Many healthcare providers are stuck in legacy systems with huge licensing fees and time-sucking maintenance requirements that tie up valuable team members.
Profisee MDM allows you to integrate, manage, and insure the quality of your data across all your platforms at a fraction of the cost and time investment.
Profisee MDM offers flexible integration, meaning it works with any data or platforms you choose. This frees your organization to migrate and modernize your software and solutions without losing data.
Gone are the days of being held hostage to a massively bloated “all-in-one” solution to have your data consistent across your operations.
Onebridge has implemented full Profisee MDM integrations for large healthcare providers in as little as three months. Required updates and changes are also implemented rapidly.
This means your organization sees the benefit of investment quickly and clearly.
With AI solutions like Microsoft Co-Pilot, getting insights from your data is easier than ever. You can simply ask a question and have the AI generate the numbers and visuals you need to answer it in a Power BI dashboard. No more waiting on your IT or analytics department just to get a report you need.
However, Co-Pilot and other AI solutions DEPEND on clean, organized, and integrated data to ensure they are presenting you with correct and accurate information. Profisee MDM is about transforming the data spread across your organization into tidy “data products” or a data catalog.
This what it means to be “AI Ready” or “AI Enabled” – It means your data is ready to be used effectively by an AI solution, and you have the foundations in place to ensure trust and accuracy in its use.
1. Robust and Powerful IntegrationsMany healthcare provider organizations face a challenge of working in proprietary systems with "walled gardens" of data. But rest assured, Onebridge has the experience of integrating Profisee with all major SaaS platforms to legacy on-prem systems. Even if it lives on green-screen and floppy disks, we can get it out.
2. Intelligent Data MasteringProfisee MDM gives you the tools to clean, consolidate, and ensure you can trust your data. It matches, merges, standardizes, verifies, corrects, and synchronizes the data across systems. All this happens with awareness of your data stewardship, governance, and workflow procedures.
3. "Ready to Serve" DataThis process ensures that data can be properly integrated and that it meets the needs of downstream systems, such as business intelligence (BI), AI, and machine learning applications. The integrative Profisee platform enforces governance standards across multiple data products.
A "Data Product" is data that is sorted and structured according to relevant business needs and information domains.
This eliminates hours of preparation and work as the data is "ready to serve" for applications and analytics.
Think of your data catalog like a grocery store where everything is ready to go, you just need to pick what you need for the recipe you are cooking.
With this metaphor you can think of AI as your own personal chef or short-order cook. With all your data clean, reliable, and ready to use, it is possible for AI integrated solutions to build visualizations, write reports, and answer your questions effectively.
Of course, that's just the beginning of what you can do with AI when you have a strong MDM solution. Healthcare organizations are implementing AI in everything from operational efficiency to augmented diagnostics and imaging.
Employing Master Data Management (MDM) to refine and aggregate data into a centralized data catalog is instrumental in enhancing the utility of generative AI models like Microsoft Co-Pilot, GPT-4, and Google Gemini for an organization's specific needs. MDM ensures that the data provided to these AI models is of high quality and consistency, boosting the accuracy and relevance of the generated insights.
By establishing a structured taxonomy and metadata framework, MDM enables data categorization and tagging, simplifying data discovery and comprehension. This categorization helps data scientists identify the most pertinent datasets for training AI models, ensuring alignment with the organization's objectives.
As impressive as generative AI has been, it is still impossible to get productive and effective performance from these models without a solid foundation of clean, structured, and trustworthy data.
