The Commercial Real Estate Industry Needs to do More in Leveraging Machine Learning

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Machine learning and other cognitive computing technologies remain the hot, disruptive solutions marketed and touted by every software company out there, as the amount of money coming into AI-related startups continues to outpace other segments. However, the resulting “hype” has created a lot of unfortunate noise about machine learning’s value, and some commercial real estate leaders are having trouble navigating the buzzwords as they try to understand what this really means for them. Despite the noise, the power that machine learning can bring to an organization is absolutely real, clear and demonstrable, augmenting how we work and personalizing our experiences as consumers and employees. Instead of using rules-based programs, machine learning does just what the name implies: it learns from data and history to provide insights and patterns that are not found with the normal business intelligence and other standard, legacy analytic programs. On top of that, some of the most impactful results are those that leverage data that is sourced and specific to a company, building, employee or region, thereby providing personalized results that are unique and actionable.

A report by Deloitte earlier this year revealed that early innovators leveraging machine learning, Natural Language Processing (NLP), computer vision and other cognitive technologies are already seeing business benefits. In the survey of 1,100 IT leaders, 55% said that their company’s adoption of AI has enabled them to increase their lead over competitors, with 9% stating that it actually enabled them to leapfrog ahead. That’s pretty compelling. So what about the commercial real estate industry? Is the industry taking advantage of the power that machine learning can bring? The answer is not yet.

This isn’t to say no one is leveraging any of the cognitive computing technologies—but its adoption has been limited at best. There are some wonderful machine learning solutions available today across all segments of the industry and I’ll name a few later, but first, why is the industry slow to adopt machine learning compared to other industries? Here are four big reasons:

  1. Data and knowledge intensive – The commercial real estate industry is fairly old compared to others, and it has historically been led by a relationship-driven, “run-on-my-gut” type of management, albeit with a relatively high success rate. That success rate feeds a legacy set of leaders that are skeptical of and resistant to change. Coupled with this, the industry truly runs on data but doesn’t have a great track record for compiling, housing, cleaning and leveraging that data. The legacy software applications were historically not open and did not support data integrations well, so consolidating the data took a lot of effort. This has been a major inhibitor in the industry moving as quickly as others in leveraging data and machine learning. That’s changing rapidly on the vendor side as new entrants provide more modern alternatives while pushing the legacy vendors, but there’s a lot of data already locked up in a myriad of systems across each company.
  2. Fragmented, with a myriad of legacy and modern applications – The industry has not attracted new software innovation in the past due to its heavy fragmentation (there are tens of thousands of owners, and even the biggest are relatively small compared to other industries). Though owning or investing in real estate sounds simple, the information needed to oversee and manage commercial real estate is fairly unique and broad (rent rolls, investor administration, complex lease terms, multiple regulatory agencies, tenant support, building operations, etc.) Therefore, it takes a collection of unique applications to meet end-to-end business needs, as there is no one application that truly meets all needs for every segment (commercial/multi-family, hotels, industrial, single family, geographical regions, etc.) On the positive side for machine learning, this fragmentation also adds to the volume of data that can be captured across the investment and operational lifecycle.
  3. Lack of attention to data quality – With all this data coming at every commercial real estate organization, very few have the data governance maturity needed to yield high quality data. A recent poll that I saw of fellow commercial real estate CIO’s showed that one of the biggest hesitations in moving forward with machine learning and similar technologies was a lack of confidence in their data quality. It’s not the only reason, but most CIO’s rightfully realize they need to have good data in order to achieve the best results.
  4. Resource constrained – On the earlier point that the industry is primarily made up of small- to medium-sized companies- this factor translates to organizations not having large employee bases, so they are inherently resource constrained. However, that is the catch-22 about machine learning, as it can make employees more efficient by automating the more mundane, operational tasks, freeing up more time to focus on customers and knowledge-based tasks. The employees will also be armed with new, data driven insights to carry out this work.

Ok, so now that we’ve reviewed why the industry has been slow to adopt, let’s focus on nine ways that machine learning can add business value to the commercial real estate industry, and some examples of companies that are already bringing value:

  1. Proactive and predictive insights on asset conditions and failures: One valuable use of machine learning that is getting traction and validation is the ability to more efficiently operate and manage a building’s physical assets and IoT devices. Specifically, the ability to convert the fault alerts produced by building equipment into actionable notifications, providing early warnings when important assets might be failing or need attention. There are a number of companies that filter through these warnings based on patterns and historical resolutions, while some are also looking more broadly to include other data points—such as work order history or manufacturer/device history—to identify and predict when an asset is in danger of failing. Fixing or replacing a piece of hardware proactively is much more cost-effective and incurs less employee impact, so predicting when an asset might fail can be beneficial. Some of the companies addressing these issues are BuildingIQ, Enertiv, and Switch Automation.
  2. Gaining proactive insights into tenant space needs, operational issues and other factors affecting NOI: Most of the “insights” available today are via operational applications or traditional business intelligence tools. That’s great for understanding what happened in the past, but these tools are not ideal for helping predict what might happen next. They are also not good at leveraging the disparate sets of data available in providing proactive property and tenant insights. By combining both internally sourced data (workorder, lease expiration’s and terms, parking, a/r, etc.) and external data (weather, tenant growth metrics, etc.), Okapi is one company actually using machine learning to provide insights in this manner for commercial and multi-family, while diffe.rent and home365 are a few examples in the single family space.
  3. Occupancy and space utilization, and the personalization of the workplace: Understanding an office’s space utilization patterns is one of the most impactful, but less optimized functions of a corporate real estate organization, with the biggest issue being the ability to easily understand how office spaces get used on a daily basis. As companies fight for talent with an increasingly agile workforce and with spaces that support activity-based work (places for concentrated work, phone calls, meetings, casual conversations, collaboration, etc.), this challenge has become ever more important to solve. Understanding and predicting usage is important to ensure that space is being used optimally in enabling employee productivity, while helping identify future opportunities to support growth. Machine learning can analyze a disparate set of data coming from sensors, room booking, badging, and other siloed sources and highlight usage patterns that might be unique to a specific building, region or department. A few companies playing in this area are Digital Spaces, Density, and Vergesense.
  4. Enhanced tenant and employee engagement: One of the biggest trends in commercial real estate is the explosion of employee and tenant-facing apps that aim to connect users to the services and communities that matter most. They support conference room booking, facility and work order requests, class registrations, ride hailing, cafe menus, and many other features that are increasingly expected by today’s employees. The more sophisticated apps leverage machine learning and historical data to suggest specific conference rooms, advise of non-bookable working spaces that might become open, or recommend class registrations or specific parking spaces, among other tasks. It may not sound like much, but any friction or key strokes you can remove from an employee’s day go a long way in their job satisfaction. Some examples of these technologies are CBRE’s HostWorkwell, and HqO, to name a few.
  5. Insights into property valuations and buying opportunities: The selling price for commercial real estate has many factors, so determining the best value for an asset, or highlighting underpriced assets, are great examples of where machine learning can add value. Most buyers use discounted cash flow and other financial models to help determine an asset’s current value, so the more accurate an assumption is on rent growth, occupancy, and market rents and demand, the better the valuation model will be. In 2018, there was more than $562 billion worth of commercial real estate transactions in the U.S. alone, and this large transaction volume offers a treasure trove of data and information. It’s a lot easier said than done, but companies like skyline and others are developing machine learning algorithms that offer investors and partners access to the sophisticated insights machine learning can offer.
  6. Computer vision: Computer vision is leveraging machine learning against images and videos for insights, and it’s an area that is early but one that will be very transformative for real estate over time. Computer vision is also used by robots that can navigate and monitor both indoor and outdoor spaces in various ways. There are many use cases in production today (I’ve lumped them together for simplicity) such as occupancy counts, identifying demographics of shoppers, security notifications on crowd gatherings, license plate and visitor blacklisting, employee building access, employee or tenant sentiment, and even early warning notifications to law enforcement when an active shooter first brings out a gun. Though there are real concerns about privacy when not used properly (a larger topic on AI ethics that needs its own summary), the technology is there and already in use. Companies like trueface, aegis, Knightscope, ambient ai, and Cobalt Robotics are just a few examples.
  7. Automating the lease abstraction process: Real Estate is one of the most document-intensive industries, so it makes sense to leverage machine learning to automate some of these unique processes. The lease abstraction process in particular, is manual with the non-standardized use of leases across the industry, along with the variation of terms and clauses found in every lease. By utilizing NLP, a form of machine and deep learning that analyzes words and context from history to take actions, the lease abstraction process can be augmented to improve efficiencies and lower expenses. Leverton was one of the early pioneers in the industry and were recently bought by MRI, while DealSum is another. However, most of the cloud platform players have advanced NLP capabilities, with Google being one of the leaders in this arena with their Document Sense product. We are very early in this segment, as labeling is complex and time consuming, but it is one that has high ROI opportunities for the larger firms with a high volume of leases.
  8. Automating the work request process: Most facility management applications can be cumbersome and time consuming to create a ticket, since many of the applications require multiple inputs (location, request type, urgency, description, etc.) to process and assign the ticket. To improve and simplify the user experience, NLP models can leverage the words used in historical requests to automate the process, requiring only a basic description of the problem. Machine learning programs can learn from the language used to describe a problem, taking words like “water leak,” “broken handle,” “coffee spill” and other words used in previous requests to assist and automate the creation and assignment of the ticket. Most work order systems today don’t yet have this capability built in, but I’ve personally been involved in the development of similar work efforts that leverage some wonderful machine learning platforms like Google’s GCP AI products and Microsoft’s Azure AI.
  9. Leveraging chatbots to interact with tenants or employees: This last example of machine learning is actually one that is the most pervasive and real use case across all industries today. Known more formally as “conversational AI,” chatbots and virtual assistants leverage machine learning and historical data to automate the most redundant, typical and time-consuming requests carried out by employees. You’ve likely come across a chatbot while visiting a website, or maybe you’ve “chatted” with “someone” via web support, when in fact it very well could have been a chatbot. The beauty of a chatbot is that it’s always on and waiting, and it can handle the first level interactions that cover the majority of requests. Developed properly, they can escalate the issue to a live person if a question isn’t being answered or upon request by a user. In commercial real estate, chatbots have been deployed to answer tenant questions and resolve facility issues, with just two examples being the Bengie app from Building Engines and CBRE’s host.

This is just a short list of some of the machine learning use cases and companies being leveraged today, but I hope it provides insight into what is possible and the business value that machine learning can provide. Over time, these and other capabilities will become more mainstream in the commercial real estate technology world. For now, it’s the early innovators that are ahead of the game and leading the pack.  Are you one of them?

How Leveraging the Cloud Can Enhance Your Security Risk Profile

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It’s a given that cloud adoption is growing heavily, but I continue to hear how security is a concern or roadblock for some. Yes, the ever increasing stories of cyber attacks have ensured security remains a top priority for CIO’s, as it should be, but I’m always amazed at how security in the cloud is looked upon as a major hurdle or obstacle.  Moving your apps to the cloud does take a new way of thinking about security but it shouldn’t hold you back.  Leveraging the cloud, particularly SaaS applications, actually enhances your security risk profile.

It’s a myth that using SaaS apps or putting information in the cloud is inherently less secure than keeping everything on premise.  The data needs to be secured no matter where it lives and keeping it on-prem does not make it more secure.  What is true is that it’s different, and as long as you understand the differences, your company’s risk profile is much better off.

One great reason to use the cloud is that you’re outsourcing the development and hosting of your applications, enabling you to focus on more core, business value activities.  This also includes security, if done right. Leveraging the cloud gives you access to a great pool of resources, whether it’s with your cloud vendor or in combination with other cloud vendors.  This is because the skills and resources available with most cloud vendors are much greater than what you can muster yourself.  It’s the lifeblood of their existence and the teams and time devoted to overseeing security is far greater than what most companies can do cost effectively on their own.  This doesn’t mean that cloud vendors can’t get hacked as well.  They can.  Even though most of the highly publicized security breaches have actually been to on-premise environments, cloud vendors have been hacked. There are no guarantees but the same goes with your on-premise environment too.

However, using the cloud doesn’t mean you ignore the security concerns and just leave it to the vendors.  To fully leverage the cloud for improved security, you need to understand what truly needs to be secured, understand your vendors’ policies and procedures, implement a few tools, and ensure your users are trained and aware of how they can help prevent security attacks.  This is not an exhaustive list by any means and I just touch on a few of them below, but these items will put you in a better position going forward.

Data Classification:  First, you need to understand what information you really need to protect.  Not every piece of data your company produces is sensitive or confidential, so classifying your data as to what is truly sensitive, private or regulatory impacted is step 1.  Understanding where this data then resides (likely more than one place) is then required so you know what to focus your extra efforts on.

Implement Two Factor Authentication (TFA) . TFA is one of the best tools available to ensure outsiders aren’t accessing your applications via insecure or stolen passwords. Many of the leading Identity Management tools have this capability and there are other stand alone options available too.  End users are much more accustomed to this with their banking or other apps, and it does raise the security strength of your apps.

Internal User Training – Internal users are the biggest hole in an enterprise and ensuring the end users know the security best practices is an easy and inexpensive tool.  More companies are instituting security training as a requirement for all employees.  This is even more important with a SaaS / browser based application environment.

Understand Your Vendor Practices  Just because you’re offloading your application development and hosting to your SaaS provider doesn’t mean you absolve yourself of any oversight or due diligence up front. You still need to understand your vendors capabilities and keep ongoing oversight of your SaaS vendors.

As a buyer of SaaS applications, here are items that you should understand and investigate. Again, It’s not a exhaustive list, nor will you find consistency with the approaches or capabilities, but you still need to familiarize yourself with the following:

  • Encryption (in transit and at rest)
  • Internal Controls
  • Backups / redundant data centers
  • How quickly are your vendors patching critical vulnerabilities?
  • How does the vendor QA its product?
  • How do they test DR/Contingency?
  • Do they have best practices with continuous delivery?
  • What are their change management practices?
  • How do they handle PII or sensitive data?
  • Employee profiling/security training and programs/phishing programs
  • What is their monitoring and notification process?
  • Do they have data centers in countries that require specific data residency requirements (if applicable)
  • SSO Support
  • Dedicated CISO
  • Automated testing
  • Peer reviews on coding

These are all capabilities that any software provider should ideally have, so the more you investigate and push, the better you’ll be for it in the end.

There are also many new approaches coming out of startups, from leveraging micro services to machine learning, so you should also pay attention to emerging technologies. Keeping abreast of the new and emerging companies should be a CIO core competency, but it’s even more important today with security and the cloud.  Putting your applications and data in the cloud provides a great deal of business value, in what can be a more secure environment.  You just to need to understand the differences.

Yes – IT is Still Relevant

consmerITWe had a great discussion recently on the topic “Is IT still relevant”, where I was joined with Tim CrawfordMark Thiele and Bob Egan on a Google Hangout and Twitter chat (#CIOitk), and a few themes came out that I think are worth highlighting:

The Consumerization of IT has changed our expectations of enterprise systems, and has raised the bar on what technology should be like in the
work place.  These expectations, the speed of change in today’s business environment, and the ability for the business to obtain cloud services themselves, has turned the IT organization upside down.

The challenge for today’s IT leader is to recognize this change and adjust accordingly.  Many CIO’s have already done this, but there are still quite a few who haven’t.  There is no “model” that CIO’s can just pick up and follow, but they can follow a few simple guidelines to improve their standing with the business and ensure relevance:

  • Speak the language of the business. You can’t focus on providing business value if you aren’t talking to the other business departments and executives in their language.   See my previous blog post on this subject.
  • Get out and understand the challenges your employees are facing. IT leaders must be outward looking, fully understanding the business challenges facing the organization from within and externally.
  • Embrace shadow IT. This  means embracing how the cloud is helping bring innovation into your organization faster (and better) than you can do it yourself.  There is a need for IT to be involved, but not everything has to go through a centralized IT department.
  • Focus on customer engagement. The customer is king and this is what drives the future of your business, so understand the customer needs.  Think ahead and ensure that the IT organization is doing things that can improve customer engagement.

Culture Matters

Another point that was brought up on Twitter was about culture and how that affects IT’s perception. This is a very important point and something that can’t be overlooked.  Culture really does matter.  Yes, technology has become a big part of everyone’s business but not all organizations have completely caught up to this thinking throughout the C-suite. Without a culture of valuing and leveraging technology, IT leaders face headwinds on change. Change is hard for many organizations and for those that are slow to adopt, they’ll likely be left behind.  Just ask Blockbuster.  So, all the hard work can easily be met by cynicism and doubt, but you can’t give up.

The IT Organization and the CIO of the Future

The future of the IT organization was also discussed, and a common theme was that staffing is an issue.  Cloud adoption, embracing shadow IT, and an agile mindset change the way IT organizations operate and think and the skills are different. I went through these in a presentation last year on the Future of the CIO, but the highlights are that IT leaders need to be:

  • Consultants to the business
  • Conductors vs builders
  • Entrepreneurial
  • Social
  • Evangelists for innovation and agility
  • A business enabler, focusing on what’s core and strategic to the business

Many thanks to Tim for moderating the session, Mark and Bob for their great insight, and Amy Hermes for her drive and unparalleled PhotoShop and marketing skills.  Keep an eye out for the next CIOitk (in the know) chat session.

The Next Gen CIO’s are Leading Today

I had an opportunity to speak about The Next Generation CIO at the Constellation Connected Enterprise conference a few weeks back, and the topic brought to light the theme of what really makes a CIO an effective business leader both today and in the future.  In my view, the skills required for the next generation CIO aren’t much different than what’s required today.  In line with what I presented previously on the subject, one must possess business savvy, leadership, relationship building, and social skills, have the ability to act as a consultant and integrator to the business, embrace the cloud and Shadow IT, and understand the power of data and mobile.  It’s also knowing that it’s all about the business and not the technology, a crucial skill for success.  All of the skills needed in the future are already present today in those CIO’s who are on the leading edge.  Therefore, if you’re currently embracing these trends and skills, then you’re already a Next Gen Leader.

A CIO, both today and in the future, needs to be a business leader, always focusing on how IT can be leveraged in growing and improving business capabilities.  This means the CIO needs to understand the business just as well as the other executives, while always speaking the language of the business.   That’s the Golden Rule I wrote about earlier this year, and if a CIO isn’t doing this when speaking with the other executives, then they’ll just be viewed as the “IT guy” and not a business leader.  Everything IT does needs to be focused on adding business value. It should not be about the technology, and that point is what has given the CIO a bad name in the past.  Truly understanding how technology can best be leveraged for business improvement is a requirement, but the CIO of the past didn’t always get that point.  Translating technology capabilities into new business and customer engagement opportunities is what sets the “Next Gen CIO” apart from the others.

The Next Gen CIO is a consultant to the business and an integrator, and should be embracing Shadow IT.  Embracing Shadow IT means you don’t require everything to come through a central IT funnel, but CIO’s and their teams can still add tremendous business value to these decisions with contract expertise, integration direction, security oversight, and vendor partnering among other things. This is where the consultant role also comes into play.  There is a great deal of innovation happening today that addresses specific business problems, and many times those in the business are the first to discover these new tools and approaches.  They have the most knowledge on value, so letting the business champion and drive discovery is a great approach that helps IT from having to say no. What does need to happen though is that IT needs to be included in the discussion, particularly on the points mentioned above.  Without it, the risk of having insecure applications, bad and expensive contracts, and data silos increases exponentially.

Lastly, a CIO needs to understand the power of data and mobile, and leverage the cloud as much as possible.   My team has been cloud all-in for many years, and the business benefits go way beyond pure costs.  The speed in which we gain access to new product functionality, while significantly reducing our in-house development staff has been transformational.  On the infrastructure side, we’re almost out of the data center business and are relying on the mass scale and capabilities of others to meet our needs.  Unless your company is in the hardware business, moving your infrastructure to the cloud, whether pure public or hosted private, is a requirement now and in the future. In addition to the cloud, a Next Gen CIO recognizes the demands, and capabilities of mobility and data. Using data to make critical business decisions is not a new concept by any means, but the availability of new data sources  in the digital and Internet of Things world, and the amount of unstructured data being consumed has made this more critical and complex.  When talking about transformation, digital business, and new business capabilities, leveraging data and the insights it brings is even more important.  Helping the business take advantage of this data trove is a capability that will make IT critical for business success.

New skills are definitely required in the future but I believe that future is already here for many CIO’s that already have these skills.  Are you one of them?

Cloud’s Biggest Benefit is Agility and Adding Business Value

Between the recent IT conferences and some interesting twitter chats, I continue to hear discussions on the top cloud benefits.  Cost savings in particular has come up a few times.  But, is cost savings really a top cloud benefit?

Using the cloud is really about agility and adding business value. It allows IT organizations to focus their attention on doing things that help grow revenue, increase customer engagement, and open up new product channels. IT can spend less time managing commodity infrastructure and maintaining in-house developed code for non-core programs. They can leverage other resources in monitoring and security; resources that many SMB firms just don’t have access to.  That’s critical.

Getting to specifics, my top benefits that come from using the cloud are as follows:

  • Agility – Being nimble and agile should be the mantra of all IT organizations today.  Getting away from long and drawn out development efforts and implementations is expected today and critical for businesses who are fighting to develop market share and grow their company.  Being able to quickly respond to changing business needs is a must  and that’s what the cloud provides.  Firing up new infrastructure in minutes or securing a new, focused SaaS app are fantastic business enablers and exactly what every forward-looking CIO should be focused on.
  • Scalability – Acquisitions, mergers and high business growth trajectories are forcing IT organizations to quickly grow their capabilities and reach.  Typically, you don’t have a lot of time when a merger, acquisition, or some other critical event is upon you, so setting up a quality model for quickly scaling is essential.  Even with some time, the effort involved to scale adequately is time and resource intensive.  Again, this is exactly what the cloud offers.  Additional infrastructure is the obvious and easy scenario, but cloud apps that support business services are just as important.
  • Time to market – For companies bringing out new products or rushing to gain market share, IT has unique challenges in responding.  The cloud is made for this with the ability to quickly deploy new software, configurations or the infrastructure required to be first to market.  In industries that depend on this for their survival, the cloud is a business priority.   How long would it take to develop a new application for a new product line if you weren’t leveraging the cloud in some manner?  Platform as a Service products are great for this.
  • Access to broad and deep skill sets – Particularly for SMB’s, the cloud provides unheard of access to a trove of smart and focused people who have skills that are hard and expensive to source and access on your own.  I like to use security as a good example of this benefit.  Many say the cloud is less secure than on-prem infrastructure, but I argue the opposite.  Just because you wrote it or have it in your own data-center doesn’t mean you’re doing a better job than a cloud vendor.  While it’s sure not a guarantee that a cloud company will do a better job, they typically have a much larger staff with a better focus on security than you do.  Their business depends on it and they have the resources to quickly respond to ever-changing threats.  What’s required for a CIO is to understand these differences, do the right due diligence on a new cloud vendor, and maintain an ongoing relationship with the vendor to ensure you know how they’re managing security.  It’s not something you look at once and forget, but managing vendors becomes a critical competency.  It’s still easier and more efficient than managing and finding (and keeping) a team of developers and ops guys who really know security.
  • Access to quality, pre-developed software – Developing software programs that address very little core, company specific business processes are a big mis-management of internal resources.  There are an amazing number of high quality applications that are already developed that address most of your business needs, and the number and quality is growing daily.  This isn’t just for commodity applications like email, but there are a lot of industry specific SaaS vendors that provide applications that no IT organization can match.  The platforms available are worth it alone.   A cloud product is also constantly growing with critical features and they’re more in-tune with new software designs and usability trends than you can be.  As cloud vendors continuously update their products, you’re immediately getting access to these new features and capabilities.
  • Speed of upgrades – This is one of my personal favorites.  It’s not always seamless for some of the less mature or new SaaS vendors, but the speed of upgrades and the reduced requirements on internal organizational resources is transformational in my mind.  I have seen plenty of organizations spend a countless amount of time and energy in analyzing, testing, and deploying upgrades to large on-prem applications.  The effort spent on these upgrades are a tremendous drain and they take the focus away from helping grow revenue or providing top notch customer service.

Notice that “cost” is not on my above list?  I’m not saying that long term, the cloud can’t be cheaper, or that it enables you to spend money in a different and more efficient manner (Operational vs. Capital), but those benefits don’t make my top 6.  In fact, the cloud can be more expensive on a pure license perspective in the long run, but there is a lot more to this equation than licenses.  Reductions in your ongoing IT resource needs and the savings I mentioned earlier on organizational resources, all go to the bottom line and are savings over time.  I just don’t focus on that as agility and business value is what I’m concentrating on.