Data matching explained: The key to clean, connected data

From making smarter decisions and enhancing customer experience to fueling growth and reducing risk, businesses bank on data for practically everything today. However, inconsistent and fragmented data is a challenge.

For instance, variations in customer records, duplicate supplier data, or inaccurate patient details can trigger many issues.

Your customers might end up dissatisfied, operations can become inefficient, or the analytics can be flawed. It's no wonder that annually, businesses lose close to $13 million on average when working with poor-quality data.

Data matching can address the problem of dirty, disconnected datasets though.

In other words, even when data is inconsistent, incomplete, or differently formatted, you can detect, compare, and connect records associated with the same entity. Hence, data matching helps you arrive at information that's clean, unified, and dependable.

Let's explore its importance, challenges, perks, and more.

Decoding data matching

It involves the comparison of records from one or multiple datasets to figure out if they are linked to the same person, products, household, or business.

Spotting similarities between records is the objective of data matching, even if identical information is missing.

For instance, the following records might be associated with the same customer:

• Maggie Smith, 40 Main Street, maggie.smith@email.com
• Margaret Smith, 40 Main St., msmith@email.com

Although there are subtle differences, intelligent matching technologies can identify commonalities and confirm whether they are linked to the same individual.

Currently, from identity verification and fraud prevention to customer data management and financial compliance, data matching finds extensive usage. This helps maintain correct, connected data across various systems.

What is the importance of clean and connected data?

As a contemporary organization, you probably operate across marketing databases, CRM systems, financial applications, customer support systems, and other platforms. However, with time, these systems create records that are obsolete, duplicate, or inconsistent.

And subpar data results in:

• Duplicate records
• Communication failures
• Incorrect analytics
• Compliance risks
• Elevated operational expenses
• Unsatisfactory customer experience

Fortunately, leveraging data matching can help you obtain clean data that's connected too.

And this boosts everything, from operational efficiency, customer insights, and decision-making to personalization, targeting, compliance, and reporting.

Data matching: How it works

Advanced data matching techniques combine artificial intelligence (AI), rules, and algorithms to recognize how records are related.

Let's break down what happens:

Standardization

Data requires standardization before matching. So, addresses are formatted consistently, phone numbers are normalized, abbreviations are corrected, and capitalization and punctuation are standardized. This enhances matching accuracy.

Exact matching

This process compares records to spot values that are identical, such as the same customer ID, email address, or phone number.

Though accurate and straightforward, this method is limited in scope since real data features many inconsistencies.

Fuzzy matching

This method helps in identifying records that aren't identical yet similar. It usually takes into account nicknames, misspellings, typos, and differences in formatting.

Hence, it's suitable for messy real-life data.

Probabilistic matching

This type employs statistical models to estimate the probability that two records are connected to one entity.

Probabilistic matching assesses multiple data points simultaneously, like address similarity, name similarity, phone number, birth date, and email address. Each field has a role to play in the confidence score.

AI-Driven matching

Modern matching systems are increasingly incorporating AI and machine learning to boost accuracy.

They learn from match decisions made in the past, minimize false positives, spot concealed relationships, and adapt to changes in data patterns.

What are some common data matching types?

The following types of data matching are common in different industries:

Customer data matching

Businesses create unified profiles for customers across various systems by leveraging data matching.

This improves customer support, sales accuracy, marketing personalization, and the overall customer experience.

Identity matching

Through matching, identity verification systems confirm if records are associated with an actual person.

This is vital for complying with anti-money laundering (AML) regulations, ensuring effective know your customer (KYC) verification, and preventing frauds.

Healthcare record matching

Matching enables healthcare organizations to connect records of patients across different systems.

This minimizes duplication and averts grave safety risks.

Vendor and supplier matching

Identifying duplicate suppliers or vendors across procurement systems is easy with data matching.

Organisations can reduce the risk of fraud, payment errors, and inefficiencies related to procurement.

Data matching challenges you should know

Though powerful, data matching has certain shortcomings:

Poor data quality

Matching accuracy is hit in case of incomplete records or outdated ones.

Missing fields, inconsistent formatting, typos, and obsolete addresses are common issues.

Duplicate records

Duplicate records don't just trigger confusion, but also make operations inefficient.

If the matching isn't effective, businesses might contact the same customer multiple times. Marketing spend might also go to waste or analytics reports might be inaccurate.

False positives (and negatives)

Ideally, matching systems should ensure that accuracy and sensitivity are perfectly balanced.

That's because false positives can match records that aren't related, by mistake. And false negatives might be unable to spot records that are related.

Complexities due to scale

If your business handles millions of records across multiple geographies and systems, complexity might increase owing to different languages, naming conventions, and regional formatting standards.

How does effective data matching help?

Data matching, when implemented properly, offers multiple perks:

Better data precision

Matching enhances the overall quality of data by eliminating records that aren't consistent or duplicates.

Improved customer experience

When customer profiles are unified, it's possible to deliver smooth and fast experiences with a personalized touch.

Stronger analytics

When data is clean and connected, you can forecast accurately and gain correct customer insights.

Business intelligence and operational reporting gain a more robust edge too.

Reduced operational expenses

With data matching, you can reduce the human effort that goes towards correcting inaccurate or duplicate records.

It also minimizes the expenditure associated with duplicate outreach and administrative tasks.

Enhanced compliance

Players in regulated industries depend on data matching to satisfy compliance requirements, whether it's connected to AML, KYC, patient safety, or audits.

Successful data matching: Best practices to adopt

How to ensure data matching is truly effective?

Standardize data

Consider normalizing names, dates, phone numbers, and addresses for maximum possible match accuracy.

Don't stick to one matching technique

Using multiple techniques can enhance the matching system's overall performance.

Monitor data quality constantly

Audit datasets, validate records, do away with duplicates, and update obsolete information on a regular basis.

Leverage AI and automation

To reduce the workload of manual reviews, boost scalability, and efficiently process big datasets, use AI-powered matching systems.

Integrate matching across systems

Make sure data matching connects different systems like marketing, finance, enterprise resource planning (ERP), customer relationship management (CRM), etc.

This way, you will have a data ecosystem that's unified.

Embrace data matching to grow your business confidently

Whether you want to operate more efficiently, make smarter decisions, or ace compliance effectively, data matching can do the trick.

By enhancing data quality and eliminating duplicates, you can strengthen both operations and analytics.

However, for successful matching, you need cutting-edge solutions fit for messy, dynamic, and large datasets.

So, invest in the right tools for merging duplicate records, validating and standardizing data, improving identity resolution, and reducing false matches.

You won't just end up with clean data, but also a unique competitive edge.