Now imagine yourself 5 years after implementation of the Batch Watch DBMS. Although it worked impeccably for years, the DBMS is starting to cause trouble for your data analysts. With the advent of new online data tracking technology, your DBMS has become slightly outdated.
But don’t worry. You don’t need a new system — a few program modifications should do the trick, say your data analysts. You let out a sigh of relief.
While program modifications are less cumbersome than system replacements, they still require that operations shut down for a small amount of time to allow for implementation of the modification. And before you implement, you need to test the modification with dummy data.
Dummy data is mock data generated at random as a substitute for live data in testing environments. In other words, dummy data acts as a placeholder for live data, the latter of which testers only introduce once it’s determined that the trail program does not have any unintended, negative impact on the underlying data.
For example, a company implementing a new accounting system uses dummy data to ensure its bookings are stored correctly before inputting live accounts.
You can think of dummy data like a car crash dummy — you hope nothing bad happens to it while testing, but if something bad does happen, you can fix the problem with no negative impact on your valuable data assets.
Dummy data is mock data generated at random as a substitute for live data in testing environments. In other words, dummy data acts as a placeholder for live data, the latter of which testers only introduce once it’s determined that the trail program does not have any unintended, negative impact on the underlying data.
For example, a company implementing a new accounting system uses dummy data to ensure its bookings are stored correctly before inputting live accounts.
You can think of dummy data like a car crash dummy — you hope nothing bad happens to it while testing, but if something bad does happen, you can fix the problem with no negative impact on your valuable data assets.
Dummy data is mock data generated at random as a substitute for live data in testing environments. In other words, dummy data acts as a placeholder for live data, the latter of which testers only introduce once it’s determined that the trail program does not have any unintended, negative impact on the underlying data.
For example, a company implementing a new accounting system uses dummy data to ensure its bookings are stored correctly before inputting live accounts.
You can think of dummy data like a car crash dummy — you hope nothing bad happens to it while testing, but if something bad does happen, you can fix the problem with no negative impact on your valuable data assets.
Imagine you own a big e-commerce website that sells watches in bulk. Your company is called Batch Watch — it’s your baby. One of the biggest assets the company has is the data it collects on vendors, its products, and customers.
When you started the company, you didn’t have much cash to spend on an expensive database program, so you took the first inexpensive option that came your way. Unfortunately, this means your data is not well-modeled, and you’re not really sure how that initial data program’s technical skeleton is structured.
After years of growth, you’ve now got cash to invest and want to implement a high quality database management system. However, you want to ensure the transfer is smooth and requires minimal downtime. You hire an IT consultant to execute the transfer.
The IT consultant analyzes your data and its structure, and she decides on a new program that’s most efficient to store your data. The project outlines is as follows:
The key point to note here is that the data in the current database is structured in a special way. It’s the structure that must be tested, not the data itself. In fact, this dynamic is what allows us to use dummy data in the first place. It’s like taking a practice exam before the real one — it doesn’t count for anything, but it prepares you for the live event.