How to Improve Data Quality: A Practical 5-Step Process

I have watched companies spend six figures on a new CRM or analytics platform, then feed it the same dirty records they had before. The tool was never the problem. A record with a misspelled name, a dead email, and a duplicate sitting next to it does not get better because you moved it somewhere nicer. Here is the practical process my teams run to clean business data, and the honest call on when to hand it off.

What bad data actually costs

Bad data does not send you an invoice, which is exactly why it goes unfixed for years. It leaks out the sides. A sales team works leads that bounce. A finance team pays the same vendor twice because the record exists under two names. A shipment goes to an address that has not been valid since 2019. None of these feel like a "data quality" failure in the moment. They feel like bad luck. They are not. They are the same root problem showing up in different departments.

The work activities that can be automated with AI sit around 30% (McKinsey, 2023), and data cleanup is squarely in that band for the rules-based part. The catch is that 66% of tasks still need human skill or a human and AI working together (McKinsey, 2023). Verifying that an address is real, that a company still trades, that two slightly different records are actually the same customer, that judgment does not come out of a formula. It comes from a person who knows what to look for.

The 5 steps to improve data quality

1. Profile the data

You cannot fix what you have not measured. Profiling is the diagnostic pass: run the dataset and count what is broken. How many records have a blank required field. How many phone numbers are the wrong length. How many states are spelled three different ways. This step gives you the size of the problem before you touch a single record, and it tells you which fields are worth the effort. Most teams skip it and start cleaning blind, which is how a two-day job turns into a two-week one.

2. Deduplicate the records

Duplicates are the most expensive error because they multiply everything downstream. The hard part is that real duplicates rarely match exactly. "Robert Smith" and "Bob Smith" at the same company are one person. "Acme Inc" and "Acme, Inc." are one account. Exact-match dedupe misses both. This is where fuzzy matching plus a human eye earns its place, because the software flags the candidates and a person confirms the merge. Get this wrong and you either keep the duplicates or merge two records that should have stayed separate.

3. Validate and verify

These are two different jobs and people collapse them into one. Validation checks that a value fits the rules: an email has an @, a postal code matches the country format, a date is a real date. That is automated and fast. Verification checks that the value is actually true: the email deliverable, the address real, the business still operating. Verification often needs a third-party lookup or a human check, and it is the step that separates clean-looking data from data you can act on. A field can pass validation and still be wrong.

4. Standardize the formats

Standardization is making the same fact read the same way everywhere. Pick one format for dates, one for phone numbers, one for state names, one for company suffixes, and force every record to it. This is unglamorous and it is what makes your data usable by a system. "California," "CA," and "Calif." are the same state to a human and three different values to a database. Standardize once at the field level and your reporting, your dedupe, and your integrations all stop fighting you.

5. Govern it going forward

The first four steps clean the backlog. This one keeps it clean. Validate new records at the point of entry so dirty data never gets in, assign someone to own each dataset, and run a scheduled check instead of waiting for the next fire. Cleaning is a project with an end date. Governance is a habit with no end date. Skip it and every cleanup you pay for is temporary.

Make it stick: ongoing governance

The reason this matters is simple math. A dataset degrades on its own. People change jobs, companies move, emails go dead, and new records come in dirty from forms and imports. If you clean once and walk away, you are not solving the problem, you are renting a solution that expires. The teams that get this right treat data quality the way they treat security: a standing process, not a one-time event. Validate at the source, verify on a schedule, and keep one person accountable for each dataset. That is the whole governance model, and it is cheaper than cleaning the same backlog twice.

When to outsource data cleaning and verification

Outsource the work when the volume is more than your team can clear without falling behind on the actual job, when a one-time cleanup keeps getting pushed, or when the verification needs more hands than you can spare. The rules are easy. The hours are not.

For two NSI projects we cleaned and verified Excel files in the 1,500 to 2,000 record range, deduplicating and checking each field against a trusted source so the client could trust the list. For Gold Wing we ran high-volume data extraction at a pace their in-house team could not match. In both cases the client kept the decisions and we took the labor. A dedicated data verification team handles the profiling, deduplication, validation, and the manual verification that automation cannot finish, under a 99.5% accuracy SLA with double-key checks on critical fields and ISO 27001 security. The data cleansing side handles the standardization and the merge work.

It is the same logic that applies across data entry and management: hand off the repeatable volume, keep the judgment in-house. A US in-house data clerk runs $45,000 to $55,000 a year loaded. Our teams start at $7 an hour, no setup fee, and deploy in 7 days.

FAQs

How do you improve the quality of data?

Profile the data to find the errors. Deduplicate the records. Validate and verify each field against a trusted source. Standardize the formats. Then govern it so it stays clean. The first four steps fix what you have. Governance keeps it fixed.

What are the five key attributes of data quality?

Accuracy means the value is correct, and completeness means no fields are missing. Consistency means the same fact reads the same everywhere. Validity means the format is one the system allows, and uniqueness means there are no duplicate records sitting next to each other. Almost every data problem you will ever hit is a failure of one of these five.

What is the difference between data validation and data verification?

Validation checks that a value fits the rules, like a ten-digit phone number or a real date. Verification checks that the value is actually true against a trusted source, like a deliverable email or a real address. Validation is automated. Verification often needs a human.

How do you deal with poor data quality?

Stop the bleeding at the source by validating new records as they arrive, then run a one-time cleanup on the backlog: profile it, dedupe it, verify the fields, and standardize the formats. Cleaning a moving target without fixing intake puts you back to dirty data in a month.

Sitting on a dataset nobody trusts? Get a custom quote. Dedicated data teams deploy in 7 days, 99.5% accuracy SLA, US-managed, ISO 27001 certified.