Many companies are making large investments and efforts in the development of artificial intelligence (AI). However, if the data that is used is not of quality, all that effort will have been wasted. The solution to this problem is a concept that is already very familiar to us. The MDM or master data management.
Master Data Management supports artificial intelligence and machine learning projects and at the same time, artificial intelligence can take care of the most complex MDM tasks, which until now used to be managed solely by humans.
Artificial intelligence requires clean and mastered data
Data is the weakest link in the chain in the context of artificial intelligence and its open-source universe. This forces companies to prioritize their actions focused on preserving the quality of information and optimizing its management.
In this context, MDM assumes a crucial role, since:
- Refers to business-critical data stored in disparate systems.
- Provides a common vocabulary for transactions and operations.
- It takes care of the cleaning, the government, the monitoring, and the control of all the data.
- Master data is arguably the optimal starting point for any successful AI initiative. And this is reflected in the market where, today, you can already find a new wave of MDM solutions that even bring AI and integrated machine learning, to address the management, maintenance, and cleaning required for these big data systems.
The role of artificial intelligence in master data management
As already mentioned in the introductory lines of this post, artificial intelligence has the potential to improve master data management, thanks to the magnitude of the impact it can cause in all areas of MDM.
Currently, artificial intelligence allows machines to imitate cognitive human behavior, something it achieves through the application of complementary disciplines such as language processing, robotics, or machine learning, among others.
Leveraging this technology for master data management could soon mean AI would help:
- Identification of duplicate records: This is one of the most common problems in master data management, which could be solved, thus ensuring the quality of the data. For this, it would only be necessary to have an information verification system that takes into account information outside the MDM application (such as transactional data, CAD / PLM data, SOP, manuals, the history of previous corrections, typical individual errors / regional), rather than just checking records based on their attributes.
- End-user guide on data maintenance issues: In this case, artificial intelligence could help answer these types of questions, thus eliminating the need for end-user training.
- Support for new types of master data applications: With the help of a graphical user interface where the touch screen, motion detection, and voice recognition replace the mouse and keyboard, artificial intelligence would allow users to simply focus on moving graphic objects.