Lines image

Clean and correct your data automatically

Understanding the condition of your data is important, but fixing it is vital. Poor data quality can mean bad business decisions and requires a time-consuming, expensive and often manual project to put it right. Our approach to this challenge is different. Our technology can report the current condition of your data but it can repair it, too. Even better, we can set up a process of continuous, automated improvement that gets your data clean, then keeps it clean.

Altogether, we offer a smarter way to smarter data.

Automate complex, time-consuming and previously manual processes

We work with you to establish the data quality level you require to be fit for purpose. We then help you develop user-defined and user-managed data quality rules that will find and fix quality issues.

Rules-Based Approach

Once created, rules will run against your data repairing all the common errors and flagging the difficult exceptions for manual correction.

The rules – held in a single central repository –then become an automated, repeatable process that can clean and correct newly acquired data to prevent bad data polluting your dataset. The quality levels can be recorded over time to provide important metrics to enable measurements for continuous improvement.

With a shorter time to usable data, and a faster route to user-trust, 1Spatial technology makes your data both cleaner and smarter.

Contact us

For help getting your data into shape and keeping it that way.

Contact Us

Find out more...

Data Validation

Our automated, rules-based approach validates data at the point of collection, in the field on a mobile device, or before it is ac...

Explore Data Validation Explore Data Validation

Data Enrichment

Data enrichment releases greater value from existing data investments, by combining the best parts of different datasets to create...

Explore Data Enrichment Explore Data Enrichment

Data Quality

Our approach is designed to help you discover and precisely define your data quality requirements, to check how your data conforms...

Explore Data Quality Explore Data Quality
//