Forecast Financial Modelling – Forecast

Case study

Data Visualisation

UK Police Service

These dashboards demonstrate the clear benefits from data-driven decisioning and showcased cost reduction opportunities and operational improvements for the client.

Client

Our client manages one of the UK’s largest fleets, comprised of a diverse array of vehicles. They have a fleet of over 5,000 vehicles, covering nearly 47 million miles per year.

Purpose

The client required a way to understand and visualise key aspects of their fleet, utilise their data to their advantage and find opportunities for impactful operational improvements.

Approach

To understand how to provide the best solution, Forecast initially mapped the client’s data landscape and sought to understand the operational aspects and the current data usage and analysis. Working with input from key stakeholders, Forecast built a comprehensive data model using the client’s multiple sources of data in PowerBI. This was done using a combination of Power BI native Power Query and integrated Python and R languages, matching the best tool for each data source. On top of the data model, relevant KPIs were derived and visuals were chosen to best convey the information.

Finally, Forecast grouped the figures and KPIs to tell a story and delivered pivotal PowerBI dashboards that were intuitive and interactive to appeal to a broad range of stakeholders. This involved adopting an iterative approach, including collecting input from the client, to design dashboards that work best for them. In addition, Forecast sourced external data to augment and further add value to the existing datasets and give a new perspective to the client.

Outcome

Forecast delivered bespoke, visually appealing and interactive Power BI dashboards focused on:

  • Fuel expenditure, usage and trend analysis
  • Collision cost and trends analysis

These dashboards have demonstrated the clear benefits from data-driven decisioning and showcased cost reduction opportunities and operational improvements that may not have been seen previously.

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