Big Data and Predictive Analytics

Big Data and Predictive Analytics:Beyond Big Data to Big Impact A Future of Work Roundtable Co-sponsored by Sodexo and IFMA Held at IFMA’s World Workplace 2015 October 7, 2015 Denver, Colorado

James Ware, PhD Executive Director The Future of Work…unlimited

1The information and concepts contained in this document are the proprietary property of Sodexo.

As such, they cannot be reproduced or utilized without permission. ©2015

FOREWARD

IFMA is proud to have co-sponsored the 2015 Future of Work executive roundtable on big data and predictive analytics held during World Workplace in Denver. All too often, elaborate building information management systems are integrated into built environments without the simple follow-through of engaging the facility manager.

It reminds me of the old adage of using a sports car to deliver furniture. While it is the fastest way to get it there, such a delivery vehicle lacks other pertinent, albeit less flashy, attributes (namely, space). This same analogy drives through in reference to big data in the built environment: although flashy dashboards and reports are easy to sell, they can be complicated and their true value not easily extracted without proper training and implementation.

In this white paper, Sodexo shares a case study with many lessons learned in deploying its building information management system in a productive and effective manner. Of particular interest to me was the focus on using social media and sharing data with facility management (FM) clients to facilitate internal campaigns to save water and power.

This approach is truly a testimony to the success of today’s mass collaboration business model, brought about through data transparency between the facility manager and his/her client. One of IFMA’s continuous priorities has been to work toward getting the facility manager a proverbial seat at the table, particularly when it comes to corporate strategy in relationship to the built environment and employee satisfaction contributions. This white paper includes an extensive discussion on the topic and shares some key points in how to drive to the finish line.

This roundtable could not have come at a more important time for the facility management community. Open dialogue about how one size does not fit all and the essential invisible nature of FM are important conversations articulated well in the following pages.

IFMA is delighted to have been a part of this meeting of the minds. The end result offers the facility manager a rare glimpse into the senior executives’ needs — a tool that can only help both parties communicate in the same language and yield better strategies for success.

Tony KeaneChief Executive OfficerInternational Facility Management Association



BIG DATA: AN IMPENDING REVOLUTION IN FACILITIES MANAGEMENTThe tools and techniques of Big Data – the capture, analysis, and application of massive amounts of data – have hit the FM world big time. More and more buildings are equipped with sensors of all kinds, producing large quantities of data not just about the buildings and their installed equipment, but also about how people are using the buildings and reacting to the surrounding environment.

By definition, however, data that has been captured provides a historical record. Whether it is about air quality, temperature, humidity, energy consumption, or even utilization, the information is about activities and conditions in the past.

Making decisions about facilities must – also by definition – be about impacting the future; how much energy will be required to heat or cool the facility tomorrow, next month, next year? How much space will the workforce require in the next three to five years? What will be the cost of necessary equipment maintenance and replacement over the next decade? Facilities professionals cannot change the past, but they have traditionally relied on historical data to predict the future.

Yet, in a world characterized by dynamic uncertainty and change, historical data may be increasingly misleading. How can we sort out which historical data is relevant and reliable, and what data must be discarded? How can FM convert a historical dashboard into a meaningful crystal ball?

To address these questions and to equip FM leaders for the future, IFMA, Sodexo, and The Future of Work…unlimited collaborated to host a Future of Work Executive Roundtable conversation at IFMA’s World Workplace 2015 conference in Denver on October 7, 2015.

The roundtable conversation was not intended to produce definitive resolution of these challenges but, rather, to sharpen the participants’ collective understanding of the issues and to alert them to several new skill requirements and management practices that

will become increasingly central to FM success over the next decade.

The Roundtable was facilitated by Dr. James Ware, PhD, Executive Director of The Future of Work…unlimited, Global Research Director for Occupiers Journal Limited, and immediate past president of IFMA’s Corporate Real Estate Council.

Dr. Ware also prepared this summary of the roundtable conversation.

The RoundtableThe roundtable participants were invited by IFMA, Sodexo, and The Future of Work…unlimited. As a group, they represented a broad range of industries, professional roles and geographic regions. They are senior FM practitioners, workplace designers, and FM service providers.

The roundtable conversation, which lasted approximately two hours, focused on exploring the value of big data in a world characterized by continual and often unpredictable change. How can FM data be collected, harvested, and applied to help reduce future FM costs as well as provide more productive workplaces for employees and contractors who inhabit the buildings FM provides for?

The following questions posed at the outset of the roundtable drove the subsequent conversation:

1. How relevant is historical data in a world characterized by dynamic uncertainty?

2. When is trend analysis helpful, and when can it become dangerous?

3. What are the critical building and behavioral metrics that FM should include in a facilities performance Dashboard?

4. How can a Dashboard be converted into a crystal ball?

5. How, if at all, should FM/CRE leaders include predictive analytics about facilities investments in executive conversations about corporate policy, business strategy, and FM leadership development?



BIG DATA IS NOTHING WITHOUT BIG JUDGMENT

Jim Ware set the stage for the conversation by reviewing a model of how most people think about the past, present and future.

The model, Figure One, is based on research conducted by Sadruddin Boga, and reported in the Systems Thinker journal1.

At any point in time, of course, we live in the present – today. As we reflect on what happened “yesterday,” by which we include all our yesterdays, or the past, we interpret our experiences based on what we knew and experienced in the past – but we also add in whatever we have learned since. In other words, our understanding of the past depends on everything we know and have learned recently (and we tend to weight the recent past more heavily than “ancient” history).

Then, as we anticipate the future, we make short-range forecasts assuming, usually, that the conditions of the present and recent past will continue relatively unchanged into the foreseeable future.

Our predictions tend to be biased by our most recent experiences; we expect the near future to be very similar to the immediate past. For example, we anticipate that tomorrow’s weather will be very similar to today’s, partly because of our understanding of the science of weather and partly because we have innate biases toward stability.

Human beings typically expect things either to stay the same in the primary characteristics that we pay attention to, or to change gradually. We believe in trends and relative stability. That is why sudden changes are so often surprising; if there is a big drop in the stock

“Prediction is very difficult, especially if it’s about the future.” - Niels Bohr

1. Sadruddin Boga, “Shifting Vision to Lead to an Emerging Future,” Systems Thinker, vol.17 #9, November 2006

Figure One: Why Prediction is Difficult



market, or a 20-degree rise in the outdoor temperature overnight, we are surprised and we take notice. We talk to our friends about surprises precisely because they are by definition unexpected.

One important perspective worth noting as we explore how to leverage Big Data and use what we know today to predict the future is that Big Data is really nothing without Big Judgment. Technology can help us collect, compile, and even analyze data by identifying trends, anomalies, and discontinuities, but it requires human judgment to discern (or create) the meaning in that data.

Formulas that extrapolate trends into the future are nothing more than mathematical equations; whether a trend line is realistic or not is a matter for human beings to decide. We – the decision-makers – must always choose to accept or reject the predictive data that the computers produce for us to consider. The major value of baseline and trend data is that it enables us to identify not only legitimate trends but also events that fall outside our expectations – those surprises that can break a budget if they are not adequately anticipated.

BIG DATA IS MORE THAN DASHBOARDS: IT IMPACTS DECISION-MAKING CAPABILITYSodexo presented a brief case study describing how the company has worked with the International Monetary Fund (IMF) to build an aggregated energy data dashboard or kiosk, that presents a consolidated picture of the energy consumption at their headquarters buildings. That “dashboard” provides “almost live” data to both, the IMF FM division and the Sodexo FM team. A sample page from the dashboard is depicted in Figure Two.

The IMF’s facilities team manages over three million square feet of facilities, (made up of primarily office space and conferencing/events space). The majority of the space is located at its headquarters in Washington, D.C. The team also manages a smaller international footprint spread across over 100 countries. The team has a goal of operating its facilities at a world-class level, so knowledge about the facilities performance – real estate costs, energy consumption and costs, environmental

Big Data and Predictive Analytics: From Big Data to Big Impact Page A World Workplace 2015 Executive Roundtable

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degree rise in the outdoor temperature overnight, we are surprised and we take notice. We talk to our friends about surprises precisely because they are by definition unexpected.

One important perspective worth noting as we explore how to leverage Big Data and use what we know today to predict the future is that Big Data is really nothing without Big Judgment. Technology can help us collect, compile, and even analyze data by identifying trends, anomalies, and discontinuities, but it requires human judgment to discern (or create) the meaning in that data.

Formulas that extrapolate trends into the future are nothing more than mathematical equations; whether a trend line is realistic or not is a matter for human beings to decide. We – the decision-makers – must always choose to accept or reject the predictive data that the computers produce for us to consider. The major value of baseline and trend data is that it enables us to identify not only legitimate trends but also events that fall outside our expectations – those surprises that can break a budget if they are not adequately anticipated.

Big Data is More Than Dashboards: It Impacts Decision-‐Making Capability Sodexo presented a brief case study describing how the company has worked with the International Monetary Fund (IMF) to build an aggregated energy data dashboard or kiosk, that presents a consolidated picture of the energy consumption at their headquarters buildings. That “dashboard” provides “almost live” data to both, the IMF FM division and the Sodexo FM team. A sample page from the dashboard is depicted in Figure Two.

Figure Two: Sustainability Kiosk at the IMF (showing total energy consumption for one given area)

The IMF’s Facilities team manages over 3 million square feet of facilities, (made up of primarily office space and conferencing/events space). The majority of the space is located at its headquarters in Washington D.C. The team also manages a smaller international footprint spread across over 100 countries. The team has a goal of operating its facilities at a world-class

Figure Two: Sustainability Kiosk at the IMF (showing total energy consumption for one given area)



conditions within offices, water consumption, waste management and recycling, and utilization rates – is of keen interest not just to the IMF’s FM group. Similarly, such data is also valued by the organization as part of its wider environmental sustainability initiative.

As a result of the IMF’s data warehouse and the information transparency the warehouse creates, there has been a heightened awareness of energy costs all across the organization. The IMF has achieved significant energy and cost reductions, as much through changed day-to-day behavior of IMF staff as through efficiency improvements in the basic HVAC and energy management systems.

Just as importantly, the IMF is focused on the workplace experience for their staff and visitors, especially through improving the internal environmental quality conditions.

But the availability of data at the IMF isn’t just about managing the existing facilities effectively. Facilities data is also at the heart of a focused energy-awareness, conservation, and, communication program. While the kiosk is accessible for inquiries, there is also positive outreach, sharing energy data via email, special events, and other communications campaigns throughout the organization.

One additional note: the IMF’s facilities dashboard presents information in simple language and using metrics that anyone can understand. Rather than reporting building efficiencies and trends in arcane or obtuse facilities jargon or econometric terms, the kiosk translates results into easy-to-understand measures that “ordinary” employees can understand and relate to. The kiosk also enables individual departments and floors in the buildings to engage in friendly competitions based on energy and water conservation.

All this has led to behavioral-adaptations by the workplace users as they become more conscious of and participative in the process – which in turn is leading to the sustaining of achieving and expanding targets. Of particular note, the wide-spread access and sharing of such data has led to various creative suggestions coming from those workplace users. In a sense, the FM Big Data and its implications are being crowd-sourced.

This is just another example of what author Rod Collins has called the transition of our economy away from mass production to a new business model based on mass collaboration2. Put another way, in this age of networked knowledge, no one individual is smarter than everyone working together. Big Data makes the biggest difference in organizational performance when it is made accessible to the wisdom of the crowd.

EXPLORING THE ISSUESThe first two questions the roundtable group addressed relate to the value of historic data.

If the world is changing rapidly and in unpredictable ways, historic data could be not only meaningless but also misleading. However, buildings are relatively stable entities, so baseline FM data may be generally less uncertain than, say, marketing, market research, or customer preference data.

2. See Rod Collins, Leadership in a Wiki World: Leveraging Collective Knowledge to Make the Leap to Extraordinary Performance, Dog Ear Press, Indianapolis, 2010.

How relevant is historical data in a world filled with dynamic uncertainty?

When is trend analysis helpful, and when does it become misleading?



But one roundtable participant pointed out that even when the world is changing in surprising and unexpected ways, baseline data remains important because it tells you where you are coming from.

Deep knowledge of the past provides a background and context for making decisions about the future, even when that future is dramatically different. If you see a “spike” in some key measurement, you know things may be abnormal only because you know what “normal” has been by virtue of that historical data.

Perhaps the most important aspect of data interpretation is to have a good sense of what is generally predictable, or stable, and what dimensions of performance are more likely to be dynamic and less predictable. You can then focus your time and attention on the components of performance, whether buildings or behaviors, that are unpredictable, because the more stable aspects will in some sense take care of themselves – or the decisions they require can be safely automated.

Historical data is key for the “business as usual” components of the facilities, leaving FM leaders to concentrate their analysis and decision-making on the more dynamic issues that are less certain and, perhaps, more critical to achieving innovative, breakthrough performance that can serve to differentiate an organization from its competitors.

It is valuable for any functional discipline, including FM, to develop a keen sense of peripheral vision – the ability to recognize critical trends or data points that are often hidden within all the “noise” that Big Data can produce. Leaders in any organization must stay focused, know what matters, and pay attention to anomalies more than to routine data.

But they must also invest some portion of their time and attention on issues that are on the “fringe” – new developments that are not currently central to today’s performance but that might at some point have a major impact on the business. Being able to spot early indications that the future might be very different from the past is a key capability for leaders in every functional area.

Being able to see what others haven’t yet seen, or understood, is a critical leadership skill. But it also requires sophisticated analytic capabilities. It’s a combination of powerful technology and powerful intuition. In fact, this is an area where it makes far more sense of speak of augmenting human intelligence, rather than automating it.

Automating, of course usually refers to replacing a human task with technology, whether it is heavy machinery, robotics, or artificial intelligence. Human judgment and intuition is a long way from being “automated” in the traditional sense of that concept.

On the other hand, one of the roundtable participants reported that IBM and Bell Labs have had some success attempting to identify and predict potential terrorist-like behaviors by compiling and analyzing massive amounts of behavioral data – including, for example, human and automobile movement in and around major landmark buildings, clothing styles, conversations, and other factors believed to be predictive of “shady” activity.

We may be closer to real-world examples of what the character played by actor Tom Cruise experienced in the Hollywood film Minority Report – in which people were arrested before they could commit criminal activity that the authorities in that science fiction story confidently believed they were about to engage in.

Another roundtable participant described how Rolls-Royce and General Electric are using real-time Big Data on the performance of their jet engines in commercial aircraft to anticipate maintenance requirements and prevent mid-flight engine failures. Similarly, Duke Energy is capturing real-time energy consumption data from its facilities and then using that information to make immediate adjustments in building temperatures and air quality and to optimize their buildings’ performance, well before any of the occupants raise any concerns.



WHAT ARE THE LESSONS AND THE IMPLICATIONS FROM THESE EXAMPLES FOR THE FM PROFESSION?As it has become easier and less expensive to capture extensive facilities data, we run the risk of being so focused on the massive amounts of data we can access and analyze that we sometimes forget to ask what our goals are. Data, for its own sake, can blind people to what matters, and what they are trying to accomplish.

Sociologist William Bruce Cameron, commenting on behavioral data in particular, observed that:

“It would be nice if all of the data that sociologists require could be enumerated because then we could run them through IBM machines and draw charts as the economists do. However, not everything that can be counted counts, and not everything that counts can be counted. [emphasis added]3”

And part of what makes facilities data “squishy” is the fact that the comfort of the building occupants is so often not only subjective, but it varies significantly from one individual to another. A complaint that the office is too cold, or too hot, must be taken seriously, but “cold” and “hot” are perceptions that are affected as much by individual metabolisms, and by a person’s expectations and state of mind, as by any absolute and “objective” measures of temperature.

FM professionals armed with Big Data must be careful not to rely too heavily on data, or to use it to prove a point, because they can be perceived as hard-hearted or overly analytic (as opposed to caring about results, or about the actual experiences of the occupants they are supporting). Ultimately FM is a service and support function, and the value that it provides is defined by end users’ perceptions and emotions, not by data alone.

As important an outcome as client satisfaction is, “satisfaction” is highly subjective and individualistic. At the risk of stereotyping, for example, software engineers most likely have a very different way of determining whether they are “satisfied” with a conference room or a cubicle workspace than graphic designers do. And those two groups – and others – no doubt differ widely in what they are willing to tolerate, as well as what they expect, in terms of air quality, temperature, lighting, interior design, and any other building characteristic we can identify.

Knowing when and how to use data, and to blend it with subjective judgment, is another critical skill for FM professionals. And it will only become more important as Big Data becomes more and more prominent in facility management practices.

THE PREDICTIVE POWER OF BLENDING BUILDING AND BEHAVIORAL DATAIt also important to blend building and behavioral data in order to anticipate needs and to prepare for surges and spikes in usage. For example, if the company is holding an all-staff meeting or entertaining several important customers on-site, there will be predictable changes in food and beverage requirements, use of the parking lot, and building utilization on particular days (which will also affect heating and cooling requirements, air flow, and even hours of operation requiring changes in security requirements, reception staffing, and so on).

These examples are obvious and familiar to experienced FM professionals, but they provide another impetus for ensuring that building and behavioral data be linked to provide higher-quality service.

The importance and value of trend analysis most likely varies from one industry to another. In some business sectors, like restaurants, demand for individual foods, for example, is reasonably stable and predictable. In other industries such as museums and sports, for example, factors like weather and special events clearly disrupt “normal” trends.

3. William Bruce Cameron, Informal Sociology: A Casual Introduction to Sociological Thinking, Random House, 1963.



COMPARING PROBABILITIES WITH POTENTIAL CONSEQUENCESAnother approach to predictive analytics classifies future events along two independent dimensions: (1) the likelihood they will occur, and (2) the impact they could have. Both dimensions are related to trend analysis, or at least can be estimated based on past experience. Our estimate of the likelihood that an event will occur is based on our experience with similar events in the past. For example, even though earthquakes are relatively rare and extremely difficult to predict, geologists understand enough about their causes to provide reasonable – if approximate – estimates that they will occur in a particular geographic region.

However, there is also the possibility of complete surprise – events that have no historical precedent. The most obvious example of an unexpected event (at least for most members of the general public today) was the attack on the World Trade Center on September 11, 2001. While we understand today that there were in fact informed predictions that terrorists were plotting to attack buildings in the United States, for most of us the events of that day were a complete surprise.

The second dimension that determines the importance of a future event is the event’s potential impact. Clearly, an earthquake, a tornado, a hurricane, or a major terrorist attack, though unlikely to occur, can have a major impact on those affected.

Why is this way of thinking about the future important? Because, like any two-dimensional analysis, it enables us to think differently about four kinds of prototypical events, as shown in Figure Three.

In quadrant one of Figure Three are those events that are generally predictable but have a relatively low impact, or minor consequences. Quadrant two includes events that are highly predictable and have a high impact. Quadrant three represents events that have a low probability of occurring, but when they do their impact is also low. You don’t have to worry very much about them.

Finally, in quadrant four are those low probability/high-impact occurrences like major storms or violent attacks. While they may occur only rarely, when they do the consequences can be severe so it is critical that you be able to respond.

This probability/impact diagram helps organizational leaders allocate resources appropriately and prepare for an uncertain future, based on the potential

1 23 4

Expect but Routine

Focus Attention

Here

Safe to

Ignore

Surprise! Be Ready to

Respond

Probability of Occurence

Impact

Low

High

Low High

Figure Three: Probability-Impact Matrix



consequences of various outcomes for the organization. For example, even if an earthquake or flood is deemed highly unlikely, it makes a great deal of sense for a financial services organization or an airline reservation system to invest in a backup data center; the impact of a systems outage would be so significant that it might put the company out of business.

On the other hand, a short-term power outage in a retail store or a restaurant might be inconvenient, but it would probably not be as disastrous as the closing of an airline’s national reservation system or call center. On the other hand, a strong earthquake could shut down a retail business for months; that is what business continuity insurance is all about.

This kind of hypothetical analysis clearly demonstrates the value of predictive analytics for the FM profession. And it also highlights the importance of understanding your own business, of having clear business goals, and of thinking through the potential consequences of various future possibilities.

GIVEN THESE FACTORS AND FRAMEWORKS, HOW SHOULD FM LEADERS IDENTIFY WHAT DATA TO COLLECT AND ANALYZE?There are many different kinds of data sources; as noted earlier, combining them in creative ways can lead to new insights and innovative planning for the future. For example, linking credit card transaction data with geo-location data, weather data, demographics, and general economic conditions, can produce deep understanding of shopping patterns and thus the changing value of real estate and retail properties in a particular town or shopping center.

Ask the Right QuestionsThe key is learning how to ask the right kinds of questions. In many situations the data that can

answer those questions already exists; it just requires acquiring the various databases, cross-linking them, and identifying the underlying correlations and other patterns. In other cases, a critical question might trigger an entirely new data collection initiative.

But knowing what data to collect and examine is only the beginning. The most effective way to leverage Big Data is to use it as a stimulus for additional questions – and to let the data lead you to new insights, new questions, and new data.

Leverage the Five “Whys”In that context it is worth remembering the “Five Whys” approach to getting to the root of a sticky problem. It’s a central technique in the Six Sigma quality management approach4.

Simply put, just keep asking “Why?” over and over again (at least five times) to work your way back from symptoms to causes and get closer and closer to the root cause(s) of the problem. This approach is generally attributed to Sakichi Toyoda, a Japanese industrialist and inventor, and the founder of Toyota Industries Company, Ltd. It is essentially a mechanism for persistent inquiry that continues until a particular challenge or problem has been deconstructed all the way to its basic underlying causes.

The most important aspect of the Five Whys approach (and any other analytic methodology, for that matter) is to be prepared to go where the data takes you. Be willing to “listen” to the data even when it doesn’t match your expectations or desires. As the late New York Senator Daniel Patrick Moynihan was fond of saying, “You are entitled to your own opinions, but not to your own facts.” Let the data speak.

But never forget that what ultimately matters is what data you choose to pay attention to. With all the data-capture capability that we have today, it is too easy to be overwhelmed by the “fire hose” of data coming at us. Once again, the critical skill is knowing which data matters, and what questions to ask of the data.

4. See http://www.isixsigma.com/new-to-six-sigma/getting-started/what-six-sigma/3.

http://www.isixsigma.com/new-to-six-sigma/getting-started/what-six-sigma/3



“One size misfits all.”And as the roundtable participants confirmed, the data that matters for one business, in one particular industry, may be unimportant, or even misleading, in another. The data that a hospital needs has some common characteristics with what a clothing store needs, but not very much. Operating a restaurant requires very different data from a museum or an airport – and so on.

However, across all those industries and businesses, the common goal is supporting or enhancing the customer experience – whether that customer is an internal employee, a consultant or contractor, or an actual revenue-producing customer. FM’s role in every case is to ensure that those who occupy or visit its buildings are comfortable, safe, and productive.

At its best, FM is Essentially InvisibleRealistically, when FM is doing its job effectively, it is essentially invisible. As every FM professional knows, the only time FM is in the spotlight is when something goes wrong – the air conditioning fails, the restrooms are dirty, there is a power outage. When everything is going the way it’s supposed to, no one notices (and they rarely comment either). The routine is what’s expected. That is simply the way the world works.

The promise of Big Data and its use in predictive analytics is that FM can move ahead of the curve by anticipating problems before they occur. Today HVAC equipment can not only schedule its own routine and preventive maintenance, it can even anticipate maintenance needs and call for help in advance of a critical part’s failure.

WHAT IS THE ROLE OF FM METRICS IN CORPORATE STRATEGIC PLANNING?Several roundtable participants commented that the key to making FM data a core part of corporate strategic planning is to position the data (and what it means) in terms of how FM initiatives will impact issues that are important to the business – issues like brand identity, workforce attraction, retention and daily work

behaviors, and, of course, organizational performance and productivity.

FM must learn to present the data in terms that are meaningful to business executives. We can’t presume that they will be able to link “raw” FM performance data to workforce capabilities or business effectiveness; FM leaders must articulate and demonstrate those linkages and translations if they expect to be part of the business strategy conversation.

WHAT ARE THE IMPLICATIONS OF BIG DATA AND PREDICTIVE ANALYTICS FOR THE SKILLS NEEDED BY FM PROFESSIONALS, AND FOR THEIR PROFESSIONAL DEVELOPMENT?Thus, FM professionals must learn to “frame” the data and FM reports in terms that the business understands. For example, in a company whose brand highlights sustainability and responsible environmental stewardship, keeping the building lighted 24 hours a day is not just a wasteful expenditure of money, it’s a direct disregard for the brand itself, and all that brand stands for.

Linking FM Performance to Business SuccessThere is a general sense that the conversation about FM’s contribution to organizational effectiveness has evolved from a single-minded focus on cost and cost reduction to a new level that recognizes the importance of the workplace environment to the business and to workforce attraction and retention. And FM clearly impacts the quality of the workplace environment every single day.

But FM doesn’t act alone; it is clear that the quality of the workplace environment is the result of a collaborative effort by FM, HR, technology, and other parallel infrastructure functions. To be fully effective, FM must operate in a highly interdependent and



richly interconnected mode. The skills of listening, framing issues, problem-solving, and collaborative communication are all just as central to the future of FM as are core data analytic capabilities.

The “Director of Work” – A New Leadership Role?To achieve this integrated vision a few leading-edge organizations are beginning to explore the concept of a “Director of Work” or a “Director of the Work Experience” – a role that incorporates all of these very different functional disciplines and blends them into a coordinated effort that measures its success by tracking outcomes like business performance, (revenue growth, profitability), employee engagement and satisfaction, staff attraction and retention, and both individual and team performance and professional growth.

One of the most difficult career development transitions that senior FM professionals face is moving beyond being the leader of their own team to becoming a member of the business leadership team. This is a challenge that senior executives face in every functional area – “graduating” from being at top their own functional silo to being part of a multi-disciplinary team.

That is a complex and often stressful transition. It means learning to listen to – and understand – other senior leaders whose training, language, and even decision-making styles are radically different from what the FM professional has mastered over the years. It also means part of a more senior leader’s team and contributing constructively to business decisions. That is a vastly different experience that calls for significantly different communication, negotiation, and persuasion skills.

From How to WhyUltimately, becoming an effective leader requires that FM professionals move beyond focusing on the technical Hows and How-tos to paying more attention to the Whys. Yes, how things work and how to get them done will always be important, but to know, and to communicate, why they must be done – and how they support business goals and outcomes – is what effective leadership is all about.

Big Data and predictive analytics are really nothing more than tools for supporting FM decision-making. They are new and important tools, but they are only tools. Mastering the tools and understanding their power is critical for the future of FM; but it is equally important not to let the tools become an end in themselves.

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