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Global Community for Artificial Intelligence in MDM

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.

Why join?

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  • Latest thinking, discussions and exchange for Artificial Intelligence in Master Data Management
  • Exclusive insights for community members as well as guest lectures from AI experts and AI pioneers
  • Experience AI through joined realization of selected use cases with latest AI technology

Community Members

MDM and AI Experts
from the following companies

Abbvie
Aptargroup
B. Braun
Bayer
Biogen
Böhringer Ingelheim
Bosch
Bristol-Myers Squibb
BSH
Cardinal Health
Carl Zeiss AG
Colgate-Palmolive
Coloplast
Conagra
Covestro
Daichi Sankyō
DB Schenker
Diversey

Eli Lilly
Endress+Hauser
Evonik
F. Hoffmann-La Roche
Farfetch
Fresenius Medical Care
Genentech
General Mills
Goodyear
Grundfos
GSK
Harro Höfliger
Heidelberg Cement
Heineken
Henkel
Honeywell
Infineon AG

KION Group
Knauf
Komatsu Australia
Leoni
Louis Dreyfus
Mann+Hummel
Mars UK
Merck
Nestle Skin Care
Norsk Medisinaldepot
Novartis
Olympus
Paul Hartmann AG
Philip Morris
Philips
Porsche
Procter & Gamble


Reemtsma
Roche Diagnostics
Roche Pharma
Rockwool
Rudolf Wild
Sartorius
Schaeffler
Shell
Siemens
S.Oliver
Tesa
Tetra Pak
Teva
Thyssen Krupp
Tüv Süd
Vaillant Group
Weidmüller Interface

Community Events

Event

Hyper Automation with Data Science and Analytics Workshop

December 3, 2020
Virtual

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What is Artificial Intelligence and how will it affect MDM?

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.

Video Series:
Ask the Expert

How to start with AI in MDM?

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What are the use case areas of AI in MDM?

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We have only a few thousand master data records. Is it sufficient to apply AI?

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Can we trust AI?

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What are the organizational options for AI in MDM?

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The new AI capabilities will change all areas of MDM practice

Reasoning

Problem solving by identification of typical user errors with reasoning. Considering transactional errors, frequent data changes, corrupted data etc. for autonomous development of standards.

Affective Computing

Perception of non-verbal behavior and adjustment. Assessment of correlations and causality between mood and data quality as well as usability improvements based on measured customer satisfaction.

Computational Creativity

Ideation process for MDM related topics could be supported by computational creativity. In combination with problem solving it leads to innovative approaches for MDM issues.

Natural Language Processing

Understanding human language by deriving master data attributes from free text. Replacing mouse and keyboard with voice command leading to new user experience and usability of MDM application.

Machine Learning

Autonomous data maintenance based on experience and training. Ensuring compliance of governance rules, principles and standards. Considering transactional errors for autonomous data corrections.

Planning

Algorithms can be used to plan data maintenance activities in advance. Adjusting and planning data cleansing activities based on upcoming transactions.

Robotics

Control of motion by robotic processes. Not expected to have huge impact on data management itself, but it could lead to new object definitions.

Knowledge representation

Identification of existing information by describing objects, entities and relationships. Building ontological model of the information by linking all elements to an information network.

Machine perception

Deriving master data attributes from pictures, videos and sound, and replacing mouse and keyboard by gesture control. Provides a new user experience and usability of MDM application.

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Selected AI use cases for MDM

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.

Personal assistant that guides and assists users in the master data system

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.

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Automated vendor and customer master records creation & data validation

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.

AI in MDM – focus areas

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:

Automation

  • Non-value adding, repetitive and time-consuming activities executed by AI
  • Use case examples: mapping of records, data determination for migration, dynamic mapping of standards (e.g. eCl@ss), automatic data population in the system, automated data validation and approval, metadata derivation/updates, technical drawing analysis

Usability

  • New interaction approaches between users and master data based on voice and natural language
  • Use case examples: personal assistant, data visualization, graphical maintenance, visualization for master data-based supply chain configuration, master data maintenance scenarios, fully automated cost center/profit center request via e-mail

Insights

  • Making sense out of big amounts of data, patterns, and rules 
  • Use case examples: data profiling, bias/pattern recognition, outlier detection, record classification/clustering, plausibility checks, error root cause analysis, data change simulation, dynamic rule mining, data usage assessment, duplicate record detection, fraud analysis

Structuring

  • Extraction of information from various sources into a machine-readable format
  • Use case examples: building ontological models, extract master data from pictures or flat text files, master data catalog creation, holistic picture of (all) ERP data, customer/supplier data derivation from webpage, business partner data crawling and vendor hierarchy, structured OCR and validation

Predictive

  • Intelligent and dynamic help with field values based on historical data, web, product lifecycle and business rules
  • Use case examples: data extension prediction, data proposals, upstream system (e.g. PLM) sensing to predict new records, lifecycle change prediction

Interested? Get in touch with us!

Henrik Baumeier

Partner Data & Analytics

Leads the data & analytics division at CAMELOT, helping global clients in their digital transformation

Aleksandra Baumann

Community Lead

Leads the AI in MDM Community and consults companies in the area of Enterprise Information Management at CAMELOT.

Marc Hoffmann

Head of Enterprise and Information Management, North America

Recognized expertise in data management and governances. Leader in the Artificial Intelligence (AI) in MDM community.

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