From reactive to proactive: how data intelligence is reshaping UK security operations

Security operations have a data problem, but it’s not what you think. You’re already collecting massive amounts of operational data through workforce management systems and ERP platforms – guard movements, client interactions, scheduling details, performance metrics. The problem is getting useful answers out of it.

Think about it like this. You don’t want to wait until quarter-end to know which contracts ran over budget. You want to know about what budgets ran over last week. Or, better yet, you want to know which contracts are in danger of going over budget before they do. If you’re using a sophisticated workforce and contract management software, then your systems already have all the data needed to answer these questions – the problem is aggregating the data in the ways that can get you the answers fast. Meanwhile, your teams are operating under the same onslaught of challenges: last minute absence management, contract gaps, employee churn and administrative overhead.

What putting your data to work looks like

Analytics platforms change how you use information you already have by addressing fundamental operational questions that drain management time and profitability. Consider the possibilities within scheduling alone, where advanced data analytics can make a tangible difference:

• Schedule stability: Instead of wondering why certain shifts consistently require last-minute coverage, you will identify patterns in unfilled positions within master schedule templates and track what percentage of adjustments happen within 24 hours of shift start. This visibility helps operations managers understand whether scheduling problems stem from template design or execution issues.
• Resource utilisation: Data analysis reveals how effectively you’re using different resource pools – subcontractors versus salaried employees, assigned teams versus flexible coverage. Historical patterns will show which sites consistently require overtime coverage and whether you’re deploying the right mix of personnel types for different contract requirements.
• Predictive possibilities: Analytics may identify employees most likely to drop shifts within 24 hours of start time based on historical patterns, letting managers proactively plan backup coverage. A simple query like “Who is at highest risk to not show up for their shift?” could return employee names with their percentage of last-minute cancellations, enabling targeted scheduling decisions.
• Reconciliation accuracy: Supervisors may quickly identify discrepancies between scheduled coverage and actual time records, ensuring accurate billing and payroll while catching potential compliance issues before they affect client relationships.

In these examples, real-time monitoring replaces monthly surprises through budget tracking that shows contract performance daily rather than at month-end, while alert systems flag problems immediately – missed checks, unusual activity, overtime costs climbing beyond targets.

A foundational step of making this future a reality for your business is ensuring you’re operating from a clean and curated data warehouse; one that supports a true single-source-of-truth database. This way, each team sees relevant, accurate information that can be spliced-and-diced based on their own unique needs, without dealing with overwhelming data clutter or digging through multiple systems.

The financial impact of organised data

These operational improvements translate directly into measurable financial benefits.

• When schedule stability increases and last-minute adjustments decrease, overtime costs drop while client satisfaction improves. Fewer emergency callouts mean lower premium pay rates and reduced administrative time spent on crisis management.
• Better resource utilisation delivers immediate savings. Understanding which sites consistently require overtime coverage allows for more accurate initial staffing, while optimising the mix between salaried employees and subcontractors reduces unnecessary labour costs. Accurate reconciliation between scheduled and actual time prevents billing discrepancies that can damage client relationships and delay payment cycles.
• Predictive analytics around shift coverage reduces the financial impact of no-shows. When managers can identify high-risk employees and plan backup coverage in advance, sites avoid expensive emergency staffing solutions or potential contract penalties for inadequate coverage.

The cumulative effect can markedly improve contract profitability.

Moving forward with data as a differentiator

Security operations generate enormous amounts of data through normal business activities. This information sits in workforce management systems, contract management platforms and scheduling databases across the industry. Now, security companies have the opportunity to leverage the data in ways never before possible.

Data Factory, TEAM Software by WorkWave’s Wavelytics data warehouse solution, is on the forefront of delivering this kind of unprecedented business intelligence.