The Analytical Edge: Leveraging AI and ML in Master Data Management

What is Artificial Intelligence and Machine Learning?
Artificial Intelligence (AI) has revolutionized various industries by enabling machines and software to perform tasks that typically require human intelligence, such as reasoning, learning, problem-solving, and understanding natural language. Within AI, Machine Learning (ML) is a critical subset focused on developing algorithms and models that allow computers to learn from data and make informed decisions without explicit programming for each task.

What are the different AI Use Cases in MDM?

● Data Discovery: Identifying and classifying data assets.

● Data Lineage: Tracking data’s origin and transformations.

● Data Modeling: Creating and maintaining data models.

● Data Quality: Ensuring data accuracy and consistency.

● Match and Merge: Identifying and merging duplicate records.

● Data Relationship Discovery: Understanding connections between data entities.

● Data Governance: Enforcing policies and regulations.

● Privacy and Protection: Safeguarding sensitive data.

● Data Sharing: Controlling data access and usage.

When integrated with Master Data Management (MDM), AI and ML can significantly enhance the consistency, accuracy, and reliability of an organization’s key data assets. Here’s how these technologies contribute to MDM:

1. Data Quality Improvement

a) Data Enrichment:
AI enriches data by linking it with external sources, improving its completeness and relevance. For example, adding demographic information to customer profiles from public databases and providing URLs.

b) Automated Data Cleansing:
Machine learning models learn from existing data patterns to continuously improve the accuracy of data cleansing processes. Verdantis Harmonize tool learns by example where ML algorithms play a vital role in improving data quality by learning from historical data.

c) Automated Data Classification:
AI can automatically classify and categorize data based on content and context, reducing the need for manual intervention. For instance, AI can categorize Materials into the appropriate categories in the master data repository.

d) Success Story:
AI and ML integrated into the Harmonize tool significantly transformed the master data cleansing process for a global $53 Billion food manufacturing company headquartered in Virginia, USA. This resulted in identifying and correcting data errors, deduplication, and standardization across multiple systems and regions. By leveraging ML algorithms, business rules provided by the customer across different phases were automatically extrapolated across all data, leading to significant improvements in data quality and consistency.

2. Data Integration and Matching

a) De-duplication:
Duplicate records in master data can lead to operational inefficiencies, increased costs, and poor decision-making. Traditional methods of identifying and eliminating duplicates are often manual, time-consuming, and prone to error. AI and ML technologies can significantly enhance the accuracy, speed, and effectiveness of master data cleansing processes.

b) Success Story:
For the world’s largest $11.9 billion mining company headquartered in USA, duplicate records in master data were causing several issues. Verdantis AI models were trained to identify duplicates based on various data attributes and key identifiers. These models used advanced matching techniques, including semantic search and pattern recognition, to identify potential duplicates even when data was inconsistently formatted. The AI and ML system successfully identified 8% duplicates in a dataset of 350k items within a few weeks, showcasing its effectiveness in large-scale data matching.

c) Semantic Search:
AI’s ability to interpret and analyze data enables it to make informed approximations when exact matches aren’t available, identifying patterns and connections within the data.

d) Entity Resolution:
ML algorithms can match records across different systems, even when data variations exist, which is particularly useful in unifying customer records from various departments.

3. Predictive Analytics

a) Forecasting Trends:
AI leverages historical data to predict future trends, enabling organizations to make data-driven decisions.

b) Anomaly Detection:
ML algorithms detect anomalies in data, preventing potential issues before they escalate.

4. Data Governance

a) Compliance Monitoring:
AI can monitor data for compliance with regulations and standards, automatically flagging discrepancies and generating alerts when needed.

b) Policy Enforcement:
ML helps enforce data governance policies by ensuring that data entry, updates, and access adhere to predefined rules and procedures.

5. User Experience Enhancement

a) Personalized Experiences:
AI tailors user experiences by analysing master data to understand preferences and behaviours, leading to more personalized interactions.

b) Intelligent Search:
ML enhances search functionality within MDM systems, making it easier for users to find and retrieve relevant data.

c) Success Story:
A global retail chain with thousands of stores worldwide faced challenges with inconsistent and duplicate customer data across multiple systems. The company adopted an AI-based Master data management solution, Verdantis, that automated the onboarding process by validating and enriching supplier data in real-time. ML algorithms were used to identify potential risks and recommend actions to mitigate them. The AI algorithms successfully identified and merged duplicate customer records. Additionally, ML algorithms learned from historical data to detect anomalies and suggest corrections, significantly improving data quality.

What are the benefits of AI and ML in MDM?

1. Accelerated Data Processing and Accuracy

○ Faster Processing: AI swiftly processes large datasets, delivering timely insights for quicker decision-making.

○ Reduced Human Error: Automation minimizes mistakes, ensuring consistent and reliable data across the organization.

2. Pattern Recognition and Analytics

○ Identifying Hidden Trends: AI uncovers patterns that might be overlooked by human analysts, enabling deeper understanding.

○ Optimizing Strategies: Insights from AI analytics help refine business strategies to align with market trends and consumer behaviours.

3. Enhanced Data Governance and Security

○ Automated Quality Checks: AI ensures data adheres to quality standards, reducing the need for manual oversight.

○ Strengthened Security: AI proactively detects threats and ensures compliance, safeguarding sensitive information.

4. Efficient Data Cleansing and Validation

○ Error Correction: AI automates the detection and correction of data inconsistencies, streamlining the cleansing process.

○ Organized Data: AI categorizes data systematically, making it easier to analyze and manage.

5. Intelligent Data Enrichment

○ Completing Missing Data: AI predicts and fills in missing attributes, ensuring a more complete dataset.

○ Broadening Data Sources: AI pulls in relevant information from various sources, providing a holistic view for better analysis.

Challenges and Considerations:

While integrating AI and ML into MDM offers numerous benefits, it also presents challenges:

● Data Privacy: Ensuring that AI and ML systems comply with data protection regulations and maintain user privacy.

● Bias and Fairness: Designing ML algorithms to avoid biases that could affect data quality or decision-making.

● Complexity: Implementing AI and ML solutions requires expertise and can add complexity to MDM systems.

Conclusion:

AI and machine learning have the potential to greatly enhance master data management by improving data quality, enabling better integration, providing advanced analytics, and automating various tasks. However, addressing the associated challenges is crucial to fully leverage these technologies and maximize their benefits.

By partnering with Verdantis, you can unlock the full potential of AI and machine learning in your master data management initiatives. Contact us today to learn more about how our solutions can help you achieve your goals.

Get In Touch: info@verdantis.com / www.verdantis.com

Flori Bercus Manoj Kukreja Rui Carvalho Steve Jones Towards AI Editorial Team Maxime Labonne