AI Without Master Data Management is like…
Treating the symptoms without understanding the disease
AI’s potential to revolutionize the healthcare industry has many organizations eager to harness its promise for efficiency and automation. However, they must address a crucial step before diving in: data readiness.
Even the most sophisticated AI models will fail to deliver without clean, reliable data. Here are three essential considerations before implementing AI.
1. AI is Only as Good as the Data Supporting It
AI is a powerful tool, but it is only as effective as the data that fuels it. According to a Gartner report, 85% of AI projects fail due to poor data quality or a lack of relevant data.
AI will not function effectively if your organization is challenged with data issues like fragmented data sources or inconsistent records. It can’t produce meaningful results without good, clean data.
One area of potential in the healthcare industry is AI’s ability to produce data matches and eliminate false ones more efficiently. Today, teams program each match strategy to tell the tool what to look for. For example, look for the Social Security number and date of birth; if they match, it’s the right person. It’s possible AI could do this and eliminate the programming step. However, it will be difficult for AI to do this across multiple disparate data sources.
2. AI Struggles with Federated Data Sources
Many healthcare organizations made a strategic move to a federated data environment to capture cost savings. This also enabled them to select specific tools and functionality to serve different needs, creating multiple disconnected data sources.
Unfortunately, this is not a data model where AI thrives. AI models will fail to perform when confronted with varying data standards, incomplete records, or conflicting data sources. Inconsistent data makes it difficult for AI to make accurate predictions.
This is where the power of MDM comes in. It can deliver a clean source of data that feeds the rest of the system, creating an ideal environment for AI to add value.
3. Understanding the Intricacies of Healthcare Data is Essential
Healthcare data comes with unique challenges, from electronic health records (EHRs) to credentialing systems and claims data. Yet, many organizations are working with data models built by people who don’t understand the complexity of the healthcare industry.
Choosing a partner with healthcare expertise will set you up to manage your data in a way that aligns with industry standards, regulations, and best practices, which is a critical foundation before AI.
We’re Healthcare-Focused and Outcome-Driven
Our team specializes in helping healthcare organizations manage and improve their data so that AI can deliver on its promises. Let’s talk before you embark on your AI journey. Contact us.
About Rachel
Rachel Chen, Executive Director at Providence Health and advisor to Azulity, is an expert in enterprise foundational data stores, including Master Data Management (MDM) and Data Governance, with extensive experience leading large-scale MDM implementations for healthcare organizations. She specializes in cloud migrations, data management frameworks, strategic data initiatives, and data literacy, consistently delivering successful outcomes.