From Instinct to Insight: The Evolution of Data in Facilities Management

Good data is a fundamental part of making good decisions (and vital for those aspiring to increase their use of artificial intelligence). We have found organisations often evolve through various levels of data management maturity, progressing from an ad-hoc approach with a few organisations attaining highly strategic and adaptive models.

Increasing maturity of data management enhances decision-making, operational efficiency, and overall business performance. However, most organisations lack a clear data strategy across their FM service delivery. Even those that have a clear data strategy often have poor data that is not updated regularly and accurately. A common complaint at the recent Institute of Facilities Management AI Conference was the failure by nearly all organisations to maintain an accurate database of assets.

Landmark have been particularly involved shaping data strategy during the preparation phase for FM procurement as this is normally a cost effective time to significantly improve data maturity by improving:

● systems, data management and reporting

● data to manage supplier performance

● data processes to ensure data is more effectively and efficiently updated

Evolution of Data Maturity

Facilities Management is increasingly becoming driven by data, with the value and sophistication of data processes becoming strategically important. Landmark recognise the following typical levels of data maturity:

● Level 1: Unknown (Unstructured and Ad-hoc) Data

● Level 2: Defined (Basic Structure and Awareness)

● Level 3: Repeatable (Standardised and Consistent) often accompanied by digitisation

● Level 4: Strategic (Data-Driven Decision-Making)

● Level 5: Adaptive Stage (The Ultimate Data Maturity)

Level 1: Unknown (Unstructured and Ad-hoc) Data

Many facilities management teams operate without structured data collection. Decisions are predominantly based on intuition, previous experience, or reactive responses to issues as they arise. This approach limits operational efficiency, makes predictive maintenance nearly impossible, and can result in significant unplanned costs due to unforeseen failures or inefficiencies. It also means that informed strategic space and asset planning is impossible.

At this level, data management is often unstructured and random, with no clear procedures or frameworks in place. The scope of managed data is inconsistent, leading to minimal or no data analysis. Decisions and performance are independent of data insights, and any performance assessment occurs informally. As a result, outcomes are largely unpredictable, and data remains an underutilised asset.

Level 2: Defined (Basic Structure and Awareness)

The next level often involves data collection, often through checklists, paper-based logs, or manual input into spreadsheets. Facilities managers rely on this manually collected data to make informed operational and strategic decisions. For example, manually recorded energy usage or occupancy levels can inform scheduling and resource allocation. Although manual data collection improves decision-making over intuition alone, it remains labour-intensive, prone to errors, and typically delayed in providing actionable insights.

Examples of data collection in FM at this stage may include:

● Periodic inspections of HVAC systems logged on a PPM tracker.

● Occupancy surveys conducted periodically to optimise space utilisation.

As an organisation begins to recognise the importance of data, internal data management systems should emerge including procedures for handling data. Key metrics for comparative analysis are identified, enabling post-process performance evaluations. Reporting is introduced, albeit on an ad-hoc basis, laying the groundwork for a more structured approach.

Level 3: Repeatable (Standardised and Consistent) often accompanied by digitisation

With increasing awareness, data management should become standardised through clearly defined procedures. Internal benchmarking and comparative analysis highlight gaps that require attention. Organisations implement deep data analysis to improve operational insights, and performance measures (KPIs) become consistent. At this stage, data and results are systematically linked, allowing for more structured decision-making.

Digitising manual records into electronic formats is an intermediate step that enhances data accessibility and usability. This involves converting paper-based records into digital databases, enabling easier storage, retrieval, and analysis such as scanning and digitising historic maintenance logs to create a searchable database.

Level 4: Strategic (Data-Driven Decision-Making)

At this advanced level, the organisation proactively identifies and manages information limitations, ensuring high-quality data governance. Data analysis is used systematically to indicate organisational capabilities, and stretch targets drive performance improvement. The company moves toward a forward-looking approach, where results shape future performance strategies.

Advances in technology have enabled automated data collection, significantly enhancing decision-making capabilities. Sensors, IoT devices, swipe cards, and IT login data provide real-time insights into facility usage, environmental conditions, security, and equipment performance. This shift allows for proactive rather than reactive management, enabling rapid responses to changing conditions and efficient resource allocation.

Examples of automated data collection:

- Motion and occupancy sensors optimising lighting and heating systems.

- Swipe card systems providing data on space usage, team clusters and foot traffic.

- Sensors monitoring air quality and temperature for improved occupant comfort and energy management.

A more sophisticated approach to data management emerges, where both internal and external benchmarking enhance comparative insights. Compliance audits validate data accuracy, ensuring reliability in decision-making. Strategic planning is closely linked to data insights, enabling long-term change initiatives. Organisations start using forward planning to integrate data-driven decisions seamlessly into their operations.

Automated data often begins in isolated systems. Integrating these data streams into a centralised management platform provides a holistic view, allowing comprehensive analysis and more effective management decisions. Often this will include centralised dashboards combining occupancy data, energy use, and maintenance schedules. Real-time visualisation tools and dashboards help FM teams quickly interpret automated data, improving responsiveness and clarity for decision-making. For example interactive dashboards will show live updates of HVAC efficiency, occupancy heatmaps, and resource utilisation.

Level 5: Adaptive Stage (The Ultimate Data Maturity)

At the pinnacle of data maturity, information management is recognised as a core strategic capability. Performance feedback loops continuously, refining data management processes, whilst data accuracy is classified with risk and probability evaluations. Workforce empowerment ensures accountability for outcomes, fostering a culture of data-driven decision-making. Data is used flexibly to fine-tune performance, ultimately enabling rapid and informed decision-making at all levels.

The highest level of data-driven FM involves integrating automated data collection with machine learning (ML) algorithms. Machine learning uses large volumes of collected data to identify patterns, predict outcomes, and automate decision-making processes, leading to significant operational improvements, cost savings, and better strategic planning.

Examples of ML in FM:

- Predictive maintenance: ML algorithms analyse sensor data from critical equipment (such as lifts, chillers, and boilers) to predict potential failures before they occur, significantly reducing downtime and maintenance costs.

- Space utilisation forecasting: ML analyses historical occupancy data to predict future space requirements, facilitating more efficient space management and strategic facility planning.

- Energy management optimisation: Automated analytics systems using ML optimise HVAC and lighting systems, reducing energy consumption and enhancing sustainability.

Data Strategy

We recommend that organisations have a clear data strategy and, for many smaller uncomplicated organisations, just having a defined basic level of data management is sufficient. Typically, with any strategy, the main components are:

● Assess organisation need for the data

● Assess the current data position in your organisation

● Define the data Gap and Evaluate Options

The starting point is to identify what you want to achieve with your data strategy and the organisation's need for data. Typical FM goals may include one or more of the following:

● Demonstrable regulatory compliance

● Reducing operational costs

● Improving asset performance

● Enhancing sustainability efforts

● Increasing space utilisation efficiency

● Improving maintenance scheduling and reducing downtime

● Improve performance of supply chain

● Preparation for better procurement of FM services

Following establishment of the main drivers of data, it is important to understand what are your current data sources and data systems and how good is the data they provide? Typically, these may include:

● Business data such as:

o HR & Workforce Data - Employee headcount & locations, Work patterns (shifts, remote working, attendance), New hires & terminations

o Visitor data

o Finance Data

o Utility Bills & Sustainability Reports – Energy, water, and waste tracking

● Property and occupancy data such as:

o Lease data & property portfolio details

o Workplace Management Systems (IWMS) – Space utilisation, employee movement

o Meeting room bookings & desk reservations

● Asset data

o Building Management Systems (BMS) – HVAC, lighting, energy consumption

o IoT Sensors – Temperature, occupancy, air quality

o Computerised Maintenance Management Systems (CMMS) – Work orders, maintenance logs

o Enterprise Asset Management (EAM) – Asset lifecycles and depreciation

● Security Systems – Access logs, video surveillance

● Health & Safety - incident reports & compliance data, training records

To be able to develop the data strategy it is important to understand the data gap including how good the data is and does it provide the information you need to achieve your FM objectives. Once you are clear of the information you require it is important to understand how you will want to combine the data to provide intelligence and improve the FM service to better align with the organisation need

To properly evaluate the options available you will need to consider a number of variables when assessing which is the best strategy for your organisation including:

● Affordability of each option

● Data Ownership – Who owns and manages data?

● Access Control – Who can access what data?

● Data Quality Standards – Ensure accuracy, completeness, and consistency

● Compliance – Extent you need to follow GDPR, ISO 41001, and other relevant regulations

● Data Storage – Cloud vs. On-Premises Databases

● Data Integration – APIs, middleware for unifying data from multiple systems

● Analytics & Dashboards – BI tools like Power BI, Tableau

● AI & Predictive Maintenance – Machine learning for anomaly detection

● Data Governance and the policies you require for managing data

Increasingly, a data strategy and having effective data management is becoming fundamental to organisations. This is particularly important if you have the opportunity to plan these changes at a more cost effective time such as the re-procurement of your FM supply chain.

To learn more about how Landmark can assist you in this critical phase, please contact us info@landmark-and-associates.com for further information.