Data analysis is the cornerstone of strategic decision-making in virtually every industry today. In the property management sector, where understanding performance metrics can be the difference between growth and stagnation, the reliance on data is even more crucial. However, using a traditional property management system’s data structure for this purpose presents a unique set of challenges. These systems, designed primarily to handle legal information, often fall short when it comes to offering real estate business intelligence and analytical insights. As we'll see, what starts as a seemingly straightforward task can quickly become a complex and error-prone endeavor.

In the realm of business, we're often in search of performance insights that span various time frames. Sometimes, we might want a summary of a quarter's performance, while at other instances, we may wish to detect trends by examining specific periods.

In the property management industry, the primary data source for business data is usually the property management system. However, these systems essentially act as contract management platforms, storing legal information about which tenants are permitted to use specific building spaces for given time spans. Because of this focus, these systems lack the essential attributes required for effective data analysis.

Take, for example, a property management company interested in tracking the trend of rent income from their office spaces over a 36-month period. The initial step for many would be to access the property management system and export data, specifically contract objects that pertain to the selected time frame.

To visualize this data as a trend, it must be organized and plotted on a timeline. The steps involved include:

(1) Data cleaning by allocating rent figures specifically to the office areas under each contract object while removing unrelated areas like storage, common, and outdoor spaces.

(2) Calculating the number of days between the start and end dates for each contract object and distributing the total rent across those days.

(3) Eliminating any days from the dataset that do not correspond to the chosen 36-month period.

After completing these actions, a spreadsheet should emerge with 1,098 columns, each representing a day within the 36-month interval, and rows for each contract object. This layout enables the summation of daily rent, which can then be graphed for visual interpretation in the Excel sheet or a real estate dashboard.

But what if the analytical goal extends to metrics like "Monthly Square Meter Price per Rented SQM"? To compute this, we need to introduce two additional dimensions—'vacant' and 'occupied'—and add a new variable for square meters. Furthermore, vacant square meters must be removed from the dataset on the specific days they were unoccupied.

As you can now appreciate, even straightforward analyses can quickly become complicated, giving rise to inconsistencies. Imagine the variability if five different individuals were to undertake this task, each using their own Excel sheet. These individuals would likely pull data at different times and apply personal biases in data manipulation, especially when evaluating their own performance metrics. This would inevitably lead to conflicting conclusions and inconsistent decision-making, not to mention the substantial amount of time spent on manual efforts each time data for a specific period is needed.

As illustrated, the process of extracting useful insights from property management systems is far from straightforward. Even basic analyses can spiral into complex procedures fraught with the potential for inconsistencies and errors. This complexity not only requires significant manual effort but also poses a risk of generating conflicting results, particularly when multiple stakeholders are involved. Given these challenges, it becomes increasingly clear that specialized analytical tools designed to work in conjunction with property management systems are not just a luxury, but a necessity for reliable and consistent decision-making.