Key Highlights
- Identity solutions unify customer data across channels and touchpoints.
- Irrelevant data reduces accuracy and drives up operational costs.
- Advanced filtering and data quality management improve identity resolution.
- Accurate identity resolution boosts targeting, personalization, and ROI.
- Ad tech challenges include data fragmentation, privacy, and cross-device matching.
- Reducing irrelevant data improves efficiency and enhances the customer experience.
Identity Graphs: The backbone of modern customer insights
Consumers interact with brands across dozens of devices and platforms—from mobile devices and laptops to in-store visits and connected TVs (CTV).
However, these interactions cause much of the crucial data companies need to run effective campaigns to become fragmented. That fragmented data landscape makes it challenging for businesses to recognize and engage with their customers consistently, no matter their device.
That’s where identity graphs come in.
What is an identity graph, and how does it work?
An identity graph (ID graph) is a database or data structure that connects identifiers, such as email addresses, device IDs, and phone numbers, to build a unified profile of an individual or household across different devices and touchpoints.
By linking these identifiers, ID graphs enable brands to understand who their customers are, no matter where or how they interact. This improves the precision of ads, marketing campaigns, and personalization, ultimately leading to greater customer engagement.
Why does this matter?
Identity graphs empower businesses to:
- Provide consistent and personalized experiences across various channels.
- Accurately measure advertising and marketing effectiveness.
- Analyze customer behavior patterns.
- Enhance targeting accuracy while upholding consumer privacy, particularly with the decline of third-party cookies.
ID graphs are crucial to AdTech, modern marketing, and data strategy. They facilitate cross-device advertising and customer journey visualization and can even aid in detecting fraud.
In the next section, let’s take a look at how identity solutions operate and why focusing on relevant data is critical to their (and your) success.
Good data, better outcomes: Why quality drives identity solutions
Effective identity solutions give brands a clearer view of their customers by connecting those diverse identifiers mentioned earlier across channels and platforms. By consolidating that data into comprehensive, accurate customer profiles, these solutions enable personalized marketing strategies, accurate analytics, and compliance with privacy regulations.
However, data quality is critical. Irrelevant data—such as outdated, incomplete, or unnecessary information—can undermine the effectiveness of identity solutions. And there is so much irrelevant data to go around, like time zone data, browser types, and screen resolution.
Irrelevant data burdens storage systems, increases operational costs, and introduces the risk of errors. But beyond that, the irrelevant data can influence campaigns, lead to wasted advertising dollars, and significantly impact your ROI. According to Gartner, organizations lose an average of $12.9 million annually due to poor data quality. That includes ad spend and marketing dollars.
Avoiding irrelevant data is imperative. Organizations need to collect and maintain relevant, validated data to do that.
This is where enterprise identity solutions come into play. Businesses, from startups to global enterprises like Amazon, rely on enterprise identity solutions to gather and manage customer profiles.
By tracking behavior across platforms and connecting actions such as in-app purchases and website activity, identity solutions transform raw data into actionable insights.
The right data powers accurate profiles—and ultimately, better business outcomes.
How irrelevant data undermines accuracy, efficiency, and ROI
Irrelevant data can significantly undermine the performance of identity solutions by reducing the accuracy, efficiency, and cost-effectiveness of identity resolution processes. These systems rely on clear and precise data to create accurate customer profiles. When data is outdated, duplicated, or irrelevant, it leads to wasted ad spend and can drive up operational costs.
Poor-quality data complicates everything. It leads to inaccurate matches, identity fragmentation, and inconsistent customer profiles. For example, outdated email addresses or duplicate entries prevent businesses from getting a clear view of their customers.
These challenges slow decision-making and increase identity matching errors. As a result, marketing campaigns may target the wrong audiences, deliver irrelevant messaging, or send personalized content to the wrong recipients. That damages personalization effectiveness and erodes brand trust.
By contrast, systems that proactively eliminate irrelevant data benefit from improved matching accuracy, streamlined operations, and more effective marketing and engagement strategies. That means you’re looking at a better ROI on your campaigns.
Understanding the costs of irrelevant data is just the beginning. Next, let’s explore practical strategies businesses can use to keep their data clean, accurate, and ready to power high-performing identity solutions.
Common challenges with identity graphs in AdTech
While identity graphs offer powerful capabilities, they also present challenges, especially in the AdTech landscape. Some common problems include:
- Data Fragmentation: Customer data often comes from multiple sources and platforms that don’t always integrate seamlessly, leading to incomplete or inconsistent profiles.
- Privacy and Compliance: Evolving regulations like GDPR and CCPA demand complex, resource-intensive compliance efforts.
- Limited Data Access: Major platforms (like Google and Meta) restrict access to user data, complicating the creation of fully unified identity graphs.
- Data Decay and Outdated Identifiers: As consumers change devices, emails, and behaviors, data can quickly become outdated.
- Cross-Device and Cross-Platform Matching Complexity: Accurately matching identifiers across an increasing number of devices requires advanced methods without over-reliance on probabilistic models.
Overcoming these challenges requires robust technology, sophisticated data management practices, and a commitment to continuously refining and validating data sources.
Strategies to minimize irrelevant data in Identity Systems for marketers
To maintain accurate and cost-efficient identity solutions, enterprises must adopt strategic data management practices that minimize irrelevant information. Implementing robust data quality management processes and leveraging advanced filtering technologies can significantly enhance the reliability of processed data.
Implementing Data Quality Management practices
Strong and consistent data management ensures data is clean, valid, and verified before integration into customer profiles. It’s important to have routine audits to identify issues early, clean the data through deduplication processes to eliminate redundant or outdated entries.
This process is imperative as it enhances marketing performance, audience targeting, and all your data-driven initiatives (we know there are a lot).
Utilizing AI and advanced data filtering technologies
All of our systems are increasingly reliant on artificial intelligence (AI) and machine learning algorithms for almost everything. That’s no exception when it comes to analyzing large datasets to detect anomalies, outdated entries, and duplicates.
These tools include:
- Probabilistic Matching: Uses statistical models to uncover hidden relationships between data points.
- Artificial Intelligence: Automates data validation and highlights duplicates and inconsistencies at scale.
- Pattern Recognition: Identifies and removes data points unrelated to core identity signals.
By adopting these tools, businesses can significantly reduce irrelevant data signals, improve the accuracy and scalability of their identity solutions, and achieve greater operational efficiency.
How identity resolution impacts campaign performance
Accurate identity resolution technology is the backbone of successful marketing campaigns. By correctly matching identifiers across devices and platforms, businesses completely understand their customers’ behaviors and preferences. This unified view enables:
- Better Audience Targeting: Identity resolution allows marketers to segment audiences more precisely and deliver relevant content to the right users at the right time.
- Personalized Customer Experiences: With clean, connected data, brands can create personalized messaging and offers that resonate with individual customer needs and behaviors.
- Improved Attribution and Measurement: Accurate identity graphs help track customer journeys across multiple channels, improving the ability to measure campaign performance and ROI.
- Reduced Wasted Ad Spend: By eliminating duplicate profiles and bad data, businesses avoid targeting the same user multiple times or reaching outdated contacts.
- Enhanced Customer Trust: Reliable identity resolution supports privacy-compliant personalization, which builds consumer trust and strengthens brand loyalty.
Data-driven marketing relies on high-quality identity resolution. It’s not just a technical advantage—it’s a competitive necessity for optimizing campaign performance and achieving business objectives.
Cost savings and improved operational efficiency
Reducing irrelevant data directly improves ROI by lowering infrastructure, processing, and operational costs. Leaner datasets reduce the need for expansive storage systems and speed up data processing, allowing for quicker customer interactions and more agile marketing responses.
Efficient systems free up resources and enable teams to focus on core goals like activation, personalization, and campaign optimization. Together, cost savings and operational improvements empower businesses to compete more effectively in today’s crowded digital landscape.
Conclusion
Irrelevant data can significantly undermine the effectiveness of identity solutions. As we’ve explored, maintaining relevant, high-quality data is essential for ensuring accurate identity resolution and reliable customer profiles. By implementing strong data management practices and leveraging advanced filtering technologies, businesses can reduce unnecessary costs, enhance efficiency, and improve the performance of their identity systems.
Prioritizing data relevance will be key to maximizing the value and scalability of your identity solutions. Contact our experts today for more insights or assistance optimizing your identity systems.
Frequently Asked Questions
What are the key components of an effective identity solution?
An effective identity solution integrates diverse data sources like emails, devices, and customer interactions to create a unified customer view. It also employs sophisticated identity resolution logic to deliver accurate, actionable insights.
How does identity resolution work?
An identity resolution platform connects identifiers from multiple platforms using both deterministic and probabilistic matching techniques. Combined with data signals and advanced algorithms, these methods accurately link identifiers to build robust customer profiles that support marketing, personalization, and analytics.
How does an ID graph work?
An identity graph collects and organizes anonymous identifiers from various data sources, linking them to form a continually updated view of customer data. By applying precise algorithms, businesses can create detailed profiles that enhance marketing, personalization, and customer engagement.
Why is irrelevant data such a problem for identity graphs?
Irrelevant data clutters identity graphs with noise, leading to inaccurate matches, duplicate profiles, and wasted spend. It increases processing time, storage needs, and operational costs—all while reducing confidence in campaign performance and customer insights.
What types of data are commonly considered irrelevant?
Examples include outdated identifiers (like old email addresses), device/browser settings not linked to identity (e.g., screen resolution), and anonymous behavioral data without associated identifiers. These data points don’t enhance customer understanding and can distort matching logic.
Can identity graphs adapt as customers change behavior or devices?
Yes, modern identity graphs are dynamic and designed to update as new data signals are ingested. The best systems can handle changes like new email addresses or devices by linking them back to the same customer, ensuring continuity over time.
Is identity resolution still relevant in a cookieless world?
More than ever. As third-party cookies disappear, identity graphs provide a privacy-conscious way to understand and connect with customers across platforms, without relying on tracking cookies. They support first-party strategies and compliant personalization at scale.
How often should identity data be audited or cleaned?
It’s best to audit identity data on a rolling basis or at regular intervals (e.g., monthly or quarterly), depending on data volume. Automated tools and machine learning can also monitor for anomalies and signal decay in real-time, keeping the data fresh and relevant.