The previous post on this blog was called Machine Learning, Artificial Intelligence and Data Quality. In here the it was examined how Artificial Intelligence (AI) is impacted by data quality and how data quality can impact AI.
Master Data Management (MDM) will play a crucial role in sustaining the needed data quality for AI and with the rise of digital transformation encompassing business ecosystems we will also see an increasing need for ecosystem wide MDM – also called multienterprise MDM.
Right now, I am working with a service called Product Data Lake where we strive to utilize AI including using Machine Learning (ML) to understand and map data standards and exchange formats used within product information exchange between trading partners.
The challenge in this area is that we have many different classification systems in play as told in the post Five Product Classification Standards. Besides the industry and cross sector standards we still have many homegrown standards as well.
Some of these standards (as eClass and ETIM) also covers standards for the attributes needed for a given product classification, but still, we have plenty of homegrown standards (at no standards) for attribute requirements as well.
Add to that the different preferences for exchange methods and we got a chaotic system where human intervention makes Sisyphus look like a lucky man. Therefore, we have great expectations about introducing machine learning and artificial intelligence in this space.
Next week, I will elaborate on the multienterprise MDM and artificial theme on the Master Data Management Summit Europe in London.
Liliendahl on Data Quality 