Stop your data lying to you: How to get your operational data to tell you what’s really going on
Comparing the performance of water utility field teams working in different environments is challenging. If you don’t normalise the operational data that the whole business relies on, you can present a distorted picture of your operations impacting workloads, efficiencies, and operational decisions.
Stakeholders want transparency and fairness in how performance data is used, so how can you ensure that your operational data is balanced and true?
Understanding the need for normalisation.
Water utility field teams handle a huge range of tasks under very different conditions, which can significantly impact their operational outputs. These include…
- Travel Time: The physical distance between job sites.
- Task Complexity: The nature of the tasks assigned, whether routine maintenance or emergency repairs, varies in complexity and effort.
- Infrastructure Discrepancies: The condition and type of infrastructure (iron vs new plastic) can influence the ease and duration of maintenance tasks.
- Urban and rural: Teams in densely populated urban areas may complete more jobs per day because of shorter travel distances and easier access. Conversely, rural teams might face longer travel times and more challenging conditions.
- Weather: Weather impacts different areas in very different ways.
To normalise performance across different teams requires a comprehensive understanding of these variables and a robust mechanism to manage out the impacts.
FME gives you a comprehensive view.
Delivering this normalised view involves assimilating large quantities of disparate and non-matching data, then transforming it in a consistent and robust fashion.
This is where FME comes in. FME is a global leader in transforming just this form of data, and has recently been recognised as such by Gartner.
FME lets users create workflows to manage their data through a simple drag-and-drop interface. This sophisticated platform excels at handling complex spatial data and can connect hundreds of applications, manage various data formats, and automate processes to save time.
Build a normalisation model with FME.
FME is perfect for building normalisation models for water utilities because it can…
- Merge Data: Water utility companies typically use various workforce management systems to track employee schedules, job assignments, completion times, and other operational metrics. FME can access and integrate these systems along with third-party data, like weather information, by using its extensive library of connectors and transformation tools.
- Apply Logic: By applying normalisation logic to this dataset and weighting job performance metrics based on key factors relevant to water utilities, FME helps create accurate and fair performance assessments.
- Automate Real-Time Processes: Automated workflows in FME can continuously update and normalise new data as it comes in, providing almost real-time results. This means the operational data always reflects the most current and relevant information.
- Give Geospatial Insights: FME is excellent at spatial data analysis, giving insights into the physical locations and relationships of water infrastructure.
By aligning geospatial data with operational metrics and external conditions, FME allows water utility companies to fully assess and normalise the performance of their field teams.
This approach ensures that performance evaluations are fair and accurate, leading to better management decisions and fairer compensation for field teams.