Data Summarisation and Visualisation

Data Summarisation and Visualisation

Additional Insights and Missed Oversights

Today ‘data driven decisions’ is a term that is widely used. But what does it actually mean and can you have too much data? 

With the realisation of the AI discipline, organisations are recognising that there is a lot to gain from gathering different sources of information that can then be analysed. 

In short, data-driven decisions are decisions based on real data and not intuition. An example you might be familiar with is how organisations collect customer browsing information on their webpage to better design and execute their promotional activities. 

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While having this available data is seeing an increase in productivity, efficiency and return on investment across industries, there is also a risk in overloading on disaggregated data without being able to make sense of the outputs. Too much data and analysis may not have the same positive effect if the information is not presented in the right way.

Intelligent data management and visualisation is one way to extract insights from data. Presenting key performance indicators in a logical way and summarising information to the intended audience using data dashboards is something solution providers are making use of to better visualise information.

Visualisation in Wastewater

For the wastewater industry, there are many examples of this including treatment plant alarms, selective monitoring and customer feedback visualisations. When we talk more specifically about pipe inspections, the key summarizations and visualisations we see used are the structural and service grades, and GIS.

Pipe condition is  typically graded on a 1-5 scale. These grades are calculated by assigning each defect a score and identifying the worst stretch of pipe (peak score) or the average of the scores over the asset length (mean score). Utilities will typically take the worst of the peak and mean as the final grade both for the structural and service grade.

This is a very easy way to classify pipes at a glance for the purpose of identifying risk and prioritising repairs. For serviceability, the 1-5 grading is often adequate and directly correlates to answering the question “how blocked is the pipe”. However, this method can also be too general to understand the risk of failure for structural defects. A combination of both service and structural grade should be used to indicate which pipes require attention and decision making.

Considering utilities and councils may be maintaining large networks of wastewater pipes, getting relevant information to the right people is critical in having a rehabilitation plan executed successfully, and optimising the efficiency of their asset networks.

GIS application in wastewater

GIS can be a great tool to get an overview of pipe asset locations and surrounding infrastructure. It overlays different information and can start to point to root cause analysis for failures, but also indicate consequences of failure, raising the priority on assets in certain areas.

Multi-layered information presented in a single medium can help to understand context and consequence of failure. Consequence of failure helps to prioritize remediation on aging pipes.

Pipe length and location, locations of manholes, further details on assets (e.g. depth, material, diameter), and transport routes are some examples of information that can be explored in GIS.

This information is particularly important when considering location and pipe degradation with respect to surrounding infrastructure (e.g. main roads, proximity to trees, surrounding businesses). This can also start to point to reasons for certain defects and their rate of degradation when considered in context with the surrounding environment.

If monitoring a large area, the GIS interface may present too much information. Users need to have the ability to segment, group and filter results to be able to make sense of the visualisation.

Final thoughts

In the past 10 years we have seen data granularity become finer, this indicates industries are truly understanding the value of data. As a consequence, the impact of presenting the right amount of data and extrapolated insights to the right audience has become equally as important. 

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