"Synthetic data is the perfect input into machine learning and AI" - read the EU report on synthetic data
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Why synthetic data is important for your business

The benefits of synthetic data are cost reduction, greater speed, agility, more intelligence and cutting-edge privacy. From transforming test data generation to AI governance, synthetic data can deliver high value use cases across organizations. 

Data scientists, advanced analytics teams and CTOs should take note

Data challenges & the synthetic data solution

“By 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated”
- Gartner
But synthetic data is much more than an AI and analytics tool. Synthetic data will have a far-reaching impact not only on data management level, but also on C-level decision making. Here is why:

Synthetic data is highly flexible

You can create, share and discard synthetic data at will. It is as good as production data and capable of improving data quality. You can even modify existing data sets, e.g. to correct for present bias.

Synthetic data will replace real data

AI-generated synthetic data is currently mainly used for training machine learning models. The technology is also getting popular in software development and testing. Synthetic data can replace radioactive production data in non-production environments. At the same time, the solution cuts time-to-market significantly.

Other use cases are continuously emerging and the future of business decisions is synthetic.

Data-driven innovation and transformation continues to accelerate

Data being the lifeblood of modern businesses poses enormous challenges to decision makers. Especially in regulated environments. On the one hand, data use is restricted by privacy, safety or other regulations. On the other hand, data access is critical in driving innovation and accelerating digital transformation. Synthetic data helps overcome this dilemma.

Synthetic data is safe and fully anonymous

Synthetic data generation is better than traditional anonymization methods. Instead of changing and in the process destroying an existing dataset, synthetic data is generated from scratch. First, a deep neural network learns all the structures and patterns in the actual data. After the training, the model uses this knowledge to generate new synthetic data. This artificially generated data is highly representative, yet completely anonymous. It does not contain any one-to-one relationships to actual data subjects, eliminating the risk of re-identification.

Synthetic data is highly accurate

MOSTLY AI is capable of retaining 99% of the value and information of your original datasets. This unprecedented accuracy allows using synthetic data as a replacement for actual, privacy-sensitive data in a multitude of use cases.
Synthetic data vs. Legacy anonymization (1600 × 900 px)

The potential of simulated data

AI-generated data allows for data simulation. Companies can develop synthetic business scenarios and simulate customer behavior. The business opportunities arising from this are endless.

The potential of cross-industry collaboration

Synthetic data enables not only privacy-compliant data sharing within organizations. It enables a new level of cross-company and cross-industry collaboration - with huge economic benefits for everyone involved.

Synthetic data increases efficiency and profitability

In many ways, synthetic data helps speed up business processes and reduce bureaucracy. For data scientists, actuaries, pricing analysts and many more, it reduces the time to data massively and frees up their time to focus on value creation. It leads to leaner processes, higher employee loyalty and increased competitiveness.

Are you ready to start your synthetic data journey?

Download the guide
Synthetic data journey ebook
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