Gartner Market Guide for MDM External Service Providers
Get insights into what capabilities are relevant for third-party vendors in MDM initiatives.
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The Global Community for Artificial Intelligence (AI) in Master Data Management (MDM) is designed to foster systematic knowledge transfer and exchange with other companies, researchers and experts.
Community members are provided with continuously updated content, comprising interesting lectures, latest research information, innovative use cases, lessons learned and how-to guidance.
Furthermore, design thinking workshops will be conducted in Europe and the United States of America to ensure continuous inflow of new ideas. The workshops also offer the chance to share thoughts and ideas as well as challenges that will be discussed within the community. Benefit from this great opportunity to find partners for co-innovation joining forces in the endeavor to bring first AI & MDM light house uses cases to life.
Carl Zeiss AG
F. Hoffmann-La Roche
Fresenius Medical Care
Nestle Skin Care
Paul Hartmann AG
Procter & Gamble
December 3, 2020
January 28, 2021
February 11, 2021
March 11, 2021
Artificial intelligence is a computer science that enables machines to mimic cognitive human behavior. As human behavior is quite complex, AI is typically divided into several disciplines e.g. robotics, planning, problem solving, language processing, machine learning, etc. Some of those capabilities will have no/small impact on MDM, some will have huge impact. However, if scientists were able to put all disciplines together into one general artificial intelligence machine, this machine would be able to perfectly mimic human behavior. Although this sounds futuristic and there are controversial discussions whether a generic artificial intelligent machine is possible at all, we will see the impact of AI in MDM with first use cases soon to be realized.
Based on the CAMELOT Artificial Intelligence Innovation funnel, many use cases have been identified which solve information management challenges posed by AI. In three selected examples, CAMELOT outlines the relevance of AI for Master Data Management and how solutions could look like.
Research forecasts that chatbots will be responsible for cost savings of more than $8 billion per year by 2022, up from $20 million 2020. Less (or even no) end-user training and support will be required as chatbots will guide users and answer MDM and tool related questions. AI speech and AI gesture control will allow to process commands and to capture nonverbal feedback while AI machine learning will process the information and will have access to the AI knowledge representation of the company.
AI will replace the familiar business partner creation process in master data systems. Teams can directly trigger the creation step by sharing the relevant supplier/customer documentation with the system. With the help of OCR, the data will be automatically populated and will send a change request to a master data expert for data validation. The case has minimal training requirements and has high potential for improving overall data quality, decreasing time in data maintenance and freeing up extra capacities. The concept is scalable to further processes and document types.
Innovative ideas for use cases always address a concrete need of a specific user group and build on a wide range of topics. Following our experience gathered in multiple ideation and design thinking workshops, we group the use cases for the application of AI in data management as well as across processes in various value chain functions in the following categories:
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