As businesses increasingly rely on software tools to manage their operations, it is critical that these systems function correctly and efficiently. Testing these systems is vital for identifying potential faults and preventing problems from occurring. Accurate and efficient software systems can boost a company’s productivity, customer satisfaction, and income signiﬁcantly. Using real-world production data to test these systems, however, can frequently result in incomplete or inconsistent data, leading to skewed results and possible challenges for the business.
A Cautionary Tale
Let us explore how a logistics company may have saved themselves a lot of hassle by testing their inventory tracking system with synthetic data.
The company manages a large inventory of items and uses a software system to track each item’s location and status. However, the company tested the system using actual inventory data, which was incomplete and contained errors. As a result, the system did not perform as planned, resulting in shipment delays, lost goods, and a rise in customer complaints.
The unreliable nature of real data makes it unsuitable for accurately testing a system. The data lacked essential information such as item descriptions, shipment dates, and tracking numbers, making inventory tracking and management difficult. Furthermore, the real data only reflected a limited number of products, locations, and shipping statuses, making it difficult to adequately test the system’s capabilities on a broader number of test scenarios.
The use of synthetic data could have resulted in a controlled and structured environment to test the system. Synthetic data would include various data configurations, such as different item categories, quantities, and shipment statuses, and could be utilised in a wider variety of scenarios to ensure the system accurately tracks and manages the inventory as intended.
The Take Away
The consequences of testing with real-world production data can be severe. These problems can harm the company’s reputation, cause financial losses, and decrease customer satisfaction. Additionally, using real production data can expose the company to legal liability, especially if the data contains sensitive customer information or violates privacy laws.
Using synthetic data to test software systems is becoming rapidly more important for companies to make sure that their systems work accurately and efficiently. Synthetic data can help test the system thoroughly, finding any potential issues before implementing it in a real-world setting. By using synthetic data, companies can improve customer satisfaction and increase its revenue by preventing the release of a faulty system.
In conclusion, companies should recognize the limitations of real production data for testing software systems and incorporate synthetic data testing strategies. With the growing importance of accurate and efficient software systems, utilizing synthetic data can help businesses improve their operations, prevent potential problems, and increase customer satisfaction.
Datamaker, our fake data generator, helps exactly with that, as it is a powerful tool with which anyone can generate massive amounts of synthetic data sets at the click of a button, without any knowledge of coding or anonymization techniques. There’s no need for production data either, with Datamaker you can generate synthetic data, that behaves just like real data. You can simply choose the data types and patterns and quickly create high-quality data for your specific testing needs.