How Descriptive Analytics Should Be Utilized in Your Real Property Portfolio Management
Summary: Descriptive analytics allows organizations to identify patterns and trends with facility data. Together with diagnostic analytics, it elucidates the past to inform decisions that will affect the future of your facilities and assets. There are multiple aspects to the process, and each confers unique benefits.
Descriptive analytics is an excellent way to assess your facility’s performance metrics. It’s a straightforward approach that uses past performance to help key stakeholders better understand what happened so that they make better, more informed decisions for the future.
Before detailing the many ways that descriptive analytics can benefit your property portfolio management, it’s helpful to first review its function and its relationship with diagnostic analytics.
What Is Descriptive Analytics?
Descriptive analytics is a branch of analytics that focuses on summarizing and interpreting historical data to gain insights and understand patterns, trends, and relationships. Essentially, it involves analyzing data to identify past occurrences.
To illustrate this, consider an example: A facilities management department wants to analyze its facilities’ data from the past year. To accomplish this, it collects data on asset inventory, condition, and expected life. Through descriptive analytics, it discovers that assets have exceeded their expected useful life or are in poor condition. It also finds that many assets of the same type are in need of major repair or replacement.
Using these insights, the company can make informed decisions, such as prioritizing deferred maintenance to ensure its assets’ full expected useful life or choosing to replace the assets to avoid high costs or inefficient performance. Descriptive analytics provides valuable insights about what happened in the past and helps organizations understand their data in a meaningful way.
Descriptive vs. Diagnostic Analytics
While similar, descriptive analytics and diagnostic analytics are two distinct stages in the analytics process.
Descriptive analytics focuses on explaining past events, and it involves data aggregation, data visualization, and basic statistical analysis. Examples of descriptive analytics include generating reports, dashboards, and key performance indicators to present historical data meaningfully and concisely.
In contrast, diagnostic analytics goes beyond the surface-level understanding provided by descriptive analytics and aims to identify the reasons behind specific events or trends. It does this by examining historical data in more detail to uncover patterns, correlations, and causal relationships. It may involve root cause analysis, hypothesis testing, and data mining to explore potential causes and factors that contributed to certain outcomes.
In summary, descriptive analytics provides a high-level overview of historical data, while diagnostic analytics delves deeper into the data to explain the reasons behind specific outcomes. Descriptive analytics focuses on the “what,” while diagnostic analytics aims to uncover the “why.”
The Top 7 Benefits of Descriptive Analytics
So, how does descriptive analytics work in practice? There are several aspects to the process, and each confers its own unique benefits. Here’s a breakdown of the elements:
- Data visualizationDescriptive analytics often involves presenting data visually through charts, graphs, and dashboards. These visual representations can help identify patterns, trends, and anomalies in the performance of your facilities. Utilizing data visualization tools enables stakeholders to quickly grasp complex information, identify areas of concern or improvement, and make data-driven decisions.
- Performance monitoringBy examining historical data, you can identify performance trends over time, compare actual results against targets or benchmarks, and gain insights into the factors driving your facilities’ costs. This information can help you assess the effectiveness of strategies, identify areas of improvement, and make necessary adjustments to optimize performance.
- Asset sectioning and profilingUsing characteristics such as asset types, criticality, condition, and priorities, descriptive analytics can assist in sectioning your facilities and systems. By analyzing past data, you can identify assets that require the most funding, have the highest priority, or are in the worst condition. This sectionalization enables strategic capital planning and more effective resource allocation.
- Demand forecastingAnalyzing historical facilities data with descriptive analytics techniques allows you to identify patterns, cyclical trends, and other factors that influence capital requirements. By understanding past patterns, you can make informed predictions about future budgets, enabling better asset management, capital planning, and resource allocation.
- Root cause analysisDescriptive analytics can aid in identifying the underlying factors or root causes of specific outcomes or events. By analyzing historical data, you can uncover correlations, associations, and dependencies that contribute to certain business outcomes. This information can help you diagnose problems, understand the impact of different variables, and take appropriate actions to address issues or capitalize on opportunities.
- Risk assessmentYou can also use descriptive analytics to assess and mitigate risks in your business. By analyzing historical data, you can identify potential risk factors, patterns of poor facilities maintenance, and anomalies in financial or operational data. This information can support risk management strategies, governance processes, and regulatory compliance efforts.
- Performance benchmarkingDescriptive analytics enables companies to compare their performance against industry benchmarks or competitors. By analyzing historical data and utilizing external data sources, they can assess their relative performance, identify areas of strength or weakness, and benchmark against best practices. This information helps set realistic performance targets and guides strategic decision-making.
Overall, descriptive analytics empowers businesses to leverage their historical data for insights and informed decision-making. By employing data visualization, performance monitoring, asset segmentation, demand forecasting, root cause analysis, risk assessment, and performance benchmarking, businesses can gain a competitive advantage, optimize operations, and drive growth.
Written by Frank Quigley.
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