Ensuring Data Quality in Mobility Usage Management: Principles to Improve Accuracy and Insights

In the Telecom Expense Management (TEM) field, accurately analyzing mobility usage is essential for helping businesses make informed decisions. Unfortunately, usage data extracted from invoices or other sources often falls short of expectation. Issues like missing domestic usage, incomplete voice and messaging data, or incorrectly parsed information lead to inaccurate reports, ultimately resulting in misguided business decisions. Addressing these issues begins with applying core data quality principles. In this blog, we explore how attributes like relevance, integrity, timeliness, and others can enhance mobility usage management.

The Challenge of Mobility Usage Data

Managing data extracted from mobility invoices comes with unique challenges. Often, usage reports are incomplete, focusing only on international data while missing domestic usage, or showing zero usage for certain months even when usage occurred. These inaccuracies are not only frustrating but also undermine the integrity of the analysis and the decision-making processes that rely on it, potentially leading to incorrect actions such as disconnecting active mobile lines.

To better understand how we can overcome these obstacles, let's explore a few key data quality attributes that are crucial for effective usage data management:

1. Relevance

Relevance is key to ensuring that the data we collect is meaningful for clients. In the context of mobility usage, it means providing a comprehensive picture that includes both international and domestic data, as well as voice and messaging data - not just fragmented insights. Ensuring relevance requires us to prioritize capturing the full breadth of mobility data: voice, messaging, roaming, and data usage, so businesses have the information they need to make strategic choices.

2. Accuracy

Accuracy is the backbone of data quality. Incorrectly parsed data, such as omitting domestic usage or providing zero usage where data wasn't capture, leads to flawed analyses and wrong conclusions. This underscores the importance of refining our methods to extract and validate data from invoices, ensuring that every element - voice, messaging, and data - is captured without error.

3. Completeness

Missing data can lead to gaps in understanding usage behavior. For instance, capturing only international roaming while missing domestic usage leaves clients with an incomplete picture. Completeness involves ensuring that every aspect of mobility - data, voice, messaging, and all relevant months - is accounted for, allowing for a comprehensive usage analysis.

4. Timeliness

While we provide ourselves on delivering data in a timely fashion, timeliness is only meaningful when paired with other attributes like completeness and accuracy. It is crucial to ensure that data delivery is not only on time but also provides a complete and accurate reflection of mobility usage. If data is captured from multiple sources, assessing risks and considering alternative sources can help avoid delays that compromise timeliness.

5. Uniqueness

Duplicate data entities can lead to inflated or misleading usage metrics: By implementing processes that identify and eliminate duplicates, we ensure that each data point is unique and provides true value to the analysis. For example, any repeated entries from multiple sources should be flagged and reviewed to maintain the accuracy of the dataset.

6. Consistency

Data consistency is crucial across different sources. For instance, mobility usage reported in one part of an invoice should align with summaries provided in other sections. Cross-verification between data sources helps ensure that all values match and that there is consistency in the data being delivered to clients.

7. Integrity

Data integrity involves maintaining logical relationships between various data elements. For example, ensuring that the relationship between domestic and international data is accurately maintained or that voice and messaging usage are properly linked within the dataset makes the analysis coherent and meaningful. Ensuring integrity allows for actionable insights that are trustworthy.

The Way Forward

Improving the quality of mobility usage data is an ongoing process that requires attention to all the key data quality attributes discussed above. From revisiting our methods of data extraction to implementing regular cross-checks, adopting these principles can significantly enhance the quality of the data businesses rely on.

By addressing issues like accuracy, completeness, and integrity, we can provide a more reliable basis for analysis and empower clients to make better business decisions. Mobility usage management isn't just about data collection - it's about ensuring that data is high quality, consistent, and complete, so clients can navigate their telecom data with confidence.