How is AI impacting data quality initiatives?

In today’s data-driven landscape, the quality of data directly impacts the success of an organization’s decision-making, analytical insights, and operational efficiency. Poor data quality can lead to misguided strategies, and costly inefficiencies. This challenge is universal, affecting industries of all sizes and types.

Drawing from my experience in the telecom sector, an industry where clean, complete, and real-time data is mission-critical, I’ve seen how poor data quality can disrupt operations and erode trust. Telcos have long been pioneers in leveraging big data, analytics, and AI to ensure network reliability, optimize performance, and deliver seamless customer experiences. These lessons are highly relevant to other industries as well.

"Most data leaders feel real pressure to have a Generative AI strategy and use cases in flight." Gartner

To address these challenges, organizations are increasingly turning to AI techniques to enhance data quality at scale. From detecting anomalies and correcting inconsistencies to predicting and preventing errors, AI is transforming how businesses manage their data. In this article, we’ll explore how AI is reshaping data quality initiatives and of course with effective use cases applicable to various sectors.

The true cost of bad data

I’ve seen the true cost of bad data in a real-time environment, where a single error from inaccurate information impacted millions of users. In one case, incorrect data in our inventory caused the network monitoring system to trigger a false P1 alarm, wasting valuable resources and causing unnecessary disruptions, along with sending incorrect notifications to customers. Enterprise clients were frustrated, and the financial impact, including damage to trust, was significant.

This experience highlights that bad data isn’t just an operational issue, it directly affects both customer satisfaction and revenue. In telecom, and beyond, the cost of bad data is simply too high to ignore.

The telecom industry, in particular, feels the real pain of bad data, as it operates in a real-time environment where one simple error can impact millions of users. For instance:

• Revenue Leakage: Telecom operators could lose up to 10% of their annual revenue due to bad data in billing systems, inaccurate customer records, and operational inefficiencies.
• Network Downtime: Incorrect data in network configurations or monitoring systems is a leading cause of outages that are difficult to resolve quickly. Just one hour of downtime can cost up to $1 million in lost revenue and customer compensation.
• Customer Experience: Poor data quality in CRM, ITSM & Billing systems can lead to misdirected marketing campaigns, incorrect bills, delays in incidents resolutions, and frustrated customers. Imagine the impact of a 20% increase in churn rates due to data quality !

These cases underline the direct financial losses as well as indirect costs such as reduced customer trust and operational inefficiencies caused by poor data quality in the telecom and beyond.

Baseline Expectations from a Data Team

In telecom, real-time & accurate data is not just a luxury, it’s a necessity. A small data error can lead to network outages, incorrect billing, or poor customer experiences, which cost time, money, and trust.

Fixing bad data is not just about preventing financial losses, it’s about unlocking innovation, improving operations, and achieving long-term success.

To safeguard and enable IT & Network initiatives, telecom organizations, and of course any Business, rely on their data teams to deliver on the following:

• Uptime: Data must be available in real-time to support network availability and customer services.
• Security: Adherence to regulations like GDPR is crucial, particularly given the volume of sensitive customer data handled by telecom companies.
• Reliability: Accurate billing, services/resources activation & provisioning, customer services management, performance & capacity management, and network configuration & monitoring all hinge on high-quality data.
• Scalability: The ability to process and analyze massive data volumes generated by 4/5G networks, IoT devices, and customer interactions.
• Innovation: Telecom leaders leverage data to fuel AI advancements, such as predictive maintenance, network optimization, fraud detection, churn prediction, and of course personalized customer experiences.

Key metrics for ensuring data quality

I’ve seen how bad data can lead to costly incidents, whether it’s delays in network outage resolution, incorrect billing, or service provisioning/activation issues. In the telecom world, where every process relies on accurate & real-time data, poor data quality can quickly lead to major headaches. Ensuring data quality isn’t just important, it’s critical for keeping business operations running smoothly.

By focusing on these metrics, telecom companies can ensure their data is reliable, driving operational efficiency and supporting data-driven decision-making.

To keep the wheels turning, we relied on a set of key metrics to measure and maintain data quality. These metrics aren’t just technical standards—they are the foundation for every step of the business process framework (eTOM).

• Consistency: Data must remain synchronized across systems without loss or alteration. For example, when customer service updates a user’s contact details in the CRM, these changes must immediately be seen in the billing system, network management systems to avoid discrepancies in service delivery.
• Timeliness: Data must be delivered within the required time frame to support real-time operations. For example, network monitoring systems should transmit performance data to the operations team as close to real-time as possible, typically every minute or, at most, within 5 minutes (as per old snmp standards). This timely data flow enables rapid identification and resolution of issues, such as slow internet speeds or dropped calls, minimizing customer impact.
• Uniqueness: Each data entry should be unique, with no duplicates. For example, in a customer database, if a user has multiple entries for the same account due to an input error (e.g., same phone number and address appearing under different IDs), it can lead to incorrect billing or missed service upgrades. This can be avoided by ensuring each customer has one & unique profile.
• Validity: Data must comply with predefined rules to ensure it is accurate and meaningful. For example, in the UK, addresses must include a valid postcode that matches the geographic location. If a system records an invalid postcode, such as "AB12 3XY" for a London address, it can result in failed deliveries or incorrect customer communications. Ensuring validity prevents such errors and maintains the integrity of business operations.
• Accuracy: Data must precisely reflect reality to ensure reliable operations. For example, misrecording a customer’s location as London instead of Edinburgh, can lead to irrelevant offers, pricing errors, or misallocated network resources. Accurate data is essential for effective service delivery and informed decision-making.
• Completeness: Data must contain all essential attributes to enable accurate decisions and seamless operations. For example, missing details like a customer’s account status or recent payment history can result in service suspensions or delayed issue resolutions.
• Traceability: Data should be traceable to its origin and able to be audited. For example, telecom operators need to track data from the point of collection such as the customer interaction through its various transformations and uses in billing or ITSM systems.

Use Cases for AI in Data Quality

Not long ago, data integration was a slow and complex task. Profiling and discovering data from different sources often took days, or even weeks.

Synthetic Data has revolutionized our approach to data integration, turning what once took days into tasks completed in seconds.

Today, with AI, we’ve accelerated time consuming tasks. For example, by simulating data sources with an AI-driven synthetic data factory, we can quickly integrate systems and create customized datasets for our specific needs, therefore transforming what once took days into tasks completed in minutes.

Here are some key use cases where AI is transforming data quality management:

• Data Cleansing: Data cleansing is the process of identifying and correcting errors, inconsistencies, and inaccuracies within a dataset. Generative AI can easily enhance this process by generating plausible replacement values for missing or erroneous data. For example, in customer data, AI can suggest corrections for misspelled names or fill in missing contact information based on contextual patterns.
• Deduplication: Generative AI enhances deduplication by identifying duplicate records based on patterns such as names, addresses, and other key attributes. AI can suggest merging or eliminating duplicates, resulting in a cleaner datasets.
• Synthetic Data: When real data is scarce or sensitive, generative AI creates synthetic data that mirrors the original dataset. This synthetic data can be used for testing, model development, and sharing without exposing sensitive information. When global telecom companies across multiple countries collaborate, especially during project development, synthetic data becomes the only viable option for ensuring compliance and data privacy. In situations where teams are spread across regions, such as working with offshore teams in India or other countries, sharing real customer data can pose significant challenges due to strict privacy regulations like GDPR. Using synthetic data allows telecom companies to share and work with data without violating privacy laws. It enables teams to simulate real-world scenarios, test new features, and refine models without exposing sensitive customer information.
• Anomaly detection: AI excels at identifying patterns and detecting deviations from the norm, making them invaluable for maintaining data integrity. These models can automatically scan large datasets, compare them to established patterns, and flag inconsistencies, errors, or outliers. By identifying anomalies early, organizations can take corrective actions to update or fix their data. In the telecom industry, AI can detect anomalies in network traffic by learning normal usage patterns like data consumption or call durations. If a sudden spike occurs, such as unexpected data usage, AI flags it as an anomaly.
• Data Profiling & Discovery: Generative AI can streamline data profiling and exploratory data analysis (EDA) by quickly analyzing large datasets to identify key patterns, outliers, and data quality issues.
• Data Enrichment: AI can enhance datasets by predicting missing or incomplete attributes based on existing data. For example, AI can predict a customer's age or demographic information based on available usage patterns, improving customer segmentation and targeted marketing efforts.
• Data Standardization: AI can automate the process of standardizing data formats across various systems. This can include unifying address formats, phone numbers, and other data elements to ensure consistency across systems and platforms.
• Data Validation: AI can automatically validate data as it is entered into systems. For example, AI can verify that customer records are complete, accurate, and compliant with regulations before they are stored, improving the overall quality of the data from the moment data is captured.
• Automated Data Classification: AI can classify data more effectively by learning from patterns and context, ensuring that data is stored in the right categories. This can be applied to categorizing network data, customer complaints, or service requests, ensuring that they are processed efficiently.
• Data Consistency Across Systems: AI can help maintain data consistency between different systems by continuously monitoring and reconciling data flows. For example, this could involve ensuring that customer data, billing information, and network usage stats are always aligned across multiple systems, reducing discrepancies.
• Self-Healing Data Systems: AI can automatically detect and fix data errors as they occur. For example, if a customer’s address is incorrectly entered into a system, the AI can cross-reference it with other data sources to suggest corrections, minimizing manual data entry errors.
• Automated Root Cause Analysis: AI can help identify the root causes of data quality issues by analyzing patterns and correlating data points across systems. For example, this could help uncover why billing errors or network service disruptions are happening and suggest corrective actions.
• Data Reconciliation: AI can reconcile data discrepancies across multiple systems, ensuring that records match across different platforms. For example, AI could reconcile customer information between the CRM, ITSM, billing system, and network usage data to ensure accuracy in customer charges.
• Data Classification and Tagging: AI can classify and tag data based on predefined categories or learned patterns. For example, this could involve classifying customer support tickets into different types like billing issues or service outages, and tagging them appropriately for better tracking and resolution.

Benefits of using AI for data quality

In today’s data-driven world, ensuring the quality and integrity of data is more crucial than ever. As organizations continue to rely on data for decision-making, predictive modeling, and operational efficiency, maintaining high-quality data has become a complex and resource-intensive task.

This is where AI steps in, revolutionizing how organizations approach data quality. By leveraging AI’s capabilities, businesses can automate data cleansing, enhance data quality, ensure compliance, and uncover valuable insights with unprecedented speed and accuracy. In this section, we shall explore the key advantages of using AI to elevate data quality and drive business success.

• Time-to-Value: AI automates data quality tasks, significantly reducing the time required for data cleansing, anomaly detection, and profiling, enabling faster decision-making and more agile operations.
• Cost-Efficiency: By automating labor-intensive processes like data labeling, cleansing, and validation, AI reduces the need for manual intervention, lowering operational costs and freeing up resources for higher-value tasks.
• Trust: AI ensures cleaner & accurate datasets by identifying and rectifying errors, leading to better-quality insights and greater confidence in business decisions.
• Innovation: By generating high-quality synthetic data, AI enables experimentation and testing in environments where real data is scarce or sensitive, driving innovation without compromising privacy or regulatory compliance.
• Compliance & Privacy: AI-generated synthetic data is designed to comply with data privacy regulations like GDPR, allowing businesses to work with data safely while mitigating the risk of data breaches and compliance violations.
• Customer Satisfaction: By ensuring the quality of customer data, AI is enabling personalized marketing, customer support, and service delivery, ultimately driving customer loyalty and retention.
• Decisions: With improved data quality, businesses can trust their insights and make more informed decisions that align with strategic goals
• Competition: By leveraging AI for faster data cleaning, anomaly detection, and profiling, organizations can outperform competitors who rely on traditional methods, gaining an edge in market responsiveness and innovation.
• Real-Time: Generative AI’s ability to detect anomalies and inconsistencies in real-time allows businesses to act quickly on data issues, reducing downtime and operational disruptions.

Conclusion

AI is rapidly transforming the landscape of data quality management by offering smarter, faster, and more scalable solutions that address the complexities of modern data challenges. From automating tedious tasks like data cleansing and deduplication to enabling real-time anomaly detection, AI ensures data accuracy, consistency, and completeness across diverse sources.

With AI, it's no longer about managing data, it's about making data work for you, not against you!

In industries such as telecom, where data privacy and compliance are critical, AI facilitates secure data sharing through synthetic data generation, ensuring GDPR and other privacy regulations are met. This shift not only enhances operational efficiency but also fosters innovation by providing teams with reliable, secure & high-quality datasets.

Embracing AI for data quality is no longer an option, it’s a business imperative that accelerates digital transformation, reduces costs, and drives competitive advantage, paving the way for more agile, data-driven organizations.