Bad data in, bad data out: Protecting your investment in ADMS

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Bad data in, bad data out: Protecting your investment in ADMS

Congratulations! Your utility has successfully implemented a cutting-edge ADMS application. Your GIS team has spent months working closely with the implementation team to clean and correct the data within the GIS application. The teams have validated voltage and phasing, eliminated loops, resolved open points, populated missing attribution with default values, and ensured the connectivity of […]

Congratulations!

Your utility has successfully implemented a cutting-edge ADMS application. Your GIS team has spent months working closely with the implementation team to clean and correct the data within the GIS application. The teams have validated voltage and phasing, eliminated loops, resolved open points, populated missing attribution with default values, and ensured the connectivity of circuits and devices for every feeder in your distribution system.

This extensive data validation, updating and cleanup effort was costly, but it should significantly enhance the management of your transmission and distribution (T&D) operations. It will improve your reliability statistics, boost customer satisfaction ratings and earn you recognition with the Public Utility Commissions in your service territory.

Now comes the hard part: Keeping your GIS and ADMS current

Utilities must recognize that their efforts to clean up and validate their GIS Connectivity model is not a one-time task performed during ADMS implementation As new or replaced assets are installed in the T&D system, those new assets must be updated into the GIS in near real time.

The data and attribution requirements of ADMS are far more demanding than the current data and general mapping requirements or your legacy GIS. Real-time access to new construction data is critical for the effective use of ADMS applications. For example, if new switching devices are not updated, you won’t be able to optimize your outage isolation switching orders. Without the new transformer data in GIS, ADMS won’t be able to account for transformer load-splitting results, impacting your Reliability Statistics. Additionally, you won’t be able to optimize FLISR if the assets are not accurately accounted for in GIS. One Tier 1 utility reported that 2 years after cleaning their GIS data before launching ADMS, 50% of their substations are no longer analysis-ready in ADMS due to the lack of a process to continuously push high-fidelity construction data to their GIS in near real time.

The most effective time to collect new asset data is at the time of installation. For ADMS, simply knowing that the installed transformer is a generic 50 kVA unit is not enough. ADMS requires detailed data about that specific transformer, with unique attributes that vary across each device.

If you don’t reimagine your current data capture processes, you’ll quickly lose the value of your ADMS investment.

Field workers and installers are the best sources for identifying newly installed asset data—they know what was installed, where it was installed, and when it was installed. But collecting data in the field using traditional manual, paper processes is not viable in light of ADMS data requirements. The answer is to utilize modern and automated data collection technology to simplify the data capture process. Barcodes, QR codes and RFIDs are the obvious solutions. These scanning technologies are widely used in everyday life—whether at the grocery store, coffee shop, or hardware store—and can be easily leveraged for asset tracking.

Strategies for optimizing ADMS

Ensuring that high-fidelity, accurate data is fed to ADMS not once, but on a perpetual basis, requires a different approach. By providing a “digital thread” from planning through construction execution, through close out, Digital Construction Management (DCM) solutions can provide a seamless process integrated with GIS that provides continuous updates to ADMS.

DCM solutions start with a fully digital job packet that includes design and Compatible Unit (CU) data. Field crews use a mobile application and barcode scanners to easily and accurately collect as-built data on the job site. But the true value for utilities is the post-construction processing of the field collected data into the various Systems of Record enabled with a DCM application.

Through the use of near real-time communications via “the cloud”, back office staff can review and approve as-built changes to the design. As-built changes can easily be captured in a single workflow whether spatial or attribute data, with the system able to report back the compatible units for WMS / accounting and spatial data / attributes for GIS. Any approved changes can be processed into the GIS and WMS applications through partial posting or at the approval of the entire work order. GIS data can then be reflected in ADMS to ensure the system is operating with a “digital twin” of actual field conditions.

This rapid post-construction processing of completed Work Orders ensures that all Work Order close-out processes are completed quickly, reducing their impact on CWIP and AFUDC levels and enabling the rapid inclusion of new assets into the Utilities Rate Base for cost recovery.

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The last time the market witnessed a shakeup like this was China’s DeepSeek, but doubts emerged quickly about its efficacy. Developers found DeepSeek’s efficiency gains required deep architectural decisions that had to be built in from the start. TurboQuant requires no retraining or fine-tuning. You just drop it straight into existing inference pipelines, at least in theory. If it works in production systems with no retrofitting, then data center operators will get tremendous performance gains on existing hardware. Data center operators won’t have to throw hardware at the performance problem. However, analysts urge caution before jumping to conclusions. “This is a research breakthrough, not a shipping product,” said Alex Cordovil, research director for physical infrastructure at The Dell’Oro Group. “There’s often a meaningful gap between a published paper and real-world inference workloads.” Also, Dell’Oro notes that efficiency gains in AI compute tend to get consumed by more demand, known as the Jevons paradox. “Any freed-up capacity would likely be absorbed by frontier models expanding their capabilities rather than reducing their hardware footprint.” Jim Handy, president of Objective Analysis, agrees on that second part. “Hyperscalers won’t cut their spending – they’ll just spend the same amount and get more bang for their buck,” he said. “Data centers aren’t looking to reach a certain performance level and subsequently stop spending on AI. They’re looking to out-spend each other to gain market dominance. This won’t change that.” Google plans to present a paper outlining TurboQuant at the ICLR conference in Rio de Janeiro running from April 23 through April 27.

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Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist Power Applications Engineer Pittsburgh, PA This position is also available in: Denver, CO and Andrews, SC. Our client is a leading provider and manufacturer of industrial electrical power equipment used in industrial applications for mission critical operations. They help their customers save money by reducing energy and operating costs and provide solutions for modernizing their customer’s existing electrical infrastructure. This company provides cooling solutions to many of the world’s largest organizations and government facilities and enterprise clients, colocation providers and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer Ashburn, VA This traveling position is also available in: New York, NY; White Plains, NY; Dallas, TX; Richmond, VA; Montvale, NJ; Charlotte, NC; Atlanta, GA; Hampton, GA; New Albany, OH; Cedar Rapids, IA; Phoenix, AZ; Salt Lake City, UT; Kansas City, MO; Omaha, NE; Chesterton, IN or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA Agents and CxA PMs. *** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive

No joke: data centers are warming the planet

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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