Data Mapping Challenges in Customer Data Onboarding

Customer data onboarding can be a daunting process. At Lume, we've encountered firsthand the complexities and challenges that come with it. One of our primary use cases has been to streamline customer data onboarding, a task that can take anywhere from days to several months to complete. Why is it so difficult?

In this blog post, we’ll delve into the intricacies of data mapping during customer onboarding, highlighting the specific hurdles we frequently encounter. Whether you’re new to this topic, looking to improve your onboarding processes, or curious about how AI can make a difference, this post will go over the difficulties of onboarding and how AI data mapping can help.

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The Challenges of Customer Data Onboarding

Schema Differences: The First Hurdle

Each customer designs their schemas differently. Even small decisions, like field names (e.g., f_name vs. first_name), can complicate normalizing datasets. This issue extends to customers creating completely different objects within their schemas for the same underlying entities. Imagine trying to merge two different puzzle pieces together – that’s what dealing with varied schemas feels like. Traditional methods involve painstaking manual mapping or custom scripts that need constant updating and maintenance. This approach is not only time-consuming but also prone to errors.

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Formatting: The Second Obstacle

Beyond fields and schemas, customers interpret fields differently. Underlying values and their formats vary per customer, making it doubly difficult to map fields and clean the data. For example, date formats can be as varied as MM/DD/YYYY or DD/MM/YYYY, and numeric fields might include different symbols or decimal separators. The challenge here is not just recognizing these differences but also transforming them into a consistent format that your system can understand. Traditionally, this task falls on data engineers and analysts who spend countless hours cleaning and normalizing data – time that could be better spent on more strategic initiatives.

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Business Knowledge: The Third Barrier

Understanding customers' decisions often requires multiple back-and-forth emails and calls, further extending the process. Each customer has unique business logic and terminology that needs to be deciphered and integrated into the data mapping process. This step usually involves various roles, from Data Science and Data Engineering to Software Engineers and Customer Success teams. The communication overhead and the potential for misunderstandings can significantly delay the onboarding process.

Importantly, there also can exists gaps in knowledge between the business owner and the engineer implementing the mapping, where teams have internal back and forth and significant sunk-cost work when restarting mappings.

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Additional Challenges and Insights

From our discussions and feedback, we’ve identified a few more challenges in the customer data onboarding process:

• Data Cleaning Logic: It doesn’t sustain for long as new variants and business logic are introduced.
• Standardizing Data Definitions: Requires buy-in from multiple stakeholders, which can be a lengthy process.
• Undocumented Data Pipelines: These can create significant hurdles in understanding and integrating data.
• Sharing Data Across Different Systems and Providers: Ensuring the data runs smoothly, fast, and accurately across various platforms is a persistent challenge.

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The Real-World Impact

Some argue that a long onboarding process in B2B can create a sticky relationship and reduce churn. While this may be true, the reality is that long onboarding times can bottleneck growth for many businesses. Even with a high price point, companies are often limited by the human resources available to onboard new clients. This bottleneck forces them to push back or decline new business opportunities, ultimately limiting their scalability.

For instance, we had a prospect reach out with an average time of nine months to onboard data from each of their clients. Nine months! Imagine the impact on their business operations and customer satisfaction. Although nine months is the record for what we’ve heard at Lume, this situation is not unique; average onboarding time is 5 weeks for teams we’ve spoken with. It highlights the urgent need for a more efficient solution.

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Lume's AI-Powered Solution

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At Lume, we believe in leveraging AI to automate and simplify the data mapping process. Our AI-driven approach addresses these primary challenges effectively, all relying on the concept of semantic similarity.

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Schema Differences: Our system uses advanced AI algorithms to recognize and map different schemas automatically. It can handle variations in table names, field names, and object structures by understanding their semantic meaning, creating a unified data model without manual intervention. Prior automation technologies relied heavily on fuzzy matching, which often led to inaccurate mappings and further complications. Lume’s AI comprehends the meaning behind table names, field names, and structures, allowing us to map schemas more accurately and efficiently.

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Formatting: Lume’s AI can automatically clean and normalize data, ensuring that all values are in a consistent format. By understanding the context and semantics of the data, our system reduces the burden on your data team and increases the accuracy of the data mapping process. This deep understanding allows our system to clean and normalize data accurately, ensuring that all values are in a consistent format without the need for extensive manual intervention.

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Business Knowledge: Our platform includes tools for teams to inject business understanding directly into the AI. Beyond the vast amount of knowledge it is already trained on, company specific business context helps bridge the gap between your business logic and your customers' data. By semantically understanding the data and the underlying business rules, our platform minimizes the need for extensive communication and reduces the potential for misunderstandings. This results in a smoother, faster onboarding process.

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Conclusion

Customer data onboarding doesn’t have to be a painful and prolonged process. With Lume, you can overcome the challenges of schema differences, formatting issues, and business knowledge integration. Our AI-driven solution simplifies and accelerates the data mapping process by understanding data on a semantic level, allowing your team to focus on what truly matters – delivering value to your customers.

Are you facing similar challenges in your customer data onboarding process? We’d love to hear your insights and experiences. Contact us or visit our website to learn more about how Lume can help you streamline your data onboarding and integration processes.

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