Problem Definition: Potential Duplicates, Tasks, Reviews
By investing in an EMPI / MPI / MDM platform you recognize the need to match your data accurately and completely but all of these platforms fail to match the entire population of data and leave the unresolved matches as potential duplicates (also known as tasks, suspects, worklists, reviews etc...) for humans to work manually.
MPI/EMPI/MDM Implementation's create Potential Duplicates for humans to review
Potential Duplicates/Tasks are resolved by data stewards
Human Data Remediation
Can be inconsistent between stewards
Confined to workdays/hours (M-F, 8-hour days)
Large Backlogs are difficult to overcome (100k – millions)
Adding sources will spike backlogs
Meanwhile new Tasks/Suspects generation continues
Potential Duplicates typically account for 10-15% of the population – affects match rate
AI Steward Implementation Process:
Production in Weeks
Implementation done in weeks
EntityWise does most of the heavy lifting
One Size Approaches Fail!
Every Client has a different belief about matching and we honor that by creating a custom fit algorithm.
We learn directly from your best Stewards and create a Ai Steward.
Producing Results in a timely manner is important.
Traditional approaches typically involve training a team of stewards which takes months.
We can execute on 100's of thousands of PD's in days to weeks
Accuracy is the most critical aspect of resolving Potential Duplicates.
Ai Steward mimics the decisions of your best data stewards which produce the most consistent and accurate results.
There are 3 steps to resolving any potential duplicate on any platform:
Learn from your Potential Duplicate Data and Data Stewards Answers
Test Solution Against Test Cases to Ensure Accuracy
Resolve 10's to 100's of thousands of Potential Duplicates in Days
This is true whether you are doing this through a team of people or through an automated process.
AI Steward works in conjunction with your existing platform to help resolve these tasks.
How do we engage?
We can engage:
As a one-time project to eliminate a backlog and allow your data team to catchup (a services engagement)
As a subscription where we continuously resolve an ongoing workload 7x24x365
As a file based engagement where we receive a file of potential duplicates and return them resolved.
How AI Steward Works
Why are Data Stewards able to resolve tasks when the MDM/EMPI/MPI cannot.
Data Stewards examine a pair of records differently than a match platform that employs a Deterministic or Probabilistic match approach.
A human looks at each of the attributes individually and makes assessments that weigh the balance of which attributes are more important in addition to how well the values match and strike a balance that is not possible from traditional approaches.
Machine Learning, what AI Steward uses, approaches the decision making process exactly the same way a human does.
It learns from the data steward and can see what the data steward see's when making the decision.