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Analytics: What does it mean to Corporate Real Estate
10/19/2017
Phil Wales's picture
Author:
Phil Wales

This blog is based upon the “Data Analytics: The focus for Corporate Real Estate” article published in the The Leader magazine.

Wikipedia defines analytics as “the discovery, interpretation, and communication of meaningful patterns in data.” In the broader sense, it involves studying historical data to assess potential trends, evaluate the effects of certain decisions or to define the parameters of a certain event. In everyday practice, the goal of analytics is to improve business decisions by enabling a deeper understanding of available data.

To make analytics work effectively within the world of Corporate Real Estate (CRE), three critical principals must be understood: management cannot have analytics without solid foundational data, they should make any analysis as simple as possible and always remember that analytics is a way of thinking, not a substitute for it.

Principal #1 – “You can’t have analytics without solid foundational data”

Optimized, validated and normalized data is a requirement for trustworthy decision-making across any service spanning an enterprise as diverse as CRE and in the CRE world, Big Data is a given. With the explosion of “intelligent” assets and the Internet of Things (IoT) playing an increasing role in modern facilities, Real Estate and Facility Managers are inundated by data sets so large or complex that traditional approaches to processing and reporting against them is no longer viable. The issue is both volume as well as value. The reality of this is easily seen – a search for Big Data on Google returned 358 million matches.

There are multiple ways to gather and consolidate data – far too many to cover in a blog. However, the easiest and most common approach is the IWMS (Integrated Workplace Management Systems) platform.  With IWMS the data is always normalized and available across the platform for reporting and analysis, and with an integration to corporate financial systems, you will have a holistic view the full dataset needed by CRE, such as HR, finance, accounting, and treasury data.

Principal #2 – “Make your analysis as simple as possible”

Analytics efforts often end up being hijacked by the assumption that with data, the more the better. However, this is incorrect in almost every case.  Remember, analytics is just a way to discern “meaningful patterns in data.” An almost inexhaustible array of patterns can be discerned within CRE data, but the operative word in the definition is “meaningful.” Too often organizations have developed extensive lists of metrics and performance indicators that add little or no value in the operations of the corporate portfolio.

Principal #3 – “Analytics is a way of thinking, not a substitute for it”

It is easy to get enamored with the tools but developing a quality analytics capability is hard. Definition of a problem and fundamentally solving the problem will drive the success of the effort. Too often analytics are deployed because they are clever or interesting to create. One way to mitigate this risk is to always look at data in the context of solving a particular problem. Also, don’t confuse association with causality.  Patterns are easy to find in large datasets but it is more difficult to determine what caused the pattern.

Conclusion

Although Big Data is not new, its exponential growth within CRE is. Today’s challenge involves getting a handle on a workable platform, taking the right steps in assembling a CRE data repository, isolating meaningful data patterns and understanding which opportunities that analytics is actually bringing to the table. 

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