Using Data Matching to Resolve Identity Resolution Challenges

data matching tool

Identity resolution – the process of connecting disparate customer data to an individual or entity as they interact across different channels and devices has become critical for marketing success and business intelligence. But with new consumer privacy laws and stricter anti-money laundering policies in place, identity resolution is no longer limited to marketing success. It’s a necessary business goal that can be met if companies implement a data quality framework, leverage the use of smart data matching software, and enable business owners to own data.  

Here’s everything you need to know about identity resolution, its challenges, its importance in today’s business landscape, and what you can do about it. 

What Makes Identity Resolution So Challenging for Businesses?  

The first reason is of course the sheer volume of data in enterprisesPetabytes of customer data exist in diverse formats within many disconnected systems (also known as disparate systems) spread across the organization.  

Because businesses increasingly rely on customer experience to deliver hyper-personalized services, they need data a range of data – behavioral and lifestyle data, firmographic and demographic data, metadata (device logins, location, IP addresses etc.) along with household data, financial and background data. But before businesses can use this data, it must be connected through the identity resolution process, especially if companies use a multitude of apps and third-party services to get this data.  

Some unique challenges with customer identity resolution our clients share are: 

  • The Unavailability of Unique Identifiers:  This was a frequent problem with most of our government clients. Limited by state laws, these institutions did not have access to unique identifiers such as social security numbers to consolidate identities. Hence, they would have to rely on other data fields such as phone numbers, email addresses, or registration numbers that would often be missing, inaccurate, or duplicated. With business clients, unique identifiers were difficult to maintain because companies are constantly restructuring their systems, making it difficult to have accurate, consistent, and stable unique identifiers. For instance, two companies merged, and both used different unique identifier systems that were not compatible with each other making it impossible to consolidate their database. In this event, the companies will require an extensive record linkage exercise and recreate new unique identifiers after removing duplicates and inconsistent data fields.


  • The Problem with Data Accuracy: In an ideal world, you would have a single, accurate version of each customer’s contact information. In the real world though, people change addresses, occupations, phone numbers, email addresses frequently. Most people will use several email addresses for one service.  Accurate customer data is a distant dream for most businesses and manually seeking the correctness of personal details is not only prohibitively expensive but also invasive. For instance, if a customer goes by multiple name variations such as Johnny Martin, John Martin, J.M, or Martin Johnny, businesses usually use business logic to consolidate these variations as one individual named John Martin. If this accuracy cannot be ascertained, the record is marked for manual evaluation. The solution lies in domain-specific algorithms and rules that help classify candidate record pairs into matches and non-matches. In the instance above, Johnny can be classified as a nickname, and therefore, any name with Johnny can be automatically replaced with John (provided there are records where John is used). This method helps business users deal with data that needs their attention, instead of data that can easily be resolved through automated rules.


  • Dirty and Duplicated Data: The nature of customer data is inherently uncertain and flawed. Dirty and duplicated data are the most critical data quality challenges that businesses are still struggling to resolve. For companies investing in big data, it is a massive undertaking to ensure customer data is accurate, complete, timely, and unique. Without these features, it would be next to impossible to perform identity resolution. The very foundation of a successful identity resolution depends on clean and accurate data. For this purpose, best-in-line solutions like Data Ladder’s DataMatch Enterprise allows for data profiling and data cleansing as mandatory steps before data matching.

For transactional information, incorrect record linkage can lead to penalties, violations of sanction lists, legal complications, and many more problems. For marketing, incorrect record linkage can lead to poor customer experience and increased customer complaints. For compliance and security, false identity resolution can result in data breaches and loss of business.

A Data Matching Platform Designed for Efficient, Automated, Accurate Identity Resolution  

Identity resolution challenges cannot be ignored, neither can it be resolved using traditional, manual methods of Excel formulas and SQL programming. It requires a modern, advanced, automated, real-time solution that enables the company to perform critical functions as:  

  1. Data Profiling: Measuring the quality of data and getting an overview of the problems with its quality.  
  2. Data CleansingIdentity resolution cannot be performed if you’ve got dirty, inconsistent data plaguing your database.  
  3. Data Standardization: NYC or NY? U.S./USA/US? Non-standardized data impacts the quality of data and makes one of the most frustrating challenges of identity resolution.  
  4. Data Matching:The ability to match data across and within multiple data sets with a high confidence score is critical to the success of identity resolution. Your goal isn’t just to match and consolidate data – your goal is to lower false-positives which is usually caused when manual methods ignore subtleties in data and cluster fields based on exact or near-exact matches.  

Considering the challenges of identity resolution and the need to have a platform that allows for automated data cleansing and matching, Data Ladder built the DataMatch Enterprise platform that was designed to empower business users to prepare, match and resolve identity problems.  

The DataMatch Enterprise platform:  

  1. Is a one-stop solution that lets users prepare data for business use. It is designed on a data quality management framework that starts with data profiling and ends at data enrichment. Users get a point-and-click interface that allows non-technical users to perform data cleansing and data matching operations on the data *without* having to learn a programming language.  
  2. Allows for data integration to over 500+ sources.The ability to easily integrate a data source into an on-premises, secure platform is tantamount to the success of cross-jurisdictional data matching. Users no longer have to extract and export data or use third-party plugins to integrate data into a platform to clean or match data.  
  3. Uses machine learning to match records within and across databases, with an accuracy average of 95% (higher than the industry average of 85%) even if unique identifiers are unavailable, inconsistent, or incomplete.  
  4. Uses established fuzzy matching and established proprietary algorithms to match complex data based on phonetic, exact, numeric variations. It rapidly matches terabytes of data within a matter of minutes, enabling efficient operational efficiency.  
  5. Reduces false positives by leveraging on defined patterns and business rules that allow for the easy classification of data. For instance, the WordSmith function allows users to standardize variations, categorize nicknames, record redundant fields, and replace them with new or standardized information.  

Identity resolution is not a simple matter of matching records. There is a whole process of preparing and cleaning data before using the match process to link records. It’s imperative for records to be free of duplicates, to be standardized and optimized before the matching process takes place. Businesses that ignore the subtle problems with data quality will have to face higher false-positive ratios, which means increased workload in validating information – beating the whole purpose of using an automated solution. 

How is Data Matching Done within DME?  

Data matching does not have to be a complicated process. In an age when real-time data accuracy and matching is required, businesses cannot afford data matching solutions that require technical expertise or programming language knowledge. Self-service data matching tools are the future!  

Data matching is part of DataMatch Enterprise’s data quality management framework that allows users to match, merge, and dedupe records across multiple data sources. What makes DME unique is its ability to allow for multiple data sources to be connected simultaneously for matching across all, between or within data sources.  

DME uses a simple process flow for data matching. The user can:  

  1. Upload a CSV file or connect their data source directly into the interface  
  2. Run the data through the step-by-step wizard that profiles, cleanses and standardizes data  
  3. Create the match definition – (all, between or within)  
  4. Dedupe records by merging them into a single entity  
  5. Create a final list without the duplicates and export  
  6. Save the dedupe records for later examination  

Building on intelligent machine learning algorithms, DME returns a matching accuracy rate of 95 – 100% as it uses several algorithms at a time to evaluate data patterns for possible matches. The software also allows for fine-tuning matches, giving the user limitless opportunities to refine data.  

You can read more about fine-tuning data matches in this extensive post.  

Fine-Tuning Data Match Results

Matching Evolution: Finding Matches Across the Enterprise and Fine Tuning Results the Modern Way

Why You Need a 95-100% Accuracy for Identity Resolution?  

Because the lower your match accuracy scores, the higher will be your false positive and false negative rates which translate to increased costs, workload, delayed operational processes, and high risks. One of the key struggles with identity resolution is the amount of effort that goes into manually reviewing each false positive while struggling with false negatives. Traditional data matching methods that rely solely on fuzzy matching algorithms or deterministic algorithms do little to relieve businesses from this dilemma. Moreover, they don’t allow for easy match fine-tuning, making it difficult for the user to truly get optimized results.  

Identity resolution depends on the efficiency and accuracy offered by a data match solution.  

Read this whitepaper to see how we’ve helped government and public institutions manage identity resolution challenges with our robust solution. 

Identity Resolution Whitepaper

Identity Resolution for Government and Public Sectors Institutions


Want to unify your customer data? Talk to our team and let us help you achieve your identity resolution goals quickly, efficiently.  


Farah Kim is an ambitious content specialist, known for her human-centric content approach that bridges the gap between businesses and their audience. At Data Ladder, she works as our Product Marketing Specialist, creating high-quality, high-impact content for our niche target audience of technical experts and business executives.

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