How AI-Driven Master Data Management Can Speed Up Your Business Outcomes

Last Published: Nov 02, 2023 |
Prash Chandramohan
Prash Chandramohan

Sr. Director, Product Marketing. MDM & 360 Applications

Artificial intelligence (AI) automates tasks typically requiring significant resources and human intervention. Master data management (MDM) creates trust in your critical business data — i.e., customers, products, suppliers, employees, equipment, locations and more — to not only support effective and efficient business operations but also to propel strategic enterprise initiatives forward with a single source of truth.

By bringing AI to MDM, the rate at which you can achieve value and reach your goals is not only faster, but your employee experience is better and your return on investment (ROI) is higher. Let’s examine how AI accelerates, scales and modernizes MDM so that, when applied, MDM can benefit your entire organization. Then, we’ll share AI and MDM strategies you can (and should) adopt to stay innovative, disruptive and differentiated. 

Accelerating Outcomes

With AI-driven MDM, you can attain your business goals faster across a broad range of capabilities by bringing automation and simplification to the data mastering process. Faster data mastering allows you to support critical use cases that uniquely differentiate your business from the competition. This includes customer experience and digital commerce, as two examples.

From data discovery, data integration and data quality to data modeling and data governance, there are many elements of data mastering where AI improves speed and efficiency. Here are five ways AI-assisted MDM accelerates business outcomes through automation:

  1. Discover master data quickly: AI-powered MDM enables you to swiftly locate and access relevant master data contained in tens or hundreds of millions of columns across thousands of sources. AI and machine learning (ML) techniques like clustering, data similarity and semantic tagging automate master data discovery and domain identification to source relevant master data entities across the enterprise data landscape.
  2. Onboard master data swiftly: AI automates master data ingestion and onboarding using named entity recognition and natural language understanding to identify fields (i.e., street) and field types (i.e., address) and subsequently map them to data models within the master data solution. By automating master data integration, AI-driven MDM significantly reduces the time required to onboard new data sources into the solution so you can adapt quickly to changing data landscapes and business needs.
  3. Detect and resolve data quality anomalies rapidly: AI-driven MDM uses natural language processing (NLP) and blended ML techniques (e.g., deterministic, heuristic and probabilistic) to rapidly detect and rectify data inconsistencies in records such as data accuracy, completeness and duplicates. This ensures your data remains standardized, clean and accurate without the delays created by manual data cleansing and data quality rule creation.
  4. Automate how master data is modeled: AI automates schema matching to recommend the mappings necessary to structure your data within the MDM solution. This includes automatically mapping data source attributes in semantically related master data models. Techniques like Bayesian probabilistic methods can match virtually all attributes between schemas and recommend a consistent and uniform set of core attributes and hierarchy structures to use in a standard master data model for a centralized MDM hub.
  5. Evolve master data governance: AI-driven MDM maps your business glossary definitions, policies and data owners to your master data using domain discovery, data similarity and NLP techniques. This automation accelerates MDM adoption bygreatly improving productivity and cross-functional collaboration between data stewards, application owners and business subject matter experts. 

Supporting Scalability

To meet the requirements of modern enterprises, AI scales MDM capabilities rapidly to support large data volumes, identifies hidden patterns and reveals relationship insights by linking master data with transaction and interaction data. Here are five ways AI-driven MDM scales to meet modern data management needs:

  1. Handle large volumes of data seamlessly: AI-powered MDM efficiently manages vast amounts of master data, seamlessly scaling to accommodate modern and future data volumes. This helps ensure that you can expand your data assets with ease and without disruption. This includes combining master data with non-master data, such as transaction and interaction data, for operational and analytical use.
  2. Identify hidden insights and relationships quickly: AI-driven MDM goes beyond traditional data management to automate the creation of a cross-domain, cross-departmental knowledge graph by inferring relationships between master data. In addition, it can help with contextual understanding by inferring the relationships between master data and transactions as well as interactions. MDM can offer new insights, which may have previously been considered impossible, by identifying insights, correlations and relationships hidden in your data that were previously disconnected.
  3. Scale to adapt diverse data sources and formats: AI-powered MDM is adaptable and scalable to handle diverse data sources and formats as the volume of master data and the number of sources grow. By finding master data and identifying domain types, intelligent MDM solutions can help you manage across a variety of requirements and complex data environments that run today’s business operations.
  4. Support global operations and ad-hoc volume needs: AI-driven MDM supports global operations where thousands of users need both regional and global views into trusted data while simultaneously matching, cleansing and enriching data in real time. It quickly adapts to sudden spikes in data volume during key events like quarterly financial close processes or mergers and acquisitions, identifies patterns and trends in master data and simulates scenarios while ensuring uninterrupted operations and faster decision making.
  5. Manage multi-departmental and enterprise-wide master data use: AI-powered MDM efficiently combines data across your various departments, lines of business and enterprise-wide applications. This helps ensure that your data remains trusted, consistent and accessible to your stakeholders while keeping data quality high by supporting two-way synchronization across applications.

Modernizing Data Management

As access to and trust in data becomes increasingly essential and difficult to ignore, MDM has moved away from being an IT infrastructure system and has become a business user-facing application. As such, it requires modern capabilities that offer intuitive ways for users to visualize and interact with master data. Here are the five ways AI-driven MDM helps modernize the practice of data management and drive insights:

  1. Enhance UX with a business-user friendly interface: AI-powered MDM provides a user-friendly interface designed for business users that offers you extreme personalization and role-based security. It includes user-specific preferences to make your users’ daily lives easier and increase productivity. Modern MDM combined with AI helps ensure employees at your organization can interact with data more effectively and efficiently.
  2. Empower employees with analytics and insights: AI-driven MDM empowers your organization’s employees by presenting easily consumable analytics and insights derived from records and profiles created with mastered data. It makes it easier to find data through more accurate and relevant search results and provides recommendations on the best course of action in each situation. This modern approach enables your users to confidently make data-driven decisions and gain valuable insights into existing or emerging business opportunities.
  3. Modernize data governance tasks, reducing manual effort: AI modernizes data governance tasks by reducing the need for manual oversight. It does so through recommendations for data governance actions based on data quality scores, historical patterns and policies. This automation will streamline your data management processes, saving time and minimizing the risk of human errors.
  4. Monitor and alert for data quality issues proactively: Imagine the impact on your organization of ongoing observability and data quality monitoring. AI-powered modern MDM actively monitors data quality and can proactively alert you to potential issues. This proactive approach helps ensure data accuracy and compliance, reducing the risk of data quality problems going unnoticed.
  5. Offer hidden insights into master data and relationships: AI-powered MDM uncovers hidden insights into data linkages, related records, data anomalies, cross-domain insights and patterns. It suggests relationships and connections based on historical and contextual information, leading to many use cases such as cross-selling and supply chain relationships

Next Steps

By leveraging AI-driven MDM, you can quickly unlock valuable insights from your master data that were previously buried in the complexity and volume of your data. These insights can help you enhance customer experiences, drive digital commerce efforts, optimize your supply chain and drive innovation within your business.

To learn more about how Informatica AI-assisted MDM provides faster time to value for both standard and advanced MDM deployments, read this blog. 

First Published: Oct 27, 2023