Meet the evolving digital needs in your organization and identify new opportunities with a future-proof data mesh architecture built in your secured environment.
New data products can be developed and deployed into your secured environment within hours.
Each new data product is deployed with default analysis apps that provide immediate access and analysis capability. Bespoke apps, unique to your data and your needs, can be built in minutes.
Existing analysis scripts in other coding languages (e.g. R, Python) can be integrated into the data product and published as apps.
Once the data product is deployed into the analysis platform, your team can start using analysis apps to analyze data on the same day.
Why this matters:
Return on investment from day one, instead of months down the road.
Milan Radovich, PhD,
Assoc. Prof. IU School of Medicine,
IU Health Vice President for Oncology Genomics,
Co-Director IUH Precision Genomics
ORIEN Network Scientific Committee Co-Chair
Promote data discoverability and interoperability
When both the data and analyses are FAIR (findable, accessible, interoperable, and reusable), you empower your team to accelerate time to value by rapidly and seamlessly connecting real world data sources to be easily analyzed by those with domain knowledge.
Promote Transparency in the Data Analysis Process
See how we reproduced an elastic net machine learning model from a research paper. Learn how we integrated python-based machine learning algorithm into the Tag.bio platform. View our surprising findings.
Future-proof your data architecture with data mesh
With Tag.bio’s data mesh approach, each of your datasets are represented as modular data products that can communicate with one another using smart APIs. These data products can be easily and independently added, updated, and removed without impacting other datasets.
Why this matters:
- Deliver an agile, scalable response to new services and changes
- As new real-world data emerges, you can seamlessly incorporate it into the mesh without disrupting your existing infrastructure
The analysis platform enables your organization to streamline data requests and access.
From the user interface, users can request the data products and types of data that they want access to and your team can approve the request in the same platform.
Additionally, any actions that your users take on the platform are automatically saved in the analysis history, providing you with complete transparency and auditability.
Why this matters:
- Confident that your users have the right access to the right data
- Deliver high-impact decisions by providing user with relevant, quality data
- Meet data demands with an efficient data permission workflow
- Peace of mind knowing that you have an audit trail on the actions taken on the data
For your team of domain experts:
The analysis platform automatically saves any analysis activities that your team has taken and stores them in the analysis history.
Why this matters:
- Your domain experts will be able to reproduce any useful data artifacts (UDATs) that they’ve discovered
- If any of your team members transition, you’ll still have access to their analysis history
For your team of data scientists:
When your data scientists build an analysis app or integrate their scripts (e.g. R, Python, Machine Learning) into the analysis platform, all of their code is saved and versioned.
Why this matters:
- All scripts are instantly accessible to other data scientists for reference
- If any of your team members transition, the analysis apps they’ve released on the analysis platform will still be accessible and reusable. The same applies for the source code.
From a 30-minute demo to an inquiry about our 4-week pilot project, we are here to answer all of your questions!
