Synthetic data is the solution for IT project's security and velocity.
Gartner predicts that “By 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated”, while by 2030, they estimate that "Synthetic data will completely overshadow real data in AI models". We may disagree with Gartner numbers, but surely we shall use more and more synthetic data. CloudTDMS is betting that synthetic data shall represent at least 50% of the data by 2024, especially for big-data, analytics & AI/ML projects.
Why should we care about Synthetic Data??
Just as an example, are you aware about the recent biggest data leak in the world ? Nearly one billion people in China had their personal data leaked ! Unknown hackers claimed to have stolen data of nearly one billion Chinese residents after breaching a Shanghai police database. The hackers claimed the database was hosted on the cloud accessible to anyone without restrictions.?
If this very sensitive database has been stored in the cloud without proper security measures, it is probably due to the fact that real data has been shared by internal team members such as IT Partners, developers/testers for analytics projects, or data scientists to train or test a new AI/ML models.
One advice from CloudTDMS.com , often forgotten in day to day data projects: Never share real data or provide access to production databases to a project's team members. Even if, for example, an important dashboard is requested urgently by the company's CEO !
Join the new era of Synthetic Data ! It is becoming the new fuel for all Data related Projects. In other words any organisation can make test data as Realistic without taking it from production
Forrester recommends synthetic data to accelerate the development of new AI solutions, improve the accuracy of AI models, and protect sensitive data. It is currently being used in autonomous vehicles, financial services, insurance and pharmaceutical firms, and computer vision vendors.
To make your IT projects secure and fully compliant with regulations, organisations can generate synthetic data either by using open source python libraries such as Faker or a No-Code Cloud Solution such as CloudTDMS.com .
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Firstly, Faker is a powerful python library that generates fake data, and is very simple to use. This in-house development approach works but to generate data using faker requires coding skills, another drawback of this approach is that it is not configurable, in instance, for each new object/table/file, the script needs to be modified completely.
On the other hand,?CloudTDMS.com solution is a No-Code platform having all necessary functionalities required for test data management such as :
CloudTDMS.com is offering an always free plan called "Starter plan".
Don’t Become a Headline !
With Synthetic Data approach, and for free, you could fuel any data project & protect your company.?
You can achieve this either with in-house development with open-source tools such as Python Faker library or by using a No-Code Cloud Solution such as CloudTDMS.com.
In a nutshell, one advice : Fake it until you make it !