Making sense from chaos: Understand your operational data with FME and AI
When building operational strategies, you need to get as honest a view of your operational data as possible. This is challenging for water utilities as their operational data is particularly complex – with overwhelming volumes and formats streamed from multiple sources.
Traditional analysis tools can and often struggle with such complexity, enabling the data to mislead. So, how can you ensure your strategic decisions are supported by the right data not just the data that looks right?
By leveraging AI in conjunction with FME (a sophisticated data transformation platform), you can gain a comprehensive view of your operations. You can use advanced tools to bring this data back under control, back to being understandable and believable.
The Challenge of Data Complexity
Developing a data-backed strategy requires analysing data from numerous sources such as historical incident records, infrastructure status, weather forecasts, regulatory guidelines, and customer communications. This is especially challenging during emergency situations as the data gathered and processed is often:
- Disparate: Data comes from various, often unrelated sources, making it difficult to combine to get a whole system view.
- Non-continuous: Emergency data in particular is irregular and temporally scattered, making it hard to visualise trends or establish relationships.
- Chaotic: Data is inconsistent in format and structure, making it challenging to analyse using conventional methods.
High-volume, complex data makes it difficult to spot those important patterns and trends that can guide your decisions. So, what can you use to make sense of it?
Making Sense of Complex Data with AI
AI, particularly machine learning, excels in pattern matching within complex datasets, making it ideal for this task. AI addresses the three big challenges of operational data:
- Breadth of Time: AI can manage non-continuous, temporally scattered data to identify trends and patterns over time.
- Breadth of Input: Utilities can provide data from thousands of sources. With more data you can develop a more accurate and holistic understanding of important factors.
- Breadth of Factors: AI considers a wide range of factors simultaneously, from infrastructure health and weather conditions to regulatory requirements and demographic data. By evaluating multiple factors together, AI can identify complex relationships and interactions that traditional analysis might miss or may appear unconnected.
Leveraging FME for High-Quality AI Training Data
The effectiveness of AI models relies heavily on the quality and comprehensiveness of the data they are trained on. Therefore, you need a solution that excels at integrating data from diverse sources and can present it in a digestible way for AI pattern matching. This is where FME comes in.
FME allows anyone to create technical workflows to manage data through a simple drag-and-drop interface. Specialising in working with complicated spatial data, this enterprise tool can also be used to connect hundreds of applications, manage multiple formats, and create time-saving automations. It ensures that the information fed into AI models is comprehensive and of high quality.
Optimising Water Utility Strategy with AI and FME
With AI, utilities can base their decisions on a holistic view of all relevant factors. AI analysis can be used for a wide range of applications such as asset management, leak prevention and detection, and water quality management.
By leveraging AI in conjunction with FME for data integration, water utilities can optimise their operations, balancing cost and readiness while ensuring compliance and service quality.