Maximize the value of your fleet data, by implementing this simple data quality assessment checklist

Fleet Data Quality Assessment Checklist

Reliable fleet analytics begin with reliable data. The Uptake Fleet Data Quality Assessment Checklist helps public transit agencies, government fleets, commercial operators, and other fleet organizations evaluate whether their data is accurate, complete, consistent, timely, and ready to support operational decisions.

This practical self-assessment includes 126 fleet data quality checks across ten essential areas:

The checklist helps fleet maintenance, operations, information technology, safety, finance, and data teams identify gaps that can undermine reporting, preventive maintenance, asset management, regulatory compliance, and predictive maintenance programs.

Each quality check is rated using a consistent maturity scale, ranging from Not in Place to Standardized and Consistent. Fleets can calculate individual section scores and an overall fleet data maturity score to determine whether their data practices are Foundational, Developing, Managed, or Advanced.

The assessment also includes public-transit-specific checks covering vehicle assignments, routes, blocks, trips, service interruptions, accessibility equipment, passenger-counting data, spare ratios, and Transit Asset Management reporting.

After completing the assessment, organizations can document their most important data gaps, assign corrective actions, identify responsible owners, and establish measurable target dates. Suggested performance thresholds provide practical starting points for monitoring vehicle mappings, required fields, duplicate records, telematics reporting, fuel and charging transactions, integration failures, and safety-critical exceptions.

Use the Uptake Fleet Data Quality Assessment Checklist to establish a baseline, prioritize improvements, and prepare your fleet data for more dependable reporting, advanced analytics, artificial intelligence, and predictive maintenance.

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The presentation focuses on AI and data strategies for fleet management, exploring why AI is revolutionizing fleets, key use cases (with a focus on predictive maintenance), and practical takeaways for implementing AI in fleet operations.

Transcript of Adam McElhinney’s Talk – Uptake (uptake.com)
Topic: AI and Data Strategy for Fleets

Hi everyone, thanks for coming to my talk—especially late on the last day of this great conference!

My name is Adam McElhinney, and I’m the CEO of a company called Uptake. Today, we’re going to cover a lot of ground about fleets and fleet data, with a particular focus on AI strategy.

Here’s a quick overview of what I’ll be discussing:

A Little About Me

By day, I’m the CEO of Uptake, where we help customers reduce unplanned downtime.
By night, I serve on:

I hold 19 patents in machine learning and AI—mostly in industrial applications—so this is something I’m deeply passionate about.

Why AI Is Revolutionizing Fleets

Let’s talk about what’s changed to make AI practically useful in the past five years:

  1. Theoretical Breakthrough
    In 2017, Google released a groundbreaking paper: “Attention Is All You Need”. It introduced the Transformer model—the “T” in GPT (as in ChatGPT).
    Most of the authors have since left Google to start AI companies, showing how commercial this space is becoming.
  2. Computing Power
    New neural networks scale almost linearly with computational resources. Unlike older models, you don’t hit diminishing returns as quickly. That’s why we’re seeing huge investments—up to $500 billion—in computing infrastructure.
  3. Data Availability
    Models are being trained on vast datasets like Common Crawl (a publicly available index of the web). Companies are now signing exclusive data licensing deals—Google and Reddit, for example—to feed proprietary AI models.

Two Key Axes for Evaluating AI Use Cases

When thinking about where to apply AI in your fleet, consider these two dimensions:

  1. Impact – If fully automated, how much would this transform your fleet?
    (0 = no impact, 10 = game-changing)
  2. Viability – Can this realistically be automated using today’s tools?
    (0 = not feasible, 10 = plug-and-play)

AI Use Cases in Fleet Management

Use CaseImpactViability
Autonomous Driving102
Predictive Maintenance7–88–9
Routing Optimization6–77
Customer Service AI49
Driver Behavior Monitoring6–76–7
Warranty Processing5–66
Inventory Management66

The Predictive Maintenance Deep Dive

Objective: Detect and alert on potential failures before they happen.

Current standard is preventive maintenance (PMs)—based on miles or hours.
Predictive maintenance takes it further by using fine-grained data from telematics, fault codes, and sensor readings.

Why Fault Codes Alone Fall Short:

Uptake’s Predictive Approach

We use:

  1. Work order data to identify historical failures
  2. Telematics & fault codes to match events leading to those failures
  3. Sensor data (especially from Geotab, a partner of ours) for real-time insights

Think of it like Netflix:

“If these combinations of fault codes and signals appear, that likely means X failure is coming.”

This leads to longer lead times, fewer breakdowns, and higher fleet availability.

AI Use Case Checklist

Use this framework before launching any AI project:

  1. Can you quantify the current cost?
    If not, you can’t measure improvement.
  2. Is the business impact material?
    Don’t spend AI resources on $10K/year problems.
  3. Do you have lots of data?
    Rare events are tough for AI.
  4. Can you validate outputs?
    Always back-test with historical data before deploying.
  5. Can a human do it at small scale?
    If no one can do it manually, AI probably can’t either.

Final Recommendations for Your Fleet’s Data Strategy

  1. Standardize Your Metrics
    Agree on how you measure things like downtime. Definitions should be consistent across departments.
  2. Accept Imperfect Data—but Improve It
    Everyone has dirty data. Use scorecards. Check if devices are on, properly assigned, and if technicians are entering codes meaningfully.
  3. Write a Data Strategy
    Align your data approach with your business goals. Data should be treated as a horizontal asset, not a siloed IT concern.

Closing & Q&A

That wraps up my talk. I’d love to take any questions—whether it’s about AI, fleet data, predictive maintenance, or anything else.

If not, thank you so much for your time.
Please feel free to connect with me on LinkedIn—I love talking data, AI, and fleet maintenance!

As manufacturers look to meet the competitive mandate to digitally transform their operations, the digital skillset of their team will be critical to their future success and resilience. For many manufacturers, finding skilled talent can be a challenge. In a recent survey from Deloitte and the Manufacturing Institute, manufacturing executives reported that recruitment is more challenging today than before the pandemic.

The study also found that the lack of available skills in today’s workforce could leave 2.1 million U.S. manufacturing jobs unfilled by 2030, costing manufacturers $1 trillion in lost productivity. For their part, 77% of manufacturing workers reported a willingness to retrain to improve their future employability according to a 2020 PwC survey.

Though industrial businesses and workers agree that training on digital skills is instrumental in building out future productivity, many manufacturers have simply not tied work performance to learning and development. A 2019 Tooling U-SME survey of manufacturers found that industrial businesses were not bridging the skills gap, contributing to a high cost of turnover rate and lost productivity. Just 12% had training and development programs in place, with one-third budgeting for external or on-the-job employee development.

To build out attractive recruitment and retention programs, manufacturers need to develop scalable digital training. Here are 7 ways that manufacturers can engage and guide their workforces as they build out their digitally transformed operations.

1. Executive Ownership of Building a Smarter Manufacturing Team

Direction from manufacturing executives can ensure their company pairs its training programs to tangible business objectives and strategic goals, holding leaders accountable for making decisions that also promote digital transformation, employee retention, and productivity. It requires leadership to be literate in digitally-enabled opportunities and communicate associated goals clearly to employees to earn their buy-in. A data integrity committee, for example, can accomplish this board-level oversight of workforce initiatives while advancing strategic data-driven business goals at the frontlines.

2. Providing access to online tech courses and nanodegree programs

Learning platforms like Udacity and Coursera offer courses in anything from Six Sigma proficiency to Robotics for Advanced Manufacturing. Training initiatives do not need to happen on the job, but creating opportunities for learning by providing access to these platforms will attract new hires and build a culture that emphasizes the importance of continual digital learning.

3. Partnering with local schools to create a talent pipeline

Take Harper College in Illinois, for example, which previously had dismantled its manufacturing curriculum. With the support of the Illinois Network for Advanced Manufacturing (INAM), local high schools, and federal grants, Harper College revamped its manufacturing curriculum, filling 30,000 open jobs paying on average $29 per hour. With input from INAM, the redesigned and manufacturer-influenced curriculum enables students to earn an associate degree debt-free with guaranteed employment upon graduation by pairing specializations in Automation, Metal Fabrication, Precision Machining, or Supply Chain and Logistics Management and apprenticeships.

4. Tapping into networks of right-skilled workers

For smaller manufacturing firms who struggle to recruit a workforce floor-ready for advanced manufacturing, contractors represent an opportunity to take advantage of the technical skills required. A manufacturing-focused talent marketplace like Veryable is key to connecting flexible and skilled workforces to manufacturing firms in need of flexible labor.

5. Using simple out-of-the-box software

A digital solution that suits Industry 4.0 is one that simply works — manufacturing firms can no longer afford to leave technology users to weave, bundle, bridge, abstract, translate, connect, develop, and outsource disparate and disjointed pieces of software.

Uptake Asset Strategy Library® Explorer was created with this need in mind, providing industry-proven preventative maintenance strategies on-demand. As asset-heavy industries struggle with knowledge transfer as many longtime maintenance and reliability professionals look to retire, PM Strategy Explorer digitizes domain expertise and provides step-by-step maintenance tasks based on specific failure modes for critical assets.

6. Software demos for employees by power users

Offering ample time and opportunity for manufacturing workers to share knowledge can help reskill and upskill employees across an enterprise. Asynchronous access to demos through video recordings and instructional guides prepared by team members can help new personnel get up to speed quicker. Think of it as your very own Coursera or Udacity, an internal one-stop-shop for learning and development.

7. Making OT data available to the enterprise

Operational technology (OT) data has long held back asset-intensive operations like manufacturing from cost-effectively taking advantage of applications like AI/ML, digital twins, and operational orchestration. In particular, the compression of OT data and its restriction to proprietary formats often keeps data locked in unusable formats for preferred methods of consumption.

Uptake Fusion, for example, makes business and maintenance users both power users of OT data, allowing them to cost-effectively develop high-value, multi-purpose industrial applications as they wish. It presents the critical context for various data consumers in the organization, retaining the same object model used to query OT data on-premise but in the cloud to scale decision-making.

Making Talent Development a Competitive Advantage

On-demand access to software, datasets, and learning are key as manufacturers look to earn buy-in from their teams. Those manufacturers that will be competitive are those that invest today in developing a culture of lifelong digital learning to guide their workforces through Industry 4.0.

Translate your data into smarter operations.

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As wind turbines age, how can wind operators get more out of their fleets?

As with any heavy asset, turbines are less productive the more they are used. That simple fact of aging is taking on new resonance in the North American wind industry, where the average age of fleets is estimated to rise from 7 years old in 2020 to 11 in 2025 and 14 in 2030.

With many operators now having expanded their installed capacity and conducting maintenance for the first time with service in-house, the management of aging turbines may come as an organizational first. OEMs, independent service providers, and many of the larger owner-operators have managed turbine performance decline before, but not like this new maintenance landscape ahead, with new turbine makes and advanced controls technology still untested by the usual wear and tear.

Aging Turbines, Young and Old

After accounting for the “teething” issues in years one and two because of site configuration and maintenance strategy adjustments, the Berkeley Lab found turbine performance decline to be linear in a survey of the North American fleet. This gradual loss of output productivity is consistent across turbine makes and models.

For pre-2008 sites, this annual decline in output equals about 0.53 percent. Newer turbines with a greater blade-to-generator ratio installed at post-2008 sites, by comparison, are holding up better so far. They’re losing on average about 0.17 percent in output each year.

These newer turbines are also contributing to the expectation of a longer turbine lifecycle, from around 20 years in the early 2000s to an expectation of 30 years today, according to a recent survey of wind executives.

Despite the improved lifespan and maintenance programs for repowered turbines and newer sites, the tenth year remains a critical point in the turbine life cycle. Between the tenth and eleventh years, the

Berkeley Lab found that output productivity slipped for older sites by about 3 percent. The extent of that drop off for newer sites is still uncertain, but ten and eleven year old sites have so far underdelivered on their productivity projections, suggesting that the drop off continues still.

Explaining the Year Ten Drop Off

Performance drops off for a few reasons around the tenth year — some of it attributable to technical and normal mechanical degradation, but much of it the result of market incentives and controlled wind farm operations.

1. Component Replacements: The replacement of key components like gearboxes (which alone explains about 30 percent of total performance decline) introduces some of the same teething problems around turbine and site configuration in year ten. After the second bout of component optimization which follows, turbines recover some productivity into their second decade before falling off once more. As many executives project operating turbines into a third decade, it is possible that another round of component replacements and a third set of teething issues may realize another drop off and slight recovery.

2. Expired OEM Warranties or Service Agreements: When operators bring service in-house, cost-effective maintenance — especially for underperformance — typically becomes less of a priority. As we’ve written before, the wind industry has historically treated downtime as more identifiable, measurable, and addressable, which accounts for why underperformance issues become more challenging to document, investigate, and fix as maintenance moves in-house.

Without due correction for underperformance, the revenue left on the table is significant. The Electric Power Research Institute estimates that just a 1 percent boost in productivity at a typical wind farm with 100 two-megawatt turbines would increase revenue by $250,000 – $500,000.

3. Productivity Goals Framed by Power Purchase Agreements: A power purchase agreement (PPA) often determines the span of peak productivity and the associated maintenance strategy at a park. Similar financial instruments to hedge risk in wind operations like Proxy Revenue Swap Financing, which exchanges the expected value of power generation for fixed payments from third parties, encourages more moderate approaches toward power performance.

Whether a PPA benchmarks on both availability or performance, hitting agreed-upon output without exhausting non-PPA productivity can see varying drop-offs depending upon the length of the PPA.

Turbine Productivity Decline

4. Slowdowns Incentivized by the Production Tax Credit (PTC): As sites lose eligibility for the PTC in their tenth year of operation, performance declines by about 3.6 percent. Losing the 1-2 cents per KwH writeoff encourages more moderated power performance becoming the norm in order to stretch out mechanical stress and pre-empt costly failures and subsequent replacements on turbines that have already been repowered.

In Europe, where a PTC equivalent is not available by kilowatt-hour, wind farms have operated with a more gradual linear rate of decline, without the sharp decrease in year ten. Similarly, upon expiration for the Treasury Department’s lump sum 1603 Grant, wind projects dropped off at 3.2 percent. Instead of pursuing vigilant maintenance on turbines at peak performance, projects took a more measured approach and saw a less steep decline in year ten.

Powering Performance for Older Fleets

Into the second decade of the turbine life cycle, when “teething” issues again subside, performance decline slows and averages about 1.23 percent annually for the rest of a turbine’s lifespan. By year seventeen, older turbines installed in the 1990s and early 2000s have averaged 87 percent of their peak productivity. Younger turbines now appear that they will age more gracefully, though to what extent remains to be seen.

Capitalizing on heavy investment in wind turbines requires cost-effective upkeep. Repowering initiatives are just one step in an O&M approach to stave off productivity declines. And as performance decline draws greater attention with the demand for carbon-free energy increasing, caring for an aging fleet through power performance will be fundamental to the long-term profitability of wind projects.

Looking to power wind performance?

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