Synthetic data: agile data analytics and open innovation
Eliminate time consuming data access roadblocks to realize an agile data access infrastructure.
THE OLD SITUATION
Time consuming data-driven innovation
Agile; this is thé typical way of working that we observe within many organizations. Here, those companies aim to move and deliver fast in a complex environment to realize innovation (fast).
However, where those organizations are (often) master in setting up an agile way of working, they typically forget setting up an agile infrastructure. Consequently, those organizations frequently face infrastructure related dependency in many projects that aim to realize innovation (fast).
Here, specifically for the realization of data-driven innovation, data access is key. It is relatively simple: without data, no data-driven innovation. However, what we typically observe in practice is a non-agile setup. First, we see lacking data access infrastructure and various time consuming trajectories before data access could be granted. Second, we see a variety of legal restrictions and risk assessments. Finally, we see a lot of overhead and wasted time resulting in a non-agile organization for the realization of innovation.
Agile data analytics and open innovation with synthetic data
Our solution: work with synthetic data as alternative to using original data. This allows our customers to eliminate those aforementioned time consuming and non-agile trajectories. Ultimately, this creates a strong foundation to realize data-driven innovation, but then, in an agile way.
Our clients realize agile analytics in 2 formats:
Ad hoc synthetic data
We see ad-hoc data synthetisation when agility in data access is desirable. As alternatively to realizing data-driven innovation with original (sensitive) data, here one can realize data-driven innovation on synthetic data. This situation will boost agility by avoiding the data access hurdles one would normally face.
Setup a synthetic data warehouse
Many organizations have a data warehouse containing original (sensitive) data. Our suggestion would be to introduce a data warehouse with synthetic data next to the data warehouse with original data. Now, your employees (or even 3th parties) can easily access synthetic data from the synthetic data warehouse to realize data-driven innovation upon and will not face those data access hurdles.
Why our customers use synthetic data
Build a strong foundation to realize data-driven innovation with ...
FASTER DATA ACCESS
Boost the realization of data-driven innovation now!
First, Synthetic data: use our software to generate an entirely new dataset of fresh data records. Consequently, information to identify real individuals is simply not present in a synthetic dataset. Furthermore, the key difference at Syntho: we apply machine learning to reproduce the structure and properties of the original dataset in the synthetic dataset. This results in maximized data-utility. As a consequence, you will be able to obtain the same results when analyzing the synthetic data as compared to using the original data. In addition, Syntho offers a quality report for every generated synthetic dataset to demonstrate this. Our quality report contains various basic statistics, including aggregates, distributions and correlations, enriched with more advanced measures, such as multivariate distributions. Here, you can observe the highlights from our quality report. 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. Finally, our customers use synthetic data to boost innovation, mitigate data biases and even train AI models. Hence, why use real (sensitive) data when you can use synthetic data? We believe our technology is what you need. Let us help you!