In today’s data-driven world, organizations are increasingly relying on data integration to manage, move, and connect their vast troves of data. While building your own platform may seem like an attractive option, it often presents many challenges that outweigh the benefits. Let’s compare the potential risks and benefits of:

 (1) building everything from scratch, 

(2) customizing a paid solution so data engineers can build a tailored solution, or 

(3) buying & adopting a data platform outright from a vendor.

Building a Data Platform: A Risky Proposition

1. 1. Ongoing Maintenance: Custom builds are often purpose built for particular use cases, and often have to be mostly, if not completely, reconfigured to accelerate future use cases. What happens as data is democratized and the custom solution only works for technical users?
   2. Unanticipated Costs and Delays: Building a data platform from scratch is a complex and time-consuming endeavor. It often leads to significant cost overruns and delays, as organizations grapple with unforeseen technical challenges and resource constraints.
   3. Limited Flexibility and Customization: Custom-built data platforms may struggle to adapt to the ever-evolving data landscape. They often lack the flexibility and extensibility needed to accommodate new data sources, technologies, and use cases.
   4. Security and Compliance Concerns: Building a secure and compliant data platform requires specialized expertise and ongoing maintenance. Organizations may lack the in-house resources to adequately address these critical aspects.
   5. Resource Drain on Internal Teams: Building and maintaining a data platform can divert valuable time and resources from core business functions. It can also create internal silos, as teams focus on platform development rather than data analysis and insights.

• Institutional Knowledge Risks: What happens when the engineer who wrote the code leaves the company? In-house solutions often lean on one or several data heroes to champion and own the project, but when they leave who will pick up the slack?

Customizing a Data Integration Platform: Reinventing the Wheel

In the middle of adopting a paid solution and building wholly in-house, companies with specific needs and requirements can also explore combining and customizing paid platforms with their own code. This way, in-house data engineers are focusing on value add for specific use cases rather than reinventing the wheel for every aspect of data integration. This may comprise of solutions like Airflow, dbt, or Glue combined with a data integration platform solution to orchestrate complex flows, apply custom transformations, or trigger a series of pre-built flows when an action occurs. This can result in all the benefits of a paid solution while enabling engineers to automate the time-consuming tasks like setting up, building, and testing data pipelines. However – if considering this route, also be wary of choosing tools that actually aid your engineers, not hold them back. Look for data integration platforms that support external integrations to services like Airflow, Glue, and dbt, with support for data engineering customization like custom Python code, a well-documented API library, and even a CLI for scripting. 

The Advantages of Purchasing a Data Platform

Purchasing and adopting a data integration platform can often solve most if not all of the data integration challenges in a company. Today there are tools on the market that enable companies to solve their data integration challenges with flexibility and ease, while reducing cost. Data integration platforms have evolved to tailor to most of the needs of even the most ambitious data engineers, accelerating their work without holding them back.

1. Rapid Deployment and Scalability: Pre-built data platforms are ready to deploy and scale quickly, allowing organizations to reap the benefits of data-driven decision-making sooner rather than later.
2. Proven Technology and Support: Reputable data platform providers offer vetted technology and comprehensive support, ensuring that organizations have access to the latest advancements and expertise.
3. Focus on Core Competencies: By purchasing a data platform, organizations can free up their internal teams to focus on their core competencies, such as data analysis, business intelligence, and innovation.
4. Cost-Effectiveness and Predictability: Reputable data platforms offer predictable pricing models that can be aligned with organizational budgets and growth plans.

Building a Compelling Case for a Purchased Solution

Once you’ve decided to explore options in the market for purchasing a data platform, whether to customize with in-house code or fully adopt, here are some practical guidelines for building your case to internal stakeholders.

1. Quantify the Value: Calculate the cost savings and revenue opportunities that a data platform can bring. Highlight how it can improve efficiency, enhance decision-making, and drive innovation. What will the alternative cost your company? For example, Arcadia Cold was able to eliminate 3-4 other integration tools and reduce integration costs by 50-60% across the company by adopting the right integration platform.
2. Address Risks and Concerns: Proactively address potential risks and concerns, such as security, compliance, and user adoption. Emphasize the vendor’s expertise in mitigating these risks and ensuring a smooth transition.
3. Demonstrate ROI: Articulate the clear return on investment (ROI) that a data platform can provide. Demonstrate how it can pay for itself through improved productivity, cost savings, and increased revenue.

We can conduct a simple back-of-the-envelope analysis to see that in all but most unique cases, the costs often far outweigh the benefits of building. Between the extra full-time data engineers required as well as steeper infrastructure and maintenance costs, whatever perceived downsides of a purchased solution pale in comparison.

Key Considerations in the Purchasing Process

• Security and Compliance: Verify that the vendor’s data platform meets industry standards and regulations for data security and compliance.
• User Adoption and Onboarding: Ensure that the vendor provides comprehensive onboarding and training materials to facilitate user adoption and maximize the platform’s value.
• Future-Proofing: Evaluate the platform’s extensibility and ability to adapt to future data needs and technologies.
• Cost Scaling: Understand how costs will change as usage increases, ensuring that the platform remains cost-effective as the organization grows.

Embrace the Power of a Proven Data Platform

Building a data platform from scratch can be a costly and time-consuming endeavor, often diverting resources from core business functions. By purchasing a data platform from a reputable provider, organizations can gain access to proven technology, comprehensive support, and rapid deployment, empowering them to harness the power of their data and drive business success.