1Spatial rules engine
The automated rules-based approach disrupting data management
Careful management is key to unlocking the value of data. With huge volumes flowing through today’s data-rich organisations however, governance is an ongoing challenge.
1Spatial’s pioneering rules-based approach uses automation to give you power over your data, putting you in control so that you can be confident it is current, complete, consistent and compliant.
With 1Spatial, the rules are written to match your requirements, by you - in collaboration with our experts or for you - and managed within a central rules catalogue, shared across the enterprise and easily applied to any GIS platform.
How does it work?
- The rules in our rules engine are used for two things:
- Validating the data to measure quality levels and identify the specific problems within it.
- Enhancing the data such as fixing problems, synchronising and integrating different sources of data, inferring missing data or transforming it to a more useful and valuable form.
- You create a rules catalogue (or adapt an existing one) to meet your specific data requirements and automate the data management process.
- These rules form the basis of a data quality management system for auditing, measuring and improving existing data as well as enabling a data delivery gateway for data flowing into your systems.
- In this way not only is existing data improved but incoming data can be rejected or rerouted if it does not meet the quality requirements defined by the rules.
- As a result, our rules-based approach provides automated yet flexible data management processes that can be shared and are repeatable across enterprises and different platforms, and always applied consistently and objectively.
By using our patented 1Integrate rules engine, you’ll join some of the largest geospatial databases in the world embracing automation to deliver well-structured, well-managed data.
What’s running our rules engine?
Our data management expertise takes inspiration from cutting-edge technologies such as artificial intelligence and robotic process automation whilst also overcoming their shortfalls. For example, we know that machine learning is adversely affected when your data is not clean, so we’ve built in post-machine learning cleansing, and we’ve designed a pre-process to ensure rectify lack of definition within automatic image detection.VIDEO: See how it works
Driving forward a ground-breaking safety project
“Having this new data in place will mean any planner or field worker can ask “How deep should I expect to find the pipe at this point and how certain are you of the answer?” As a result, NWG can continue to deliver and exceed excellent customer service levels, knowing their teams can work in a safer way while minimising disruption to the water supply.”Operational Solutions Manager, Northumbrian Water Group