Problems We Solve
RecordLinker gives you the power to quickly and accurately link records, freeing up valuable time and organizational resources that would have otherwise been spent correcting data errors. We ensure your manual effort is minimized so you can focus on what really matters: actionable insights.
Reduce Manual Data Work for Your Team
Efficiently Convert and Harmonize Your Data
With RecordLinker you can standardize your input data based on names and identifiers. Our Machine Learning will auto-link records that pass a confidence threshold.
Whenever Machine Learning is not sure what to do, we let your data conversion specialists decide on their own based on ML suggestions, manual search options, and industry-wide external sources of data. Your teams can label problematic items for investigation as well as leave comments and attachments.
RecordLinker knows various data formats and constantly gets updated with new ones. Your experts don’t need to waste time on deciphering how to handle data from input systems they may not even know.
Standardize and Dedupe Your Data Mart
Deduplicate, merge, and standardize records from your data marts to achieve holistic view of business and operations.
With data coming from various systems into your data mart, you may find that you end up with duplicate records, differences in naming conventions and codes for identical things. RecordLinker helps you overcome that to consolidate analytics.
Our Machine Learning solutions helps you identify and merge records that mean the same thing.
Regardless whether it’s data conversion, standardizing core system reference data or an entire data mart – everything happens in a single consistent UI that is agnostic to data formats and category of data you are working on.
Increase Quality of Your Master Data
To effectively link or standardize input data, you need to ensure that what you are standardazing to is pristine.
RecordLinker can inform you about misspellings and incorrect identifiers assigned to companies, products etc. Then our Machine Learning suggests a possible way of correcting these errors. It can help you solve years of errors and lack of maintenance ofdata in your core system.
RecordLinker can also help you keep track of changes that happen in your industry (e.g. for insurance, companies tend to merge and change names). Based on an industry standard source of your choice, we may help you track the history of these changes. Thanks to that, you will always standardize to updated reference records.
When you edit, merge or create reference records in RecordLinker, our ML goes through linked input data and accordingly reassigns anything that was previously linked to your golden set of records.
