The Benefits of AI-Powered Semantic Search in Master Data Management

We’ve come a long way since the early days of search. Back then, queries were based on traditional lexical search (exact match), making them more about luck and precision rather than intelligence and understanding. This method, while revolutionary at the time, lacked the ability to understand meaning or context, leading to irrelevant results and a frustrating user experience. 

However, what started as a simple word-matching tool has evolved into a sophisticated engine that interprets context, intent, and relationships between concepts. And while many people associate semantic search primarily with advancements in large language models (LLMs) and vector databases, its application extends far beyond simple search into areas such as entity resolution and master data management (MDM). 

‍What is Semantic Search?

Semantic search is a data retrieval technique that leverages the meaning, intent, and context of a user's query to find the most relevant results. In contrast, traditional lexical keyword search only matches the literal text of a user's query against the indexed records.

By employing advanced AI, now, instead of focusing on exact keyword matches, semantic search leverages the hidden, contextual meaning of words to improve the relevance and accuracy of search results. Because it uses the internal representations of text generated by LLMs, semantic search can capture the true context and relationships between words, making it particularly effective in scenarios where understanding nuances is crucial.

While many semantic search applications work on unstructured data, an MDM system provides a clear and complete definition for what an entity means in the context of your business. This means that when AI-powered semantic search is applied to MDM, it can handle both the variety that is inherent in natural language and also leverage a high-quality data model that captures user intent and expectations, the relationships between entities, and how entities have changed over time.

Healthcare Provider Mastering Use Case
In lexical search, the terms “general practitioner” and “family doctor” are distinct. But in healthcare, they mean the same thing. Semantic search accounts for the nuance between these terms, enabling healthcare organizations to resolve these entities and ensure their golden records accurately represent the relationship between these entities.

Retail and Logistics Use Case
In retail or logistics, semantic search helps standardize product categorization across multiple geographies, aligning SKUs that may be labeled differently in the US, Europe, and Asia. Similarly, resolving address formats from 'Main St.' in the US to 'Mainstraße' in Germany with a semantic understanding could avoid shipping errors.

Challenges with Implementing AI-Powered Semantic Search
As semantic search, buoyed by advancements in AI, takes hold, many people expect it to be the silver bullet that enables them to deliver high-quality search experiences. However, the reality is that implementing semantic search is more challenging than it appears. 

To start, setting up the metrics pipelines needed to measure relevance and accuracy are complex and require continuous feedback to adapt and learn from user behavior. Further, semantic search requires a degree of specificity in order to be effective. Even though semantic search can be applied to many kinds of data, it still needs to be calibrated to a user's needs and expectations based on the use case. In practice, this often means that semantic search should leverage generative AI, machine learning, and traditional lexical search to achieve a balance of fuzziness and precision.

Finally, because many organizations resolve entities using established MDM solutions with traditional search mechanisms, implementing semantic search requires integration without disrupting existing workflows. 

Semantic Clustering of Healthcare Practitioner Specialties

Connections indicate semantic relationships based on meaning rather than lexical similarity.

The Importance of Context: Applying AI-Powered Semantic Search in MDM
Traditional MDM solutions rely on conventional search techniques that focus on predefined data fields and attributes. Because they are primarily rules-based, traditional MDMs rely on predefined logic and rules to match entities. While effective for small datasets, these systems struggle with scalability as data complexity increases. Consider these scenarios: 

• Variations in Business Names: a human may know that “ABC Corp,” “ABC Corporation,” and “A.B.C. Ltd” are the same company. But unless an MDM solution establishes a specific rule stating that these variations are, in fact, the same, the MDM will fail to match the records. As a result, duplicates will persist in the system because the MDM simply doesn’t know that the records are a match.

• Multilingual Support for Addresses: Companies with global operations often deal with addresses in multiple languages and character sets. For example, “Rue de Rivoli” (French) should be matched to “Rivoli Street” (English). Resolving these addresses in a traditional MDM requires companies to create complex rules that encompass every language scenario, an effort that does not scale as organizations expand globally.
• Resolving Entities Across Domains: Matching MDM entities is crucial, but when two seemingly different products are described in different ways across business units, traditional MDM solutions struggle to recognize that they are the same.

Financial Services Use Case
In the finance industry, semantic search plays a crucial role in Know Your Customer (KYC) processes. Financial institutions must resolve entities across various sources to avoid duplications, spot suspicious entities, and maintain compliance with regulations.

AI-powered semantic search helps organizations to overcome these issues by providing the context needed to match data entities at scale. And when combined with pre-trained machine learning models, organizations can achieve the most accurate entity resolution possible. Further, by employing human feedback, AI-powered semantic search becomes smarter over time. Think of it as a metrics flywheel where the organization collects information about how well the search is performing and then applies the feedback in order to adapt to changes in the data or improve the models or parameters it's using to determine search results. The result is more, better-quality matches and minimal duplicates in the system. 

Tamr: AI-Native MDM with Semantic Search
Tamr’s AI-native MDM solution combines AI with human intelligence to enhance data quality and enrich it with both first- and third-party data. Tamr integrates AI-powered semantic search directly into its MDM workflow to deliver:

• Search and Matching in Real-Time: Tamr RealTime enables users to search data while it's still in motion, using modeling of entities to provide accurate, up-to-date information and avoid creating duplicates.
• Feedback-Driven Refinement: Tamr’s feedback mechanisms capture the input needed to continuously adapt to new data and evolving business requirements.
• Contextual Integration: LLMs don’t know your data. But Tamr does. Tamr semantically understands the broader context of your data, enabling it to resolve entities and deliver more relevant and meaningful search results. 
• Virtual Chief Data Officer (vCDO): Powered by Generative AI (GenAI), Tamr ‘s vCDO enables users to ask questions about their data and resolve issues and enrich it in real time.

AI-Powered Semantic Search: Build vs. Buy
When considering the implementation of a new technology like semantic search, the question of “build vs. buy” often emerges. And while semantic search seems like an easy-to-implement solution, the reality is quite different. Without a deep understanding of the data, a well-designed feedback mechanism, and domain-specific optimizations, your semantic search implementation will likely fall short of expectations.

To accelerate their time to value, organizations should consider AI-native MDM solutions like Tamr which has AI-powered semantic search built in. Tamr puts our experience and expertise with machine learning, LLMs, and semantic search to work, enabling organizations to experience the benefits of semantic search faster. Using our AI-native MDM solution, organizations can not only access better, more accurate search results in real time, but they can also feel confident that the solution will scale as their data and their business evolves over time. 

To experience the true power of AI-powered semantic search, organizations need a solid foundation of high-quality data, a mechanism that makes it easy for users to provide feedback, and a contextual understanding of their domain. Tamr’s approach to integrating semantic search into its AI-native MDM solution offers a reliable path forward, accelerating the time to value and delivering meaningful results.

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