In today’s fast-paced and data-saturated business climate, establishing clean, compliant, and unified customer data is essential. Accurate and well-organized data is key to gaining better customer insights and developing more effective marketing strategies. However, obtaining and maintaining high-quality customer data is complex and costly, especially given the rapid growth of data and its often fragmented, nontraditional forms.
Evaluating Data Management Platforms
When deciding how to manage customer data, companies must evaluate the pros and cons of building a custom in-house solution versus adopting a customer data platform (CDP). Building a solution offers customization but comes with higher development, maintenance, and troubleshooting costs and initial set up over time. In contrast, a CDP may have higher upfront licensing fees but requires less ongoing management. To make an informed choice, businesses should consider the total cost of ownership (TCO), which accounts for both immediate and long-term costs, including operation, maintenance, and training. By assessing TCO, companies can avoid short-sighted decisions and ensure they choose a solution that balances immediate needs with long-term efficiency and scalability.
Uncovering Hidden Costs and Long-Term Benefits
TCO is essential for revealing hidden costs that might not be evident when initially selecting a data management solution, including concerns like data quality, usability, future-proofing, governance, and evolving standards. By examining the full lifecycle of a system—beyond just upfront costs—TCO helps businesses identify expenses related to maintenance, upgrades, training, and downtime. These hidden costs can accumulate over time, impacting long-term efficiency and overall profitability. A comprehensive TCO analysis allows companies to evaluate not just the immediate financial impact but also the operational sustainability of a solution, ensuring that it delivers value and efficiency over the long term.
Evaluating Data Management Costs and Efficiency
When evaluating the TCO for a data management solution, several key factors should be considered. These include the cost of software licenses, the time required for implementation—including the number of teams and individuals involved—and the conversion of these costs from dollars to hours. Additionally, ongoing maintenance costs should be assessed, taking into account the number of applications, the complexity of the solution, and the level of expertise needed, as higher-skilled engineers command higher rates.
It’s also crucial to factor in the expected “time to value”—the time it takes to see returns from customer data—as longer deployment periods can introduce risks such as shifting priorities or organizational changes that could delay or cancel the project. Finally, consider the opportunity cost of opting for slower or cheaper solutions, which might impact overall efficiency and effectiveness.
Industry-Specific TCO Examples
Using a model-based calculator, let’s look at how different industries might estimate their in-house data management expenses if they used TCO as a measure from the start.
Airline/Hospitality Industries: Air travel and hotels face unique data management hurdles. These sectors must contend with a multitude of customer identifiers, including booking numbers, loyalty programs, and email addresses. Additionally, they need to accurately associate group bookings, such as shared hotel rooms or multiple flight tickets.
- Assuming seven data engineers at a given company (which is the average in the US today) just the initial set-up of developing in-house customer data infrastructure can consume approximately 1,400 hours of work.
- Ongoing maintenance can require an additional 300+ hours a year.
- Over the course of a year, hidden costs, like aligning automated flight status reminders with mobile software updates, and the physical, browser-based, and potentially siloed data across multiple customers and households, can add expenses of approximately $160,000.
Financial Services Industry: Financial services companies operate in a landscape of stringent regulations and high security demands. They require impeccable data quality for effective marketing, accurate credit analyses, and integration with rating agencies. Furthermore, these organizations must ensure data accessibility across various departments while maintaining strict confidentiality protocols.
- Establishing this robust data infrastructure, including all the concerns mentioned above, would require around 2,600 hours.
- Annual maintenance demands approximately 500 hours.
- Additional costs, such as maintaining integrations and accommodating alterations in standards to ensure up-to-date credit ratings and effective adaptations to fluctuations in customer financial data, would likely reach approximately $130,000 in wages and compute for a typical business annually.
Weighing Custom Solutions with Lakehouse CDPs
Evaluating the total cost of a custom-built, in-house data management solution should account for the considerable resources needed for its ongoing maintenance after implementation. These platforms demand ongoing, dedicated resources, including skilled developers and data engineers who might otherwise focus on revenue-driving projects. Maintaining these solutions requires continuous effort to keep them up-to-date, secure, and compliant with evolving standards and regulatory requirements, which can vary significantly across regions and industries. The need for continuous compliance and adherence to changing standards can make these solutions resource-intensive and challenging to manage over time.
This is why many organizations are shifting to specialized customer data platforms to take better control of their data. Adopting a Lakehouse CDP architecture can provide significant long-term benefits. These platforms are designed to integrate with existing tools with faster setup, built-in compliance, and automated maintenance. By reducing the burden on data engineers and minimizing the time spent on maintenance and compliance, a Lakehouse CDP enables businesses to achieve faster time to value and allows technical teams to concentrate on strategic, revenue-driving initiatives. While each industry faces unique data challenges, customer data platforms provide a more efficient and cost-effective way to maximize the value of customer data while minimizing hidden long-term costs.
Businesses need to fully examine the full scope and lifecycle of their data management strategies with comprehensive total cost analysis. This approach ensures they select a solution that not only meets their immediate needs but also supports long-term efficiency, scalability, and cost-effectiveness.
By Caleb Benningfield