Key Takeaways:
- Building an in-house data pipeline can cost significantly more than anticipated and introduce hidden risks, from maintenance issues to scalability problems.
- Pre-built solutions help businesses avoid these pitfalls by offering scalable, secure, and maintainable data pipelines.
- The real question isn't just about building vs. buying — it's about choosing the right solution that aligns with your business goals and data needs.
Data shows 74% of companies that invested in ‘high-quality and reliable’ data pipelines increased their profits by an average of 17%, according to ScienceDirect.
This is proof that how you manage your data can directly impact growth.
But should businesses build their own custom data pipeline or purchase a pre-built solution? It’s a common crossroads most organizations will face, and the answer is highly individual to the company in question.
While building in-house offers customization and control, it can lead to unforeseen complexities and costs.
Challenges with scalability and compliance, as well as unexpected hidden expenses, can make what seemed like a good idea quickly turn into a time-wasting headache.
Editor’s Note: This is a sponsored article created in partnership with Adverity.
Pre-built solutions like Adverity’s offer a streamlined alternative, helping businesses scale without the headaches of custom development.
To help guide your decision, here are four critical checks to consider before choosing to build or buy your data solution.
1. Assess the True Cost of Building vs. Buying
Building a custom solution from scratch might seem like the best option for achieving a highly tailored system, but it often comes with hidden costs.
These costs are not only financial — consider the internal resources, time, and ongoing maintenance required to support a custom system.
API connectors, for example, require constant monitoring and updates, as even minor platform changes can break data pipelines and disrupt reporting.
When considering building in-house, you need to factor in the cost of skilled personnel, such as data engineers, analysts, and developers, who will need to continuously update and manage the system.
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"There are so many different data points, especially in media, that there can be a danger of having too much data. We needed to filter through the noise to identify key measures and support our clients’ vision while also re-directing them to identify new areas of impact," said Robert Francis, global data & platforms director at Wavemaker Global.
Additionally, the cost of testing, security, and scalability can add up quickly.
On the other hand, buying a pre-built solution offers a more predictable pricing structure, often including support and updates from the vendor.
If you opt for a robust platform, you may find that the cost of buying is more effective in the long run due to the reduced operational overhead.
2. Evaluate Your Long-Term Data Needs
Another critical factor is the scale and complexity of your data needs. Custom-built systems might initially seem like a good fit for businesses with unique requirements.
However, as your company grows and your data needs evolve, a custom solution may struggle to scale without constant investment and adaptation.
Out-of-the-box solutions, like those offered by platforms such as Adverity, come with scalability built in, making them a more sustainable option as your business expands.
"With our platform LOOP we struggled with maintaining the breadth of APIs, whereas with Adverity it is automated, offering us a rounded proposition, keeping Accord on top of the fast-paced and ever-changing digital landscape," said Dan Ward, head of performance at Accord.
When purchasing a solution, ensure it has the flexibility to grow with your business, offering advanced features like data integration, analytics, and AI-driven insights that can evolve with your changing needs.
3. Speed of Implementation
Time is often a critical factor when choosing between building or buying a data solution. Custom-built solutions can take months, even years, to implement effectively.
This is due to the need for planning, development, and testing phases, followed by any adjustments required for seamless integration into your existing infrastructure.
If your business needs a solution that can be deployed quickly to start driving results, purchasing an existing solution can be far more efficient.
A good data management platform will come with ready-made integrations, user-friendly interfaces, and pre-configured tools that make implementation much faster.
Companies that implement solutions like these often see improvements in data management and decision-making within just a few weeks.
4. Focus on Core Competencies and Expertise
Building a custom solution requires not just technical expertise but also a deep understanding of your business’s data strategy.
It can be tempting to think that your in-house team can handle everything, but without the right expertise, you may miss out on best practices or fail to identify key performance indicators that are crucial to your business’s success.
Out-of-the-box solutions designed by experts who specialize in marketing data management ensure that best practices, security, and performance are built into the system from the start.
Choosing a platform built with your industry in mind gives you instant access to proven tools and expert-led best practices.
"Too many get caught up in the complex, foundational task of simply moving and preparing data, thinking it's a minor hurdle before they can truly drive competitive advantage. Your competitors are already focused on innovating their strategies," said Cameron Benoit, director of U.S. Solution Consulting at Adverity.
"Don't let building foundational infrastructure distract you. Invest in the right data pipeline/ETL tool, and free your teams to focus on what truly differentiates your marketing: strategy, customization, and impact."
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Building might sound great in theory, but in practice, it often means more time, cost, and complexity than expected.
Before going custom, gut-check your needs against cost, speed, scale, and expertise.
In many cases, buying smart means moving faster and focusing on what really matters.
That’s where integrated data platforms, like Adverity, come in to help teams get reliable data without the DIY headaches.
