Duplicate customers, addresses in four formats, a status field with eleven spellings of the same status, and a join key that stopped being unique in 2019. Nobody wants to buy data cleaning and every stalled AI project, failed migration and disputed dashboard traces back to it. We profile what is actually wrong, fix it at the source where we can, and leave you with the rules that stop it coming back.
Profiling, deduplication, standardization and validation for the data your analytics and AI depend on, with rules that keep it clean after we leave.
Data cleaning and preparation is the work of finding and fixing errors in a dataset before it is used: duplicates, missing values, inconsistent formats, broken keys and definition drift. It is the step that determines whether downstream analytics, migrations and AI models produce trustworthy results or confidently wrong ones.
Data cleaning and preparation finds and fixes duplicates, missing values, inconsistent formats and broken keys before data reaches a report or a model. Thinklytics profiles the real error rate first, remediates at the source where possible, and installs validation rules so the same problems do not return after the engagement ends.
Profiling first: a measured error rate by field and by record, so remediation is aimed rather than guessed.
Deduplication and entity matching, including the hard cases where the same customer exists four times under different identifiers.
Standardization of formats, codes and status values so a join actually joins.
Validation rules installed at the point of entry, so the dataset does not degrade again the month after we leave.
A one-time scrub. Cleaning without validation rules buys you a clean snapshot and a dirty dataset six months later.
A tool purchase. Data quality platforms help, but they do not decide what correct looks like.
Deleting inconvenient records. Remediation is reversible and evidenced, because an auditor may ask.
A substitute for fixing the source. Where the source system can be corrected, we correct it rather than patching downstream forever.
A data quality profile: measured completeness, uniqueness, validity and consistency by field, with the error rate stated as a number.
The remediation itself: deduplication, standardization, gap filling where it is defensible, and flagging where it is not.
A matching ruleset for entity resolution, tuned against a sample you sign off on rather than a default threshold.
Validation rules wired into the pipeline or the source system, with alerting when new records break them.
A before-and-after quality score, so the improvement is a measured figure rather than an assurance.
Policy data quality score in 16 weeks, unlocking $8.4M in AI underwriting projects that had been blocked on data.
Patient data quality score in 12 weeks, past the vendor's 80-point benchmark, enabling a $3.2M population health programme.
Federal grant funding recovered after data accuracy was fixed across 12 city departments and the re-audit passed.
The model is fine. The matching underneath it is wrong often enough that the output cannot be trusted, and that failure is invisible until someone checks record by record.
The same customer appears several times and nobody agrees how many customers you have.
No entity resolution. Records arrived from different systems with different identifiers and nothing ever matched them.
Duplicate or malformed master data. The target system enforces rules the source never did, so problems that were tolerable for years become blocking.
The clean-up happened downstream and no validation was installed at entry. Without rules at the source, cleaning is a treadmill.
Profiling to measure what is wrong, then deduplication, standardization of formats and codes, resolution of broken or non-unique keys, and handling of missing values. The final step, and the one most often skipped, is installing validation so the dataset stays clean.
We profile it. Completeness, uniqueness, validity and consistency are measured per field and reported as numbers, not impressions. That profile is what scope is built from, and occasionally it shows the data is better than feared.
The source, wherever it can be corrected. Cleaning only the downstream copy means doing it again every cycle. Where the source cannot be changed at all, we install rules at the boundary and say so explicitly.
Yes, and it is a common reason clients arrive. Models amplify whatever inconsistency exists in their training data, so the matching accuracy and label quality underneath a model usually matter more than the model architecture.
Only with explicit sign-off, and reversibly. Merging duplicates and archiving is preferred to deletion, because an auditor or a regulator may later ask what was there before.
Validation rules at the point of entry, plus monitoring that alerts when new records violate them. Without that, any cleaning engagement is a snapshot with a short shelf life.
Cleaning is worth buying when something downstream is blocked on it. If nothing is blocked, the profile alone may be enough.
An AI, migration or reporting project is stalled and the data underneath is the suspected cause.
The same entity exists multiple times and nobody can say how many you really have.
A trial conversion or load keeps failing on data the source system always tolerated.
The problem is that nobody owns the definitions: see Data Governance Consulting.
The same entity spans several systems and needs a permanent single record: see Master Data Management.
You need the pipelines and warehouse built, not just the data cleaned: see Data Foundation.
You are not yet sure the data is the problem: start with the Analytics Truth Audit.
We do not publish a rate, because the same record count can be an easy job or a hard one depending on how the errors are distributed.
Customer, product, vendor, finance and asset data each carry their own rules. Cleaning one domain is far cheaper than five.
A single systematic format problem across a million rows is quick. Ten thousand rows each wrong in a different way is not.
Matching on a shared identifier is mechanical. Matching on name, address and fuzzy attributes across systems needs tuning and human review of the edge cases.
Fixing at source is the durable answer and needs access, approvals and sometimes a change window. Patching downstream is faster and recurs forever.
A regulated dataset needs a reversible, evidenced remediation trail. That is real effort and it is what makes the result defensible.
Profiling comes first, so scope is measured against your actual error rate rather than an assumption about it.
Four phases. The last one is the one that makes the first three last.
Completeness, uniqueness, validity and consistency measured field by field. The output is a number you can take to a steering committee.
Agree what a valid record looks like, and who decides. Most cleaning projects that fail, fail here rather than in the tooling.
Deduplicate, standardize, resolve keys and handle gaps. Reversible and evidenced throughout.
Validation rules at the point of entry and monitoring that alerts on new violations, so the dataset does not degrade again.
Almost anyone can improve a dataset once. The difference shows up a year later.
Ownership and definitions, so correct stays defined after we leave.
A permanent single record where the same entity spans systems.
The entry point when you are not yet sure the data is the problem.