Advanced Analytics – Go Where The Data Takes You

Strategic issues, like rapid business ramp-up, mergers and acquisitions (M&A) and digital platforms, impact a company's real estate strategies in profound ways. These issues are creating dramatic market shifts. They’re also changing our business processes. Real estate-related data help drive business decisions, large and small, potentially impacting everything from the long-term trajectory of business to workplace design to the lives of employees. However, exploiting the decision-making power of that data requires business leadership to recognize its full promise, not only as an instrument for short-term analysis and problem solving but as a forward-looking, predictive tool.

When positioning data to support strategic business decisions, it’s important to understand the difference between metrics and analytics. Metrics focus on the descriptive behavior of the data, giving us a picture of what’s happening now in a given real estate (RE) portfolio. Advanced analytics provide an understanding of why things have happened and what might happen - all to create a context for strategy development. Advanced analytics are designed around strategic issues and represent a culmination of data points mapped together to understand the risks, opportunities, and constraints that a portfolio could have on business strategy.

Unquestionably, general RE metrics have a critical role to play within the four walls of a company – after all, every organization should know metrics like space utilization (static, dynamic and/or sharing ratios) and lease management. This data provides insight into what's happening now and surfaces emerging issues in the RE portfolio. Additionally, many of these metrics are benchmarked against internal or external peer data where the gaps become the basis for analytical questions exploring why.

Advanced RE analytics help organizations plan for change by understanding the risks, opportunities, and constraints impacting their business strategy. Through data visualization, they structure dynamic views into qualitative and quantitative factors providing an objective source-of-truth to help decision-makers compare strategic, financial, and operational dynamics.

With advanced analytics focused mostly on strategic business issues, problems relating to portfolio agility, talent constraints, market optimization, capital efficiency, collaborative environments, and vacancy costs are generally uncovered. These types of analytics require dynamic data sets critical for identification.

The process of defining the need for business change, setting priorities to impact that need (in a positive way), developing the right set of questions, and collecting data points to answer those questions is grueling and complex but, ultimately, a necessary journey.
Natalie Maiolo

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