Your business applications and decisions depend on clean, consistent data. And, there are seemingly limitless ways for your data to wind up incomplete, invalid, inaccurate, messy, and inconsistent. Analight Group can help you ensure that your data is clean, accurate, and complete right now and forever in the future.
- Data Integrity Measures
In addition to analyzing and cleansing individual datasets, we leverage powerful algorithms and software to compare different sets of data against each other for completeness, consistency, and uniformity.
- Data Parsing
Data can be corrupt and useless if it does not conform to a given syntax. We apply the most modern and powerful technologies available to clean your data.
- Duplicate Removal
Part of data cleansing is removing duplicate data. We have simple tools and complex algorithms to fit your duplicate removal needs.
- Error Correction
No only will we help you find the errors in the data, but our talent team of intelligent data analyst can help correct many errors on their own.
- Data Type Constraints
Improve your ability to analyze your data by ensuring that particular data points match a particular data type (i.e. boolean, string, integer, decimal, date, etc.)
- Mandatory Constraints
We can help you ensure that required fields are not omitted from your data.
- Range Constraints
Our team can help ensure that particular data fields only contain data within acceptable bounds.
- Unique Constraints
Some data fields should be unique! We can help ensure that duplicate keys and duplicate values do not occur where they shouldn’t.
Text values are often required to conform to a specific pattern, e.g. phone number, SSN, or mailing address. We’ll help you ensure that your data conforms to your standards.
Data Integrity Measures
We can compare your data to trusted external sources to ensure that the data is accurate.
Above and beyond cleansing individual records of data we can ensure that entire datasets contain all required entries.
Our experienced analysts will evaluate your various datasets and ensure that data is uniform. For example, your data may contain different units of measures (pounds vs kilos, currencies, etc.). We can ensure that all your data is uniform.