1. Introduction: The Universal Problem of “Dirty Data”
Imagine scrolling through your phone’s contacts and finding multiple entries for the same person: “Jen Smith,” “Jenny S.,” and “Jennifer Smith-Jones.” While you know they are all the same person, your phone sees them as three separate individuals. This is a simple example of a universal problem known as “dirty data.”
Businesses face this same challenge on a much larger scale. They collect customer information from many different places—online sales, in-store loyalty cards, support calls, and marketing emails. This often results in fragmented and duplicate records for a single customer scattered across various systems. The solution to this digital mess is Identity Resolution, which is the process of connecting all these scattered pieces of data to build a single, complete view of each customer, often called a “golden record.”
This isn’t just a theoretical data-tidying exercise; it has significant, real-world consequences for how businesses understand and interact with their customers.
2. Seeing the Problem in the Real World: Two Examples
Messy data affects all types of organizations, from luxury retailers to professional sports leagues. Let’s look at how identity resolution provides a clear solution.
Understanding the Complete Shopper
The famous retailer Fortnum & Mason faced a classic data problem: they couldn’t see how a single customer was shopping across their different channels. Was the person who bought tea online the same one who later had lunch in their restaurant? Without connecting these records, the complete picture of the customer was missing.
“For the first time, we’re able to understand how customers are shopping with us—online, in-store, over the phone, or in restaurants. Zingg has helped us unify this data and gain insights we never had before.”
— Jonathan Moss, Director of Customer Engagement, Fortnum & Mason
Consolidating Fan Profiles
Industries like sports frequently deal with “dirty data” from ticket sales, merchandise purchases, and fan club sign-ups. The Canadian Football League used identity resolution to clean up their fan database, creating a much clearer view of their audience. Zingg’s solution helped them consolidate 10-15% of their records into single profiles.
“Zingg provides a sophisticated solution for fuzzy matching that is crucial in industries like sports, where dirty data is common. Thanks to that, we’ve seen 10-15% of our records successfully consolidate into one profile—this has been a huge win for us.”
— Dave Musambi, Senior Director, Business Intelligence, Canadian Football League
These examples show what the problem is. Now, let’s explore how a tool like Zingg actually solves it.
3. How Zingg Creates a Single, “Golden” Record
Zingg uses a combination of smart techniques to find and link related records, even when the information isn’t a perfect match. The process relies on three core features.
Feature 1: Fuzzy Matching – Finding “Almost” Perfect Matches
Computers are typically very literal. They see “Rob” and “Robert” as two completely different names. “Fuzzy matching” is a technique that allows Zingg to find matches that are close but not identical, understanding context in a way that simple databases cannot. This ensures every possible connection is found.
- Matching people on nicknames (e.g., “Robert” and “Rob”).
- Matching companies on abbreviations (e.g., “International Business Machines” and “IBM”).
Feature 2: Smart AI Training – Teaching Zingg About Your Data
You can train Zingg’s AI model specifically on your own private data to teach it what a match looks like in your unique context. The process is straightforward: Zingg shows you pairs of records, and you simply label them by answering “Yes,” “No,” or “Can’t Say” if they represent the same entity.
You only need to label around 40-50 pairs for the AI to learn your patterns. A key benefit of this approach is that the AI learns not to compare every single record with every other record. This intelligent filtering allows it to work very quickly, even with millions of records.
Feature 3: The ZINGG_ID – The Ultimate “Master Key”
Once Zingg determines that several records belong to the same person or entity, it assigns them a ZINGG_ID, which is a “globally unique and persistent identifier.” Think of this as a permanent “master key.” No matter where a new piece of information about that customer appears, it can be linked back to their single profile using this unique ZINGG_ID, allowing you to cross-reference every record across every system.
It is the combination of flexible Fuzzy Matching, context-aware AI Training, and the persistent ZINGG_ID that transforms a chaotic collection of data points into a stable and intelligent identity map.
4. The Result: A Continuously Updated Identity Graph
The final output of this process is an Identity Graph. You can think of this as the complete, connected map of all your customers, where each customer is represented by a single “golden record” linked by its ZINGG_ID.
Crucially, this graph is not static. Zingg is designed to automatically update the identity graph with new and changed information without having to reprocess all the data from scratch. This ensures your view of the customer is always current. Furthermore, this is achieved while prioritizing data ownership and privacy.
| Feature | Benefit for You |
| Deploy Natively | Use your existing data setup (like Snowflake, Databricks, etc.) with zero data copying. |
| Train Privately | Your sensitive data is used to train the AI but never leaves your control. |
This process delivers a clean, dynamic, and secure map of your data, laying the foundation for deeper understanding.
5. Conclusion: Why It All Matters
We’ve seen the journey from the common problem of messy, duplicated data to the elegant solution of a clean, unified “identity graph.” By connecting scattered records into a single source of truth, businesses can finally understand their customers on a deeper, more meaningful level.
This transformation from confusion to clarity isn’t merely incremental; for organizations that implement it, the shift is fundamental—a sentiment echoed by Redica Systems’ CTO:
“Compared to the previous approach, it’s obviously much better—probably more than 90% better.”
— Arijit Saha, Chief Technology Officer, Redica Systems
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