Two customer records share a name, a street address, and a phone number, so your identity resolution process links them into one customer. A week later, someone on the customer service team notices that one record belongs to Robert Alvarez Sr. and the other to his son, Robert Alvarez Jr., who lives in the apartment over the garage.
Deterministic matching is precise when records share an exact identifier, and probabilistic matching reaches the records that don’t. Used together, they resolve more records correctly than either method can alone. Even then, a few cases like the Alvarez family will land in a gray area that a person should review.
That leaves every data team running identity resolution in Snowflake with the same question: when you find a match you disagree with, what happens next?
Matching and correcting are two halves of one job
Most conversations about identity resolution focus on matching logic: which fields to compare, how to weight them, and where to set thresholds. That determines how many records resolve correctly on the first pass.
The correction workflow determines what happens to the rest, and the rest is rarely random. We’ve found that the hard cases tend to cluster where accuracy matters most:
- High-value customers with multiple emails, addresses and shipping destinations
- Patients and members whose records span providers, clinic locations and plans
- Households that share a phone number, an email, or a last name
- Consent and suppression records, where a wrong merge has compliance consequences
When correction isn’t part of the design, fixes still happen, just in less visible places: a spreadsheet of known bad merges, a Slack thread, or a one-off SQL patch that the next full run reverses before anyone notices. Each fix works once, but none of them adds to the team’s confidence in the identity table. Good matching keeps errors rare, but a good correction workflow keeps the errors you find from coming back.
Six things a strong match review workflow needs
Whether you built your identity pipeline or bought it, these six capabilities make match review something your team can rely on instead of something it works around.
- Show why records merged. Before anyone decides a match is wrong, they need to see which identifiers drove it. A shared address and phone number tell a different story than a shared email alone.
- Correct at the right level. Reviewers need to break apart a bad merge, join a missed match, and act on a single group.
- Track the decision. Match runs, rule changes, reviews, and overrides should each be logged with the user and a timestamp. That history shows the team what changed between runs and who changed it.
- Make corrections stick. A correction that disappears on the next match run creates more work than it saves, so overrides need to persist as new data arrive, and matching runs again.
- Make changes easy to consume. Dashboards, machine learning features, audiences, and data shares all join on identity keys. Ideally, records a correction doesn’t touch keep the same key from run to run, and the identities that do change are easy to detect from the data itself, by comparing runs or reading a change table.
- Learn from the pattern. One override fix one record, but twenty overrides on the same kind of case usually point to a threshold or field setting that needs to change.
Data stewards review the matches, and engineers fix the patterns
Engineers build and maintain the identity pipeline, but judging whether two records belong to the same person is a different job. In most organizations, that work falls to a data steward or data quality analyst on the data team. They know the source systems well, and they know when to ask the business for context.
We recommend an operating model that splits the work:
- Data stewards review a random sample of match results to check accuracy across the whole dataset, and they correct the groups that are wrong.
- Engineers watch for patterns in those corrections and treat them as configuration questions.
- Business owners in teams like customer service or operations help set expectations up front, such as how cautious to be with customer or consent records, and weigh in on the occasional case a steward can’t settle alone.
This keeps identity questions out of the sprint backlog and puts each decision with the person best equipped to make it.
AI agents treat your identity table as fact
AI agents reading resolved identity tables are answering more of the questions people ask about customers. Agents take those tables at their word, so they won’t notice that Robert Sr. and Robert Jr. share a record.
That makes the human-in-the-loop an important part of AI-ready data. When stewards fix merges in the same governed Snowflake environment where analysts and agents read customer data, every correction improves the foundation those agents rely on.
What this looks like in practice
In Redpoint Identity Studio, our Snowflake Native App for identity resolution, a steward reviewing the Alvarez group can see that a shared name, address, and phone number drove the merge. They break the group apart, and that override holds through future match runs. If the same pattern shows up in other households, engineers can test a threshold change as an experiment against the full dataset and compare it with production before anything changes.
Five questions to grade your current identity resolution setup
Bring these to your next team review:
- When two records merge, can you see what caused it?
- Can you break apart a bad merge or join a missed match?
- Do corrections include a record of who made them and when?
- Do manual overrides or connections survive the next match run?
- Do your corrections indicate when the matching configuration itself needs to change, and are you able to make those configuration changes?
A “no” on any of them points to your next improvement, whatever tool you use.
Take a clickable tour of Redpoint Identity Studio to see how you can set match rules, review results, and turn raw customer records into trusted identities – inside your own Snowflake environment.
To see the review and experiment workflow on your own data, check out the 30-day free trial for Redpoint Identity Studio on Snowflake Marketplace.
