Data has become increasingly important for building machine learning models, testing applications, and drawing business insights.
However, for compliance with the many data regulations, it is often vaulted away and strictly protected. Accessing such data could take months to get the necessary sign-offs. Alternatively, businesses can use synthetic data.
What Is Synthetic Data?
Synthetic data is artificially generated data that statistically resembles the old dataset. It can be used with real data to support and improve AI models or can be used as a substitute altogether.
Because it does not belong to any data subject and contains no personally identifying information or sensitive data such as social security numbers, it can be used as a privacy-protecting alternative to real production data.
Differences Between Real and Synthetic Data
The most crucial difference is in how the two types of data are generated. Real data comes from real subjects whose data was collected during surveys or as they used your application. On the other hand, synthetic data is artificially generated but still resembles the original dataset.
The second difference is in the data protection regulations affecting real and synthetic data. With real data, subjects should be able to know what data about them is collected and why it is collected, and there are limits to how it can be used. However, those regulations no longer apply to synthetic data because the data cannot be attributed to a subject and does not contain personal information.
The third difference Is in the quantities of data available. With real data, you can only have as much as users give you. On the other hand, you can generate as much synthetic data as you want.
Why You Should Consider Using Synthetic Data
It is relatively cheaper to produce because you can generate much larger datasets resembling the smaller dataset you already have. This means your machine learning models will have more data to train with.
The generated data is automatically labeled and cleaned for you. This means you do not have to spend time doing the time-consuming work of preparing the data for machine learning or analytics.
There are no privacy issues as the data is not personally identifying and does not belong to a data subject. This means you can use it and share it freely.
You can overcome AI bias by ensuring that minority classes are well represented. This helps you build fair and responsible AI.
How to Generate Synthetic Data
While the generation process varies depending on which tool you are using, generally, the process begins with connecting a generator to an existing dataset. After which, you then identify the personally-identifying fields in your dataset and label them for exclusion or obfuscation.
The generator then begins identifying the data types of the remaining columns and the statistical patterns in those columns. From then, you can generate as much synthetic data as you need.
Usually, you can compare the generated data with the original dataset to see how well the synthetic data resembles the real data.
Now, we will explore the tools for synthetic data generation to train machine learning models.
Mostly AI has an AI-powered synthetic data generator that learns from the original dataset’s statistical patterns. The AI then generates fictional characters that conform to the learned patterns.
With Mostly AI, you can generate entire databases with referential integrity. You can synthesize all sorts of data to help you build better AI models.
Synthesized.io is used by leading companies for their AI initiatives. To use synthesize.io, you specify the data requirements in a YAML configuration file.
You then create a job and run it as part of a data pipeline. It also has a very generous free tier that allows you to experiment and see if it fits your data needs.
With YData, you can generate tabular, time-series, transactional, multi-table, and relational data. This allows you to dodge the problems associated with data collection, sharing, and quality.
It comes with an AI and SDK to use to interact with their platform. In addition, they have a generous free tier that you can use to demo the product.
Alternatively, you can use their REST API or CLI, which will come at a cost. Their pricing is, however, reasonable and scales with the size of the business.
Copulas is an open-source Python library for modeling multivariate distributions using copula functions and generating synthetic data that follows the same statistical properties.
The project started in 2018 at MIT as part of the Synthetic Data Vault Project.
CTGAN consists of generators that are able to learn from single-table real data and generate synthetic data from the identified patterns.
It is implemented as an open-source Python library. CTGAN, along with Copulas, is part of the Synthetic Data Vault Project.
DoppelGANger is an open-source implementation of Generative Adversarial Networks to generate synthetic data.
DoppelGANger is useful for generating time series data and is used by companies such as Gretel AI. The Python library is available for free and is open-source.
Synth is an open-source data generator that helps you create realistic data to your specifications, hide personally identifiable information, and develop test data for your applications.
You can use Synth to generate real-time series and relational data for your machine-learning needs. Synth is also database agnostic, so that you can use it with your SQL and NoSQL databases.
SDV stands for Synthetic Data Vault. SDV.dev is a software project that began at MIT in 2016 and has created different tools for generating synthetic data.
These tools include Copulas, CTGAN, DeepEcho, and RDT. These tools are implemented as open-source Python libraries that you can easily use.
Tofu is an open-source Python library for generating synthetic data based on UK biobank data. Unlike the tools mentioned before that will help you generate any kind of data based on your existing dataset, Tofu generates data that resembles that of the biobank only.
The UK Biobank is a study on the phenotypic and genotypic characteristics of 500 000 middle-aged adults from the UK.
Twinify is a software package used as a library or command-line tool to twin sensitive data by producing synthetic data with identical statistical distributions.
To use Twinify, you provide the real data as a CSV file, and it learns from the data to produce a model that can be used to generate synthetic data. It is completely free to use.
Datanamic helps you make test data for data-driven and machine-learning applications. It generates data based on column characteristics such as email, name, and phone number.
Datanamic data generators are customizable and support most databases such as Oracle, MySQL, MySQL Server, MS Access, and Postgres. It supports and ensures referential integrity in the generated data.
Benerator is software for data obfuscation, generation, and migration for testing and training purposes. Using Benerator, you describe data using XML (Extensible Markup Language) and generate using the command-line tool.
It is made to be usable by non-developers, and with it, you can generate billions of rows of data. Benerator is free and open-source.
It is estimated by Gartner that by 2030, there will be more synthetic data used for machine learning than there will be real data.
It is not hard to see why given the cost and privacy concerns of using real data. It is, therefore, necessary that businesses learn about synthetic data and the different tools to help them in generating it.
In the information age, data centers collect large amounts of data. The data collected comes from various sources such as financial transactions, customer interactions, social media, and many other sources, and more importantly, accumulates faster.