Modernising Corporate Performance Management

Engaged to overhaul Corporate Performance Management (CPM) hindered by complex Excel models, Exent implemented a streamlined approach with a target-state architecture. The result: a modernised CPM technology, streamlined processes, improved decision-making, efficiency gains, and risk mitigation—all endorsed on the first pass.

The Client

Our client, a major Real Estate Investment Trust, was looking to move away from traditional Excel-based Budgeting and Forecasting modelling, which was untimely, complex and error-prone and move to a structured and modern Corporate Performance Management (CPM) platform to deliver greater efficiency, scalability, and accessibility.


A series of complex, custom-built Excel-based models were in place supporting planning and decision-making across all business domains, included Property Management (Valuations, Leases, Asset Management, Marketing, etc.), Investment Management (Asset returns, Feasibility, etc.), and Fund Management.

Due to constraints within the existing business tools and technology, data quality issues, and process inefficiencies, the business encountered difficulties in making prompt, well-informed decisions. Using unsupported systems, notably Excel, had heightened operational risks, as these systems had reached their limits in handling complex modelling.

Our client had shortlisted a planning platform and had engaged Exent to build out the path to modern, fit-for-purpose Corporate Performance Management. This path included developing a target-state conceptual architecture, building and quantifying associated benefits, establishing a comprehensive transition roadmap and consolidating this into a robust business case.


A pragmatic approach was essential to navigate challenges and strike a balance between achievability now vs a ‘gold’ target state. Both client and Exent agreed on avoiding the risks of lengthy and costly transitions, and over-complicating the near-term, while also building out deployment horizons that delivered the absolute optimum in cost and impact over time.

The existing models were complex with overlaps and interconnections facilitated by end-of-life legacy systems and Excel spreadsheets, often lacking supporting documentation. A substantial amount of data was necessary for modelling across various applications/tools. This data was predominantly managed manually, entailing individuals’ creation, input, extraction, and validation of datasets for their respective models, which were typically distinct for each asset and fund/portfolio. This approach to data management was neither scalable nor reliable.

Our structured approach commenced by establishing a clear comprehension of modelling requirements and data needs, subsequently translating these into a conceptual architecture aligned with business objectives and long-term technological implications:

  1. The first step was to visualise the design of models, their purpose, interactions, and data requirements, allowing us to articulate a more modular and simplified “Models ecosystem.” The model design’s direct implications on performance, ongoing costs (e.g., storage costs), and sustainability were duly considered.

  2. Next, we built five layered Business Architecture for Modelling and captured the business needs for each. This also involved defining the underlying data flows and detailed planning and forecasting process flows.

  3. Workshops and interviews were conducted with each model SMEs to gain a deep understanding of each business and functional requirement. Each requirement was mapped to the optimal future-state technology capability (new or existing), considering factors such as the required level of flexibility, the performance of the existing solution, cost-benefits, and alignment with the technology strategy.

  4. This mapping outlined how the required capabilities can be supported by the proposed Business Planning Software, integration with other systems, or the continued use of Excel.

  5. Getting data governance right was key to quality outputs. This focussed on common business terminology, data glossary, data quality verification, automating and streamlining data ingestion, managing access to sensitive data, and optimising data volume to minimise storage costs.

  6. Working with Systems Integration partner, we defined a conceptual architecture for the future state, which included integration with core systems like CRM and ERP.

  7. To ensure seamless operationalisation of the target state capabilities, we documented the necessary changes to the operating model and outlined high-level future state processes, including the roles responsible for developing and producing the model and associated change management processes.

  8. To optimise the licensing needs, we identified the users and their specific requirements, whether for model building, configuration, or reading, with direct cost implications.

  9. Based on the architecture, a detailed transition roadmap was developed to implement the requisite capabilities, following a top-down design approach and building from the bottom-up, commencing with the lowest data model to address most inefficiencies and data issues.


Exent delivered a comprehensive strategy, technology pathway and a detailed business case clearly articulating the need for change, the proposed solution, delivery schedule, costs, and anticipated benefits. This business case was endorsed and approved through all gates on its first pass.

Key outcomes delivered:

  • A clear and pragmatic roadmap to implement a modern, best practice CPM technology capability, considering long-term benefits, maintainability, and scalability. This solution will also reduce the number of systems used, decrease the model calculation time, and enhance the information granularity.
  • Greater control and higher quality of data, timely and accurate analytical capabilities, and enhanced reporting led to improved business decision-making (e.g., identification of underperforming assets and new project opportunities, improving the returns for the investors).
  • A comprehensive conceptual architecture delivering “Budgeting and Forecasting” capabilities to facilitate swift decision-making based on reliable data.
  • Increased efficiency through end-to-end automation, particularly in data management, which will reduce operational costs and alleviate employee frustration stemming from manual, repetitive tasks.
  • Operational alignment was achieved through a solution design that the business can effectively operate, marked by introducing standardised processes.
  • Key operational risks were effectively addressed, including security, compliance, and reliance on outdated legacy IT solutions.