Decoding the Financial Landscape of Data Integration Solutions
Glassdoor statistics reveal that the average cost of employing a data engineer can reach up to $100,000 per year, amounting to approximately $500,000 annually for a team of five data engineers dedicated to data pipelines.
In our pursuit of unraveling the complexities surrounding the build vs. buy dilemma, we now turn our attention to the critical question of calculating costs for data integration tools. Specifically, we will focus on the realm of data pipeline building and maintenance, which is an integral aspect of data integration.
As you reach for your calculator, it is worth noting that beyond the initial investment, there are ongoing costs associated with maintaining a bespoke data integration tool. Dev support and upkeep naturally come with additional expenditures. However, it is equally crucial to factor in the expense of adding new connectors and features, which may appear as a never-ending cycle as your organization continues to scale and evolve.
So, let us bridge the gaps, filling in the dots that form the foundation of a comprehensive cost calculation framework for both the build and buy options.
The true total cost of ownership consists of two major groups—hard costs and soft costs.
Hard costs:
- The cost of the initial build. These expenses are associated with designing, building, iterating, and testing the launch of initial features.
- The cost of maintenance. This includes expenditures for bug fixes, adaptive maintenance, performance enhancements, and quality assurance to ensure sustained efficiency and reliability.
- The cost of adding features. These are engineering costs involved in developing and integrating new features to adapt to evolving product changes.
- The cost of keeping all software components and frameworks up to date. That is especially important and often overlooked at a compliance-heavy organization where you deal with sensitive data.
But in our experience, the most important is soft cost. They are hard to calculate, but they, in many cases, outweigh the hard cost.
Soft costs:
- The opportunity cost. These costs are associated with the cost of opportunities you might be lost by distracting your resources from developing your main product.
- “Isolation cost”. It’s a term that we coined by building our own tools. That cost comes from the fact that when you built your own toolset, DSL, etc., every person you hire to work in your data team won’t have any experience with that toolset. You won’t be able to scale your team quickly. The training period will be long and expensive.
- The cost of a lower-quality solution. These expenses are incurred when launching a feature without the robustness and additional capabilities of a third-party solution.
Setting the Stage for Informed Decision-Making
Before we explore the cost breakdown of both build and buy options, ensure you are prepared and gathered the necessary information. To estimate the cost of building data pipelines, focus on the following key factors:
- Average yearly labor cost. Determine the annual cost of data engineers, analysts, or scientists involved in pipeline development and maintenance.
- Number of data sources. Assess the total number of data sources your organization handles, as it affects the effort required for building and maintaining them.
- Time for a typical data source onboarding. Evaluate the average time needed to construct and sustain a typical data source within your organization.
- Concurrency. How many projects you might be doing in parallel? How will you scale your data operations?
The Initial Investment: Getting Your Data Flowing
Accurately estimating the initial costs is a critical aspect when navigating the decision between building or buying data integration tools. For organizations with a clean slate, limited legacy systems, and a small data footprint, setting up a few data pipelines may appear as a no-brainer. However, if the build option is chosen, scaling the organization and managing an increasing number of data sources can quickly escalate expenses. The development of connectors and the integration of diverse sources require significant investments of time and resources. Moreover, training new data engineer talent adds to the financial burden.
On the other hand, some third-party tools may require substantial upfront investments. This is where gaining insights into future plans and understanding the implications of data integration becomes crucial. Fortunately, many data integration tools now offer a pay-as-you-go model, eliminating the need for a significant upfront investment. Additionally, some vendors provide pricing models based on the number of sources to be integrated. Evaluating a tool that aligns with your existing and near-future needs is a wise approach, particularly in preparation for the growth of your business and data operations.
Sustaining Efficiency: The Ongoing Maintenance Investment
Soon after the initial setup, companies often find themselves caught in a perpetual drain of time and resources required to maintain their data infrastructure. Daily challenges faced by Data Engineering teams include unstable data submission, evolving data contracts, APIs, schemas, data quality issues, scalability concerns, documentation, and team attrition. These demands can overwhelm resources and impede progress.
In the case of developing an in-house Data Integration toolset, proper maintenance and periodic updates present substantial costs. Keeping every library and component up to date requires dedicated resources, and forming a specialized pool, even if it starts with just one developer. Considering this expense is crucial, as it can be substantial when compared to the overall cost of Data Operations.
A proactive approach to mitigate maintenance costs is opting for fully managed data pipeline solutions. These solutions incorporate regular and predictable maintenance costs, freeing data teams from the burden of ongoing overhead expenses. Having some functionality readily available, even if it doesn’t meet all requirements, is advantageous as it reduces maintenance costs, often significantly. By partnering with a trusted vendor, maintenance responsibility falls on their shoulders, enabling your team to focus on core business objectives without routine upkeep distractions.
Embracing Growth: The Expenses of Adding Features
When considering the expansion of a data integration tool’s capabilities, the cost implications differ depending on the chosen approach: building in-house or opting for a pre-built solution.
While building an in-house tool offers flexibility and customization, adding new features can be costly. Expenses arise from designing, coding, and integrating these features, requiring resources like developers and data experts. The complexity and scale of the project influence the overall costs.
Pre-built tools offer varying degrees of flexibility. Larger vendors provide a wide range of features but may have predefined pricing and limited flexibility. However, smaller vendors present an opportunity for negotiation and customization, reducing expenses. Start-up companies often prioritize client needs and can be more responsive to specific feature requests, combining affordability with flexibility.
Unleashing Potential and Embracing Competitive Advantage: The Opportunity Costs
When estimating the total cost of ownership (TCO) for data integration, opportunity cost plays a vital role. It signifies the untapped potential and missed opportunities that arise from choosing one path over another.
Building data integration tools in-house comes at a cost. It diverts resources and expertise, impacting strategic initiatives and extending project timelines. Data engineers, valuable contributors to the organization’s data ecosystem, spend considerable time on pipeline development, limiting their involvement to higher-value tasks.
To make informed decisions, organizations must carefully assess opportunity costs. They should explore whether their engineering team’s time is best spent on pipeline development or directed towards initiatives that foster competitive advantage and deliver tangible value to customers. Streamlining data processes, such as laborious uploads and data cleaning, reduces support costs, empowering teams to showcase the true value of their product.
The Hidden Perils of Lower-Quality Data Integration Solutions
Choosing a lower-quality data integration solution may seem like a cost-saving measure, but it brings a host of long-term challenges. Recognizing the true cost implications is vital for informed decision-making and sustained success.
Beyond the initial expenses, subpar solutions demand additional resources to fix errors and enhance functionality, leading to accumulative costs over time.
Moreover, the real cost goes beyond finances. Inaccurate data hampers decision-making and leads to operational inefficiencies, damaging customer trust and brand reputation.
Additionally, relying on an inadequate solution incurs opportunity costs, risking missed business prospects and diminished competitiveness. Embracing quality solutions is key to thriving in a dynamic market landscape.
In conclusion, opting to buy a data integration solution offers significant advantages, outweighing the initial perception of expense. Building in-house requires extensive time, resources, and development efforts, with added complexity due to AI-powered transformations and custom integrations. Hiring and compensating a developer team add to the cost.
However, the decision should not be solely based on cost. The value of a timely solution and saved team time must be considered. Sometimes, seemingly uncertain solutions can prove cost-effective when evaluating the time and business value they unlock. For a deeper analysis of the build vs. buy dilemma, explore our other articles: