The DataFrame Implementation Methodology has been developed through work with infrastructure organisations that are heavily resource constrained. The implementation process requires engagement with the organisation’s asset managers to identify information requirements, and the IT team to identify data sources and create data connections. Typically a DataFrame implementation requires less than 20 total hours of internal support.

You can be assured that the security of your organisation’s data and information is of the highest importance to DataFrame. We offer two implementation options. The first is on premise, where the DataFrame software environment is setup within your organisation’s IT environment. The second is cloud-based where the data DataFrame uses is managed within our secure cloud environment.

Asset data and information assets exist to enable infrastructure providers to deliver safe, reliable, efficient and high quality services to customers and communities. The correct starting point for planning data quality improvement is to understand precisely what data at what level of quality is required to support asset management decisions and actions.

This is in contrast to many data quality projects and software tools whose starting point is the data itself. A data-first approach can lead to resources being deployed inefficiently, and a focus on correcting data rather than addressing the root causes of data issues.

DataFrame has been created specifically for infrastructure asset managers and is designed to integrate with a modern asset management framework aligned with the ISO 55000 standards.

The DataFrame model is completely configurable. This includes the ability to fully reflect your asset management operating model and process landscape, determine the most critical data to be subject to data health monitoring, and define acceptability thresholds for data quality checks.

We recognise that this high level of configurability can make it difficult to get started. Fortunately, we have a range of preconfigured templates that can be selected from. Our DataFrame implementation experts will support you all the way through the implementation process to ensure your data health monitoring goals are fully realised.

Changing the configuration of your organisation’s operating model within DataFrame is a straightforward process, meaning that if you go through an asset management transformation or improvement process, we can help you ensure your DataFrame implementation continues to reflect your operating model.

If you are going through change in your information systems environment, it is likely that some of your data quality checks will need to be updated to ensure they are still current and pointed at the right data. In most cases this will not require extensive work and we can assist you all the way through the technical change management process.

Missing data is a good reason to start monitoring data health, as it means your processes are not fully being supported by data. We first need to determine the criticality of that data to your processes, and then understand the size of the issue so that appropriate steps can be taken to improve. DataFrame allows you to see how important one set of missing data is compared with any other data issue by modelling the impact on business processes.

Relevant, reliable, and accessible data and information are critical inputs into infrastructure decision-making. Infrastructure organisations are often trapped in a reactive loop of responding to issues in the equipment or facilities, which prevents the improvements that are required to prevent those issues reoccurring.

DataFrame provides the insights required to focus the organisation on the most important data and information issues, enabling asset management practitioners to work together to determine and resolve the root causes of those issues. This approach of continual improvement is the solution to exiting the reactive loop.

Contact us and we can assist you to make the case for improvement.

The DataFrame Implementation Methodology and solution architecture is highly flexible, and we can work with most information systems and data sources. Once we have worked with you to identify the critical information requirements and the sources of that data, we will take care of surfacing the insights in the DataFrame dashboard.