Integrating various systems that hold customer information is a crucial step in understanding your company’s customers and products. A comprehensive customer view in your data warehouse or data lakehouse can provide better context to help your team answer important business questions instead of having to work with sales, product, and marketing information in data silos. It allows you to leverage a single source of truth to answer questions across the business and deliver customer insights.
Despite the benefits, many teams resort to developing their own custom systems to manage data integration from various sites, apps, and source systems. This approach can be valid for some companies; however, it often leads to difficulties in managing and maintaining these systems, resulting in technical debt and trade-offs.
Data integration challenges can come in various forms and sizes. To reduce these issues and develop a better integration system, creating a customer data integration strategy and building a scalable framework are crucial.
When integrating data, different approaches can be taken, such as building custom batch frameworks or custom event-based frameworks. Large tech companies have dedicated frameworks for managing constant data requests. However, not all companies have large data teams or resources to constantly invest in building and maintaining their integration systems.
In such cases, using third-party data integration tools to manage batch and event-based integrations can provide a sustainable solution to the data integration problem, minimizing the risk of integration debt impacting your data team. These solutions keep up to date with changing event schemas, saving time and resources for the data team and allowing them to focus on delivering ROI to management.
In the build vs. buy decision, there are reasons for both approaches. Most companies end up with a combination of both built and bought solutions. A checklist for deciding whether to buy or build includes considering factors like the team’s focus, budget limitations, timeline, resources, and technical knowledge required for building the solution, as well as the unique function or ability needed by the executive team.