Automate complex data management processes with advanced, user-managed, find-and-fix rules
The 1Spatial rules engine lies at the heart of our approach to spatial data management.
A rules-based approach ensures that processes are easily automated and repeatable, across the enterprise and across different technology platforms. By turning expert knowledge into user-managed rules, we also ensure that the best judgements are applied objectively and consistently.
Our technology is designed to manage complex, multivariable rules on large volumes of data. In fact, our technology manages some of the largest geospatial databases in the world.
Our rules-based approach saves time and money
Automating complex, time-consuming and previously manual processes dramatically reduces the time and cost of essential tasks such as data integration, data validation, data cleansing and data enrichment.
Our rules-based approach is enterprise-wide and technology-neutral
Because we use a single central repository for user-defined rules, the same rules can be applied right across your organisation. A single change can ripple instantly out to all users whether on server or desktop, browser or mobile device.
Using our technology you can, for example, integrate datasets from different organisations or departments into a single coherent database. Along the way, you can automatically re-format data, fix common errors and, of course, match the edges of mapping data from neighbouring authorities.
Data validation rules can run at the point of collection, on a surveyor’s mobile device, as well as on your central system.
Technology lock-in becomes a thing of the past. Some organisations find themselves tied to legacy GIS applications simply because the cost of re-writing bespoke code is prohibitive.
We believe in an open technology approach. With 1Spatial, rules are written by the user, managed within a central repository and easily applied to any GIS platform.
Our rules-based approach is objective and consistent
Expressing your experts’ knowledge as a set of user-managed rules means that expertise is always applied consistently and objectively, over and over again.
It is an approach that turns your best into your normal.
Consistency ensures that the same spatial features or scenarios are treated in the same way, building user-confidence in your data and supporting user safety.
Our rules-based approach is collaborative and quick
Rules are user-defined and user-managed. No programming or developer-coding. The rules are held in a central repository and edited using a simple user interface.
It is an explicitly collaborative approach. Anyone in your team can view and comment on the rules. You can easily agree any changes and quickly see the results. No coding, no programmer and no waiting for a gap in the developer’s schedule to have your changes made.
With an inherently faster feedback loop, your organisation becomes more agile and adaptive.
Our rules-based approach is a form of knowledge management
Encoding your experts’ knowledge, experience and skills as a set of central, user-managed rules means you always access the best.
It means that your experts’ time is freed up to focus on innovation and creating greater value while even complex tasks are executed automatically.
Effective knowledge management ensures you retain expertise, even as skilled individuals move on to new roles.
Our user-managed approach also makes sure that you own that knowledge, not a third party developer.
Other benefits of 1Spatial’s rules-based approach include:
- Easier management of the rules catalogue – Rules management is simplified because our system is grounded in an explicit set of identified, enumerated and named rules. These rules can be easily shared and discussed. Changes can be made instantly by the user.
- Designed for scale – our rules engine is designed to deal easily with large volumes of complex and disparate spatial and non-spatial data. In fact, it was originally designed for one of the world’s largest geospatial vector databases. Rules are executed efficiently on data loaded into an object-oriented disk-based cache which means that inter-feature rules can be rapidly performed across large numbers of features.
- Designed for complexity – our rules engine is designed to deal efficiently with complex data structures, queries and rules. For example, a data inference rule will involve rapid contextual checking of a number of nearby relevant features that would be beyond the performance limits of traditional relational databases or applications. We use an object-oriented architecture with a single spatial index that ensures performance even as data complexity grows.
- Designed to manage through certainty – our rules-engine uses simple, declarative rules that describe how your data should be. It will then find and, if specified, fix the exceptions. Traditional approaches require you to define every possible error instead. Not only is the positive, declarative approach faster to define and run, it is a more fail-safe approach. After all, you know what “good” is, you may not be able to define every example of “bad”.
For help getting your data into shape and keeping it that way, please contact us.