What is Data Enhancement? What to Look for and Key Benefits

Poor data quality can cost businesses an average of $12.9 million per year, according to Gartner. In the financial industry, the stakes are even higher — unclear, raw data doesn’t just impact businesses. It can negatively affect customers. 

Raw transaction data is messy, confusing, and can cause unintended poor decisions — for both financial providers and their customers. With raw transaction data, consumers have a hard time understanding their finances, potentially even confusing legitimate transactions for fraud. And, financial providers are unable to analyze this data and take action based on random strings of characters. 

That’s why data enhancement, sometimes called data enrichment, is a game-changer and essential. At its core, data enhancement transforms raw, chaotic transaction data into enriched, structured, and actionable insights. 

In this post, we will cover:

• What is Data Enhancement?
• How Does Data Enhancement Work?
• What is the Difference Between Data Enhancement and Data Enrichment?
• What Metrics Matter for Data Enhancement?
• What Are the Benefits of Data Enhancement?
• MX Data Enhancement

What is Data Enhancement?

Data enhancement is the process of improving data quality by cleansing existing data and adding additional information or context to the data. For financial services, it involves taking incomprehensible transaction strings — like "WLMRT0001234 UT" — and converting them into user-friendly descriptors, such as "Walmart Supercenter, Salt Lake City, UT." 

Enhanced data also includes metadata, like merchant logos, geolocation, recurring payment classification, and spending categories. This practice converts billions of raw transaction data from incomprehensible strings of letters and numbers, into clear and easily understandable transactions.

How Does Data Enhancement Work?

At MX, we have processed hundreds of billions of transactions — applying an ever-growing and ever-improving set of rules, machine-learning models, and feedback loops to continuously refine data quality. 

Here's how the process works:

1. Cleansed Data: Raw transaction descriptions often contain noise, such as special characters or incomplete merchant names. MX’s cleansing process applies different methodologies to remove unnecessary elements and standardize descriptions. For example, "AMZ*12345 Prime" becomes "Amazon Prime."

What is a consumer supposed to do with that mess? In many cases, all they can do is call customer support and ask about the transaction in question. In fact, our research shows nearly 1 in 4 consumers see transactions on their account they don’t recognize at first glance at least sometimes. And, at least 22% of those consumers immediately call their financial provider for help while 14% just report it as fraud. 

Ideally, the transaction feed should be cleansed to its simplest, clearest, and enriched form. That string of characters now becomes a clearly identifiable transaction, like a business name, transaction description, or both. That’s it. Something as simple as financial data cleansing will help drive engagement and loyalty — and minimize unnecessary calls from consumers wondering about those weird transactions. 

Offering clean transaction data is the first step to creating a digital banking experience that will delight consumers.

2. Categorized Data. Categorization organizes transactions into meaningful groups like "groceries," "entertainment," or "utilities." MX has more than 119 granular categories, giving both customers and businesses a clear picture of spending behaviors. In addition to making the transaction data more readable for consumers, transaction categorization takes it a step further by automatically categorizing data based on descriptions, merchants, and other classifiers. 

Now, consumers have a clear view into how much they spend on entertainment versus groceries without manually adding together various transactions. This also makes it easier for the financial provider to understand how consumers are spending their money so they can create better products and experiences to meet their needs. 

3. Enriched Data with Context. The final step is to add metadata to help better identify when, where, and how consumers are engaging with their finances. For instance, it can include:

• Merchant logos and URLs
• Transaction geolocation (state, city, zip)
• Recurring transaction detection (subscriptions, bills, income)

With the additional metadata on transaction data, financial providers can uncover  insights for their consumers, like opportunities for underwriting (i.e. cashflow) and creating more personalized customer experiences.

What is the Difference Between Data Enhancement and Data Enrichment?

Data enhancement and data enrichment both involve improving data quality to make it usable for consumers. The main difference is the name that providers assign to their product. In some cases, data enrichment is also defined as adding additional metadata to a dataset while data enhancement is correcting errors and filling in missing information within a dataset. 

Deciding between data enrichment tools or data enhancement tools is less important than the decision to optimize your data quality for your consumers. 

At MX, we call our solution “data enhancement” because it includes multiple aspects of enhancing and enriching data, so we use the broadest term in the industry. MX’s Data Enhancement not only transforms raw transaction data into human-readable descriptions, but adds missing information and additional metadata so that the financial data can be used to enhance customer experiences and analytics. 

What Metrics Matter for Data Enhancement?

At MX, we measure data quality through two critical metrics: coverage and accuracy, evaluated across multiple variables. But how can you ensure your data enhancement solution meets the highest standards of quality? 

The key lies in assessing its performance against these metrics. Accuracy determines how effectively data is cleansed, categorized, and transformed into actionable insights, while coverage evaluates the breadth of transactions processed with high confidence. Together, these metrics define the gold standard for quality in data enhancement.

• Coverage: Coverage evaluates the breadth of transactions processed with high confidence. It assesses how many transactions the data enhancement solution can effectively recognize and categorize. This includes merchant coverage — accurately identifying and classifying a wide range of merchants — and geolocation data, ensuring the solution pinpoints where transactions occurred with precision. High coverage ensures a broader, more comprehensive dataset for actionable insights.
• Accuracy: While strong coverage is important, accuracy truly defines the reliability of a data enhancement solution. At MX, we rigorously evaluate accuracy across multiple dimensions, including merchant name identification, transaction categorization, and cleansing transaction descriptions. Accuracy in data cleansing measures how effectively messy, confusing descriptions are transformed into clean, user-friendly ones. For transaction categorization, it reflects how accurately transactions are assigned to the right categories.

What Are the Benefits of Data Enhancement?

When transactions are cleansed, categorized, and enriched with contextual insights, the impact extends beyond operational efficiency — it transforms how financial providers and consumers engage with financial data.

Empowering Financial Providers

Data enhancement empowers financial providers to: 

• Uncover Competitive Insights: Enhanced transaction data enables financial institutions to discover external accounts their customers hold with competitors. This visibility allows providers to analyze customer behavior, identify gaps, and develop strategies to position themselves as the preferred primary account.
• Make Better Data-Driven Decisions: By analyzing granular spending trends and audience segments, providers gain deeper consumer insights. This strategic intelligence helps refine product offerings, personalize marketing efforts, and prioritize resource allocation to meet customer needs more effectively.
• Mitigate Fraud and Manage Risk: Enriched data simplifies anomaly detection, allowing financial institutions to identify and mitigate fraud risks quickly. This proactive approach strengthens security and builds trust with customers.
• Improve Operational Efficiency: Enhanced data reduces confusion in transaction descriptions, lowering call center volumes and improving the overall customer experience. Clear data not only drives satisfaction but also minimizes operational costs.

Empowering Consumers

For consumers, data enhancement helps to: 

• Create Financial Clarity and Control: Enriched transaction data provides consumers with an accurate, organized view of their finances. Precise categorization helps them track spending, understand trends, and make informed decisions.
• Prevent Fraud and Gain Confidence: Simplified and accurate transaction descriptions reduce false alarms, making it easier for consumers to distinguish between legitimate and fraudulent activities.
• Unlock Hidden Costs: By identifying recurring transactions, such as subscriptions, consumers can pinpoint overlooked expenses and take control of ongoing costs, potentially saving significant amounts each month.

For all stakeholders, data enhancement bridges the gap between raw data and actionable insights, enabling providers to foster loyalty, innovate offerings, and reduce risks, while empowering consumers to achieve their financial goals. This holistic approach not only strengthens relationships but also drives sustainable growth and competitive differentiation.

How To Choose a Data Enhancement Provider

Building an in-house data enhancement solution demands significant time, resources, and expertise. By partnering with an established provider like MX, businesses can bypass these challenges and gain access to innovative capabilities from the beginning. 

When evaluating potential data enrichment tools or data enhancement solutions, it’s essential to consider the following criteria to ensure your chosen partner delivers measurable value:

• Security
• Consumer-Permissioned Data Sharing
• Coverage and Accuracy Metrics
• Improvement and Learning
• Solution Speed
• Data Volume and Experience
• Turning Data into Action

Want to dive into more detail? View the full guide on how to choose a data enhancement partner. 

MX Data Enhancement: Unlocking the Power of Financial Data

MX Data Enhancement transforms raw transaction data into actionable insights, empowering organizations to achieve their goals — whether it’s increasing deposits, boosting loan originations, understanding customer behavior, or delivering financial wellness tools. By cleansing, categorizing, classifying, and enriching data, MX enables businesses and their users to make informed decisions with precision. Key features include:

• Merchant Details: Provide users with detailed merchant information for every transaction, making it easier to organize finances and reducing dispute calls over unrecognized transactions.
• Geo Location: Add geographical insights to transactions, helping users visualize their financial journeys and deepening your understanding of where and how spending occurs.
• Cleansed Descriptions: Deliver clear, user-friendly transaction descriptions, eliminating the need for manual interpretation and fostering greater financial clarity.
• Investments: Seamlessly manage increasing volumes of holdings and transactions data with automated cleansing and classification, ensuring you can adapt to changing market dynamics effortlessly.

MX Data Enhancement provides the foundation for building personalized, data-driven financial experiences that delight users and drive business growth.