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Maximise return on your existing data investments

Data enrichment releases greater value from existing data investments, by combining the best parts of different datasets to create something new. You can also use third party and public data to augment your core information giving you a faster route to reliable, trustworthy data.

case study

United Utilities unites its data

“1Spatial have helped us to find innovative solutions to some of our long-standing business problems that have then been delivered quickly and efficiently.”

Paul Williams Data and BI Strategy Manager | United Utilities
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Is your data fit for purpose?

Keeping your data fit for purpose can be an ongoing challenge: business requirements evolve, data standards change and of course, data decays over time.

The Little Book of Spatial Data Quality

In our free Little Book of Spatial Data Quality, we look at how ensuring data quality is critical and how organisations are beginning to treat this as an ongoing process by deploying solutions that automate their data quality and data management procedures.

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Data Enrichment

A strategy of data enrichment ensures you make best use of your existing data assets to keep your core spatial data current and accurate.

Perhaps the customer addresses in your billing system are the most accurate in your business, but your asset database has the best view of your network. Taking an automated, rules-based approach, you can combine the best of both data-sets, even if the data exists in different formats, within different systems, in different data silos. And, you can then fill any gaps with data you purchased from a third-party provider.

Our solutions help reduce your “time to value”, helping you to achieve and maintain usable, trustworthy data with minimum time and cost.

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

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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...

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Data Quality

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

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Data Cleansing

Understanding the condition of your data is important, but fixing it is vital. Poor data quality can mean bad business decisions a...

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