Melissa Data Quality Services for SQL Professionals

Problem

Making data quality integral to the data engineering process is a continuous challenge for data professionals. While there may be multiple reasons related to the lack of or insufficient data quality, the reality is that integrating it is a complex problem to solve. Ideally data engineering workloads should embed quality checks into a scalable data architecture from the very beginning and for every data product. How do we ensure our data products have sufficient data reliability?

Solution

Melissa Data Corporation offers a wide range of data quality services covering global address verification and enrichment, global identity verification, matchup, property, geo tagging services, and much more. The services are available for a wide range of target systems spanning SSIS, almost any imaginable cloud service via APIs and Melissa’s proprietary fully integrated platforms such as Melissa Alert Service. Regardless of the platform, the Melissa Identity and Data Quality suites are designed to ensure continuous data consistency and reliability. In this document we aim to provide a broad overview of how to benefit from the Melissa Data Quality services in different scenarios to guarantee the ultimate data reliability for any final reporting product.

Data Integration Architecture

Data quality is a ubiquitous problem pertaining to many different business scenarios. In general, data quality refers to the degree to which a data product (database table, Power BI report, or single record) is accurate, consistent with previous ones, complete and as a result trustworthy. Frequently, data teams realize data quality is low once they have served their final product. Then, usually end users find out the data product may not be reliable, which leads to low trust and poor adoption. Therefore, at the beginning of a data journey, the data professional faces the following questions:

1. How do we guarantee data quality throughout the lifetime of the data product?
2. How do we implement an audit log of records that did not comply with the initial quality check?

Question one is the core problem for which we will try to propose a conceptual solution using Melissa Data Quality services. Question two takes the matter of data quality a bit deeper – it shows that integrating data quality services into the data pipeline early on may save a lot of effort later, while providing additional insights. In short, data quality needs to be accounted for from the very beginning of the design of typical data architecture.

Conceptual Overview

Let us begin our data quality journey by considering this conceptual overview:

The image reveals several noticeable moments in any data architecture:

• There are always one or many data sources.
• Some sort of integration layer is responsible for extracting, transforming, and manipulating data.
• The integration layer, where usually the heavy lifting occurs, can serve as the basis for integrating data quality solutions.
• Once data with sufficient data quality is available it can be stored and made available to end users via reporting and analytics end products.
• Any compromises with data quality from the onset may propagate and affect the final product.

The vast landscape of data technologies (e.g., SSIS, Azure Data Factory, API over microservices, database stored procedures and triggers, scheduled scripts running batch jobs, and/or real-time data validation) additionally compounds the data quality challenge. Naturally, these technologies fall under the scope of responsibilities of separate roles: Data Engineers, ETL Developers, DW Specialists, Cloud Engineers, etc. While there is a degree of overlap among the roles, the scopes frequently remain segmented per role. In the next section we look at different Melissa Data Quality services and how they can play a role in ensuring data quality in any architecture for various roles.

Technology-Agnostic Data Quality Solutions

The Melissa Identity Suite

U.S. and Canada Address Verification and Contact Verify

The Personator Consumer is a core verification and enrichment service providing next-generation name-to-address matching, full contact data cleansing, precise geocoding and change of address processing. Data professionals can leverage Personator Consumer to verify, correct, and standardize U.S. and Canadian contact data, including names, email addresses, and phone numbers so only correct data enters the target system. Additionally, developers receive access to millions of move records for the U.S. and Canada with partial coverage for select EU countries.

Personator Consumer enables powerful data appends and consumer demographics reference. Integrators can add missing addresses, names, phone numbers, and email addresses. Detailed demographic information on over 250 million U.S. individuals and 170 million households, including household income, marital status, credit information, and more is available as well. Geocoding enables converting U.S. and Canadian postal addresses to a precise rooftop latitude and longitude coordinate in real time for mapping, logistics, location analysis, target marketing and more.

Global Identity Verification

Melissa’s Global Know-Your-Customer (KYC), Know-Your-Business (KYB) and Anti-Money Laundering (AML) checks in Personator Identity are designed for organizations that need high-quality, real-time electronic identity verification (eIDV). The service delivers eIDV using a layered approach of data quality checks. Each check has a dedicated engine that corrects, parses, and understands names and addresses, empowering you to safely onboard new customers while protecting your organization’s business operations.

Global identity verification utilizes contact data verification as the first step to identity resolution. Melissa is the only eIDV service that employs a data quality verification layer that verifies each piece of the identity puzzle before it gets passed to the next step. Developers can verify global addresses, emails, phones and recognize millions of unique-to-culture first and last names on a per record or batch mode basis. The robust verification processes utilize mechanisms such as:

• eIDV: for data cross-reference
• 2+2: a form of advanced eIDV using two different sources to corroborate two different attributes.
• Watchlist screening: increasingly important in identifying politically exposed individuals or sanctioned individuals.

The services from the Melissa Identity suite are available as a set of cloud or on-prem hosted application programming interfaces (APIs) enabling plug and play integration with real-time validation solutions or batch data flows.

The Melissa Data Quality Suite

With Data Quality Suite developers and integrators can standardize, verify, and correct contact data like postal address, email address, phone number and name for effective communications and efficient business operations. Some of the services included in this suite are:

• Address Verification: Validate, correct, and standardize postal addresses for the U.S., Canada and 250+ countries.
• Phone Verification: Authenticate mobile and landline phone numbers, geolocate and ensure mobile numbers are live and callable.
• Email Verification: Verify and correct email domains, syntax and spelling and perform real-time inbox check to ensure an email address exists.
• Name Verification: Identify and parse 650,000+ ethnically diverse first and last names and genderize.

The Data Quality suite is also exposed as a set of cloud or on-prem hosted APIs, enabling plug and play integration with real-time solutions or batch data flows.

Software Integrations

In addition to the Melissa Identity and Data Quality suites, various software integrations are available to facilitate clean, standardized address and contact data on preferred platforms. Melissa services integrate seamlessly in popular CRMs, ETL and MDM platforms. End users can even clean business data in popular spreadsheet editors like Microsoft Excel and Google Sheets. Additionally, power users can use the Melissa Alert Service, a robust stand-alone platform for tracking data changes and offering its own automation and integrations options.

Flexible Deployment Options

The various data quality services are available as a toolkit of on-premises APIs or web services to meet today’s needs for speed, security, and convenience. The on-premises multiplatform APIs work with Windows, Oracle, Solaris, HPUX, and AIX and can be used with any programming language like Visual Basic, C++, and C. The web services version of the suite supports REST, JSON, and XML. Both flavors can be integrated into webpages, forms, and custom applications to provide superior data verification at point of entry (real-time) or in batch.

Service Reference by Role, Technology and Service Type

Having an idea of the service offering, let us examine the following table providing an overview of some of the core Melissa Data Quality services from the perspective of different data professionals, along with their primary related technologies:

Having completed an overview of the data integration services for data quality and the associated benefits, let us look at three distinct scenarios, where the data architect has used different approaches to embed Melissa Data Quality services into different layers of the setup.

Scenarios

SSIS/ADF Data Quality Pipeline

Our first scenario covers a classical ETL approach using SSIS or Azure Data Factory. In this scenario, we have multiple data sources. Then, depending on the requirements data is being extracted:

• from on-premises sources – mostly using ETL in SSIS, or
• from cloud/hybrid source – mostly using ADF.

For on-premises workloads Melissa offers SSIS data quality components. Once deployed, developers can easily reuse them across different ETL packages. On the other hand, if it is a cloud-first setup, the data verification services can be used from an ADF REST action, where data for validation and enrichment is sent to a single Melissa API endpoint. As a result, clean, report-grade data can be stored in a data warehouse or date lake, from where they can be visualized in any reporting platform, such as Power BI. Additionally, data that did not get validated, or the service simply rejected, can be stored in dedicated error log tables. An error log workflow can be attached to these tables for reprocessing or historical analysis of the bad data.

This setup uses the following technologies and services as an example:

SQL Batch Validation

In this next scenario, in focus is purely SQL-driven architecture. The starting point is a workflow orchestration tool, such as Logic Apps. The workflow can commence at a regular frequency or have an event-based programmatic trigger. In the core integration layer, we have an SQL Server 2025 stored procedure. With this approach, data already residing in the database does not have to move to another layer. Using the built in functionality for invoking a REST endpoint from a stored procedure and the data at hand, we can call any Melissa service API endpoint. As a result, we can have two sets of tables: one for validated records and one providing a quality audit trail.

This setup uses the following technologies and services as an example:

Real-Time Verification and Validation

Finally, the third scenario is suitable for real-time validation at the data entry point. First, the data are sent to a microservice, such as an Azure Function. The function executes a call against a Melissa API endpoint and either returns the verified data to the form, or stores it in a database, or both. This entire process can be logged for data quality metrics and subsequent app analytics.

This point-of-entry quality check approach includes the following technologies and services:

Democratizing Enterprise Data Quality

As a final note, here is a summary mapping between most of the data roles discussed thus far and groups of Melissa Data Quality services. As previously pointed out, data quality is an integrated team effort that should be implemented as early in the data ingestion process as possible. Modern data technologies offer diverse integration possibilities with varying degrees of complexity. Separate roles can benefit from similar services:

Starting from the core Identity Verification services, we see that most data professionals can benefit from those, using either the software integration, on-prem or cloud service approach. Working our way out toward the edge, we have the Melissa Data Verification tools. Whether you are verifying personal data attributes or performing batch cleansing, these tools are designed to help everyone early on, including DBAs. Finally, when it comes to the fully integrated data quality platforms, such as the Melissa Alert Service or Unison, the scope of roles benefitting expands. Master data specialists, data stewards, and higher organizational tiers such as IT management are target users who can extract value from the Melissa Data Quality platforms. To whichever role you belong, Melissa anticipates your data quality needs and has you covered on multiple fronts.

Conclusion

Guaranteeing data quality and reliability on a continuous basis is an ongoing challenge for data professionals. Across the enterprise data landscape, the responsibility falls in the scope of various roles along the way of the data integration journey. The multitude of data sources, existing data silos as well as the ever-increasing complexity of data integration requirements aggravate the challenge. Nevertheless, the vast array of Melissa Data Quality services is here to support and alleviate the standardization efforts across the board to ensure data quality from the very beginning of any data collection and integration process. The different services offer options for integrating into various data ingestion architectures: classical ETL, batch processing and validation, as well as real-time data checks. If you are interested in learning more about the Melissa Data Quality Platforms, APIs or additional service, head over to melissa.com.

Next Steps

• Melissa Data Quality Solutions for SQL Server
• SQL Server Best Practices for Data Quality
• Introduction to Melissa Alert Service
• Data Verification and Enrichment for Cleaner Data
• Know Your Business Data Verification Solution

Hristo Hristov

Hristo Hristov is a seasoned data professional with 10+ years of experience spanning the intersection of data engineering and smart manufacturing solutions. Since 2017, he has specialized in implementing advanced analytics solutions for bridging the IT/OT gap.

A technical writer with over 80 published articles on data and AI technologies, Python development, and cloud solutions. Passionate about transforming complex data into business value through innovative applications of Azure Data Platform, Python, IoT solutions, databases, and other cloud technologies.

Currently applying Industry 4.0 best practices, focusing on IoT connectivity, and implementing data and AI systems in manufacturing. Hristo holds a degree in Data Science and several Microsoft certifications covering SQL Server, Power BI, and related technologies.

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