🧪

Simon Dedupe is currently an Alpha feature.

Please reach out to your account manager to be added to the waitlist!

Overview

Simon Dedupe is Simon Data's in-CDW entity resolution offering, designed to help you clean and unify your data within individual sources and ensure that data is accurate, consistent, and free of duplicates. This foundational step precedes identity resolution and is crucial for maintaining high data quality, which in turn supports better decision-making and more effective marketing strategies.

Simon Dedupe can be used with or without Simon Resolve, Simon's identity resolution offering, and is followed downstream by Simon's composable CDP application. It can be further enhanced via Simon’s Managed Identity Services.

What is Entity Resolution?

Entity resolution is the process of identifying and merging records that refer to the same entity within a single data source that may contain duplicates after normalization. Unlike identity resolution, which focuses specifically on individual contacts, entity resolution can apply to any definable object, including individuals, organizations, products, or locations.

Why is Entity Resolution Important?

Entity resolution is essential for any business looking to create personalized, data-driven marketing strategies. Without it, your customer data can remain fragmented, leading to incomplete or inaccurate profiles that undermine your marketing efforts. Here’s why it’s critical to get entity resolution right:

• Data Accuracy: Eliminates duplicate records and ensures that each entity is represented accurately.
• Enhanced Insights: Provides a comprehensive view of entities, facilitating better analysis and decision-making.
• Operational Efficiency: Reduces data redundancy and inconsistencies, leading to more efficient data management and operations.
• Regulatory Compliance: Ensures data integrity and helps maintain compliance with data governance regulations.

Key Components of Entity Resolution

Entity resolution involves several critical steps to accurately identify, match, and merge records across multiple systems. These components work together to ensure that disparate data points representing the same individual or entity are unified into a single, cohesive profile.

• Data Collection: Gather data from various sources such as CRM systems, databases, transaction records, and third-party data providers.
• Data Cleansing: Ensure data accuracy by removing duplicates, correcting errors, and standardizing formats.
• Data Matching: Use deterministic (exact match) and probabilistic (pattern matching) methods to identify records that refer to the same entity.
• Record Linkage: Link matched records to create a single, unified representation of each entity.
• Entity Merging: Merge linked records while maintaining data integrity and accuracy.

Best Practices for Entity Resolution

Entity resolution is a complex but essential process for businesses looking to unify customer data and create accurate, holistic profiles. To ensure success, businesses should follow best practices that enhance the accuracy, efficiency, and scalability of their entity resolution efforts.

• Cleanse Data: Utilize standard libraries for cleansing and standardizing data.
• Use Advanced Matching Algorithms: Employ both deterministic and probabilistic matching to improve accuracy.
• Regularly Update Records: Continuously update and refine entity records to maintain accuracy over time.
• Leverage Technology: Utilize entity resolution platforms and tools that incorporate machine learning and artificial intelligence for enhanced performance.
• Cross-Functional Collaboration: Work closely with IT, data science, and compliance teams to ensure a holistic approach to entity resolution.
• Measure and Optimize: Regularly assess the effectiveness of your entity resolution process and make necessary adjustments.

Simon’s Take

Entity resolution within Simon is meant to provide you with a first line of defense against dirty data, primarily focused upon single dataset normalization, standardization, and deduplication. It allows for flexible matching, providing options for both deterministic and probabilistic methods, whereas downstream identity resolution via Simon Resolve is purely deterministic.

In summary, entity resolution is foundational to any successful data-driven marketing strategy. It empowers businesses to make sense of complex customer data, providing the clarity needed to deliver more personalized, efficient, and impactful marketing. Simon Dedupe is designed to make entity resolution simple, scalable, and accessible to both technical and non-technical teams, ensuring your business gets identity right from the start.