AI in Usage Data Management

Introduction

Businesses are generating more data than ever before. Yet, many struggle to extract meaningful insights. To stay competitive, companies are increasingly turning to artificial intelligence (AI) to unlock the hidden potential within their data. 

One area where AI is making a significant impact is usage data management—the process of collecting, interpreting, and utilizing data about how customers interact with products or services. 

In the latest Data for Subscriptions podcast episode, we spoke with Gaurav Dixit, Head of Data and AI Products at DigitalRoute. Gaurav shared valuable insights into how AI is revolutionizing the way businesses manage usage data, particularly in optimizing processes like the order-to-cash (O2C) cycle. 

In this blog, we’ll explore the key takeaways from that conversation, including: 

📊 The importance of structured, high-quality data 

🤖 How AI delivers granular insights and enhances forecasting

🔎 Two powerful AI use cases in usage data management: anomaly detection and usage forecasting

Understanding Usage Data 

Usage data refers to any data about how a product or service is used, spanning industries like software, cloud infrastructure, telecommunications, and more. This data includes everything from: 

• Computing resources (like data and connectivity) 

• Physical telemetry (like distance travelled) 

• Product consumption (like materials, ingredients and chemicals) 
• Service utilization (like licenses, software features and support) 

Usage data captures detailed information about how customers engage with products or services, such as: 

• Consumption metrics for SaaS and subscription models (e.g., number of active users, data storage) 

• In-app analytics and engagement tracking (e.g., feature usage, session length) 

• Itemized phone bills showing call durations and data consumption 

• Electric vehicle (EV) charging metrics (e.g., charging times, energy usage) 

• Consumption data from metered utilities like electricity or water 

AI takes this data to the next level by transforming raw numbers into actionable insights. While usage data is valuable for improving customer experiences and operational efficiency, AI’s ability to uncover hidden patterns makes it a game-changer. 

Why AI is a Game-Changer in Usage Data Management 

AI isn’t just another buzzword—it’s a tool that helps to proactively solve problems before they escalate, enabling smarter, data-driven decisions.  

As Gaurav explains, “the key advantage of incorporating AI into usage data management lies in its proactive nature. AI predicts and helps resolve issues before they turn into costly problems.” 

Here’s how AI transforms traditional usage data management: 

1. Data quality: Ensures data cleanliness and reliability for accurate insights

2. Efficiency gains: Reduces manual intervention and operational costs

3. Predictive power: Identifies and addresses issues before they escalate

By adopting AI-driven solutions, businesses unlock efficiencies, enhance customer satisfaction, and improve decision-making. 

Building the Foundation: AI-Ready Data

The starting point for any AI initiative is high-quality data. Gaurav stresses this: 

“AI-ready data is hygiene—it’s essential to get started.” 

Without clean, organized data, AI cannot deliver reliable insights. Think of it like building a house: without a solid foundation, everything else crumbles. 

To achieve AI-ready data: 

• Regularly clean and validate data to eliminate errors

• Standardize formats and structures for consistency across systems 

• Leverage automated tools for data preprocessing to save time 

When businesses prioritize data quality and avoid dirty data, they establish a foundation for AI to deliver accurate predictions and valuable insights.

The Power of Granular Data 

Granular data—detailed, unaggregated information—is a treasure trove for businesses. As Gaurav notes: 

“Usage data is a very rich and important source of signals.” 

Accessing granular data in real time allows businesses to: 

• Detect trends and patterns early 

• Make more precise decisions
• Gain deeper insights into customer behavior

Gaurav uses Legos as an analogy.

“Granular data is like smaller Lego pieces. When combined, they can form something complex that bigger pieces could not.” This level of detail empowers businesses to understand their operations and customers at a much deeper level, enabling smarter, more informed actions. 

Unlocking Hidden Insights with AI 

Once businesses have clean, structured data, AI can extract hidden insights from granular usage data.

AI can reveal things such as:

• Sudden spikes or drops in usage
• Early indicators of customer churn
• Unresolved support issues

By identifying these patterns, businesses can improve customer engagement, address problems early, and uncover new opportunities for growth. 

Key Applications of AI in Usage Data Management 

1. Anomaly Detection 

Anomaly detection is the process of identifying unusual patterns or deviations in data that may indicate issues like fraud, equipment failures, or customer dissatisfaction. 

“If you can detect anomalies in real time and fix them early, that’s the best thing that can happen,” said Gaurav.  

Gaurav brought the power of AI to life with a real-world example: electric vehicle (EV) charging stations. 

“Imagine an EV charging network spread across hundreds of locations. Each station generates streams of data—from the amount of energy consumed to the duration of each charge. But what happens when something goes wrong?” 

This is where AI steps in. By monitoring charging data in real time, Gaurav explained that AI can detect unexpected patterns, like sudden surges in energy usage or irregular charging times. If anomalies occur, the system immediately flags them as potential issues, such as malfunctioning equipment or even fraudulent activity. 

The benefits of using AI for anomaly detection are clear: 

• Address operational failures quickly: AI helps businesses identify and resolve equipment issues before they escalate, minimizing costly downtime. 

• Prevent fraud: Detecting unusual usage patterns early protects financial resources and ensures fair billing. 
• Ensure smoother operations: With fewer disruptions, companies deliver a better experience for customers. 

“By spotting these anomalies as they happen, businesses can stay one step ahead—fixing issues before customers even notice them. It’s a game changer for operations and customer trust.” 

With clean, reliable data feeding these AI systems, companies can unlock their full potential—turning raw information into actionable insights that drive real-world results. 

2. Usage Forecasting 

Another powerful use case Gaurav mentioned was usage forecasting.  Usage forecasting uses historical data to predict future consumption patterns. This allows businesses to better plan resources, pricing, and operations. 

“Usage forecasting allows companies to optimize inventory, adjust pricing models, or scale operations based on predicted demand levels,” Gaurav explained. 

Take, for example, utilities. By forecasting usage, they can prepare for high-demand periods, ensuring they don’t run into shortages or overproduce. Similarly, subscription services can use forecasting to adjust their pricing models based on predicted customer behavior, ensuring they stay competitive and aligned with customer needs. 

Usage forecasting can help businesses:

• Optimize Inventory: Plan for demand fluctuations without overstocking or understocking 

• Adjust Pricing: Fine-tune pricing strategies based on expected customer behavior

• Scale Operations: Adjust resources to meet predicted demand levels 

By predicting trends in advance, businesses can reduce waste, improve efficiency, and align their strategies with what’s coming—leading to more informed decision-making and smoother operations. 

Case Study: AI to Optimize Data Management for Utility Companies

To highlight the true value of AI, Gaurav shares a real-world case study example from a utility company.  

The Challenge 

A utility company managing hot water and electricity consumption faced significant hurdles in leveraging its vast usage data effectively. Despite having advanced tools in place, the company struggled with predicting issues early and relied heavily on manual interventions to address problems. These inefficiencies led to revenue leakage and bottlenecks in their Order-to-Cash (O2C) process, including: 

• Delayed Invoices: Inefficient workflows caused invoicing delays, affecting cash flow

• Billing Errors: Inaccurate bills slowed collections and frustrated customers
• Inaccurate Data: Poor data quality hindered timely decision-making and reporting

The Solution

DigitalRoute partnered with the utility company to develop a customized solution, transforming their O2C process. By leveraging historical data and advanced techniques like predictive modeling and anomaly detection, he team drove significant improvements. 

As Gaurav explained: “We could see anomalies on the 15th of the month rather than waiting until the 31st, rather than spending five days with five people working 12-hour days. This manual effort caused an average delay of nine days per year in issuing invoices, leading to higher Days Sales Outstanding and revenue leakage.” 

The solution helped the company: 

• Identify issues earlier: Anomalies were detected by mid-month, reducing the need for manual fixes

• Ensure timely and accurate invoicing: Proactive measures minimized delays and prevented revenue leakage 

• Streamline operations: Automated workflows reduced manual effort, saving significant time and resources 

The Results

As a result, the utility company improved cash flow, decreased DSO, and gained confidence in their data-driven decision-making, leading to substantial savings. As Gaurav noted,

“For a typical $100 million revenue U.S. company, they saw net saving in the range of 1.5 to 2 million a year just by doing this. So, it kind of pays for itself even before it gets started.” 

Key Features that Made the Difference: 

1. Data-Driven Insights: Advanced machine learning techniques, like anomaly detection models, identified billing issues early, saving time and resources. 
2. Iterative Data Testing: Starting with three months of data, the team refined models to account for seasonal trends, ensuring predictions grew increasingly accurate. 
3. Business Value Validation: Frequent testing and validation demonstrated how proactive issue detection could save millions annually. 

The Human Factor

The primary challenge wasn’t the technology but integrating it into the customer’s existing workflow. Effective change management was crucial. As Gaurav noted, “The bigger part is how do we integrate it into the business flow? Technology is useless if it’s not used.” 

Conclusion

This use case highlights the power of co-innovation in solving real-world challenges. By working closely with the customer and utilizing data-driven insights, we provided a solution that reduced manual effort, saved costs, and ultimately drove measurable business value.  

Wrapping Up: The Transformative Power of AI

AI is revolutionizing usage data management, offering businesses the ability to: 

• Detect anomalies in real time 

• Accurately forecast future usage 

• Make data-driven decisions that drive growth and improve efficiency 

As Gaurav wrapped up, he emphasized the importance of proper data preparation: 

“Invest in AI-ready data, and you’ll realize the transformative potential of this technology.” 

By prioritizing AI-ready data, companies can unlock transformative potential and stay ahead of the curve. The real question isn’t whether to adopt AI—it’s how quickly you can integrate it into your strategy.

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