Aperture Match is the next evolution of data matching technology for Aperture Data Studio, designed to empower organizations that seek to match similar records for critical use cases such as duplicate prevention, single customer view or identity resolution.
Inaccurate or duplicate data can lead to poor customer experiences, wasted marketing spend, and flawed analytics. Data matching helps businesses identify and consolidate duplicate records, whether it's customer profiles, product listings, or supplier information, into a single, accurate view.
This ensures consistency across systems, improves decision-making, and supports compliance with data regulations. Matching is especially critical as data volumes grow and organizations rely on real-time insights.
Aperture Match brings a new level of performance and flexibility:
Scalability: Leverage dynamic resource allocation to handle large datasets without sacrificing performance.
Real-time ready: Interact with the duplicate store in real time using real-time workflows, with an optional high-availability setup.
Precision control: Refine clusters manually after they’ve been automatically matched by merging or unmerging records.
Hierarchical clustering: Clearer visibility into the match levels between records, making results easier to understand and analyze.
Aperture Match delivers advanced standardization and intelligent matching algorithms to seamlessly group records with similar data. Its powerful fuzzy matching capabilities mean cleaner, more connected data and smarter insights.
Together with Harmonization, it is used for data cleaning and deduplication with a common use case to create a single customer view or golden record store. The process may also be known as entity resolution.
Matching uses natural language processing (NLP) techniques including:
Aperture Match’s capabilities are designed to support a wide range of deployment scenarios, including on-premises environments and all major cloud platforms. It can support very large data volumes due to supporting auto-scaling PostgreSQL databases.
The system can be configured for high availability using container orchestration platforms such as Kubernetes.
Bulk operations in Aperture Match make large-scale data matching simple and efficient. Instead of handling records one by one, you can compare entire datasets against your entity store. Whether you’re doing an initial load to process millions of records or running incremental updates to keep data fresh, bulk matching ensures consistency and saves time.
Aperture Match fully supports real-time operations and can perform search, update, and delete actions with low latency through Realtime Workflows. Records can be transformed and validated before being added to the duplicate store, ensuring data quality and avoiding adding records that already exist.
Real-time matching helps your business maintain data integrity. For example, as a manager of a retail brand that offers discounts for first-time sign-ups, you can use Aperture Match to check new registrations against existing store records in real time. This prevents duplicate accounts, reduces marketing costs, and ensures accurate single customer views for inbound and outbound communications.
Aperture Match uses the improved and adapted Find Duplicates steps in Aperture Data Studio. If you’re using the previous version of this module - Find Duplicates, please refer to the relevant documentation.
