Imagine you’re about to launch a critical localization program, but as you scale up, data inconsistencies between your systems lead to delayed deadlines, frustrated teams, and unhappy clients. All that hassle just because the data was incomplete or flawed…
More than people usually acknowledge, the efficiency and accuracy of your localization projects largely depend on the data quality within your technology ecosystem. And this is a very common problem with daily consequences for many translation teams working with different technologies and services. Unfortunately, this is often overlooked or even ignored by people in charge of the executive decisions and that don’t work directly within the translation supply chain.
BeLazy is responsible for linking business management aspects to Vendor Portals (VP) and Translation Management Systems (TMS), and knows about these kinds of technical issues that can be hindering your projects. As a metadata-driven automation solution, BeLazy is the primary platform capable of highlighting data inconsistency. During the onboarding of the solution, BeLazy reads all metadata and either fixes inconsistencies or highlights data issues. If the data from these systems is accurate, complete, and consistent, the magic can happen with ease.
Why is data quality so important in every system?
- Solid foundation: If the data in your BMS, VP, and TMS is wrong or incomplete, the connections established by BeLazy will not yield the expected results. It would be like building a house on weak foundations.
- Increased efficiency: Correctly uploaded data allows BeLazy to better automate tasks and reduce manual errors, which translates into greater efficiency in your localization processes.
- Better decision-making and reporting: With accurate data, you can more effectively track the performance of your projects, identify areas for improvement, and make strategic decisions based on reliable information.
- Cost reduction: By having correct and consistent data, the loading of data between systems is smoother, reducing the manual time of loading it and optimizing time.
Working with AI: If you want to use LLMs at scale, you have to segment and select your prompts based on metadata. Wrong data means suboptimal functioning.
What can you do to improve the quality of the data in your systems?
- Establish clear standards: Define a set of rules and procedures for data management across all systems.
- Perform data audits: Regularly review your data to identify and correct errors, and inform internally about the issue and its correction.
- Clean and normalize data: Remove duplicates, correct spelling errors, and ensure consistency in terminology.
- Train teams: Make sure everyone on your team understands the importance of data quality and knows how to handle it correctly.
- Use data management tools: Some tools can help you automate data cleansing and validation tasks. Automation tools such as BeLazy highlight data inconsistencies very quickly and can be effectively used to identify improvement areas.
Best Practices for BeLazy Integration
System integration via BeLazy is a very important step in optimizing localization processes. However, to achieve a successful integration, it is recommended to follow some practices to improve the process:
- Map data: Perform detailed mapping of the fields and attributes of each system to ensure that data is transferred correctly.
- Data validation: Implement validation mechanisms to verify data integrity and consistency before and after integration.
- Testing: Test in different scenarios to identify and fix any issues before large-scale deployment.
- Document: Maintain clear and up-to-date documentation of the integration setup and processes involved. Recording a video of the setup on BeLazy is a great help.
- Manage Changes: Establish a process for managing changes to systems and integration settings. A RACI matrix can also be very helpful.
Sometimes, simple fields like “Last name” or “First name” are not identified in the system layout.
Check the Information icon in your system to get a tooltip to see what type of information is expected.
This is how BeLazy shows the previous information
How you are keeping names in the same system or two distinct systems affects very much the data binding. For example, in the Hubspot integration, names must be easily mappable to the Plunet or XTRF names. While you can build similarity detection capabilities, like the ones we built for company names, to allow similar names such as “Acme Ltd” and “Acme Limited”, ambiguity is best resolved at the source of the data.
Take Charge of Your Data Quality Today with BeLazy!
BeLazy is a great tool to help you identify and mitigate the risks associated with data quality. By connecting your systems, BeLazy gives you a clearer view of your data and allows you to detect inconsistencies and errors faster. In some cases, BeLazy (in conjunction with other platforms we use) may map data during the process but does not modify it in any of the systems.
During the onboarding we perform with every customer we discuss the methodology of improving the data quality and as you become successful in your automation efforts, as a side effect your data quality improves. In conclusion, data quality is critical to achieving a successful automation process, but it is something that you can continuously improve. By ensuring that the data in your BMS, VPs, and TMS is accurate and complete, you’ll be maximizing the value of BeLazy and improving the efficiency and quality of your projects and automation.
