![]() Here’s a scenario: walk to any point outside and ask yourself: what utilities are buried under my feet? What depth are they located? What encasing material are they made of? What contents do they convey? How old are they? Which nearby facilities are they connected to? Are they in use or abandoned? How many people will lose service if they are cut? What is the predicted risk of working in their vicinity? What is the financial penalty of disrupting their service? Who owns these utilities? What is their contact information? On the other hand, there is a huge range of information gathering that would best be described as utility guesswork. Central utility operations depend on huge amounts of data to monitor network usage, fluctuations, efficiencies, outages or leaks, historic patterns, safety levels, etc. For example, a customer utility bill is a miniature data report including type of service, metered usage, geographic point of service, unit pricing, ownership, contact information, etc. Utility data is collected and recorded in an organized manner at regular intervals and shared transparently with stakeholders. In fact, an important distinction needs to be made between utility data and utility guesswork. That being said, the general definition of data does not necessarily apply to all information collected about utilities. Utility data is the basis for all informed interactions with our infrastructure, whether that entails planning and designing a new project, constructing or altering utilities or the areas around them, or managing or assessing areas of land. And it should have an estimated threshold of accuracy based on the sources from which the data was generated. It has to be standardized to a degree that allows for useful comparison but does not remove meaningful detail. It has to be discoverable and accessible from public or private sources. The most important but least-mentioned aspect of data is that it has to be generated by human actions in the first place. to create a valuable entity that drives profitable activity so must data be broken down, analyzed for it to have value. It has to be changed into gas, plastic, chemicals, etc. It’s valuable, but if unrefined it cannot really be used. That assumption is captured in the well-known saying: “data is the new oil.” Many CEOs and magazine editors have used those words over the past 10 years, but the earliest recorded use comes from a 2006 blogpost by Michael Palmer, where he attributes the quote to British data scientist Clive Humby. ![]() Today, data is so abundant, and so intensively processed using higher-level analysis, that we tend to think of “raw” data as something that simply exists in the wild, waiting to be transformed into sophisticated insights. Compatibility between different pieces of dataĭata is easy to define: a collection of qualitative and quantitative observations, organized in a way that allows for systematic analysis to serve a specific purpose, whether that is prediction, evidence, strategic action, or recorded longevity.Geospatial, physical, and societal data.Utility data may be crucial to our future, but in most cases it is incomplete, unreliable, and incompatible-if it can even be found. That might sound obvious in a world underpinned by data in all forms, from Internet communication to logistics networks.īut there’s one big caveat: when it comes to data, the utility industry is practically in the Dark Ages. If utilities are the lifeblood of our modern civilization, then utility data is the lifeblood of our future planning.
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