Introduction
When working with Customer Journey Analytics (CJA), encountering "No Value" entries in reports and dashboards prompts important questions about data quality, collection methods, and reporting accuracy. These instances need careful monitoring as they may reveal hidden gaps in data collection. The main challenge lies in distinguishing between two scenarios: when "No Value" entries need investigation by data source providers, and when they simply reflect the natural flow of data into CJA. Understanding this distinction is crucial for maintaining efficient analytics operations. This guide will help you make informed decisions about "No Value" appearances in your CJA implementation.
Understanding "No Value" in CJA
"No Value" appears in CJA reports when a dimension has no corresponding metric value during the reporting period, regardless of the report type. Seeing "No Value" in your reports isn't always a sign of an error or data collection issue. It's often an expected and valid part of your dataset's structure. This distinction is crucial for CJA product administrators and CJA analysts to determine whether they need to take action or if these entries simply reflect normal user behavior.
This diagram illustrates the three main ways that values can appear in CJA data collection:
- Expected No-Value Scenarios (Purple) - These represent natural "No Value" manifestation states in the data, such as when a user hasn't logged in yet or when certain dimensions aren't contextually relevant as applied against a given metric
- Problematic No-Value Cases (Red) - These indicate potential problems that need investigation, such as failed data collection or implementation errors where dimension values should contextually exist but are missing from reports
- Valid Values (Green) - These represent properly collected dimension values containing the expected information. They showcase successful data capture and serve as the opposite manifestation of "No Value"
The flowchart illustrates how CJA evaluations should center on incoming data by first checking for value presence, then determining whether missing values are expected or problematic. This clear assessment helps administrators and analysts differentiate between "No Value" cases requiring source investigation and those representing normal operations.
Scenarios Where "No Value" is Expected
The following scenarios illustrate when "No Value" entries in your CJA reports are both normal and expected. These situations reflect legitimate data collection patterns that match typical user behaviors and platform architectures. Understanding these cases helps CJA practitioners avoid unnecessary troubleshooting and focus on genuine data quality issues.
- Dimension values that only apply to specific tracking scenarios, such as traffic sources, marketing channels, and device indicators
- First-time visitors to a website before specific dimension identifiers are set
- Pre-login dimension states where user information isn't yet available
- Features or section content dimension indicators that aren't relevant to all user journeys
- Specific product interactions that only apply to certain user activities
- Cross-device or platform scenarios where dimension continuity isn't maintained
These scenarios reflect natural and expected data states that originate from Adobe Experience Platform (AEP) datasets and appear in CJA dimensions. Such instances align with anticipated user behavior patterns and properly reflect the fundamental design of the CJA architecture. When the "No Value" line item appears in these contexts, they actually serve as valuable signals indicating normal user interactions and expected data collection states across the platform. Understanding these natural valid scenarios helps analysts distinguish between legitimate data patterns and actual collection issues that require attention.
The diagram shows how user data flows naturally through CJA. It starts with a pre-login state (represented in purple) where "No Value" entries are expected and normal because user identification hasn't occurred yet. When users authenticate and log in, the state transitions to post-login (shown in green) where user information is properly populated with meaningful values in the CJA. This progression from unidentified to identified states is one of the most common scenarios in data collection. It shows how "No Value" entries can effectively indicate where users are in their authentication journey. This pattern is both expected and useful for analyzing user behavior and system functionality.
When "No Value" Needs Attention
While many "No Value" instances are expected and natural in your data collection process, certain scenarios should warrant thorough investigation. These particular cases often serve as warning signs, potentially indicating underlying technical issues, gaps in data collection methodology, or fundamental implementation problems that could impact the integrity of your analytics. Such scenarios may compromise not only the accuracy of your data but also the reliability of insights derived from CJA. When "No Value" appears in these contexts, it frequently suggests deeper systemic issues that require careful examination and prompt remediation. These situations deserve heightened attention because they can represent breakdowns in your data collection infrastructure, gaps in your implementation strategy, or technical misconfigurations that could have far-reaching implications for your analytical capabilities. Here are situations where "No Value" appearances warrant comprehensive review, thorough analysis, and strategic remediation efforts:
1. At Source Implementation Issues
- Missing data elements or null values
- Incorrect variable mapping
- Improperly configured data layer
- Failed data collection
- Schema mismatches and invalid schema configurations
2. Data Quality Issues
- Broken tracking code
- Incomplete data collected
- Integration failures
- Data transformation errors
- Data pipeline disruptions
Managing “No Value” Options in Data View Component Settings
Customer Journey Analytics provides an extensive set of configuration options that have been engineered and optimized for managing and controlling how "No Value" entries are handled within your Data View dimension settings. These settings can be configured by CJA product administrators at the Data View level, giving precise control over how "No Value" entries are processed, interpreted, and displayed in your CJA implementation. This configuration framework enables you to fine-tune the handling of "No Value" according to your specific needs, ultimately ensuring your reporting output aligns with your organization's analytical requirements, data governance standards, and broader business objectives:
When configuring these settings, evaluate your reporting requirements and assess how the presence of "No Value" affects your analysis. Consider both immediate effects on data visibility and long-term impacts on trend analysis and reporting consistency. Well-chosen configurations enhance data clarity while keeping business insights accessible and actionable, regardless of how "No Value" entries appear in your reports. The ideal configuration balances data representation with practical analytical needs, creating a reporting environment that delivers accurate and meaningful insights even when "No Value" data is present. Let's examine the specific configuration options available:
If shown, call “No value”
This setting lets you customize how "No Value" rows display in reports. You can enter a custom name for the "No Value" dimension item in the text field, providing more meaningful context through the "If shown, call 'No value'" field. Using clear, business-friendly terms instead of "No Value" helps your organization better understand report values. While you cannot use "No Value" directly as a string in segments, you can achieve the same effect using the "does not exist" operator.
You can replace "No Value" with descriptive terms like "Pre-login User" for authentication status, "No Customer Tier" for customers without tiers, or "No Tracked Marketing Channel" for unidentified marketing sources. This creates more intuitive reports. "Pre-login User" clearly shows where a customer is in their journey, while "No Customer Tier" provides specific context. Remember that your chosen description applies to all "No Value" instances for that dimension, so select terms that accurately reflect all scenarios where dimension values are absent.
Don’t show “No value” by default
This setting determines whether to hide "No Value" rows by default in reporting. When enabled, these rows will be filtered out initially but can still be shown within a freeform table if needed by check box selection within freeform table search filter. Note that hiding "No Value" rows will affect the percentage distribution of the remaining values, as percentages are recalculated based on the visible items only.
Show “No value” by default
This setting controls whether "No Value" appears by default in reports. When enabled, "No Value" entries will be visible, though users can exclude them using the checkbox in the freeform table search filter. Including or excluding "No Value" rows affects percentage distributions, as percentages are calculated based only on visible items.
Treat “No value” as a value
This setting treats "No Value" as a string value (except for numeric dimensions), allowing you to customize its representation as a dimension value. This customization affects both attribution and the "Include No value" option in the Freeform table search filter. Keep in mind that when you assign a custom string value, all matching values in your dataset will be consolidated under that same dimension string value.
The "Treat 'No value' as a value" setting serves a fundamentally different purpose than simply showing "No value" by default. While showing by default only controls visibility, treating as a value changes how CJA logically handles these entries. Here's why this distinction matters:
- It enables more granular control in filtering and segmentation, making "No Value" a distinct, actionable dimension value
- It maintains consistent attribution and representation throughout your analytics by treating "No Value" as a legitimate dimension value in both attribution models and visualizations
You might choose to treat "No value" as a value when:
- The absence of data itself is meaningful to your analysis (such as pre-login states or unattributed traffic)
- You need to create segments or calculated metrics that specifically target or exclude these cases
In contrast, simply showing "No value" by default is better suited when you need basic visibility of missing data—without the complexity of additional logic and attribution that comes with treating it as a value.
“No value” support for numeric dimensions
For numeric dimensions, several configuration options are available. In the Data View dimensions settings, you can configure all "No value" options except "Treat 'No value' as a value." You can also manage "Include 'No value'" for numeric dimensions by check box selection within freeform table search filter. When creating segments, you can use the "exists" or "does not exist" operators with numeric dimensions.
"No Value" Solutions and Best Practices
Once you've identified problematic "No Value" instances, you'll need to develop and implement a remediation strategy. This can be done in two ways: by adjusting Data View component "No Value" option settings, or by fixing issues at the data collection source. Choose your approach carefully, as each path has different implications for both quick fixes and long-term data quality. Your implementation should follow a methodical process that fixes current issues while preventing future ones. Success depends on planning, systematic execution, and ongoing monitoring. Here are key strategic considerations for your remediation plan:
Prevention Strategies
- Implement data validation before processing
- Set default dimension values when appropriate. However, never set default values for Person IDs in CJA, as this would compromise person identifier data integrity
- Create clear documentation for expected "No Value" cases
- Establish data quality checks at collection points
- Monitor data model compliance consistently
- Implement error logging for validation failures
- Create automated testing for data collection processes
- Enforce data point tracking by making fields required in the schema, which helps prevent "No Value" instances.
CJA Validation Techniques
- Create segments to isolate and study "No Value" patterns
- Develop QA dashboards to monitor "No Value" trends
- Use alerts to track "No Value" trends over time
- Generate automated reports and dashboards that track and highlight significant changes in "No Value" patterns across dimensions, enabling quick identification of unusual spikes or concerning trends that may require immediate investigation
- Cross-reference "No Value" patterns across multiple dimensions to identify potential correlations or systemic issues
- Conduct regular audits of dimension configurations to ensure proper handling of "No Value" cases
- Maintain a changelog of "No Value" management strategies and their effectiveness
- Develop standard operating procedures for investigating and resolving "No Value" anomalies
- Create documentation templates for tracking and reporting "No Value" issues to stakeholders
Conclusion
Not every "No Value" in your CJA reports signals a problem requiring immediate attention. The key is developing a thorough understanding of both your AEP and CJA data architecture, along with the various user journey patterns. This contextual knowledge helps you distinguish between natural "No Value" states and genuine data collection issues. When you invest time in managing "No Value" Data View components, implement regular monitoring, and maintain detailed documentation of expected "No Value" scenarios, you strengthen your analytics system's reliability. This proactive strategy creates a clear baseline for normal data patterns, making it easier to identify and resolve genuine anomalies when they occur.
Remember: The goal isn't to eliminate all "No Value" instances from your CJA implementation, but rather to establish and maintain CJA organizational data heuristics and administrative oversight that clearly distinguish between expected and problematic "No Value" scenarios. These guidelines should be grounded in your understanding of user journeys, system limitations, and valid business cases where "No Value" dimension values are expected and reasonable. By developing and documenting these data interpretation rules, you create a robust framework for evaluating when "No Value" cases require intervention versus when they represent valid data states.
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