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SEP 2016

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SEPTEMBER 2016 59 W ikipedia defines analytics as "the W ikipedia defines analytics as "the ikipedia defines analytics as "the ikipedia defines analytics as "the discovery, interpretation, and discovery, interpretation, and communication of meaningful communication of meaningful patterns in data." In the broader sense, it involves studying historical data to assess potential trends, evaluate the effects of certain potential trends, evaluate the effects of certain decisions or to define the parameters of a decisions or to define the parameters of a certain event. With the exponential growth of data bom- With the exponential growth of data bom- barding corporate real estate (CRE), the use barding corporate real estate (CRE), the use of data is becoming pervasive. In everyday of data is becoming pervasive. In everyday of data is becoming pervasive. In everyday practice, the goal of analytics is to improve practice, the goal of analytics is to improve business decisions by enabling a deeper business decisions by enabling a deeper understanding of available data. To make analytics work effectively within To make analytics work effectively within To make analytics work effectively within the world of CRE, three critical principles the world of CRE, three critical principles must be understood: You cannot have analytics without solid foundational data, you analytics without solid foundational data, you analytics without solid foundational data, you should make any analysis as simple as pos- sible, and you should remember that analyt- sible, and you should remember that analyt- ics is a way of thinking, not a substitute for it. ics is a way of thinking, not a substitute for it. Principle #1: You can't have analytics without solid foundational data Optimized, validated and normalized data Optimized, validated and normalized data Optimized, validated and normalized data are required for trustworthy decision-making are required for trustworthy decision-making are required for trustworthy decision-making across any service spanning an enterprise across any service spanning an enterprise as diverse as CRE, whose critical nature as diverse as CRE, whose critical nature is substantial interaction between various is substantial interaction between various functional or operational activities. These functional or operational activities. These include space management, operations and maintenance, energy management, leasing maintenance, energy management, leasing and transaction management, and program/ and transaction management, and program/ project management. All these activities and project management. All these activities and project management. All these activities and others interact. In today's world, big data is a given. With the explosion of "intelligent" assets and the the explosion of "intelligent" assets and the the explosion of "intelligent" assets and the Internet of Things (IoT) playing an increas- ing role in modern facilities, real estate and ing role in modern facilities, real estate and facility managers are inundated by data sets facility managers are inundated by data sets 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 and value, with the reality easily both volume and value, with the reality easily both volume and value, with the reality easily seen as Googling "big data" returns 358 million matches. million matches. Too much data is fl ooding in to easily fi nd critical pieces needed to make reliable deci- critical pieces needed to make reliable deci- sions. As if this isn't problematic enough, vali- sions. As if this isn't problematic enough, vali- dating and testing all this data for reliability is dating and testing all this data for reliability is dating and testing all this data for reliability is dating and testing all this data for reliability is dating and testing all this data for reliability is almost impossible. So even if the right dataset is found, its usefulness is often suspect. is found, its usefulness is often suspect. is found, its usefulness is often suspect. Fortunately for CRE, a workable platform Fortunately for CRE, a workable platform does exist. The major IWMS (integrated does exist. The major IWMS (integrated does exist. The major IWMS (integrated workplace management systems) providers have recognized the need for both consolidat- have recognized the need for both consolidat- ing and validating critical CRE datasets. IWMS ing and validating critical CRE datasets. IWMS ing and validating critical CRE datasets. IWMS ing and validating critical CRE datasets. IWMS provides these advantages: 1. IWMS is a robust platform with all 1. IWMS is a robust platform with all 1. IWMS is a robust platform with all the built-in operational functionality the built-in operational functionality the built-in operational functionality the built-in operational functionality to support real estate transactions and to support real estate transactions and lease management; space management lease management; space management and strategic planning; operations and maintenance, plus condition assessments; capital project manage- ment; and energy/environmental data ment; and energy/environmental data management. 2. It is not necessary to determine who 2. It is not necessary to determine who owns which data in the model or to worry about why that data may not worry about why that data may not match data in other disparate systems. With IWMS, data are always normalized With IWMS, data are always normalized With IWMS, data are always normalized With IWMS, data are always normalized and available across the platform for reporting and analysis. 3. Because all data are centralized in 3. Because all data are centralized in 3. Because all data are centralized in IWMS, everything is already associated IWMS, everything is already associated IWMS, everything is already associated IWMS, everything is already associated to key assets and, with the top-tier solutions, the data has been processed through industry-leading business through industry-leading business processes so the data reliability is high. processes so the data reliability is high. processes so the data reliability is high. processes so the data reliability is high. 4. All the major IWMS systems are 4. All the major IWMS systems are 4. All the major IWMS systems are 4. All the major IWMS systems are designed to integrate with and share data from other corporate systems. This provides access to the rest of the dataset needed by CRE but not the dataset needed by CRE but not normally tracked in IWMS solutions normally tracked in IWMS solutions normally tracked in IWMS solutions (such as HR, fi nance, accounting and treasury data). treasury data). 5. With the leading IWMS vendors actively 5. With the leading IWMS vendors actively embracing integrations to the IoT world and the introduction of analytic world and the introduction of analytic engines to their products, the ability engines to their products, the ability engines to their products, the ability to access and evaluate a holistic set of facilities' data is now viable even of facilities' data is now viable even though it's still in the early stages of though it's still in the early stages of becoming truly robust. The advantage of a solid, readily available The advantage of a solid, readily available dataset makes an IWMS solution a logical dataset makes an IWMS solution a logical platform of foundational data. However, recognize that some cannot – or will not – recognize that some cannot – or will not – accept this simpler approach and keep these points in mind when assembling a CRE data points in mind when assembling a CRE data points in mind when assembling a CRE data repository: repository: • Make sure the data sources are complete and trusted. Starting an analytics effort and trusted. Starting an analytics effort and trusted. Starting an analytics effort without a solid data foundation is not without a solid data foundation is not only a waste of energy and money, only a waste of energy and money, but will lead to erroneous portfolio but will lead to erroneous portfolio but will lead to erroneous portfolio choices. Realistically, no data is actually choices. Realistically, no data is actually choices. Realistically, no data is actually choices. Realistically, no data is actually better than bad data. • When assembling a data repository • When assembling a data repository • When assembling a data repository based on a set of disparate data sources, based on a set of disparate data sources, follow the mantra, "The tools are follow the mantra, "The tools are cool but the processes rule." Often, cool but the processes rule." Often, CRE organizations get enamored with CRE organizations get enamored with "system" without fully understanding "system" without fully understanding "system" without fully understanding that technology is just an enabler. It that technology is just an enabler. It can enable a strong business model or it can speed up a poorly structured one. The bottom line is: know how the one. The bottom line is: know how the data are generated and evaluated while understanding the rules on which they are based. • Don't give away the ship. Many CRE organizations have justifi ably CRE organizations have justifi ably outsourced tactical services, often as outsourced tactical services, often as outsourced tactical services, often as a logical business decision. However, too often CRE leaders get lazy in their too often CRE leaders get lazy in their too often CRE leaders get lazy in their too often CRE leaders get lazy in their fi duciary responsibilities and assume fi duciary responsibilities and assume fi duciary responsibilities and assume that if a vendor offers to utilize its own that if a vendor offers to utilize its own that if a vendor offers to utilize its own technology that it is automatically better technology that it is automatically better technology that it is automatically better and/or cheaper. Neither is necessarily and/or cheaper. Neither is necessarily the reality. Giving away control of the the reality. Giving away control of the the reality. Giving away control of the data is rarely a prudent practice. Much of the oversight may be lost along with of the oversight may be lost along with access to the deep operational dataset. Most often facility managers only get Most often facility managers only get Most often facility managers only get Most often facility managers only get metrics and summary data the vendor metrics and summary data the vendor determines it wants to share. Although determines it wants to share. Although determines it wants to share. Although not an absolute catastrophe, this should not be the default practice, either. Principle #2: Make your analysis as simple as possible Analytics efforts often end up being Analytics efforts often end up being Analytics efforts often end up being Analytics efforts often end up being 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 because technology can in almost every case because technology can in almost every case because technology can also make things more complicated than also make things more complicated than necessary. This is a notion that goes as far necessary. This is a notion that goes as far back as to the 14th century, when William of back as to the 14th century, when William of back as to the 14th century, when William of back as to the 14th century, when William of Ockham, an English Franciscan friar and phi- Ockham, an English Franciscan friar and phi- Ockham, an English Franciscan friar and phi- losopher, promoted it until it became known losopher, promoted it until it became known as the principle of parsimony. Albert Einstein as the principle of parsimony. Albert Einstein

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