Synthetic data use case: synthetic data for model development (e.g. dashboards [BI] and advanced analytics [AI & ML])
Becoming a data driven organization is priority number one for many organizations. Nowadays, it is hard to find an organization that does not include one of the many buzzwords in its strategy. Think about it: business intelligence (BI), artificial intelligence (AI), machine learning (ML) and many more. Does that sound familiar?
Start with building a strong data foundation: easy and fast access to usable, high quality data
This is no coincidence. They are cool, trending and the future will most definitely be full of them. Consequently, to get with the program is to get acquainted with these techniques and to be able to understand how they can profit your business and day-to-day operations. When you do, the most sensible action to start with, is to look at what is at the foundation of these innovations: easy and fast access to usable, high quality data. It is simple: without data, no data driven innovation. However, we see that many organizations that struggle with these basics. Many organizations suffer from a sub-optimal data foundation.
3 key challenges that come with a sub-optimal data foundation
Our solution: develop models with as-good-as-real synthetic data with maximized data quality
Yes – a strong data foundation, with easy and fast access to high quality data is essential. We are here to make that possible. How? Stop using original data and start using synthetic data.
Syntho is expert in end-to-end synthetic data generation and implementation. We excel in both generating (1) synthetic data twins and supporting various (2) synthetic data optimization, augmentation and simulation features. When used for model development (e.g. dashboards [BI] and advanced analytics [AI & ML]), generating a synthetic data twin with maximized data quality in comparison to the original data is most suitable.
Synthetic data twin
When generating a Synthetic Data Twin, Syntho mimics the original data as closely as possible while realizing privacy. Syntho generates completely new datapoints and models them in such a way that the properties, relationships and statistical patterns of the original data are preserved. Even complex, hidden patterns, relationships and inefficiencies are captured, so the synthetic data can be used as a direct alternative to the original data.
Synthetic data for model development in practice
A strong data foundation to develop models with AI generated synthetic data allows you to have easy and fast access to high quality data. After having established this data foundation with AI generated synthetic data (step 1), you will be able to develop models on the generated synthetic data (step 2), after which it is optional to score your developed models on the original data ([optional] step 3).
Step 1: synthetic data generation
Syntho supports various possibilities to establish your strong data foundation with AI generated synthetic data. 3 examples are:
Ad hoc data synthetization
Synthetic data warehouse
Sandbox infrastructure with synthetic data
Step 2: model development
After our visit, you will have high quality synthetic data, which can be accessed easy and fast by everyone within or even outside your organization (with your permission). Now, you can test, develop and train your models with synthetic data. This is the solution to minimize (and even mitigate) the use of original (sensitive & personal) data and thereby improve compliance with the data minimization principle, while facilitating a stronger data foundation for your developers, data scientists and data engineers.
Step 3: model scoring on the original data [optional]
[optional] Although models developed on AI generated synthetic will yield similar results in comparison to when developed on original data, it is possible to later score the models on the original data.
The benefit: instead of bringing the original data to your development team to develop the model, you will now be able to bring the developed models to the data. Thereby, your developers will never see the actual data.
Moreover, this allows your internal, or maybe even external organization, to explore and test hypothesis on synthetic data. Then, only when it makes sense, one could score the relevant developed models on the original data. For scoring, you will exactly know which data is relevant and which models makes sense to score, which allows you to minimize the use of original (sensitive & personal) data.