The importance of delivering continuous data improvement
25/03/2021 - 1Spatial
Driving data quality can be expensive and time-consuming, especially without a clearly determined goal. Too many initiatives are planned and run as one-off exercises. Many run aground by targeting the most difficult problems, rather than considering where the return will be largest. At 1Spatial they find that successful projects are run in accordance with six data excellence principles.
1Spatial follow methodologies for agile data consultancy which have evolved through their experiences of many similar, data-driven projects. These enable collaboration and provide feedback as quickly as possible while eliminating wasted time.
The key factors that underpin 1Spatial’s approach are:
- Establish and document the standard to which you need your data to conform, then perform an initial assessment to determine what needs to be done to achieve this standard.
- Once you have decided on your target, put in place processes that will validate the data to determine every non-conformance. Apply manual or, where appropriate, automated fixes to the data, then re-validate to confirm conformance and to allow adoption of the data.
The two-stage approach includes the following:
Data Quality Assessment
This is the process whereby the current data condition is assessed and the target data quality is defined.
- Data Discovery
Identify all of the participating datasets and sources and any implicit or explicit relationships between them.
- Agree Quality Mission
Determine the target data quality measures. This can include both spatial and non-spatial data constraints and any combination of these.
- Define Rule Metadata
This is how the Quality Mission metrics are formulated as logic rules that can be applied to the source data to determine its fitness-for-purpose. The rules are defined for objects in their context so that the relationships between features, both spatial and non-spatial, can be assessed.
- Baseline Assessment
The application of the defined rules to the source data using 1Validate producing a detailed non-conformance report with quantified levels of compliance across the various rules.
Data Quality Management
This is the process whereby data that is maintained in a master repository with ongoing operational updates and revisions is automatically validated, and if necessary repaired, before it is released as an authoritative product to stakeholders.
- Master Data Management
This is where the master data is maintained and updated using whatever business processes and technologies are best suited to an organisation’s needs. If 1Spatial Management Suite is utilised then the data quality process is automatically enabled as it is an integral element of the flowline. If an alternative data management solution is utilised then the our technology components 1Validate and 1Integrate are readily incorporated into the workflow to ensure that the required data quality standards are met.
- Check Conformance
The rule metadata defined in the Data Quality Assessment is applied to the complete master data holding, or just to data that has been tracked as changed, using 1Validate. Any non-conformances are recorded and made available for efficient automated or manual data correction or reconciliation.
- Perform Data Reconciliation
Any identified non-conformances need to be addressed through Data Reconciliation before the data can be approved for publication. The process of reconciliation can be fully automated using the rules-based facilities in 1Integrate or it can be done using manual editing processes. Where manual editing processes are utilised they are significantly enhanced through the use of the spatial non-conformance reports generated by 1Validate. These don’t just identify a feature failing a validation rule but identify precisely where on that feature the spatial non-conformance has occurred.
- Data Certification
Once data has passed all the required validation tests it is certified as being in compliance with the stated Quality Mission and is made available for publication to stakeholders who can now adopt this data with confidence.
- Data Publication
The data that has been certified as being in conformance with the agreed quality measures is now ready for publication. This process may be as simple as releasing it for distribution to stakeholders or it may entail automated processes where the approved changes to the data are propagated into specific products (vector or raster).
The 1Spatial Platform enables this comprehensive approach to delivering data quality. The software components in the 1Spatial Platform are available in the cloud, but they can also be utilised in a standalone mode as part of any data management workflow.
At 1Spatial, they build simple elegant solutions and have worked hard to productise innovative ways to remove waste in the process and use data smarter.
To find out more about 1Spatial and what they are able to offer, please visit their website.
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