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Continuously tightening customer requirements, new use cases, and the utilisation of AI require the ability to recognise the opportunities offered by data and the courage to make more significant changes to enable its various usage models. Data must be seamlessly, efficiently, and securely accessible and transferable. To utilise essential data in real time, changes must also include the renewal of architecture and the development of new technological capabilities.
Bridging platforms for seamless operations
Traditionally, data platforms like Data Lakes and Data Warehouses are critical in data-driven organisations. They provide a centralised, controlled environment where data can be combined, stored, and transformed for analysis. Advanced analytics solutions give businesses real-time visibility into key performance indicators.
While data platforms are essential for long-term strategic decision-making, day-to-day operations rely heavily on process and application integration. This is where integration platforms (such as iPaaS and Integration Platform as a Service) come into play, enabling real-time connectivity between systems and applications. Whether automating customer service processes, managing supply chains, or handling product information, integrations ensure that systems operate seamlessly together.
The same data should not be retrieved multiple times
If application integrations and data platforms operate entirely separately, the same data may be collected and synchronised multiple times across different systems. For example, customer data may be transferred to operational applications via application integrations on the integration platform. In contrast, similar data is separately collected in batches for analysis on the data platform. This creates redundancy, increases maintenance costs, and raises the risk of errors. Customers and internal users may also encounter inconsistent data depending on which system they retrieve it from.
Since both data and integration work often involve many of the same processes, it makes sense to break down these separate silos and combine them into a seamless whole. This approach avoids redundancy: data doesn't need to be retrieved or stored multiple times, and the same functions and processing logic don’t need to be repeated across different systems. By integrating these two areas, operations become more efficient, achieving clear synergy benefits. We can utilise the data collected in data platforms more effectively to create better data products (data-as-a-product), taking integration needs into account instead of focusing solely on reporting and analytics.
An integrated solution architecture brings many benefits
When combining data platform thinking and traditional integration models, companies can harness the best of both worlds. This requires a unified target architecture and governance model, where data platform and application integration design and implementation are based on a joint approach rather than point solutions.
Data and integration platforms form a seamless whole in a combined solution architecture. Data platforms provide deep insights and support decision-making through analytics and artificial intelligence. Similarly, integration platforms, such as iPaaS solutions, efficiently bring necessary data from various sources, like ERP systems or CRM solutions, into the data platform by leveraging different integration styles. This enables continuous and real-time data transfer, addressing analytics and business needs. While iPaaS platform data transfer and transformations can be efficiently automated and managed centrally. Ready-made connectors and integration features offer powerful tools to manage and monitor the entire data transfer process. Additionally, a Data Lakehouse architecture can enhance scalability and enable real-time data processing and refinement of data products.
