SYNTHETIC DATA WEBINAR

The Power of Synthetic Data for overcoming Data Scarcity and Privacy Challenges

Watch this webinar to find out how to generate synthetic data with machine learning and use synthetic data for overcoming data scarcity and data protection challenges.

What you’ll learn during the webinar:

  1. Why classic ‘data anonymization’ offers no real solution.
  2. How AI-generated synthetic data works
  3. Why you should use synthetic data instead of real data
  4. How Synthetic data boosts data-driven innovation

Get instant access!

What is Synthetic Data?

Synthetic data, as the name suggests, is data that is artificially created rather than being generated by actual events. It is often created with the help of algorithms and is used for a wide range of activities, including as test data for new products and tools, for model validation, and in AI model training.

AI-GENERATED SYNTHETIC DATA CREATION

Generate Synthetic Data with AI

Synthetic data generation tools generate synthetic data to match sample data while ensuring that the important statistical properties of sample data are reflected in synthetic data. Generate synthetic data with Syntho. The software can be used to generate an entirely new dataset of fresh data records. Information to identify real individuals is simply not present in a synthetic dataset.

Machine Learning

Machine Learning with Synthetic Data

The key difference of Syntho is that machine learning is applied to reproduce the structure and properties of the primal dataset in the synthetic dataset, resulting in maximized data-utility. Accordingly, you will be able to obtain the same results when analyzing the synthetic data compared to using the original data.

Quality Report

Synthetic Data Quality Report

Syntho offers a quality report for every generated synthetic dataset to demonstrate this. The quality report contains various basic statistics, including aggregates, distributions and correlations, enriched with more advanced measures, such as multivariate distributions.

Syntho generates a quality report for every generated synthetic dataset. The quality report contains various common statistics such as averages and distributions, enriched with more advanced statistics, such as correlations and multivariate distributions.

Data Analysis

Use Synthetic Data for Data Analysis

Use synthetic data for any data analysis as though it is real data. Outcomes of data analysis on synthetic data will be (nearly) identical to analysis results of the original data. Syntho customers use synthetic data to boost innovation, mitigate data biases and even train AI models.

IS SYNTHETIC DATA FOR YOU?

Innovation Managers

Synthetic Data for Innovation Managers

Synthetic data enables the Innovation Manager to realize data-driven innovation within the organization.

Benefits of using Synthetic Data for Innovation Managers

  • Realize data-driven innovation (for example with big data, AI, ML etc.) fast(er)
  • Previously untouched valuable datasets can now be transformed into valuable insights
  • Reduced (legal and risk) overhead costs results in more budget for actual innovations
  • Provide the organization with solutions to realize data-driven innovation in a privacy preservative manner
  • Accelerate impactful collaborations with cross-functional teams and third party vendors

Data Compliance Officers

Synthetic Data for Data Compliance Officers

Synthetic data allows Compliance Officers to enforce compliance with data privacy rules and legislation.

Benefits of using Synthetic Data for Compliance Officers

  • Transform from innovation obstacle to innovation enabler
  • Achieve a higher level of compliance within the organization
  • Deliver good news to the board of directors
  • Implement new technologies to enforce compliance with data protection rules
  • Improve the relation (and become friends) with the rest of the organisation

Data Scientists

Synthetic Data for Data Scientists

The Data Scientist can accelerate and improve problem-solving and business value creation with the use of synthetic data.

Benefits of using Synthetic Data for Data Scientists

  • Focus on core data science tasks
  • Access to more data
  • Faster data access
  • Overcome time consuming (and energy draining) internal data access policies
  • Reduced situations with questionable data-acces

Use Synthetic Data instead of Real Data

WHY USE REAL (SENSITIVE DATA) WHEN YOU CAN USE SYNTHETIC DATA?

BOOST DATA-DRIVEN INNOVATION!

USE OF SYNTHETIC DATA IN ACTION

Testing & Development

Testing & Development with Synthetic Data

Prevent production data in your test & development environment

Let me ask you this question: does your production data contain personal data?

If your answer is yes, then GDPR impacts how you should establish your test & acceptance environment.

Synthetic data by Syntho reproduces the same statistical characteristics of your primal dataset, while warranting that no records from the primal dataset are present and specific individuals cannot be traced back. Hence, one can set up a test environment and an acceptance environment that has the same statistical characteristics of the original production environment that does not contain records from it. Consequently, using synthetic data for your test environment and development environment has 3 benefits, as illustrated in figure 2:

  1. Synthetic data approaches the statistical properties of the original data, so interactions and patterns are preserved. Consequently synthetic data is realistic and representative.
  2. Synthetic data does not contain records from the primal dataset. Hence, synthetic data rules out privacy risk.
  3. Original sensitive or poorly (classicaly) anonimized data does not leave the building, so the likelihood of data breaches is minimized.

The result: a representative test environment and a representative acceptance environment with no privacy risk.

 Figure 2: synthetic data for your test environment and acceptance environment

Figure 2: synthetic data for your test environment and acceptance environment

Synthetic data generation for non-existing data

Often when developing (new) features, data-quantity is insufficient, data is not present yet or data is not present at all to perform the desired test scenarios to assess the quality of your application. To overcome this, the Syntho engine operates as data generator to tailor the data quantity, calibrate the statistical properties or even create dummy data. This allows you to produce data for test scenario’s that you otherwise would not be able to perform.

Figure 3: data-synthetisation and data-generation

Figure 3: data-synthetisation and data-generation

Data Sharing

Data Sharing with Synthetic Data

Privacy-preserving public and third party data sharing

Freely share your data in synthetic form to enable a data-driven organization without privacy concerns.

The classic risk assessment

Sharing original sensitive data is often strictly limited. Subsequently, when data sharing with 3rd parties is desirable as illustrated in figure 1, one typically runs into a slow and tedious process. It may require a risk assessment, certificates of good conduct or it is simply not allowed. Moreover, sharing sensitive date within the organisation could be a cumbersome process because of the ‘’data minimalization’’ principle (GDPR). Consequently, access to the right data can be a time-consuming process for data analysts or data scientists that immediately would like to start with the creation of a proof of concepts instead of data-collection.

Figure 1: classic data sharing

Data sharing 1

Synthetic data by Syntho reproduces the same statistical characteristics of your primal dataset, while warranting that no records from the primal dataset are present and specific individuals cannot be traced back. When applied on premise, the desired dataset can be synthesised and shared in synthetic form resulting in 4 benefits, as illustrated in figure 2:

  1. Synthetic data approaches the statistical properties of the original data, so interactions and patterns are preserved. Consequently synthetic data is realistic and representative.
  2. Synthetic data does not contain records from the primal dataset. Hence, synthetic data rules out privacy risk.
  3. Original sensitive data does not leave the building, so the likelihood of data breaches is minimized.
  4. Time-consuming data sharing processes can be avoided.


The result: one is able to share representative data with no privacy risk.

Figure 2: the Syntho Engine on premise for data sharing purposes

Figure 2: the Syntho Engine on premise for data sharing purposes

Data Retention

Data Retention with Synthetic Data

Overcome legal retention periods and eliminate storage risks

Can I store my client data freely according to GDPR?

Since the introduction of GDPR, companies are obliged to define why they have to use personal data, and if so, obtain permission. Moreover, the ‘data minimization’ principle states that companies need to minimize the use of personal data and only use it when strictly necessary. Whenever a project finishes, the original purpose of collecting the data no longer applies, so using the data is no longer permitted. Hence, the collected data must be deleted, often alongside any products you developed using this data. So, your product – e.g. dashboard, software application or AI model – and the associated insights will evaporate.

Figure 1: Delete data after a finished project (in compliance with the GDPR)

Figure 1: Delete data after a finished project (in compliance with the GDPR)

Syntho solution: data storing and preserving data in synthetic form

Synthetic data by Syntho reproduces the statistical characteristics of your original dataset, while warranting that no records from the original datasets are present and specific individuals cannot be traced back. When applied after a project is finished, the desired datasets can be preserved in synthetic form, alongside the products you developed using this data. The benefits are fourfold:

  1. Preserve products using highly realistic and representative synthetic data
  2. Rule out privacy risks (since synthetic data does not contain records from the original dataset)
  3. Prevent re-inventing the wheel in future projects 
  4. Utilize developed projects for external purposes (e.g. for client showcases and demos)

Figure 2: Store and preserve data products in synthetic form

Figure 2: Store and preserve data products in synthetic form

Agile Analytics

Agile Analysis with Synthetic Data

Eliminate time-consuming governance blocking data access and innovation

Data Augmentation

Data Augmentation with Synthetic Data

Intelligent synthetic data sampling to reduce bias and balance datasets

Data Commerce

Data commerce with Synthetic Data

Responsibly monetize your data assets in synthetic form

Register for the webinar below

Explore the value of Synthetic Data

Book your demo now

Want to see Synthetic Data Live in action? Great! Schedule a software demo now.

We support remote demos. Explore the added value of synthetic data from any place in the world via your preferred communication tool (Slack, Microsoft Teams, Zoom, Hangouts, etc.).