3 Developer Challenges with 3 Melissa Solutions for Trusted, Accurate Data
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Data is the lifeblood of an organization, but ensuring it stays validated, standardized and trustworthy across systems is one of the most complex engineering challenges teams face. From preventing bad data ingestion to maintaining consistency across ETL pipelines, CRMs and other programs, there’s a lot that needs to be considered when handling data.
In this newsletter, we’re breaking down three of the most common challenges developers face when it comes to data quality, and solutions Melissa offers to help alleviate them.
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The Challenge: Consistent, Reliable Data Throughout the Data Product Lifecycle
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Data is captured from many ingestion points: web forms, APIs, social media, checkout workflows. Then, this data is processed in many different places by different people in separate roles.
It’s a lot to govern, especially when there’s not a data quality layer in place to help streamline these processes. Making sure your data is clean and validated is a good first step to ensuring harmony across all of these processes.
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The Solution: Data Quality Early and Regularly
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Address Autocomplete at the point of entry across all ingestion points eliminates bad data capture at the start. It verifies that each address entered is not only valid, but formatted to postal standards, saving both time and money.
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Additionally, you’ll want to periodically run batch cleansing and matching jobs to keep customer data fresh and current. Use solutions like Batch Address Cleaning, SmartMover and Data Matching, which find updates to addresses like moves and detect duplicate entities that might have accumulated since your last cleansing. These can also append any missing information, like apartment numbers or other contact information.
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Want to give your customers peace of mind and earn perks along the way?
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The Challenge: Software Integration
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Organizations store and process data across many different systems. Unfortunately, data often lives in silos across disconnected platforms, and updates don’t always sync between systems. A data quality layer across all platforms will help enforce the same rules across all systems.
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The Solution: Flexible Deployment Options
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No matter what you need—extensive electronic identity verification or a simple address verification check—Melissa solutions are easily deployed on all your preferred platforms and into web forms, landing pages and other apps and programs.
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- On-Premises: Melissa’s multiplatform APIs work with Windows, Linux, zLinux, Solaris and AIX, and can be used with any programming language like C++ and C.
- Web: Our Developer Portal is a great place to start exploring our web APIs. You have the freedom to use one or combine multiple APIs according to your business needs. They support REST, JSON and XML.
- Integrations: Make sure you keep the same data quality consistency in every platform. Melissa’s data quality tools are also available in popular CRMs, ETL/MDMs, and even Excel and Google Sheets. You can also find us on Github and Postman.
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Dive deeper into service availability by role, technology and integration.
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The Challenge: Creating & Adopting AI Apps & Programs
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In order to stay competitive, businesses are racing to adopt and create AI tools—from customer-facing chatbots and LLMs to internal AIs that update and organize data processes. But there’s also already AI exhaustion and frustration creeping in. Making sure AI outputs have minimal hallucinations and answer consumer questions precisely and accurately is crucial.
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Solution: Melissa’s Open AI Initiative
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Use Melissa as a trusted, reliable source for your data as you build your AI programs. We’ve modernized our internal documentation to make it more consumable by AI systems. This includes transitioning from legacy Swagger 2.0 specifications to the newer OpenAPI 3.0 standards, which provide richer schema definitions, clearer semantics, and better alignment with how agentic AI models interpret and reason over API capabilities.
By restructuring our documentation with AI readability in mind, we’re enabling models to understand our endpoints more accurately, generate higher quality recommendations, and orchestrate workflows with fewer ambiguities.
By tackling these three challenges, you will experience more accuracy, reliability and trust in your data.
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Think differently about your data with our new podcast What’s In Your Data? New episodes drop every other week so make sure to subscribe so you don’t miss out!
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