5 Must-Haves for Data Generation Tools

1. 5 Must-Haves for Data Generation Tools

2. Data generation has become highly significant in today’s IT landscape. For testing software, developers and testers need relevant, fresh, and valid data. However, this cannot be real data because of data privacy regulations. Creating new data is challenging and impractical because many times this data is not varied and follows a defined pattern. This has brought in the need for data generation tools that can help generate synthetic data with a more realistic outlook and feel. Artificial Intelligence-powered data generation tools produce huge amounts of synthetic data that has the right variety and density. Developers and testers rely on automated data testing and quality assurance. Thankfully there are many data generation tools available on market these days. It has become a bit tricky to pick one that is right for you, and which fits all your needs. We have tried to make it easy for you in this piece.

3. 1. Must allow scalability and extensibility The requirements for data keep changing because of the new advancements, and innovations in technology. Data meets another challenge to remain consistent across all systems. While picking a data generation tool, make sure to pick one which ensures consistency. If you are expanding an enterprise and involving more systems, the data generation tool must allow extensibility so that you can scale data generation over time. In the absence of scalability, you will have to rely on generated copies of produced data. Therefore, a good data generation tool should have connectors so that data generation across various systems can be ensured, and data remains consistent.

4. 2. Must be efficient and User-Friendly The idea behind an automated data generation tool is to ensure efficiency. A complex tool compromises efficiency because it is difficult to understand and use. Ideally, a good tool is easy and intuitive when defining data generation functions. Therefore, make sure your selected data generation tool is quick and easy in data handling which involves analysis, modeling, and generation. Even after data generation, an efficient tool makes sure that the analyzed data remains reusable from centralized repositories. With an efficient tool, data generation with complex rules also becomes a piece of cake.

5. 3. Allows integrations with tool chains A desirable data generation tool should function well with a manual as well as an automated mode of working. Even within an automated system, there is a need for manual interventions otherwise it will lead to bottlenecks and dependencies. Therefore, a good tool must enable the system to function optimally in both work modes i-e automated and manual. Other than this, a good should ensure that it can perform with innovative technologies and techniques for data handling sot that the resulting data is a consistent and valid data set for testing software and systems. You must pick a data generation tool that allows easy integration of new tools with automated process and manual processes and with other processes of data management (such as masking and subsetting).

6. 4. Furnishes data complexity as per requirements At times generation of synthetic data does not mean producing values in a required format or mere popping up values of a specific data type. The underlying principle can be pretty complex which can involve the production of data according to a particular logic. For example, it might require the data to follow temporal trends or another such sequence. An ideal data generation tool should be able to produce complex data flawlessly and also accommodate changes down the lane. It should incorporate differences such as regions, time, geographies, and other such parameters. Furthermore, it should be flexible enough to accept and customize new functions.

7. 5. Should suffice data needs and requirements Synthetic data is required for various purposes. At times they need it to test a system and to assure quality. This means the copious amount of valid data should be available readily. For example, in development testing, the testing teams require data of different varieties, volumes, and densities. Whereas, the requirements will change for functional testing. In this testing, the tool must produce high-variety data, however, it can be in lower volumes. Likewise, for other kinds of testing such as unit testing and smoke testing, the requirements will change completely. A good data generating tool should produce up-to-date data in the right quantity with the right density and variety otherwise it will create bottlenecks, and also compromise accuracy. It should ensure that data is aligned for different versions of system components.

8. Conclusion The need for synthetic data has been on the rise for the flawless and functional deployment of software and other IT solutions. Various enterprises need test data with varied complexity levels to suffice the needs and requirements of the software being developed. Manual generation of synthetic data is not a practical solution because of the huge amounts of complex data which should be valid also. In this blog, we have discussed the desirable features of an auto data generation tool. Automated synthetic data generation tools remove data bottlenecks while producing scalable and robust data sets.

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