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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.
Data mapping involves visualizing and documenting how first-party data moves within your organization. It includes:
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:
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.
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:
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:
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.
Now that you understand the basics of data mapping, let's dive into two different data mapping techniques: manual and semi-automated.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
