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

Everyone talks about data-driven business these days, but to make your business truly data-driven you need to have full control over the data. And to do that, you need a well-designed data collection solution that gathers data from multiple sources and makes it available in one place.

When it comes to marketing data, a system like this can save days of manual work every month – freeing up more time for marketers to make innovative, strategic decisions. 

Marketers are always asking: which campaigns are performing, what platforms are most successful, and how can we be more effective? But before answering these questions, organizations need to ask the big question: build a custom solution in-house; or invest in a prebuilt, off-the-shelf software?

Why the build vs buy debate matters

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 it 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 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 the security risks, unanticipated downtime due to changes in APIs, and marketing channels that don’t provide a solution for extracting data automatically.

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 (source) 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.

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.

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 a solution, try to ensure that all of these fundamental questions have been asked and answered to ensure that you're set up for success.

If you enjoyed this, try: 13 things to consider before building marketing data pipelines

FAQ 

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 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 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?

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 strategies.
• Gain a competitive advantage: Leverage data-driven insights to differentiate from competitors and create new opportunities.