Smart Building Insight-June 2024: Interview with Drew DePriest

Aamidor Consulting continues to provide insight to smart building innovators in this section. This month, we've included an interview with our good friend Drew DePriest, MCR.w, WELL AP , who is the Director of Real Estate Operations Technology at McKesson. Drew has been in the industry for about 20 years, on both the operator and vendor sides, and shares a number insightful points.

Q: You've had a long career in smart buildings and now work at McKesson. Can you give us a bit more background and tell us about your current role?

Drew: I’ve spent my entire professional career (20 years this June) in the facilities and workplace technology space. Controls was the first thing I found coming out of undergrad that paired my education (systems engineering) with my college side hustle (web UX development) – I started as a field tech in 2004, checking out Honeywell XL10s operating unit vents and VAVs, and never looked back. Over the last two decades, I’ve had the incredible fortune to work in large enterprises and startups, sales and operations, across the entire spectrum of real estate from digital workplace to facility technology.

In my current role, I lead a team of data analysts and systems administrators as we manage the entire stack of technology used by our internal real estate team. Our purview covers the entire cycle from lease administration and transactions through construction and facility operations. I find a tremendous amount of purpose in what we do, as the real estate we build and operate ultimately enable McKesson’s mission of delivering healthcare outcomes for all.

Q: As someone who has worked on the vendor side and now the buyer/user side of the equation, what do many vendors generally miss about what the owners, operators and users actually need out of technology?

Three things come to mind:

First, Requirements – the most successful deployments I’ve been a part of have always anchored in a robust, prioritized set of business and functional requirements. The mere existence of a requirements doc means the buyer has consulted with their internal end user base and put a significant amount of thought into the “what” and “why” of a technology need, the problems they’re trying to solve. Rushing into technology in some cases “for the sake of deploying technology” does not stand as much a chance of success.

Second, It’s OK to be a Point Solution – I see quite a few workplace and facilities tech providers focus on expanding their capability set horizontally. From my perspective, there’s not always a “one ring to rule them all” – I’d more often prefer to find the best unique solution for each use case within our stack and find the right points of integration amongst them. This also applies to source data – buyers need a way to view the entire history (not just snapshots) of their data within their own warehouse architecture. YMMV – certainly my experience does not match that of every end user.

Third, Buying process – the most successful projects in my experience have followed a buyer-pull model. RFPs and construction projects generally mean a buyer is ready to start the procurement process. They know their requirements, they likely have at least an idea of budget (CapEx or OpEx), and have done some research on market options.

Q: Data readiness to deploy smart building tech is a topic you've written about and discussed recently. Can you share some of your thoughts on the current state, and what needs to be done to enable buildings to adopt more advanced technology?

Data governance is a key area of enterprise IT and data engineering that the CRE/FM world needs to adopt quickly, especially if we have any realistic hope of pushing the kinds of multi-system, multi-variate model predictive control (MPC) and machine learning (ML)-enabled use cases that horizontal architecture (IDL, independent data layer) promises. Granted, for use cases and solutions that focus on a single system (HVAC) with a homogenous stack (one provider’s hardware and software, top-to-bottom) in what I refer to as the “Apple Model,” it’s less critical as you’re deploying a siloed toolset.

For broader-reaching use cases that require multiple systems of data – OT (HVAC, lighting, PACS, DR, power, etc), assets (CMMS, IWMS, enterprise IT, etc), and business (HR, finance, calendar, visitor, capital planning, etc), you absolutely must first lay a solid foundation of data health, literacy, cybersecurity controls, enterprise schema design, ontologies, and an end-to-end support model. The teams who do this well at scale will lean into either existing internal data engineering organizations or external vendors who take an enterprise-first approach to data warehousing and access.

Q: Another big topic for real estate is the carbon impact of the assets, and a pathway to decarbonize. This requires a lot of data inputs - especially to digitize things like audits, equipment analyses, and modeling out various scenarios. Do you think there are big opportunities here, and in which angles or approaches? Related, what barriers to adoption and deployment do you see?

Plenty of opportunities, yes, and I believe the full realization of digitization and automation of those audits and analyses are wholly dependent upon data governance. Especially for companies either bound to the new SEC ESG reporting rules and/or who have publicly committed to significant decarb goals, OT system data will play a huge role. Standing up building and portfolio-level models will require significant effort around consistent schemas, specifically with regard to primary keys (i.e. building identifiers, space identifiers, etc) – predictive models will unlock significant value for use cases like scenario planning, capital spend optimization, and more.

Barriers to deployment will likely mimic existing barriers of strict data compliance enforcement and architecture hardening for securing multi-system integrations, especially for those that hook into critical business system data.

Q: Related to portfolio decarbonization planning: One issue is that there are a range of existing solutions that already provide some of these data streams or analyses - do you find that the fragmentation of existing solutions is one problem in adoption of new, slightly different offerings (with different approaches)?

Not necessarily. It goes back to knowing and understanding the needs of your internal stakeholders. Variance amongst existing market solutions will give you the opportunity to compare and evaluate each one against a set of prioritized requirements to find whatever fits your organization best. And in the case where you don’t find an existing market solution to meet your needs specifically, rolling your own solution for high-level decarb reporting could be a relatively straightforward project – facility consumption data tends to be relatively static, consistently structured, and operates with a moderately low update frequency (monthly). As such, it’s a use case where a data engineering team could build a pipeline solution without expertise in the context of the underlying data.

Aamidor Consulting provides product and GTM expertise to RE tech leaders. We work with innovators who focus on decarbonizing and digitizing commercial buildings. Our typical clients are startups, capital allocators, and industry incumbents - elevating the product and GTM approach and helping to make educated investment decisions. In addition this leading newsletter, we have an unrivaled network of owners and operators and a deep bench of consulting partners. See a?summary of our past projects?and also a?description of our consulting services?for more details on how we can help.

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