AI generated DTAP. Your one-stop shop for the delivery of all tech solutions?
Typically, organizations with software solutions, like mobile apps, client portals, CRM systems etc., have a staged delivery approach that contains the development, testing, acceptance and production (DTAP) cycle. Value drivers for such approach are enhancing the quality of work, shortening the time-to-market and boosting collaborations between developers and development teams.
Testing and development with representative data is essential. Using original production data seems obvious, but is not allowed due to (privacy) regulations in the development, test and acceptance stages. Alternative test data solutions are not able to preserve business logic and referential integrity.
Challenge
Why do we not see a DTAP approach (yet) in the development of business intelligence and advanced analytics solutions?
When making the step towards developing business intelligence and advanced analytics solutions, representative data that acts as production-like data is crucial. Why? Garbage-in = garbage-out and bad quality data will results in bad quality models. This is exactly not what you want.
Compliant production-like data is needed in the development, test and acceptance stages
As classic alternative test data solutions (like anonymization, masking, scrambling, aggregation etc.) do not preserve business logic, production data is the only solutions that many organizations see for the development of business intelligence and advanced analytics solutions.
Consequently, the valuable DTAP cycle is not present yet in the area of developing business intelligence and advanced analytics solutions. This is unfortunate, because exploring hypothesis, trial & error and cracking the numbers is valuable to deliver next-level solutions. As alternative to having endless discussions, Syntho is here with solutions.
Our solution
Create a digital twin of your production environment with AI
We mimic your (sensitive) production environment with an AI algorithm to generate a synthetic data twin. This allows you to test and develop with an AI generated synthetic data twin to deliver state-of-the-art tech solutions.
- High quality test data with preserved business logic
- High quality test data with preserved referential integrity
- Easy and fast data generation with state-of-the art AI
- Optimized test data for edge case testing
- Generate new test data for new features, when your do not have data yet
The future of DTAP
Your DTAP cycle ready for business intelligence and advanced analytics
As the data quality is preserved with AI, the generated synthetic data twin can be used as if it is original data, even for business intelligence and advanced analytics tasks. Consequently, you are able to overcome the data quality challenges of classic test data “solutions. Therefore, you will have your end-to-end development, testing, acceptance and production (DTAP) cycle also ready for business intelligence and advanced analytics tasks for your whole organization.
Business value
The value of having enterprise ready DTAP approach
- Suitable for all tech solutions
- Suitable for all business verticals
- Improve overall test, development and delivery quality
- Higher NPS scores and an enhanced customer journey
- Be smarter than the competition
- Release faster and shorten the time-to-market
- Reduces bugs and potentially unhappy customers
- Minimize re-test cycles
We are expert in synthetic data. But, don’t worry, our team is real!
Contact Syntho and one of our experts will get in touch with you at the speed of light to explore the value of synthetic data!
