Data has become a key differentiator for marketers. The ability to collect, analyze and leverage quality first-party data can be the marker of business success.
However, the abundance and diversity of data can also pose a significant challenge. This is where data mapping comes in.
This post will define data mapping, how it works and the three most common data mapping techniques. Finally, we'll highlight best practices to ensure successful data integration, migration and governance.
What is data mapping?
Data mapping involves visualizing and documenting how first-party data moves within your organization. It includes:
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Understanding what data you have within different tools in your tech stack
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Matching data fields from one source or database to another
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Organizing and distilling large amounts of data
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Making data actionable
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Ensuring adherence to ongoing data management best practices
Why data mapping matters
Data mapping is important because it helps establish relationships between your disparate data sources. This process ensures your data can effectively communicate across systems, sources and tools.
Perhaps even most importantly, data mapping also allows you to streamline data processes, reduce errors and make your data actionable. It empowers you to track real-time trends as they unfold, keeping you ahead of the curve and informing data-driven decisions.
For marketers, data mapping is crucial because it creates consistency and accuracy within data, saves time, and provides a unified language for field names across different sources, making data analysis easier and more efficient. Marketers need to be able to access the right data—and trust its accuracy—to make data-driven business decisions.
Data mapping also gives marketers a visual representation of how first-party data moves through your organization, allowing you to match and link records and create 360-degree views of each data subject, including customers. This holistic view allows for deeper understanding and insights that can enhance your competitive edge.
There are numerous methods marketers can use for data mapping, but there are also purpose-build solutions to help them do this. Those include:
- Customer data platforms (CDPs): Most brands store their data across multiple locations, making it difficult to achieve a consolidated, single source of customer truth that’s necessary to create a comprehensive marketing strategy. CDPs with built-in identity resolution are appealing because they can help marketers align, enrich and analyze their first-party data. Epsilon research found that 59% of marketers are building a CDP in response to third-party cookie deprecation.
- Clean rooms: A clean room with deprecation-resistant identity resolution at its core can help marketers better understand their customers as well as prospects and ultimately target more effectively. That way, regardless of how much first-party data a brand may have, it’s still possible to identify their best customers, build custom audiences and effectively activate on those segments.
The outcome of data mapping for marketers is a better understanding of who their customers are, which then leads to better marketing targeting and audience identification, and, thus, more effective campaigns. Overall, data mapping is a powerful tool that empowers marketers to understand their customers better, align activities with revenue objectives and drive long-term growth.
Simply put, with the exploding amounts of data and data sources within companies today, you must prioritize data mapping to decrease errors and risk, as well as increase the accuracy and accessibility of your data.
How does data mapping work?
Data mapping begins with understanding the first-party data you have and creating an instruction set that identifies data mapping sources, targets and their relationships. Once this information is defined, you can integrate it into other capabilities, including data integration, quality, governance, consistency and migration. Data mapping outlines the process for integrating data into your workflows, including:
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Data integration: Leveraging data from multiple sources and formats gives you a unified view. The data mapping process ensures you understand the relationship between the data and that the fields line up once merged. This data aggregation creates a holistic view of the data, making it more actionable.
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Data quality: Mapping your data allows you to identify and fix any quality issues within your databases. Without a strategic process for mapping and managing your data, the accuracy and trustworthiness of the data quickly deteriorate.
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Data management: Data mapping is the key to data management. With a unified, single source of truth, your stakeholders have easy access to the information they need.
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Person-first data consistency: Consistency refers to the completeness and trustworthiness of the data across your tech stack. Person-first identity resolution is crucial; marketers need to develop customer profiles without data duplicates or errors so they can create truly personalized experiences and genuine 1:1 connections with their audience.
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Data migration: Data migration allows brands to better use their data, which comes in handy for marketing strategy and campaign execution.
Common mistakes in data mapping
When getting started with data mapping, it's important to set yourself up for success to avoid mistakes—because Epsilon research shows that mistakes can be far from harmless. 76% of consumers in our recent personalization survey said they view brands negatively when receiving an ad or marketing message with inaccurate information, and a whopping 91% of consumers say they see an irrelevant ad or marketing message every single day. That's a lot of negative perception.
Here are four common data mapping mistakes to watch out for so you can avoid this negative brand perception:
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Not prioritizing data quality: Starting with clean, accurate data is important for effective mapping. If the data being mapped is of poor quality, its insights and decisions will also be compromised. Investing in data quality processes and tools is important to ensure that the mapped data is reliable and trustworthy.
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Lack of understanding of data sources: It's crucial to clearly understand where and how your data is collected. This includes knowing the different systems and touch points where data is generated and the specific fields and meanings. Without this understanding, it's easy to map data incorrectly, leading to inaccurate insights and decisions. Many publishers collect data from their users in exchange for content. Scaled and linked to a strong identity graph, this data can be used to identify consumers and personalize ads in a world in which third-party cookies need to be relied upon less and less. This is a highly effective solution when it comes from an experienced vendor with scalable technology. However, not all these solutions are built from the same cloth—and it’s important to dig into exactly what signals they’re using. For example, email addresses aren’t persistent, but name and address usually are.
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Infrequent updates: Data sources and structures can change over time. Data mappings can become outdated and inaccurate if they are not regularly updated to reflect these changes. It's important to establish a process for periodically reviewing and updating data mappings to ensure that they remain accurate.
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Relying solely on manual mapping: While manual mapping—more on this in the next section—can be useful for smaller datasets, it may not be suitable for larger or more complex jobs. Manual mapping risks introducing human errors and significantly slows down data transfer processes. Employing automated or semi-automated mapping tools can help improve accuracy and efficiency, especially when dealing with large volumes of data and complex relationships between data elements.
Avoid these common mistakes to ensure your data mapping is effective, efficient and compliant. With accurate and well-mapped data, you can extract valuable insights to power your marketing activities.
The two data mapping techniques
Now that you understand the basics of data mapping, let's dive into two different data mapping techniques: manual and semi-automated.
Manual data mapping
This technique involves manually mapping data elements from source to target systems. While it allows for granular control, it is incredibly time consuming and prone to human errors.
You also need an experienced data scientist to hardcode rules for mapping one source field to another. It's a heavy lift—and likely expensive—but it allows you to fully control your mapping.
Semi-automated data mapping
Semi-automated data mapping combines some manual effort with automation tools that suggest potential mappings based on predefined rules. It strikes a balance between control and efficiency. Artificial intelligence and machine learning are vital for mapping the amount of data that a brand would need to understand their audience successfully—but keeping a human in the loop can help verify information and make any necessary adjustments.
AI and ML can process large volumes of data and adapt to changing mapping requirements with minimal human intervention.
Data mapping best practices
To ensure success, adhering to a few data mapping best practices is important. This section will discuss tips for marketers and brands that are either new(er) to data mapping, or looking to improve data mapping organization, governance and process across their organization.
Clarify your goals
Define what success looks like before starting the data mapping process. This will help you align your efforts and set the right expectations. Be sure to involve all applicable teams and stakeholders to achieve the necessary alignment.
Document these goals and expectations and store them in a central location. Update them as things change within your data mapping process.
Understand your data sources
We've mentioned it already, but it is so important that it bears repeating. Have a clear understanding of where and how data is currently collected and stored across your organization. Dig deeply into every tool, system and solution, not just the primary data sources you use daily.
Document all data sources, including the systems and touch points where data is generated, and create a master list defining each data element's meaning and structure.
Standardize naming conventions
It's hard to keep data organized—and effectively map fields across sources—without a proper naming convention strategy. Take the time to determine and document how you'll address naming across the company.
Start with the data you already have and define a format/structure. The most critical standardization happens with easily missed data fields, like dates and times.
Implement quality checks
Regularly assess the quality of your data to identify inconsistencies, errors and duplicates. Implement data quality checks and create a process for this check-in to help maintain accuracy and reliability.
Whether using automated, semi-automated or manual mapping, it's never a one-and-done process. There is an ongoing component, and you need a dedicated person monitoring your mapping and doing periodic checks to ensure everything is mapping and integrating correctly. This should be part of your data governance protocol.
Automate the process
Embrace automation tools and technologies to streamline database mapping and reduce manual effort. Automation boosts efficiency by saving time and minimizing human error.
If you used an automated mapping technique, you already have the tools and infrastructure to automate the ongoing nature of your mapping and data management processes. But if you used a manual process to initially map your data, you may still want to invest in automation software to manage your data long term. Leverage automation to minimize human error and facilitate continuous data monitoring.
Ensure data quality
When you first map your data, quality is vital. It's not only important to ensure the data maps are correct, but it's also necessary to ensure your output is trustworthy and accurate. Mapping bad, incomplete or inaccurate data only creates more waste in your overall database, thus bringing down the quality of all your data and the related insights.
For marketers, having quality data is key to running effective campaigns, accurate segmentation and reliable 1:1 initiatives. Having quality data is critical to maximizing your marketing’s effectiveness, ensuring every customer and prospect interaction has value.
Evaluating data is no easy task. It’s a process that takes time, dedication and the right tools. Marketers should use traditional measures and review data from a contextual standpoint to ensure it meets specific business needs. And for stronger results, marketers may need to dig deeper and consider criteria they may not have before.
Regularly review and update data mapping
As already mentioned, data mapping and management is an ongoing process. Beyond your quality checks, you need dedicated resources (both people and platforms) to keep up with changes in your data sources (and regulations).
Then, establish a process for periodic reviews of your mapping to ensure all fields are integrated, even from new data sources. Watch for changes to your data systems, applications or structures that could impact how you've mapped your data.
In addition, any time you add new applications or software, develop a process for mapping that data and integrating that data with the rest.
All your data in one place with Epsilon
The overarching key of data integration is to have your data in one place and to make it actionable. Epsilon offers a comprehensive solution to empower marketers with advanced data management capabilities.
Epsilon's Customer is a customer data platform that serves as a central repository for all your data, enabling seamless integration, organization and utilization. But it's not just for data storage; you can also activate campaigns from within the tool. Epsilon provides the identify, infrastructure and expertise to harness the full power of data. Learn more.
