Operational Technology (OT) Data
By Marc Petock Vice President, Chief Marketing & Communications Officer Lynxspring
Data has been a cornerstone of business since the early days of computing in the 1960s. Changes brought different tools to collect, store, process, and manage data. OT (Operational Technology) is one of them in the built environment.
Rapidly accelerating advances in automating OT data and its recognized value are changing what it means to be “data-driven” in the built environment.
OT data holds immense value in optimizing operations, combating rising energy costs, and contributing to a sustainable environment. Data plays an increasingly critical role as we ask our buildings to do more.
Today’s buildings and facilities, along with the systems, devices, sensors, and equipment that run them, generate a huge amount of data that can be used to derive useful information that drives business outcomes and operational value. Most buildings we see were built at a time when technology was simple and unsophisticated—minimal connectivity, limited integration, and “data… what data?”
Fast-forward to now, and many connectivity options, such as building networks, smart devices, and digital transformation, deliver data to a new class of gathering and analytic platforms. This is enabling us to leverage information like we have not been able to before and operate and manage our facilities at new levels.
Beware of Data Fog
“Data fog” is a term used to describe a situation where an overwhelming amount of data is available. Still, it’s not organized or interpreted in a way that makes it beneficial or actionable. It’s akin to the idea of being lost in a fog, where visibility is severely limited and challenging to navigate. In the context of data, this means that despite having access to vast amounts of information, decision-makers struggle to make sense of it or derive meaningful insights due to a lack of clarity, structure, or understanding of how to analyze and utilize the data effectively. It emphasizes the challenge of managing and extracting value from data amid its sheer volume and complexity.
Connecting to a Building’s OT Data
Collecting a building’s data, normalizing it, storing it, ensuring its integrity, analyzing it, and using it to make business decisions are all necessary. It is no longer about more data but instead asking the right questions to get the right data. Understanding the data, solving specific challenges, and addressing specific issues all require identifying the right data. Data must be made available in the right format, democratized, and delivered to the right person at the right place and time, all within a secure environment. This is the basic requirement to create a data value chain.
Normalizing different data types and blending diverse data sets requires planning, strategy, tools, standardization, and technologies. Without OT data management planning that includes a strategy and the right tools, users only scratch the surface of their data’s full value.
Many conversations start by focusing on the technology and what type of technology I use. When that happens, initiatives can falter by not delivering the correct insights needed to drive the intended results and outcomes. A robust, successful OT data and analytics approach encompasses more than a bundle of technologies. Having the right tools is critical. Too often, executives overlook or underestimate the significance of the data type and organizational components required to build a successful data and analytic function. In addition, preset business outcomes are not identified and set.
Identifying and Correctly Tagging Your Data
As one embarks on an OT data plan, looking at all the operational data required to manage a building’s performance and day-to-day operations is essential. Start by identifying what data is available and what data will be required. Categorize the data and information that different people and groups involved with the building’s operations must have to perform their work. Much of the data will be from points on building systems, but other data may come from other systems. For example, asset data, energy bill data, and service/repair data, all of which may come from systems outside of the facility or even outside the organization. Identify where the data exists, how it must be accessed, how it will be exchanged and estimate the volume. In all these cases, deciding on a “standardized dictionary” for your data is highly beneficial. The standardized dictionary would include naming conventions, semantic modelling, and tags.
Standardize how things are identified and what they are called. Multiple naming conventions are the largest and most time-consuming issue in implementing an integrated OT data management platform. You do not want to have ten names for air handlers or pumps!
The value of any smart building is stitched in data. Regarding OT data, we need to move past “data drowning” and automate collecting, sorting, and exchanging critical information that directly correlates to improving our building operations and delivering business outcomes.
In summary, the data produced from building systems, equipment and devices is more valuable than their cost. OT data and accompanying analytics hold immense value in terms of optimizing performance and efficiency, enhancing occupant experiences, and enabling for a healthy, safe, and sustainable built environment. Building and facility data is an irreplaceable asset.
