Innovation though data protection
With synthetic data for secure AI applications
Written by Omar Ali Fadl
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Data is the biggest driver of innovation in the digital age. If we look at new technologies such as IoT applications in the manufacturing industry, artificial intelligence (AI) or self-driving applications, these are only possible through the rapid evaluation of huge amounts of data. Synthetic data must ensure a high level of information acquisition while at the same time meeting the highest data protection requirements.
Especially with regard to training Amnesty InternationalGerman apps, companies and researchers are quickly reaching their limits. Regression factor: the quantity and quality of data available in the European Union. Looking at these future technologies, it will be important to keep up with international competition in order to keep knowledge in Germany and thus be competitive in the long term. Synthetic data is the most promising solution that companies in various industries are betting on. These companies promise a high level of information acquisition while at the same time meeting the highest data protection requirements.
However, the evaluation and use of data is not only necessary for technologies still in development. It would not be possible to think of most digital business models without careful analysis of production, location and user data. These enable businesses to operate more sustainably and provide customers with an optimal shopping experience and product range.
Welcome to the land of data laws
However, many companies shy away from using the full potential of their private data. It gets even more difficult when it comes to sharing data for research projects or industry-wide initiatives. Because when it comes to data, many authorities in Germany have a say. So companies often feel caught between innovative developments and feasibility within the tight framework of powers and Data protection. The result: important information remains unused and innovation slows.
The General Data Protection Regulation (GDPR), the strictest data protection regulation to date, has been in place for four years and has changed the way companies have to handle personal data. Hannoversche Volksbank was recently fined €900,000 because the financial institution evaluated data from current and former customers without their consent in order to be able to implement more targeted advertising. Thus, the GDPR is an important step in protecting the privacy of many people, but it is also an uncertainty factor for businesses. In the future, it can be assumed that both the EU bodies and those at the national level with their digital and planned strategies Electronic Privacy Regulation Data-driven business models of companies will be further regulated.
In addition to regional data protection regulations, international agreements must also be taken into account when processing data. In particular, companies that operate all over the world or even have subsidiaries in different countries face challenges here. Data transfers between the EU and the US alone are very complex, as the regulations in the two countries are very different. The fact that the data protection agreement between the two recently expired and no new regulation has yet come into effect doesn’t make the issue any easier. The situation is similar for Great Britain, which is currently working on its own basic data protection regulation after leaving the European Union, or India, which is also currently planning an initial increase. Different national framework conditions not only make coordination between countries necessary, but also lead to the corresponding complexity.
Companies in the fields of finance, health and insurance are particularly affected, as they have to work with a large amount of personal data. But science, research and in fact all businesses, especially those that operate across national borders, need data that they can really use without any worries.
Structural data in companies
The solution to the challenges companies are facing and addressing can be the use of synthetic data. Analysts from gardener It currently expects that within the next two years, about 60 percent of the data used to develop AI and analytics projects will be generated. At the same time, the famous MIT Technology Review recently named the technology one of the most popular 10 hacking technologies of 2022.
Unlike real data, which is derived from information provided by humans, synthetic data generation relies on machine learning algorithms. Thus, synthetic datasets are not a transformation of already existing data – they create a set of entirely new data points. The statistical properties and underlying structure of the data are essentially preserved, so that new datasets can be used like the original ones – for example for analytics, AI applications, quizzes, research and more. Sharing large data sets with business and development partners is much easier and legally safer. Because tuning can be infinitely scaled using this approach, the technology can be used by companies of any size and for almost any application.
Therefore, by using synthetic data, companies can extract the same information content from personal data, only they can share it more easily inside and outside the private organization. This leads to a faster exchange of information and increased knowledge. This can be a real competitive advantage over the competition, especially when it comes to developing new products and services. In addition, intelligent applications can be trained more easily and with larger amounts of data, which also greatly improves the quality here.
Synthetic data in the insurance industry
This can be clearly seen in the application. A quick look at the insurance industry shows the additional benefits of synthetic data.
For example, German insurer Provincial used synthetic data for its “next best deal” predictive analytics recommendation engine to determine the needs of more than 1 million customers. The Next Best Offer model predicts consumers’ needs and shows them offers and products based on their habits. They achieved 80 percent usability from synthetic data while maintaining data confidentiality. In addition, the machine learning model was trained on synthetic data and a performance efficiency of 97% was achieved. Predictive analytics helps insurers gain actionable insights into every aspect of their business, improving customer experience, increasing revenue, and seeing into the future.
Safe handling of data
Building transparent and understandable data processes that the entire company can see, rather than continuing to operate in a technology black box, opens the way for innovative data use across the company and a better understanding of customers. Synthetic data plays an important role in the future success of data-driven business models. It enables companies to act independently of data protection regulations and makes the entire mass of data available in a company usable. This gives companies a significant competitive advantage and the ability to focus on developing new products and services without any distraction.
It can be assumed that companies will increasingly face regulations on personal data and AI applications in the near future. It is therefore important to set up today’s data processes that enable companies to operate competitively independent of regulations. This requires technology that puts data protection first. Synthetic data can be a solution not only to meet current requirements, but also to act in a future-oriented manner.
About the author: Omar Ali Fadl is the CEO and co-founder of the company stationary, is a modern, Berlin-based provider of data protection technologies for companies operating in the healthcare, insurance and financial sectors. With Statice software, these companies can create synthetic datasets for everyday use.