Melissa and Database Trends and Applications (DBTA) recently hosted a webinar titled “From Legacy to Leading: Building an AI‑Driven Data Strategy.” In this roundtable session, Robert Stanley, Sr. Director of Special Projects at Melissa Informatics, reviewed how organizations can turn long‑standing data quality practices into a powerful foundation for modern AI initiatives. If you’re interested in watching the full webinar, be sure to check it out on demand.
From legacy data quality to “accurate AI”
A core theme of the webinar is that trusted AI depends on trusted data. Melissa Informatics builds on more than 40 years of data quality expertise to deliver AI‑enabled data quality, discovery, integration and research.
Rather than treating "legacy" as something to leave behind, Melissa Informatics uses long‑established data quality (DQ) resources as the foundation for:
- Accurate, high‑confidence AI outputs
- Well‑defined, open APIs that establish DQ services
- Dynamic, efficient DQ workflows that evolve with your data
This “legacy first” approach ensures that AI is grounded in proven methods, causing less hallucinations and better AI output.
Open APIs as the engine of AI‑driven data strategy
A significant part of Robert's discussion focused on open, well‑defined APIs as a technical backbone for accurate AI. Melissa’s Open API Initiative makes key DQ resources accessible as RESTful microservices across databases, APIs and files. These APIs:
- Support internal AI and agent development, model training, and contextualization
- Allow customers to plug data quality directly into their own applications, platforms and vendor ecosystems
- Provide the building blocks for constrained AI execution (AE), improving both accuracy and governance
By introducing DQ capabilities through Melissa's APIs, organizations can integrate trusted data operations—profiling, deduplication, enrichment, segmentation—into every downstream analytics and AI initiatives.
Enabling deeper knowledge and business value
The webinar also examined how this approach supports end‑to‑end value delivery:
- Access and federation of data across legacy systems, EMRs and other sources
- Profiling and transformation using Melissa’s DQ resources and APIs
- Management and segmentation of business‑critical datasets
- Delivery of value to analytics, dashboards, programs and AI initiatives via open APIs and automated enrichment
By using proven DQ methods when building your AI applications, you will move from fragmented environments to a cohesive, AI‑driven data strategy. Curious about how “expert” AI, semantic technologies and open APIs can transform your own data landscape? This recap only scratches the surface. Watch the full “From Legacy to Leading: Building an AI‑Driven Data Strategy” webinar to see more concepts, architecture and real‑world use cases covered by Robert and other AI experts, plus an insightful Q&A at the end of the session.
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