by | John Young
Picking up where I left things in my last article, I’ll start this one by asserting that consumerization [read: smart gadgets] and urbanization [read: growth and concentration of human population in urban centers] are key drivers influencing technology, process, and performance goals of professionals, owners, and IT folks responsible for asset and facility management (AM-FM).
Why? Put simply, most humans in developed countries spend 85% of their lives indoors and this percentage continues to climb each year. Coupled with staff salaries, building facilities and indoor environments represent the biggest capital investment for facility owners and operators (OnO). As numbers climb, so do expectations. OnOs and the occupants for whom they provide a place to work, visit, explore and engage have expectations that typically fall into two or more of the following categories – safety, resiliency, sustainability, livability, and productivity. It’s also no surprise that generational shift in consumerization, the ubiquitous use of smart gadgets, and the ability to gain nearly instant access to data about ones surroundings is ramping up expectation levels. I mean, if a refrigerator can predict when food will go bad or prescribe lack of key ingredients for a meal to be prepared later in the week … and send you a reminder on your smart phone … well, you get the picture. The same is true for doctors and the next generation of medical diagnosis.
In both cases, the ability to provide a prognosis or set of confidence weighted recommendations (i.e. via predictive analytics) is a function of data usefulness. And while the time interval for collection of historic data may be short for food stuffs and quite long for medical treatments, the same simple fact is that these data do exist and have for-the-most-part been consistently catalogued and well-organized over time. This is largely driven by federal regulations (e.g. US Food and Drug Administration) but it underscores why the organized historic data exists. The same is not true when looking across all industries. In fact, most are not in the same boat; most are not ready for predictive analytics solutions; most have a growing AM-FM data usefulness gap.
Compounding the challenge in the world of AM-FM are the sheer number, types, and complexity of assets and materials used to construct and maintain facility indoor environments and the supporting utility and fiber infrastructure. Further still, these data are typically spread across a variety of different storage formats and locations. Fortunately, there are mature but often underutilized standards for classifying and organizing these data (e.g ISO 55000, OmniClass, etc…). There are also a number of enterprise software systems that help organize and manage this data. However, I would argue that some are better poised to help OnOs bridge the gap than others.
All this to say: the ingredients for reducing or preventing a further widening of the data usefulness gap are typically available. The crux is a paucity of business and technology system assessments -- sometimes called business transformation “health-check” assessments of “people, process, and technology” – that account for “smart era” challenges and the growing data usefulness gap. This leads me to the core topics for this article – how do we measure or score the “data usefulness gap”, which technologies and tools are most-capable of helping us bridge the gap, and how can we realistically sustain or keep pace with advances in technology [foreshadowing: importance of recurring new school “health” assessments].
Let’s begin by taking a look at the data usefulness gap presented in my August article (shown here for reference) but this time let’s turn the chart on its side and focus squarely on the shaded “gap”. Pause.
It’s one thing to acknowledge that the gap is there; it’s another thing to acknowledge that even talking about this gap brings a feeling of exhaustion or “data fatigue”. This is particularly true for those OnOs and senior operations and IT leaders in organizations struggling to get a handle on the growing -- or consistently high -- total cost of ownership (TCO) of the AM-FM technology stack and processes used to move data through it. It’s this feeling of exhaustion that is precisely why I think it’s important to take a closer look.
In Figure 1 below, I am showing the “Current …” state of data management for many organizations and the “Goal” representing the to-be state plotted along a business transformation “ladder” data management and data usefulness. I’ve carried over the “Smart Era” bracket from the graphic in previous blog to provide a relative measure of time. The width between the dimension symbol arrows depicts the current state of data management – a wider distance between arrows indicates a larger number of data types, formats, sources, and processes used to achieve current level of productivity. It’s also a relative description of the TCO required to manage most facility asset portfolios – the wider the space between the arrows; greater the TCO (see Figure 2). Most organizations sit at or to the left of a “Managed” state.
So, how does an organization “bridge” the data usefulness gap? I’m certainly not the first to recognize the presence of the gap nor the data management best practices for achieving the bridge shown in Figure 1 under the dotted line arrow. However, what I am suggesting is a novel way of measuring the gap, assigning a value to it, and prescribing a list of technologies that will help reduce the gap (and increase the score).
Measuring the gap requires a weighted scoring system. This score accounts for risk, cost; and perhaps more practically, a gauge or barometer for an organizations progress whilst stepping through the stages of business transformation. Once a score is assigned, we can then consider the technologies and processes most capable of bridging the gap. Fundamentally, I’ll submit that this score is a qualitative measure of data management and organization “fitness”. Fitness can be thought of as adaptability to change or transformation. In simplest terms, this fitness represents the ease with which data is able to move through one’s ecosystem of technologies to generate valuable information.
To keep things simple, let’s have the score go from 0 to 100. A higher score represents greater fitness and potential to help reduce the gap, reduce TCO, and move closer to a smart era “Predictive” transformation goal. Scoring is based on variables or improvement opportunities that best describe the current state. Variables such as digital record and as-built design formats (e.g. paper, XLS, PDF, CAD, BIM), data storage (e.g. file, server, cloud), use of relational databases and/or enterprise business systems, system deployment models (e.g. on-premises, cloud or a hybrid combination), hierarchical location-based data organization, use of data standards and data or information models, history-enabled data collection and storage, rule-based data integrity and quality checks, etc. The number of variables could be as low as 10 or high as 100. I’ll suggest a manageable number of variables rests somewhere between 20 and 25 for most facility asset portfolios. One must then determine a weight or what I will call an “enablement factor” for each variable or group of variables. For simplicity, I’d say no more than 20 variables each getting a score up to 5 is a reasonable option. Relative weighting with the TCO for managing these assets can also be included for each variable. Pause.
I’m not going to elaborate further on scoring assignments in this article as each qualified consultant will have a nuanced take on this approach. They will also likely weight certain variables with a higher point potential or enablement factor total based on intrinsic knowledge of an organizations systems and processes. Regardless, I will submit that adding a “data usefulness score” to one’s strategic assessment of data and system health is an important part of helping organizations reduce the growing risk of letting the gap grow unabated (see risks described in previous article here ).
With scoring complete, which technologies can we use to boost the data usefulness score and effectively help an organization transform and “bridge the gap”? I list several technologies below that have a proven track record of use as effective tools for bridging the data usefulness gap. It should be no surprise that each of the technologies listed inherently support the criteria listed under the dotted line arrow in Figure 1. OnO’s in organizations using these tools and processes (with good governance) meet or exceed the aforementioned expectations, enjoy a lower TCO, and are able to quickly adapt and utilize smart era technologies. In fact, I’d go so far as to say that IT and operations staff using these technologies are in a better position to hold the data usefulness gap in check than those that do not. These staff or professionals I am talking about are geographic information system or GIS professionals.
These technologies and the representative named vendors have a track record of success that is easily discoverable online thus will not be described further here for the sake of reader fatigue [read: attempt at humor].
Technologies that help bridge the AM-FM transformation or data usefulness gap:
ArcGIS Enterprise and the Geodatabase by Esri, Inc
FME by Safe Software
1Integrate by 1Spatial
InVision Foundation by PenBay Solutions
Voyager Server Pro by Voyager Search
At first this may seem like a marketing pitch for Esri’s GIS (ArcGIS) and GIS-related technologies, but it’s not. One need only look at the size and number (350,000 plus) of global governments and businesses using ArcGIS technology to manage a wide variety of assets -- elaborated on in previous article -- to validate and verify the benefits. The other major benefit is the presence of a large pool of highly experienced GIS professionals both in and entering the workforce. More good news: GIS professionals are inherently data scientists. This comes from the “IS” part of GIS. These professionals are innately location-based asset managers as their science is built around mapping, analyzing, and managing the natural and built environment. It’s also helpful to know that well-developed and consistently evolving GIS education and professional development options are widely available. Pause.
I will elaborate further on GIS and its expanded use as core technology for smart installation, campus and plant solutions [read: AM-FM] in my next article. I will also dive deeper into the importance of BIM-GIS interoperability as GIS becomes more ubiquitously used for managing building and building indoor geographies. For now, let me explain why each technology and process improvement vendor is in the list above.
ArcGIS is an enterprise technology platform used to collect, store, manage, analyze, and share land, infrastructure, and building asset data. ArcGIS and its Geodatabase support many database management systems (e.g. MS SQL Server, Oracle, SAP HANA, or PostgreSQL). The maker of ArcGIS, Esri Inc, provides a wide range of location-based data and information models that scale all levels of geography – from the world to the indoor widget. It also easily integrates with other data stores and enterprise systems via database or web service connections that can traverse multiple systems of record. This includes connection to and analysis of unstructured data inputs (e.g. crowd-sourced from social media).
FME by Safe Software is fundamental extract, translate, and load or “ETL” technology used to bridge or extend the use of file-based design data from a wide variety of formats into an ArcGIS Geodatabase which hierarchically organizes the data and allows it to be connected to other AM-FM enterprise systems of record.
1Integrate by 1Spatial provides critical data integrity management tools that utilize a rules-based engine to automate the process of data validation, cleansing, transformation, and enhancement for both spatial and non-spatial data across a wide variety of file stores and enterprise systems. I believe this is a key arrow in the quiver of AM-FM data stewards and consulting professionals.
InVision Foundation by PenBay Solutions is especially helpful for smart installation AM-FM professionals working to better organize, manage, and share indoor facility data. The InVision software suite in anchored by ArcGIS and the Geodatabase and utilizes tools like FME and 1Integrate.
Voyager Server Pro by Voyager Search is used to streamline the ability to search for smart installation data, maps, and apps with a search engine designed to discover any location-based information regardless of format or source.
Bridging the gap requires a strategic, adaptive approach to analyzing a health check assessment with new methods of scoring and providing a transformation roadmap with specific recommendations that include these technologies. Doing so will improve “up” to the current “Managed” state and setup or prepare OnOs for taking advantage of a “Predictive” state and all the aforementioned [expected] improvements that come with it.
Working with our Smart Installations team at Patrick Engineering (Patrick), I have developed a new-school health check assessments that include scoring the data usefulness gap. Our assessments provide recommendations that utilize the types of tools mentioned above as well as a host of others used for AM-FM. The key is understanding how to integrate these tools to achieve maximum value and lower TCO. It also means making the tough decisions on which technologies and processes to keep and which ones should be modified or tossed in the bin.
Lastly, as presented above and in previous article, the pace of technology growth will only quicken along with OnO and occupant level of expectation. To maximize benefits and achieve recurring value and preparedness, this type of assessment should be performed on an annual basis; at most, every other year. In this regard, it’s important to take advantage of an annual managed service offering that includes this type of assessment. Patrick offers managed services with a variety of options from “teach you how to fish” to fully manag