For those businesses able to gather and harness it, the rise of big data has brought unparalleled opportunities. However, the complexity associated with managing such vast quantities of information brings its own challenges, especially when the relevant data is spread out over multiple sources and business functions. This can mean data-focused organizations spend most of their time building infrastructure, ultimately hampering their ability to make the kinds of timely, informed decisions they hope to make with data.
Though there are several proposed technical solutions to this problem, generative AI (GenAI) has emerged as one of the more promising. In fact, recent reports have found that integrating generative AI into master data management (MDM) and broader data governance could accelerate their time to value significantly by combining master data management with AI solutions, thereby enhancing data management practices.
“When you look carefully at how generative AI impacts MDM, the themes that stand out are enhanced workflows, data accuracy and operational efficiency in key MDM processes,” says Prash Chandramohan, Senior Director Product Marketing, Informatica. “Put another way, these language models remove critical bottlenecks by making data easier to get, clean and manage, and as a consequence, decision-makers have the context they need when it’s time to make choices.”
With this in mind, the article will set out to accomplish two related goals:
Demonstrate how integrating GenAI in master data management improves data quality, compliance, decision-making and efforts in other data-intensive business areas.
Illustrate how these tools streamline workflows and drive automation, thereby helping organizations better manage critical data with fewer errors and lower costs.
By the end, the pros and cons of this technology will be much clearer.
What is Master Data Management?
Master data management consolidates data from various sources to create a unified "master" record of key business entities — individuals, locations, ZIP codes, products and more. This provides a comprehensive and consistent overview of critical business information.
Data modeling is the foundation of effective MDM, helping create a centralized hub that serves as a single source of truth. By organizing and standardizing information across multiple sources, well-designed data models enable seamless integration of disparate data, enhance scalability, and ensure accuracy throughout the MDM ecosystem. Additionally, AI-driven solutions can optimize master data management processes, enhancing organizational efficiency and decision-making.
How is AI Used in Master Data Management?
As the difficulty of managing data increases, so too will the reliance on AI, and especially GenAI, to handle large-scale datasets, common data tasks, data-focused workflows, and powerful, complex data stores. Implementing robust data quality rules and identifying and consolidating duplicate master data records will become crucial in enhancing data integrity across these systems. The next section details what this means in practice.
Uses of Generative AI in Master Data Management
The term "generative AI" refers to the process of training deep learning models to generate output, as opposed to models that control satellites or predict the stock market. For example, generative language models output text, generative image models output images, and generative protein models output 3D structures of proteins in the fields of drug discovery and biotechnology.
ChatGPT and DALL-E are two of the most famous generative models, but others are emerging that are capable of creating music, product designs and much more. Unstructured data plays a crucial role in this ecosystem, as these AI technologies interpret and transform it into valuable insights that enhance data governance and management.
With that context in mind, here are some of the primary applications of GenAI in MDM that Chandramohan, and other forward-thinking data leaders, are looking forward to.
1. Automating Categorization, Data Discovery, and Acquisition
One of the first challenges of modern MDM is simply working with large quantities of data, i.e., structuring, sorting, tagging and cleaning it. This is important because machine learning isn’t magic — the underlying algorithms only work properly when given quality data.
AI can effectively associate data quality rules rules with relevant master data fields to improve data accuracy and consistency. With GenAI in MDM, it’s possible to streamline the initial steps of ingesting and organizing data, reduce human error during organizing and tagging, ensure consistency and accuracy through robust metadata discovery and speed up the time-consuming tasks of data classification and acquisition. All in all, this means handling large datasets in far less time and with far fewer headaches.
Modern GenAI models, for example, can auto-tag new product entries in a retail database by identifying categories, subcategories and product attributes (e.g., size or color) from text or images. Try to imagine how much time it would take a human being to do this for 500 to 1,000 novel items, and it will become clear how big a step-up GenAI in master data management can be for a business.
2. Recommendations for Problem-Solving Next Best Transformation
Part of MDM’s value proposition is creating a “single source of truth” accessible throughout an enterprise. However, duplicate records, incomplete data, and mismatched entries present major obstacles and common pain points in achieving this goal.
AI addresses these challenges by automating the associations between data owners and master data. This automation significantly improves both productivity and accuracy in the MDM process, helping organizations maintain clean, consistent data across all business systems and departments. Additionally, AI-driven systems ensure the secure handling of sensitive and private data, enabling compliance with data usage terms and minimizing risks associated with non-compliance.
While GenAI could be a partial solution for such challenges; AI-driven match-and-merge operations reduce duplication errors and inconsistencies, which are typically labor-intensive when done by a human. Even better, this approach simplifies data-heavy downstream processes, such as reporting or analytics.
What this looks like in practice: Imagine a healthcare provider using GenAI to consolidate patient records across multiple systems to flag duplicates and merge data points like names, addresses and medical histories. Such technology could free up thousands of hours for more meaningful tasks.
3. Enhancing Data Quality and Governance
One of MDM's primary challenges is ensuring that data is both accurate and well-trusted, as this latter is what makes it suitable for compliance, analytics and effective decision-making.
Of course, achieving this level of standardization often involves a tremendous amount of effort; by automating processes such as data validation, munging (i.e., cleaning and standardizing), and enrichment, GenAI can help ensure data integrity while enforcing a company's governance rules at scale.
One of the many possible use cases, a bank could use GenAI to validate customer data by flagging inconsistencies such as mismatched addresses or incomplete profiles. Even better, once these records have been standardized, the system could enrich them by pulling in credit data from third-party sources, resulting in more complete and trustworthy datasets.
This means a consistent supply of high-quality data that meets regulatory and operational requirements, an obvious advantage for MDM workflows.
4. Optimizing User Experience and Data Insights
MDM often requires translating complex data into actionable insights for decision-makers. As businesses turn to data to find ever-more subtle trends, GenAI has emerged as a critical tool for reshaping user interfaces and automating the process of gleaning the signal in the noise.
When dashboards have GenAI integrations, for example, teams can quite literally talk to their data. Imagine a sales team querying the best-performing products using simple English prompts such as, "What were our top five products last quarter?" This minimizes the need for technical expertise, making everyone, even the non-coders, more productive.
This kind of natural language interface is one of the distinguishing features of data management tools that use generative AI. “It’s amazing to see how intuitive data management and data governance can be when handled with words instead of code,” says Chandramohan. “Just a few years ago, working with data required a lot of technical know-how, but when you can use generative AI, most of that complexity goes away. Teams can ideate and iterate almost as quickly as they can speak, and it’s been exciting to watch.”
Explore the Benefits of GenAI for MDM
GenAI is a remarkable technology driving tectonic shifts in business, education and life in general. For master data management, some of the most important benefits include:
Greater data accuracy, achieved through automated match-and-merge operations, data cleaning and data governance
Quality-of-life gains across all areas, particularly in user experience
Improved efficiency at every step, from data ingestion to generating actionable insights, achieved by reducing bottlenecks and enabling better data utilization
For its part, Informatica’s AI-powered platform, CLAIRE, acts as a copilot in creating and working with a unified metadata view. It’s also an excellent example of what’s possible through the judicious use of GenAI technology; with it, users have been able to cut data classification time in half, dramatically speed up data discovery and improve productivity by as much as 20%.
To learn more about CLAIRE’s GenAI capabilities and how they can speed up your data teams, visit www.informatica.com/claire.
