Too much of a good thing: Tackling data overload in DNO asset risk assessments.
Managing asset risk is a well-known challenge for Distribution Network Operators (DNOs). The key issue isn’t a lack of data – it’s the difficulty of managing the data they already have. Fortunately, there is a tool that most DNOs already have in place to manage this data effectively.
Why Is It So Difficult?
DNOs have access to vast quantities of data, but this data is not conveniently located in one place or in a ready-to-use format. Instead, it is dispersed, and its size, currency, presence, and quality vary enormously. Managing this data requires a highly flexible tool. For example…
Environmental Risk: Environmental conditions, like humidity, salt exposure, or extreme temperatures, shorten asset lifespans. Data sources include…
- MET Office: Predictive and historical weather data, invaluable but needs to be rebuffered to match to an asset list
- OS NGD: Hugely granular spatial data that’s intricate in nature and needs clear management
- Real-time sensors: Constant streams of raw IoT data, which need significant screening to prevent data volumes getting out of control
Community Risk: Community behaviours and cultural events drive demand spikes and dips. Data sources include…
- Network monitoring devices: Real-time consumption information, producing huge data volumes that require aggregating and threshold triggers
- UK Census: Rich demographic insights that, while valuable, are static and often outdated, requiring recasting to align with current contexts.
Behavioural Factors: Theft, vandalism, and anti-social activities risk asset reliability. Data sources include…
- Crime statistics: Useful for identifying high-risk areas but often riddled with gaps and inconsistencies, requiring reconstitution.
- Maintenance records: Often siloed in other systems, limiting access and cross-referencing. Probably with AI based tools for extracting the crucial contents of freeform notes.
Building a unified risk profile with FME
Fortunately, most DNOs already have access to a tool called FME as part of their mapping systems. FME, a Gartner-recognised leader in data integration, is specifically designed to address these challenges.
FME can integrate and transform complex datasets. It can merge real-time sensor feeds with spatial datasets, like NGD, to create a single, consistent environmental risk layer that can be fed into a BI platform. FME can combine static census data with dynamic monitoring device outputs, offering contemporaneous insights into how communities impact network demand. It can validate and clean crime statistics with internal maintenance records, creating a risk map of assets vulnerable to theft or vandalism.
With its ability to process data in hundreds of formats, FME bridges the gap between disparate sources, breaking down silos and providing a cohesive view of risk.
Use all the data available without being overloaded
The data already exists. Leveraging solutions like FME empowers DNOs to use all of the data available to them to develop accurate, data-driven risk profiles for their assets, ensuring better decision-making and improved operational reliability.