is sparse indeed) and offers opportunities for adding expert knowledge. I am skeptical to the argument that synthetic data can be used to obfuscate sensitive data. If that is the goal, I would have serious doubts about the accuracy of the results because information is deliberately removed from the data.

While I agree with the use of synthetic data to experiment, I feel that we should take caution in giving this practice the kind of popularity it does not deserve. At the core of data analysis is data accuracy and integrity which synthetic data does not have.

Synthetic data is essential for various reasons: 1 Privacy Preservation: It allows you to create a dataset that mimics the characteristics of real data without exposing any confidential or sensitive information. 2 Flexibility: You can create synthetic datasets with specific characteristics or anomalies that might not be present in your original data. Which allows for better testing. 3 Cost-Effective: Generating synthetic data can be more cost-effective than collecting and cleaning new real-world data, especially when you need a large amount of data or data with specific characteristics. 4 Data Augmentation: It can augment existing datasets, making it possible to train models even when the original data is scarce or imbalanced.

1. Cost Reduction and Accessibility: - Generating synthetic data is significantly cheaper than collecting large real-world datasets. Acquiring high-quality data can be expensive, especially when dealing with rare events or specialized scenarios. - For instance, consider self-driving cars: gathering real-world data on various road conditions, weather, and traffic scenarios is costly and time-consuming. 2. Privacy Preservation and Compliance: - Privacy regulations (such as GDPR and CCPA) restrict the collection and use of sensitive personal data. Handling personally identifiable information (PII) or personal health information (PHI) requires strict compliance.

I believe that synthetic data can be immensely beneficial when dealing with data scarcity challenges. Imagine you're trying to solve a puzzle, but you're missing some pieces. Synthetic data steps in and creates puzzle pieces that look and fit just like the missing ones.

At my agency we use synthetic data every single week. In fact it is a core part of our ability to create highly accurate forecasts on the amazon platform. How does it become valuable from an amazon lens? We can utilize synthetic data to validate both worst case scenario and best case scenario situations from a growth perspective. The key is to inform this synthetic data with actual data in order to create ranges of how products can perform in various situations but also specific to individual categories. It allows you to create what I call “predictable growth” without needed to be a data scientist, but provides the ability to drive educated and actionable results.

In my perspective, synthetic data offers substantial advantages in addressing the quandaries posed by data scarcity constraints. Envision a scenario analogous to solving a jigsaw puzzle, wherein certain pieces are absent. Synthetic data assumes the role of fabricating puzzle pieces that emulate the visual characteristics and structural compatibility of the absent elements

Creating synthetic data for analysis involves: Purpose: Define why you need data. Attributes: List data features like age, gender. Distribution: Choose how attributes are distributed. Correlation: Decide if attributes relate. Generation Tools: Use libraries to simulate data. Noise: Add randomness for realism. Volume: Generate sufficient data points. Validation: Ensure synthetic data aligns with real trends. Analysis: Use it as real data for insights.

While synthetic data offers secure testing and validation of analysis methods, safeguarding privacy. It enriches datasets with diverse examples, boosting accuracy and insights. Synthetic data allows ethical experimentation of impractical scenarios and serves as a safe platform for skill development in data analysis. But synthetic data might lack realism compared to real-world data, affecting model generalization. Poorly generated synthetic data could introduce biases and not mirror real data distribution accurately. Generating high-quality synthetic data can be complex and resource-intensive. The absence of contextual information in synthetic data may hinder meaningful inference.

I feel while synthetic data can be useful, it may not perfectly capture the complexities of real data. It's important to validate your models and algorithms using real-world data before making critical decisions based on synthetic datasets

Faker library can be used to generate synthetic data using python. The documentation is extensive and there are methods to avoid redundant data and handle SCDs. In a use case that I had worked, I wrote a python function in snowflake to generate State codes and Consumer IDs by giving a range which can be taken as arguments within the function.

Generating synthetic data for data analysis involves creating artificial data that replicates the characteristics and patterns of real data. 1. random sampling 2. data augmentation 3. GANs 4. VAEs 5. bootstrapping

Synthetic data can be obtain by using various machine learning algorithms, random sampling using statistical method, identifying the pattern and generate data through it. This can help to mimic the real data where the real data is not available or difficult get extracted

The main challenge when generating synthetic data is to have it reflect all the statistical attributes of the real dataset used for training. Reflecting simple summary statistics like min, max, or average is reasonably easy, but properly reflecting probability distributions across all columns while preserving multi-variate correlations is a massive challenge. If you need such high-fidelity synthetic datasets, the best solution I know is Diveplane (no affiliation).

I think that there is too much real data out there that are not being collected. That said, I agree you must let your audience know when you are using synthetic data for your insights.

Synthetic data can be a powerful tool when utilised properly. However, it isn't "real" data. lack of realism and accuracy is perhaps the biggest limitation of synthetic data. This has to be considered when incorporating it into your findings. This can be especially dangerous when applying forecast models for businesses to make decisions

challenges can be: 1. realism 2. complexity 3. bias 4. generalization 5. contextual understanding Limitations can be: 1. lack of ground truth 2. sensitive attribute 3. scalibility 4. domain expertise

Synthetic data does not have to be perfect, it just needs to be good enough for the use to which it will be put. For example, synthetic data sets could be used to train students developing their skills in statistical analysis. Some of the data sets might be seeded with interesting outliers, leading to classroom discussions of the various ways of handling such data.

Synthetic data is useful when you understand the limitations. For example, synthetic data is used to train AI however it cannot replace the quality of AI operating with real data. Synthetic data is like using training wheels on a bike.

Evaluating and comparing synthetic data is a crucial step to ensure its quality, validity, and relevance for data analysis metrices can be: 1. distribution matching 2. privacy preservation 3. multi-dimensional pattern

Essentially, you can use synthetic data when training AI models. It is also useful in forecasting where there is insufficient historical data available to conclude a pattern. I have found that in predictive models, choosing the best forecasting model for your particular set of data (combine real and synthetic) will generate accurate results Be transparent with the consumers of the data regarding how you came about the conclusions

Utilizing synthetic data within the framework of data analysis involves a strategic approach to enhance the quality and scope of insights derived from the analysis process. The process demands a balanced consideration of both the advantages and limitations of synthetic data, resulting in informed decision-making and meaningful outcomes.

Although synthetic data has limitations and challenges, academia can be one of its key uses. The purpose is not to use the data in research projects where other organisations might use the findings, but instead, This can be used by students working on university projects who don't have access to actual data but need to test their data analysis skills. Real data cannot be shared in all projects due to the significance of data ethics and security. Thus, some universities can teach modules on synthetic data including generating, evaluating, and synthetic data ethics as well as its integration into university projects.

"Truly synthetic" data sets (not manipulations of real data) can be shared with colleagues around the world without fear of breaching privacy laws.