Businesses are drowning in customer data – most of it, duplicated and scattered across multiple data sources. One customer can have five different name variations, email addresses, physical addresses, and phone numbers. These variations can occur within one platform, such as a CRM, or within 3rd-party platforms connected to the organization. How does a business consolidate all these variations to prove that it belongs to one individual? Through the science of identity resolution!
Simply put, identity resolution is a way to figure out who people are, what they like, how they are linked to the business and most importantly, is the identity stolen? Is the identity of a scammer or a fraudster? Are they on any criminal, sanctions, or banned list? All these questions are answered through the identity resolution process.
In this guide, we’ll help you with:
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The technical process of identity resolution is the process of taking data sets from different sources and combining them into a single unified repository to fulfill purposes like: master data management, creating singular customer views, and improving data/information quality. This involves using algorithms such as natural language processing (NLP) or other advanced matching technologies to look for patterns in the data that can be used to match records.
Identity resolution serves both functional and business purposes. In the functional sense, identity resolution lies at the heart of master data management and data quality. With access to accurate and reliable data, businesses can make more insightful & confident decisions – thus serving the business purpose of identity resolution.
In a data-driven world, where businesses have a plethora of data sources, ranging from customer databases to CRM systems, social media and web-based data, to third-party data and more, identity resolution is the need of the hour.
For a given dataset, identity resolution is a three-stage process.
1). Data profiling: The first step in identity resolution is the discovery, review, and cleaning of your data set. This involves identify errors affecting the data – such as problems with standardization, corrupt, or noisy, obsolete, dirty data. Once errors are identified, the data goes through a treatment process that involves cleaning up the data, setting rules for normalizing the data (such as using DD/MM/YYYY as a date format instead of DD/MM/YY). Once you’ve got a clean copy of the data, then you move into the next stage.
2). Data matching: The second step involves using probabilistic models to match and link data. This includes using fuzzy logic algorithms to identify possible matches based on similarity scores of attributes – such as names, numbers, and any other unique reference/identifier.
3). Data consolidation: The final stage is the creation of a final master record through data consolidation. Once the matches are identified, and duplicates are treated, the data is consolidated to form the single source of truth – a term for data that represents the most valid, accurate, and complete information in one view. While creating master records, avoid the temptation to be perfect. You can never realistically have 100% unified records. The aim is to create records that supports your organization’s use cases – nothing more, nothing less.
Identity resolution allows companies to better understand their customers throughout their lifecycle by providing a holistic view of their identities and activities.
Before identity resolution technology became available, organizations relied on manual methods to identify customers. This approach typically involved collecting limited information such as name and phone number from contact forms and manually searching through customer records. The process was time-consuming and prone to errors due to inconsistencies in customer data across multiple sources. It was difficult to obtain a reliable and unified customer view.
Even when identity resolution technology was developed, professionals still had to have programming and coding knowledge to create and test probabilistic matching algorithms. While this did cut down on the manual process, it was not able to handle complex data structures streaming in from internet-based sources such as web forms, social media forms and third-party software.
To cater to modern data structures, professionals need technologies that lets them clean, match, and consolidate data on the basis of:
The WinPure Clean & Match solution meets all five requirements with the additional flexibility of an API module that allow developers to easily and quickly integrate with different systems and treat, match, consolidate data with minimal effort.
Here’s how you can perform an identity resolution using WinPure.
Step 1: Data integration
Connect to your CRM directly, or import you CSV file. Whatever your data source, you can easily plug it in the WinPure dashboard for a manual review. You can also review multiple data sets at once within the dashboard.
Step 2: Data profiling
Identify inconsistent values, check for missing information, review duplicates, and set your own standardization rules with the tool’s data profiling feature.
Step 3: Data cleaning
Want all your dates to follow a set standard? Need to remove odd characters from text fields? You don’t need to run scripts for that. You can easily use WinPure’s data cleaning functions to remove dirty data with just a few clicks.
Step 4: Removing duplicates
You can define custom data match criteria that will be used to determine if two records are considered to be duplicates. This could include checking for identical names, emails, phone numbers etc., or more complex attributes like addresses (which may require looking at similar street names). Data deduplication is critical for identity resolution because if you have multiple records for one customer, you’re not “resolving” an identity.
Step 5: Data match
Exact, deterministic, fuzzy matching and WinPure’s proprietary algorithm is used to look at similarities between strings of text or numbers and identify links between records in different datasets even when they don’t match exactly on certain criteria such as spelling or punctuation.
Step 6: Consolidation
The consolidation process involves combining multiple source records into one master record. This can be done by taking attributes from each source record, analyzing them and selecting the most accurate attribute for the master record. The selected attribute can then be used to remove duplicates and create a single master record with all of the desired information included.
On average, developers and data analysts can spend anywhere from 100 – 200 hours on merely data profiling and resolving duplicates. The exact amount of time depends on the complexity and type of data being used, as well as the processes and technologies implemented for IR.
For example, more manual solutions like human review or document matching will take longer than data matching solutions. With tasks like data normalization, the process can be well extended into weeks.
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A data match solution is a full-fledged software that allows even non-technical (aka business users) to consolidate records. This is especially important for marketing users who constantly have to deal with the variations and complexities of customer data. With a solution like WinPure, these users no longer have to rely on IT or data teams to treat or consolidate their data.
Some other benefits of using an automated solution for identity resolution over manual solutions include:
Increased accuracy and precision in the matching process. Because data matching solutions use a combination of matching algorithms, the tools are able to detect errors that humans may not think about or consider when going through records manually. Moreover, the error detection is efficient and accurate. Over time, users can also feed the tool with specific errors to watch out by simply typing in exceptions. No coding is needed for complex operations.
Improved scalability and agility. A data match solution can reduce the amount of time it takes to process large amounts of data since it is automated. This allows teams the time they need to resolve critical issues such as human verification of suspicious data. In turn, this allows organizations to quickly respond to fraudulent activities and protect themselves against sanctions violations.
Lower costs associated with data processing. Automated data match solutions are typically more cost-effective than manual approaches, as they require less labor and fewer resources overall.
In an age when data is oil, companies simply don’t have the luxury of wasting more time in manual processes that can very much be resolved with automated solutions.
Like all data management strategies and initiatives, identity resolution is fairly complex and comes with its set of challenges. Over the years, as we have helped dozens of clients with identity resolution, some of the key challenges we recommend watching out for are:
1 . Inconsistent data sources: An average organization is connected to around 400 data sources, which makes difficult to differentiate between accurate information and outdated or conflicting data points. A simple example: A customer’s official name is John Smith, but his social media or email name could be Johnny Smith. This kind of inconsistency becomes a challenge to identify, therefore, companies must create data governance processes to ensure the credibility and accuracy of data.
2 . Poor data quality: A reason why data cleaning solutions are recommended is to tackle the overwhelming challenges of poor data quality. Inaccurate or incomplete customer profiles are particularly problematic because they can result in a misidentification or unlawful access to confidential data. Worse, it could also result in legal cases against the organization. If a company’s data is dirty, duplicated, and disconnected, identity resolution cannot be possible before the data quality is improved.
3 . Lack of standardization: Another challenge associated with identity resolution is lack of standardization between different types of customer data sources (for example, social media accounts versus emails). This makes it difficult for organizations to link different sets of customer data together in one unified view since each source may have its own unique format for storing information about customers. To overcome this issue, organizations should look into leveraging technologies like fuzzy matching algorithms which can recognize similar but not exact values across multiple sources and merge them into one record in order to create a unified view of each customer’s online presence.
4 . Scalability limitations: Another common challenge is scalability; as more data sources are added or updated over time, identity resolution becomes challenging. One way organizations can handle this problem is by using distributed processing systems, breaking up tasks into small use cases instead of trying to achieve identity resolution at an organizational level.
5 . Complexity: The final major challenge associated with identity resolution is complexity; many times there are simply too many variables involved or relationships between entities that are hard if not impossible for humans alone to analyze accurately or in a timely manner without the help of automation or matching tools. These models and tools can quickly find patterns within large datasets even if those patterns would otherwise be difficult if not impossible for humans alone to identify.
Identity and entity resolution is an essential part of modern day businesses, but the challenges that come with it need to be addressed before organizations can initiate a successful resolution strategy.
Identity resolution is increasingly being adopted by businesses as a powerful tool for building and maintaining customer relationships. In fact, according a 2019 survey, 84% of organizations report they are using identity resolution to help with automating processes, reducing costs and improving customer experience.
The four areas where identity resolution is needed includes:
Marketing: Identity resolution benefits marketing departments the most. It is also one of the most challenging. With customer data coming from multiple sources including social media, web forms, emails, and third-party integrations, identity resolution is CRITICAL for marketing departments. In fact, a Forrester report claims identity resolution is a strategic effort in marketing.
Customer Service: Identity resolution can be used to ensure customers are consistently recognized when they use multiple contact channels, such as email, phone number, and social media. This helps customer service personnel quickly identify their customers to provide personalized and efficient support.
Risk Management: By using identity resolution, organizations can detect potential fraud or other suspicious activity by cross-referencing customer information with databases of known fraudulent actors. This can help protect the organization from financial losses due to malicious activity.
Sales: Identity resolution allows sales teams to quickly identify leads and target prospects more efficiently based on existing data associated with them. This ensures that sales reps have all the necessary details about a particular lead prior to outreach attempts which increases conversion rates over time.
Data Governance: Organizations often need to collect personal data in order to do business but must adhere to government regulations governing the handling of this information. Identity resolution helps organizations ensure compliance by enabling them to track how data is collected, stored and shared internally or externally.
Sanctions & GDPR Compliance: By using identity resolution, companies can reduce the risk of inadvertently violating sanctions lists or GDPR regulations by ensuring that their data reflects up-to-date information about individuals such as name, address, phone number, etc and that duplicate identities are examined. Additionally, automated identity resolution solutions can quickly detect any changes in the records which may violate existing policies or regulations.
Identity resolution is an important process for organizations allowing them to accurately identify individuals and the data associated with those individuals. This can help with:
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