Build vs. buy: what's best for your marketing data infrastructure?

Owning your marketing data starts with a choice: do you build a custom pipeline in-house, or do you buy a managed platform to handle the engineering? Your decision will dictate where your time goes. Choose correctly, and your team gets back days of manual work every month. Choose poorly, and you trap your team in a permanent money pit that sucks up dev hours without ever delivering a clear answer on your true ROI.

In this article, you’ll learn the pros and cons of building and buying a marketing data infrastructure, the decision-making framework you need to evaluate your current situation, and how to make sure your decision stays future-proof for years to come.

Why the build vs buy debate is important

Whether or not to build or buy data pipelines or data management systems is crucial for businesses because it significantly impacts how they handle and leverage their data. With more data available than ever, it’s important that it doesn’t go to waste.

The choice can have far-reaching consequences for budget, time-to-market, flexibility, in-house capacity, security and integration. On average, companies spend $1000-3500 per employee on software tools every year. Businesses with 2500-5000 employees are commonly spending between $40-100 million on hundreds of apps every year (source). That’s why being choosy is important, and why many businesses are lured in by the apparent saving of building solutions in-house.

How hard can building it yourself be?

You have the resource in-house, you know how data works and exactly what you need. How hard can it be? Ok, let’s say you build a custom solution. First, there are some factors to consider before jumping in.

These vary for every business, but here are some of the fundamental questions that need to be answered before considering a project of this scale:

• How rigid is your time to market?
  A tight deadline might favor a pre-built solution, while more flexibility allows for building software yourself – as long as you account for potential hiccups along the way.
• What are your resources and expertise?
  A skilled team and ample resources could build a custom solution, but would their time be better used elsewhere?
• What features and functionality will you need?
  The old adage goes: buy what you can, build what you can’t. So if you have unique requirements that no tool on the market can cover, it might be best to consider an own-build for complete control over features.
• What is the sunk cost and TCO (total cost of ownership)?
  Calculate factors like licensing fees, maintenance costs, and development time when evaluating costs; don’t forget the cost of potential downtime and bug fixing.
• What are your opportunity costs?
  Delaying implementation might miss business opportunities, while investing heavily in building a custom solution might limit flexibility.
• Do you know all the security and regulations per region and business type?
  Pre-built solutions often offer built-in compliance features, but a custom solution needs careful consideration and due diligence work to ensure you meet standards and regulatory requirements.

Plus, we can ask more technical questions like:

• How will the first party data be collected, stored and analyzed?
• How often can we refresh and query the data?
• How can we scale this solution to support new channels and changes in our business?

You’ll also need to consider security risks, unanticipated downtime due to API changes and marketing channels that don’t provide a solution for automatic data extraction.

The hidden cost of building

You’ll likely encounter expenses that remained invisible during the planning stage:

API maintenance

Third-party data platforms like Meta frequently update their APIs, and changes occur on independent schedules and can break existing integrations. Consequently, a connector that required three weeks of initial development eventually demands more engineering time for permanent maintenance.

Schema drift

The way platforms organize data can change without warning. So, a piece of information that used to be a simple word might suddenly become a complex list. If your system isn’t designed to catch these changes immediately, the software will keep running but produce incorrect results. Usually, companies only notice these errors when the financial department sees that the numbers do not add up.

Talent risk

The engineers who can fix and maintain the pipeline are expensive. You need at least two people so that if one is sick or on vacation, the system still works. Between salaries, benefits, and equipment, a two-person team costs at least $500,000 per year, which is a permanent cost you must pay before you even see any useful data. If the talent decides to leave, your knowledge goes with them.

Opportunity cost

Your engineers only have a set amount of time. Every hour they spend fixing data connections is an hour they cannot spend on your product or new features. Most companies that built their own systems a few years ago have learned the same lesson: the cost to build the tool was small, but the cost to keep it running is enormous.

Build vs. buy your marketing data platform

It's worth noting that every marketing platform is unique, meaning you need a deep understanding for each integration in order to ensure that you’re extracting the right data, in the right way.

Once these questions have been answered and the data is flowing automatically, you’ll need to make sense of it before you can start analyzing your marketing performance. This means automating the following aspects using developer and/or marketing hours if you intend to do this manually:

• Data normalization
• Currency conversion
• Data grouping
• Calculated metrics
• Data enrichment
• Report building

You need to understand that the ways in which individuals want to access and view the data varies depending on their role.

Marketers may want to analyze aggregated data in a spreadsheet or as an easily-shared visualization. A data analyst may prefer to have the data delivered to them in a SQL interface. Data scientists might opt for getting the data in full in a parquet file.

It's important to ensure that all of these needs are met and the data is accessible, approachable and usable across the organization.

The build vs buy argument in a nutshell

Some businesses find it difficult to make a decision because the pros and cons aren’t always black and white. Control and customization is nice, but what about if it comes at a huge cost in developer time?

The underlying question is, where do you want to invest your time and internal resources: creating and maintaining data source integrations, or creating business value?

The average number of SaaS applications used by companies rose from 80 in 2020 to 130 in 2022 and the industry is only set to grow in the coming years. So the chance of finding a software platform that already covers your needs is fairly high.

A framework to inform decision-making

Choosing between building a data pipeline or buying is a high-stakes decision that affects your budget and your team's workload for years. Most people focus only on the initial cost, but that ignores the long-term reality of keeping the system running. You need a structured way to look at the problem so you don't commit your team to a project that eventually becomes too expensive or time-consuming to manage.

1. What is your biggest limit: money, time or control?

If you need results immediately to show that your marketing works, buying a tool is faster; you can see reports in a few days. If you have plenty of time, a large engineering team, and need the software to do something very specific, building it yourself allows for more customization.

2. Are your marketing data types unique?

If you use the same popular marketing platforms like search engines, Meta and TikTok there is little reason to build your own tool. Standard tools already solve these common problems well. You should only consider building your own system if you use niche, custom software that doesn't connect to anything else.

3. Who will fix the tool next year?

Building a tool is a permanent job. If your team was hired to analyze customer marketing data rather than fix broken code, the constant maintenance will prevent them from doing their actual jobs. If no one on your team wants to be responsible for long-term repairs, you should buy a solution instead.

How AI has changed what managed solutions can do with campaign data

The gap between build and buy has widened significantly in the past two years, and the biggest reason is how AI has changed what managed platforms can deliver out of the box.

AI-powered data handling replaces manual engineering work

Modern platforms use AI to automate tasks that previously required dedicated engineering time, like anomaly detection, data validation and intelligent schema mapping. A drop in spend or a disappearing metric gets flagged before it reaches your reports. Data organization that took a week of manual work can now happen in minutes.

MCP has changed how marketing data reaches AI tools

Model Context Protocol, the open standard developed by Anthropic and now supported across Claude, ChatGPT, Gemini and others, lets teams connect their marketing data to AI through one integration instead of building and maintaining separate connections for every platform. Funnel's MCP Server goes further: delivering 600+ connectors, a semantic layer that standardizes cross-channel campaign data and the business context your team has built into your workspace. The AI receives data it understands, so you spend less time explaining to it what your metrics mean and more time acting on what they reveal.

The cost of building these capabilities in-house continues to rise

A dedicated team could eventually replicate some of these features. But managed platforms ship updates continuously while in-house teams spend their maintenance budget keeping existing pipelines from breaking.

The power of pre-builds

Building an in-house data collection and transformation solution is usually more difficult than most companies imagine. Automated solutions like Funnel can deliver a scalable and cost efficient solution without compromising flexibility or control.

Funnel is built around a data integration-first approach to marketing data. It preserves your raw data at the source and applies transformation at query time. Now, this is important when an ad platform changes its schema, or you need to reprocess history under a different rule; your data isn't locked into yesterday's model. The result is a managed layer that gives you the speed of buying with much of the flexibility you'd expect from building it yourself.

The benefits include:

• Out-of-the-box integrations to a wide range of marketing platforms, which do not require developer assistance to configure.
• If you need something off-menu, custom integrations can be built upon request, ensuring coverage for all marketing platforms your team is using.
• A robust and flexible data transformation level makes cleaning, mapping and reporting on meaningful groups of data not only possible, but achievable in minutes by a business user.
• Integrations to all major data warehouse solutions, BI solutions and visualization tools, gives you the freedom to utilize your data anywhere.
• A dedicated provider will also perform regular maintenance – so you won't have to worry about API updates. Plus they're often better for scalability further down the line.
• Quick onboarding with pre-builds means you should be up and running in no time – with educational resources and customer help when necessary.
• With all the experts working on today's platforms, a lot of tools can plug and play with no code knowledge and no expertise in data science.

Regardless of whether you end up building or buying an ETL solution, try to ensure that all of these fundamental questions have been asked and answered to ensure that you're set up for business success.

FAQs

What is a data pipeline?

A data pipeline is a series of connected processes that move data from a source to a destination, often for analysis or storage. It's like a conveyor belt that carries data from one stage to the next, transforming and cleaning it along the way.

Key components of a data pipeline typically include:

• Data ingestion: This involves collecting data from various sources, such as databases, APIs, files, or sensors.
• Data transformation: The data is cleaned, standardized, and transformed into a suitable format for analysis or storage.
• Data storage: The processed data is stored in a data warehouse, data lake, or other storage system.
• Data analysis: The stored data is analyzed using various analytics tools and techniques to extract insights and information.

Data pipelines are essential in modern businesses for:

• Making data-driven decisions: By providing access to clean, reliable data, pipelines enable organizations to analyze trends, identify patterns, and make informed choices.
• Improving operational efficiency: Pipelines can automate data-related tasks, reducing manual marketing effort and errors.
• Enabling advanced analytics: Pipelines can support complex analytics techniques, such as machine learning and artificial intelligence.

Examples of data pipelines include:

• Marketing analytics: Collecting and analyzing customer data to optimize marketing campaigns.
• Financial reporting: Gathering and processing financial data for reporting and analysis.
• Fraud detection: Identifying suspicious patterns in data to prevent fraudulent activities.
• Supply chain management: Tracking and analyzing data related to product movement and inventory.

In essence, a data pipeline is a crucial component of modern data management, enabling organizations to harness the power of their data to drive business value.

Why do companies need a data analytics solution for customer data?

Companies need data analytics solutions to make informed decisions, optimize operations, and gain a competitive edge. By harnessing the power of their data, businesses can:

• Understand their customers: Analyze customer behavior, preferences, and demographics to tailor products and services.
• Improve marketing campaigns: Measure campaign effectiveness, identify high-performing channels, and optimize marketing spend.
• Optimize operations: Identify inefficiencies, reduce costs, and improve productivity through data-driven insights.
• Predict future trends: Forecast market changes, anticipate customer needs, and develop proactive marketing strategies.
• Gain a competitive advantage: Leverage data-driven insights to differentiate from competitors and create new opportunities.

Should I build or buy my marketing data pipeline?

The choice depends on your staff, your deadline and your data needs. Purchase a tool if you require reliable reports within a few weeks and your team has more important projects to handle. Build a custom system only if you have experienced engineers with extra time and you are prepared to pay for repairs for several years. Most successful companies use a mix, and they’ll buy a service for standard platforms and build custom code only for their most unique requirements.

What is the total cost to build a marketing data infrastructure?

Initial development usually takes four to eight months, but the true expense appears in the second year. To keep the system functional requires constant updates and monitoring, which typically consumes engineering time and doesn’t facilitate the marketing team to self-serve. For most companies, the cost of building and maintaining a custom system surpasses the price of a subscription service within 18 to 24 months.

When is building a data marketing infrastructure better than buying?

Custom construction makes sense if your business has rare requirements that no existing software can handle. You must also have a senior engineering team committed to long-term maintenance. Conversely, buying is the better option when you need to move quickly and your data comes from standard sources like Google or Meta. Using a pre-built service allows your team to spend their time analyzing results rather than fixing broken connections.