Effective data conversion: 5 best practices

Product data conversion is the process of converting data from one standard to another. It can be useful in many situations, but the most common scenario is that a supplier delivers product data, and the receiving organization needs to convert it before the data can be used. Data conversion is always part of the road to a standardized data model, with which you are able to get access to consistent information through the entire organization. This article will take a look at five practices regarding effective data conversion.

1. Protect your data

Before converting your data, it’s relevant to make a backup. Within the process of converting data, some of your data may get lost and in order to prevent this to happen, a backup is always a good idea.

2. Ensure data quality

Make sure that you define and control quality norms. With this, you can ensure the accuracy of the converted data, and this results in qualitative data conversion.

3. Understand your data & technology

Make sure that you understand both the data and technology that you’re using. When you’re transferring data from A to B, you should make sure that the entire process goes well, from source to destination. The same applies to the technology that you’re using. Even when the source and destination data are within the same database management system, the process of converting from A to B could be more difficult than expected.

4. Define the scope

Imagine that you want to go to a friend, but you don’t know the way to his house. Are you just walking away from home without a plan, or do you use Google Maps to get there? The answer is obvious, you plan how

to get there. The same applies to the road towards a centralized database: without a clear scope, there will be no success. Make sure that you define a plan to get to your goals and communicate this plan to anyone involved.

5. Make the process repeatable

The process of converting data should be able to be repeated: some suppliers will keep delivering their data in the same format. That is why you should make the process repeatable: in this way, future data conversions can be performed with an increased efficiency.

Conclusion

Successful data conversion doesn’t just happen; it requires skills, knowledge on the topic and experience as well. Now that the five key practices have been identified, it is time to take a look at something else: will you convert your data in a manual way, or would automated data conversion be a better option?

Automated data conversion

The process of manually converting data is extremely time-consuming and therefore expensive as well. It’s pretty inefficient, and along with that, manually performing data conversion is sensitive for mistakes due to the monotony of the process. That is why, particularly for retailers or wholesalers that sell over 1,000 products, automation could be a cost-saving opportunity. Certain tools would be suitable for this task, but one tool stands out above the rest: PowerConvert.ai. This software tool makes use of artificial intelligence to calculate the mathematical distance between words and uses other mathematical techniques to find similarities in (product and feature) names and values. After you upload your own and desired data structure into PowerConvert.ai, the smart tool will do the hard work for you. By this, you can save a lot of valuable costs and unnecessary time that otherwise would be spent on manually performing data conversion.