Solutions
It doesn't matter what industry you're from. ADI's ability to support any data matching use case means we have you covered.
Match Data Faster
Match your data together in real-time with pre-built, open-source and proprietary models on an ultra scalable platform.
Focus on Your Differentiators
Don't get lost trying to understand data matching techniques. ADI's out-of-the-box models and AI-driven semantic profiling engine provides you a 360 degree view of your data so you can focus on business outcomes.
Expertise That's There for You
Our world-class data science and engineering teams with experience across multiple industries will ensure you don't skip a beat.
Entity Resolution
Connect disparate data together to unlock a 360 degree view
Example
A large insurance company has multiple operating units and disconnected systems running autonomously due to a number of acquisitions.
Objective
Link entities across operating units to monitor and report on aggregate activity without an expensive data migration or mastering program.
Connecting the data at its source eliminated the need for an expensive migration and master data management program.
Standardization
Produce cleaner, more actionable data by aligning to a central taxonomy
Example
A healthcare company collects data from hundreds of hospitals and other medical facilities around the country. Each facility has their own organizational structure, job titles, and duties for each title which is not homogenous.
Objective
Analyze workforce information and provide solutions based on job function irrespective of the wide variance in job titles across the organizations.
Matching description of the job and its functions, similarities within the name, department and other details that were available per facility enabled the company to better analyze their entire workforce.
Data product Creation
Join and enrich data to create new and unique data products
Example
A vendor has over 2,000 interrelated datasets from internal and external sources. Each source represents data in a different way making connecting it non-trivial.
Objective
Combine the data to create new products, analytics and insights.
Create new data assets to quickly monetize by automating the matching process and operational workflows between internal and external datasets.
Schema matching
Align fields and tables using metadata
Example
A large company has locations all across the country which each has their own data warehouse. Additionally, the company has many teams which each operate autonomously, resulting in a significant data sprawl which is concerning compliance and risk groups.
Objective
Identify potentially duplicative tables and sensitive data across the organization's data landscape.
For a subset of "dangling" tables with no integration, identify possibilities where data could be easily integrated with the existing data model.
Matching fields and tables allowed for better management of data across a large organization.
