Cloud Data Engineering for EV Charging Networks: 3 Business Challenges You Can Solve Today

Let’s dive in! 

Heads-up: This article's a bit on the longer side—feel free to use the table of contents to jump straight to the sections that interest you.

Table of Content 

• Challenge #1: Charging Site Selection

• Challenge #2: EV Charger Uptime

• Challenge #3: Commercial EV Charger Cost Efficiency

• Key Takeaways and Next Steps

Challenge #1: Charging Site Selection

Why Site Selection Is Challenging

While charging stations site selection is one of the most critical tasks for Charging Point Operators (CPOs), it’s also one of the toughest.

First, you have to find the right spot. But what’s the right spot for EV charging stations? Ideally, it’s a high-traffic location that drives demand such as shopping centers, highways, and workplaces. Sounds simple in a country that spans 3.79 million square miles and has over 116,000 shopping malls, right? Not quite. While these locations make the most sense for EV drivers, they’re often expensive, limited in availability, or already taken by competitors.

Then comes the grid. Every EV charging site that looks perfect on paper still needs to support fast charging from an electrical standpoint. In many cases, the local grid isn’t powerful enough and/or upgrades would take months and require major investment.

Even after finding a location with the right infrastructure, it’s not plug-and-play. CPOs must navigate a maze of permits and regulatory approvals. Zoning laws, utility permissions, and incentives vary across city, state, and federal levels. All this red tape can delay deployments and/or block promising sites altogether.

And throughout this entire journey, CPOs depend on data to make smart decisions. But there’s a catch: they need the right data at the right time and that is challenging as well. In our work with EV clients, we’ve seen how disconnected tools and siloed teams can delay launches and inflate costs—especially when expansion speed is key. Here’s why. 

Why Fragmented Data Isn't Helping

When we talk about data fragmentation, we’re referring to the disconnect between all the different types of data CPOs need to make smart site selection decisions—and the fact that they live in different formats, systems, or even teams.

Fragmented data usually looks like this:

• Traffic data from one tool

• Energy grid capacity from the local utility provider’s portal

• Real estate availability tracked in spreadsheets or by external partners

• Competitor locations gathered manually or through third-party databases

• Incentive programs and regulations stored as PDFs or internal docs

• Internal data like performance of existing stations, user patterns, and peak-time usage

None of it speaks the same 'language,' and it’s rarely centralized. So when it’s time to compare options or run projections, teams often rely on static reports, manual processes and/or incomplete insights.

The impact?

• Slow decision-making: Teams spend more time searching, cleaning, and merging data than analyzing it.

• Missed opportunities: Promising sites can be overlooked simply because the data didn’t surface in time.

• Subjective decisions: When data is hard to access or interpret, decisions lean on assumptions instead of facts.

The good news is that the right cloud-native data engineering strategy can turn this around. Here’s how:

How Cloud-Native Solutions Can Help With Data Fragmentation

Cloud-native data engineering helps CPOs turn fragmented, unstructured data into actionable insights, meaning no more manual wrangling and juggling disconnected tools.

Here’s how:

Seamless data integration

Cloud-native platforms can pull data from all those different sources—traffic APIs, utility providers, internal databases, third-party platforms—and funnel it into a single, unified environment. No more jumping between tabs or exporting spreadsheets. CPOs get a complete, real-time view to support data-driven decisions.

Real-time leaning, standardizing, and enriching

Automated data pipelines clean and normalize data on the fly. That includes fixing errors, standardizing formats, and enriching site profiles with relevant metrics like grid capacity, nearby amenities, EV adoption trends, or local regulations. It means faster EV charging sites comparisons, more accurate forecasts, and less time spent wrangling spreadsheets.

Location intelligence, at scale

Once centralized and enriched, this data becomes fuel for robust location scoring models. CPOs can evaluate potential sites based on real demand, accessibility, grid readiness, and incentive availability across entire regions, not just a few hand-picked cities.

Scalable data pipelines

Cloud-native platforms use scalable pipelines that process data in real time and at scale, which means no delays or waiting for reports to update. CPOs can respond in real time to regulatory changes, shifts in demand and act on emerging opportunities swiftly.  

Visual dashboards and predictive models

Forget static reports. With dynamic dashboards and ML-powered models, CPOs can forecast performance, identify opportunities, and prioritize locations with the strongest ROI potential.

Collaboration across stakeholders

With cloud-native tools, every stakeholder—from ops to execs to partners—gets access to the same up-to-date insights, wherever they are. Cloud-based solutions streamline communication, reduce misalignment, accelerate decision-making and improve coordination across tech, business, and strategy teams.

In short, cloud-native data engineering connects the dots, giving CPOs a full picture of where to invest and the confidence to act fast.

At NaNLABS, we build scalable, cloud-native data ecosystems that empower better decision-making. More than devs, we’re your sidekick on this tech journey. Explore how we can help! 

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Challenge #2: EV Charger Uptime

How EV Charger Uptime Impacts CPOs’ Bottom Line

For CPOs, ensuring high EV charger uptime is a key business driver. Here's why it matters:

• Keep revenue flowing: When stations are up and running, so is revenue. High uptime ensures CPOs don’t miss out on profitable charging sessions, especially during peak hours. Over time, a reliable network becomes the preferred choice for EV drivers, driving more usage and income.

• Control maintenance costs: A well-maintained network requires fewer emergency repairs. High uptime is often the result of proactive monitoring and predictive maintenance—both far less costly than fixing unexpected breakdowns

• Deliver a smooth customer experience: Nothing frustrates EV drivers more than showing up to a broken or offline charger. Consistent uptime builds trust, satisfaction, and loyalty, which are all critical to standing out in a competitive market.

• Keep operations running efficiently: When chargers are reliable, support teams aren’t overwhelmed with complaints or urgent fixes. Uptime reduces friction across the board, from the backend to the business side. 

So how can CPOs ensure high uptime?

It starts with addressing the key causes of downtime:

• Hardware failures: From broken cables to malfunctioning power units, hardware issues are one of the most frequent causes of downtime.

• Software and connectivity issues: Outdated firmware, failed updates, or connectivity issues between chargers and backend systems can leave chargers unable to communicate or process transactions.

• Grid instability: Limited local infrastructure or high load during peak times can trigger interruptions, especially in fast-charging setups.

Then, it requires a proactive approach. That’s where predictive maintenance comes into play.

How Predictive Maintenance Can Help

Predictive maintenance is a game changer for charging point operators aiming to optimize uptime and keep operations running smoothly. This proactive approach powered by predictive maintenance analytics reduces unexpected outages, cuts maintenance costs, and improves customer satisfaction.

Here are five ways predictive maintenance enhances EV charging networks performance and reliability:

1. Anticipating issues before they escalate

Predictive maintenance leverages real-time data from various sources, including charger performance metrics, environmental conditions, and usage patterns, to detect early signs of failure. For example, if a charger shows signs of wear in its cables or displays unusual fluctuations in power consumption, predictive models can alert CPOs to potential issues before they become critical. 

Benefit: CPOs can plan maintenance during off-peak hours, avoiding costly emergency repairs and service disruptions.

2. Making smarter, data-driven decisions

With cloud-native platforms, predictive maintenance can integrate data from all parts of the charging network, including hardware, software, and external factors like grid stability. This unified view allows CPOs to prioritize maintenance tasks based on urgency and the potential impact on the network. 

Benefit: Resources are used more efficiently, leading to faster interventions and fewer missed risks.

3. Reducing maintenance costs

Emergency repairs often come with a hefty price tag. Predictive maintenance avoids those last-minute calls by catching small issues early, preventing them from snowballing into major breakdowns.

Benefit: Maintenance cycles are more efficient, with fewer outages, and a healthier bottom line.

4. Extending charger lifespan

Small, unresolved issues add up over time. With predictive maintenance, those minor wear-and-tear signals don’t get ignored. That keeps hardware running longer and delays costly replacements.

Benefit: Lower total cost of ownership and more value from every charging point.

5. Leveling up the driver experience

High uptime means happier drivers. Predictive maintenance keeps chargers consistently available and operational, building confidence in your network.

Benefit: A reliable network improves customer satisfaction, loyalty, and brand reputation.

At NaNLABS, we implemented predictive maintenance pipelines using real-time telemetry for an EV infrastructure client. This reduced charger downtime alerts and allowed field teams to schedule interventions ahead.

Turning Predictive Maintenance Analytics Into Real-Time Operational Alerts

Predictive maintenance analytics rely on a mix of historical and real-time data from charger sensors, usage patterns, and environmental conditions to detect anomalies and forecast potential failures. But insights alone aren’t enough. To truly optimize uptime, those insights need to trigger actionable, real-time alerts.

Here's how real-time data plays a central role in that process:

Real-time data collection from connected assets

EV chargers continuously generate performance signals, such as temperature, energy output, connectivity status, usage cycles, and more. Real-time access to this data allows CPOs to spot issues as soon as they start forming, rather than after they've already caused a breakdown.

Integrated dashboards

We create dashboards that visualize charger uptime, maintenance history, and issue severity at-a-glance, built for fast triage and response.

Intelligent threshold-based alerting

Cloud-native platforms use predictive models to set intelligent, dynamic thresholds based on charger behavior and usage patterns. When real-time data crosses a risk threshold, an automatic alert is sent to the right team—no manual monitoring required.

Automated incident classification

Real-time alerts are enriched with context from maintenance logs, known error patterns, and charger history. This helps operators prioritize which alerts are urgent, and which can be handled in the next maintenance cycle.

Multichannel, role-based notifications

Real-time alerts are sent through the channels your team already uses—whether that’s Slack, SMS, email, or dashboards. And they’re filtered by role, so each stakeholder sees only what they need to take action, faster.

Faster response loops for higher uptime

With real-time data triggering instant alerts, field teams can act before failures escalate. This drastically reduces mean time to resolution (MTTR), and keeps uptime and customer satisfaction high.

By turning predictive maintenance analytics into real-time operational alerts, CPOs can develop a more responsive, agile, and reliable charging network, built for optimal performance.

At NaNLABS, we build cloud-native data architectures that stream and analyze high-velocity data in real time so you can make faster, smarter decisions. Explore how we can help! 

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Challenge #3: Commercial EV Charger Cost Efficiency

Regular EV Charging Network Operation Costs

Running a commercial EV charging network comes with ongoing operational costs that directly impact profitability and pricing strategies for charging point operators. These costs can quickly add up, particularly during high demand or energy peaks.

Here's a breakdown of the most common operating costs and their impact on CPOs bottom line:

Energy Costs

Energy consumption is typically the largest expense for EV charging operations. Energy prices fluctuate based on demand, grid instability, and location. During peak hours, the cost per kilowatt-hour (kWh) can surge, pressuring margins and making cost efficiency a challenge.

Maintenance and Repairs

Routine maintenance is necessary to keep EV chargers functioning reliably. As mentioned above, hardware issues and software bugs can result in downtime and expensive emergency repairs. A proactive maintenance strategy helps minimize unplanned costs and disruptions.

Payroll Costs

A skilled team is essential for smooth network operation, from monitoring performance to troubleshooting issues. But it adds to the budget. If you’re willing to work with a nearshore data engineering company, outsourcing can be a more affordable option than an in-house team. Find out which one would work best for you.

Software Licensing and Backend Management

Commercial EV charging networks rely on complex software systems, including payment gateways, session management, and energy monitoring. A team with deep integration expertise ensures these systems function seamlessly, avoiding inefficiencies that can increase operational costs.

Regulatory Compliance and Taxes

Staying on top of regulatory compliance and taxes is an essential part of running a successful commercial EV charging network. By proactively managing reporting, audits, and system updates, CPOs can ensure they meet all local, state, and federal requirements, and avoid penalties. Discover how to anticipate regulatory shifts in EV, and still meet your roadmap.

While the operational costs of running a commercial EV charging network are significant, there are strategic ways to optimize them without compromising performance.

How to Optimize Costs While Ensuring Performance

Optimizing costs in commercial EV charging networks while maintaining top-tier performance requires leveraging smart strategies and cloud-native solutions.

Here’s how cloud-native solutions help optimize costs while ensuring optimal performance:

Load Management in EV Charging

Effective load management—powered by real-time cloud data pipelines—ensures that charging stations avoid demand spikes that lead to high energy costs. With a cloud data infrastructure, CPOs can forecast demand in real-time and distribute energy usage accordingly across multiple stations.

Benefit: By managing energy usage in real-time, CPOs minimize peak-hour energy surges and lower overall costs.

Real-Time Cost Management

Cloud-native platforms provide insights into energy consumption patterns, enabling CPOs to adjust charging speeds and schedules dynamically to minimize high energy prices during peak demand periods. Predictive analytics can also forecast energy prices, adjusting charging processes to ensure cost efficiency.

Benefit: This real-time adjustment prevents price surges, ensuring better margin control and reducing operational costs.

Smart Load Balancing

Smart load balancing allows for easy scaling of operations as demand increases. With scalable data processing and infrastructure, CPOs can expand their network without substantial additional costs.

Benefit: This ensures high uptime while cutting down on unnecessary energy consumption and reducing grid strain.

System scalability

Cloud-native systems allow for easy scaling of both data processing and infrastructure. This flexibility allows CPOs to manage increased demand without significant additional costs.

Benefit: Pay-per-use scalability means you only incur costs when you need additional resources, ensuring efficient growth without overspending.

Demand Forecasting for Strategic Planning

Cloud-based demand forecasting leverages historical data, weather patterns, and local events to predict future charging demand. With this information, CPOs can prepare for peak periods, adjust charging prices, and optimize the number of chargers available to users.

Benefit: Accurate demand forecasting ensures the right number of chargers are available without overburdening the system, improving overall efficiency.

Optimizing costs and streamlining operations are essential steps toward building sustainable growth. When complemented by data-driven strategies, you can enhance the performance of your EV charging stations and unlock even greater revenue potential.

How Data-Driven Efficiency Unlocks EV Charging Station Revenue Growth

Once your EV charging station business is running efficiently, data becomes your biggest growth lever. With the right insights, you can not only scale but generate new revenue opportunities. 

Let’s look at how data can power your EV charging station business:

Dynamic Pricing Models

Real-time data enables you to adjust pricing based on demand, location, or time of day. This flexibility helps you stay competitive while maximizing profitability during high-traffic periods. As a result, you get more control over margins and optimized pricing strategies for different user profiles. 

User Behavior Insights

By analyzing session data and charging patterns (e.g. session duration, time-of-day usage, and charger preference), you can understand what keeps drivers coming back—or turning away. These insights are critical for improving service, loyalty programs, and even UX. Ultimately, when data is turned into actionable insights, it allows you to enhance customer retention and increase station visits.

Smart Site Expansion

Performance data across locations highlights where demand is rising and where your next station should go. With these insights, you can prioritize high-ROI areas and accelerate payback on new installations.

Fleet & B2B Optimization

For fleets and recurring business users, data helps design custom plans, forecast usage, and ensure charger availability. That means stronger partnerships and stable revenue from high-usage clients.

Performance Benchmarking

A data-driven strategy makes it easier to compare site performance, spot underperforming assets, fine-tune pricing or maintenance, and replicate what’s working. The result? Continuous optimization that drives both operational and revenue growth.

At NaNLABS, we specialize in building scalable, cloud-native data infrastructures that transform your EV charging data into actionable insights. Explore how we can help! 

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Key Takeaways and Next Steps

5 Ways Cloud Data Engineering Can Help You Optimize Your EV Charging Business

Cloud data engineering empowers your EV charging network to scale, optimize, and make data-driven decisions. 

Let’s break down how it can benefit your business:

1. Real-time data insights for faster decisions: A cloud-native architecture gives you instant access to data from your entire charging network. These real-time insights help you spot issues, adjust pricing, optimize energy usage on the fly, and keep your operations agile. That means faster go-to-market, faster iterations and fewer delays from outdated reports or missing data.

2. Scalable infrastructure for seamless growth: Your cloud infrastructure scales alongside your EV charging business. Whether you’re adding more chargers, expanding to new locations, or increasing data capacity, your cloud native platform offers the flexibility and performance you need without the overhead of on-premise hardware.

3. Smart load balancing and energy efficiency: Cloud-native data architectures optimize energy use across stations in real time. Smart load balancing dynamically distributes power, reducing grid strain and costs, and maintaining service quality.

4. Predictive maintenance for optimized uptime: By analyzing historical and real-time data, your cloud native platform predicts when maintenance is needed, preventing costly downtime. This improves uptime, increasing customer satisfaction and ROI.

5. Data-driven strategies for revenue growth: With cloud infrastructure for data analytics, you can adjust pricing models, gain insights into user behavior, and optimize station placement. These insights unlock new revenue streams and help you stay competitive.

Let’s elevate your EV charging network together!

At NaNLABS, we specialize in building robust, cloud-native data architectures that help businesses like yours scale, optimize, and drive growth with real-time data insights. Whether you’re scaling EV infrastructure or streamlining operations, we’ll be your tech sidekick, helping you make it happen.

The Journey Starts Here

6 Reasons Why NaNLABS is the Right Cloud Data Engineering Partner for EV Businesses

Choosing the right cloud data engineering partner can make or break your growth strategy. At NaNLABS, we combine deep technical expertise with a product-thinking mindset to help EV businesses scale smarter, faster, and more efficiently.

With NaNLABS, you get more than a nearshore dev team; you get a reliable tech sidekick. It means…

Let’s propel your EV charging business forward with tailored, cloud-native data solutions. The journey starts here! 

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