At The Consumer Goods Forum (CGF), and particularly within my work in the End-to-End Value Chain and Product Data, we are focused on making things easier and more effective for businesses along the entire value chain. This could be to help make a business more sustainable, or it could be to support efficiency and reduce costs and waste along the supply chain, for example.

I am, therefore, excited about the introduction of the CGF Data Reference Sheet (DRS), which aims to considerably speed up the coupling of any two databases. It does this by leveraging the increasingly available power of Machine Learning Tools and a “red pill”.

Every single day, databases need to be connected and disconnected somewhere in any given supply chain, be it onboarding a new supplier
or customer, or creating new connections among inner-company databases. Usually, this is achieved via an API adoption through a
process called “mapping”, client by client, item by item, attribute by attribute. An IT project is set up, the data models of the two databases are compared and then jointly mapped. 

This consumes time and resources.

In the past, one way to “simplify” this process was to force all participants to apply and implement the same data model and field names hard coded into their software, which poses different challenges in the setup and requires all participants to map the API: that is, each single company is spending the same amount of money and time to make things compatible.

The author assumes that most companies would rather not change their data model and storage system and hence prefer to delegate much of the mapping and tedious tinkering to software that is enabled to automatically adopt and map any two databases.

This got us thinking. Is there an efficient “middle ground”? Can we leverage software and machine learning to do the heavy lifting and speed up the integration? Can we fully automate the coupling of any two databases?

We believe the answer is “YES!”. The core component of achieving this is a simple process utilising the aforementioned CGF Data Reference Sheet.

Click here to download the Data Reference Sheet and continue reading.