Sound familiar? Every month, the dashboards appear on the management team's screens. They show the performance of the past period: maintenance costs, rental changes, rent collection. Finally, there is insight. Yet the conversation rarely revolves around the future, but almost always around the past or the present.

The discussion gets bogged down in questions such as "Why is this figure red?" and "Is this data correct?". This is a direct consequence of a deeper problem: employees still spend 80% of their time collecting and validating data and only 20% on actual analysis. The dashboard thus becomes a tool for confirming yesterday's gut feeling, rather than shaping tomorrow's strategic decision.

Reporting is a hygiene factor, but it is not a strategy. The real value of data only emerges when you shift your gaze from the rearview mirror to the road ahead.

From looking back to looking ahead: the business case

Imagine that the dynamics in the boardroom change. What if the discussion is not about last month's unexpected maintenance costs, but about the predicted maintenance requirements for the next quarter? What if you don't reactively flag up rent arrears, but can act proactively because you see risks coming?

This is the crucial step from reporting to predicting. It is the difference between reacting and governing. The business case is clear and straightforward:

• Predictive maintenance: Analyze data from your real estate to predict which components need maintenance and when. This leads to fewer ad hoc breakdowns, more efficient planning, and lower costs.
• Financial scenarios: Model the impact of external factors, such as a possible rent freeze or changing construction costs, on your investment scope. This allows you to make decisions based on data, not assumptions.
• Proactive service: Identify patterns that may indicate future rent arrears or social issues, enabling you to provide support earlier and more effectively.

The essential condition: a mature foundation

Many corporations aspire to make predictions. The pitfall is the assumption that this is purely a technological project. However, this predictive power is not a technological quick fix. It relies entirely on the quality of the data foundation.

According to the data value pyramid, you cannot sustainably reach the top (levels 4 and 5: predictive analytics, AI) if the foundation (levels 1, 2, and 3) is unstable. Before you can make predictions, three things need to be in place:

1. Excellent data quality: A prediction is worthless if it is based on incomplete or incorrect master data about your properties and tenants. The principle of "garbage in, garbage out" is an ironclad rule here.
2. Supported data governance: There must be clear agreements about definitions and ownership. If the Real Estate department and the Housing department use different definitions of 'rentable unit', any prediction is meaningless in advance. Ownership ensures the right choice is made because one responsible party makes decisions based on clear definitions and the common interest.
3. An integrated data platform: Data must be reliably and automatically collected from various source systems into a single central repository. As long as this is a manual process, you will remain stuck in reporting. Only when data flows are automatically and accurately collected can you truly progress to predictive analytics.

Start with the foundation, not the dream

The path to becoming a predictive organization does not begin with the purchase of a complex AI tool. It starts with an honest and critical look at the current state of your data foundation. The most valuable step you can take today is to recognize that "data-driven working" is not a technical project, but an organizational change.

It is the shift from a culture that asks "What happened?" to a culture that asks "What is going to happen, and what are we going to do about it?" That is where the real transformation lies. And it starts with the foundation.