Three Aspects of Spatial Data Management

Data management means more than simply handling updates. Here are three scenarios that we often see.

Data migration and transformation

Deploying new software systems or adopting new industry standards (like the European Community INSPIRE specification) can require significant changes to your geospatial data. Taking an automated, rules-based approach can dramatically reduce the time and cost of data migration or transformation.

Automation makes the process repeatable and provides an audit trail so that you always know what happened when.

Automated processes can also include intelligent manipulation, for example, to break single linear assets like roads or pipelines into separate records at every junction or join.

Data quality can be monitored and even actively improved as part of the same process, making your data smarter and fit for purpose.

Data enhancement

Incomplete data seriously hampers an organisation, yet it’s surprisingly common. Business needs change, regulatory requirements evolve but too often data doesn’t keep pace.

Sometimes, you simply inherit partial and fragmented data – a particular problem for the likes of water or public transport companies.

Organisations often feel that a full re-survey project is the only answer – revisiting their entire geography to re-evaluate the assets in question. That can be expensive and time-consuming.

We take a different approach. We leverage the data you do have, along with the knowledge of your experts, to determine what’s missing.

We work with your experts to distil the experience, knowledge and expertise in their heads into consistent, objective and repeatable rules. For example, “a house of this age, size and location will have a sewer pipe of this size and construction located to the rear of the property.”

This approach enables clients to infer what their missing data should be with a high degree of certainty. Results can then be confirmed, as required, by surveying a small sample rather than the entire estate.

The savings can be substantial, as Northumbrian Water found. The company’s Head of Wastewater Networks and Developer Services Mike Madine said: “Our original plan was to physically map just one third of the transferred network within our current, five-year asset-management period. That would have cost £10 million. Working with 1Spatial, we’re able to deliver a map of the whole transferred network in just two years, for £1.25 million.”

Data integration and conflation

Data integration, or conflation, takes the best of every data set and combines it into a consistent and reliable whole. It lets you re-use existing data investments by matching disparate data sets and taking the best of each – even when that data is in different formats, of different ages and in different systems.

Data from departmental silos, from large groups of partner organisations or from third parties can be extracted and combined by automatically applying user-defined and user-managed rules. For example, when the US Federal Highways Administration (FHWA) introduced its ARNOLD initiative, we helped Arizona Department of Transport integrate data from over 30 counties and other local government agencies.

Rules-based automation dramatically reduces the time and cost of integration. It turns data integration from a major, annual project into an easy, ongoing process ensuring your data is always as current as possible.

Automation can also validate the data or fix common errors during integration, speeding up the time it takes to trust your data and build organisational confidence.

Smarter data management

Overall, our enterprise-wide, cross-platform automation helps you manage your core data asset quickly and cost-effectively, helping to make your data smarter.

If you’ve missed earlier posts in the series, you can find them here:

  1. Are you leveraging the Location Opportunity?
  2. Journey to the centre of decision-making
  3. Spatial data: making management cost-effective

To learn more about managing spatial data, and to read some real-life examples from organisations like the US Census Bureau and Ordnance Survey Ireland, download our Little Book of Spatial Data Management, here.

 

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