Data is a fundamental asset of modern software systems. However, as software development is getting increasingly complicated and layered, it’s very difficult to manage sheer volumes of data and ensure such data is consistently discoverable, reusable and governed across all layers of the IT factory.
This difficulty opens wide the doors of competitiveness for all those organizations that are striving to provide seamless digital experiences across a growing number of touchpoints and applications, enabling new digital business models.
The problem is that data comes from disparate sources but without proper tools it ends up being excessively fragmented, inconsistent, and poorly accessible. This fragmentation inevitably leads to dependencies and scalability issues among individual components and services, eventually affecting data integration.
Navigating this complexity requires creating layers of abstraction that isolate components from each other, allowing changes within supply channels to have minimal impact on the backend.
This process is known as decoupling and aims at minimizing dependencies and bottlenecks while enhancing data retrieval, data lineage, and data access control.
The Data Decoupling Layer is a software layer that is part of a broader Data Fabric architectural approach. It pursues the dynamic decoupling, integration, consolidation and harmonization of data for optimized consumption, ultimately empowering scalability, flexibility, and informed decision making.
Let’s find out how it works and why it could benefit both modern and legacy systems.
Understanding Data Decoupling
Nowadays, organizational requirements happen to change frequently, abiding by a fast-paced market. The use of modern technologies and innovative solutions to enforce responsiveness, scalability and adaptability has therefore become mandatory.
This necessity becomes particularly relevant when it comes to the massive realm of data. Data is indeed exposed to various channels from plenty of sources called Systems of Records (SoRs) – like legacy mainframe, CRM, e-commerce, ERP – and gets ready for consumption.
However, such data consumption is not the easiest thing. When applications directly access and interact with data sources, several pain points could emerge. Firstly, direct application access to data sources often leads to tight coupling, creating system bottlenecks. Additionally, applications may issue multiple separate queries to various data sources, risking overload. Lastly, redundant, scattered, or inconsistent data can complicate integration and sharing.
Data decoupling aims to isolate data from specific applications and systems – basically it separates application and business logic from the data layer – by creating layers of abstraction that offload and decouple the Systems of Records and empower data aggregation and integration into unified views, which are more suitable for discoverability, ingestion, reusability, and managing.
The data decoupling layer is a bridging facilitator: it ensures that individual services and components can be upgraded or replaced without changing the entire underlying infrastructure. What happens in the supply channels (mobile apps, websites, customer care interactions, etc) is separate from the backend (on-prem or cloud orchestration, software updates, migrations, etc).
Explaining the Data Decoupling Layer
Imagine you have several devices of different brands in various rooms within your house, each device with its own control system. Now, think of a data decoupling layer like a smart home assistant. Whether you ask it to play music or turn off the lights, you’re not speaking directly to the audio streaming service or your light bulbs. The assistant figures out what you mean, knows where to send the request, retrieves data or performs the action, and gives you a simple response.
Basically, the smart home assistant simplifies control by abstracting device complexity and differences. This means your commands remain consistent, even if backend services or devices change. You are decoupled from intricate device operations and don’t need multiple controllers. Updating a device only requires modifying the settings of your smart home device, not your entire system control.
In short, the data decoupling layer acts as a smart intermediary between your applications and the many underlying data sources, formats, and systems. It provides a simplified, consistent interface to manage many different, complex parts behind the scenes, making your interaction easier and more flexible.
Data Decoupling Layer: How Does It Work?
Think about a big e-commerce company with many systems holding different pieces of customer information, like the website, mobile app, CRM, marketing tools, order system, and loyalty program. Without a data decoupling layer, anyone trying to understand a customer’s full story has to jump between all these separate systems. This is slow, confusing, and often inconsistent, like using many different controllers for one home system.
A data decoupling layer acts, in this case, like a smart home assistant for customer data. Instead of dealing with each system separately, it connects to all of them, understands their unique data formats, and brings everything together. It collects, links, cleans, and combines all the customer info into one simple, unified profile.
Now, whether a sales rep or marketing analyst wants to know about a customer, they just ask this single layer. They instantly get a complete picture (contact info, browsing history, purchases, loyalty points, marketing interactions, and support tickets).
Basically, the data decoupling layer simplifies access to complex, scattered data by hiding the messy details and delivering one clear, consistent view. In this specific case, it’s much easier to understand customers fully and act on insights quickly and reliably.
Digital Integration Hub (DIH) to Realize and Support Data Decoupling
Traditional approaches create siloed, inconsistent, and hard-to-access data, hindering efficient management and requiring direct SoR interaction. Data decoupling solves these issues, overcoming the limitations of traditional SoR integration. But how to accomplish it?
The data decoupling layer manifests itself through the Digital Integration Hub (DIH), which is somehow its embodiment. The DIH is an architectural approach based on event-driven patterns and solid data management components to increment data integration and synchronization.
Event-driven patterns, like the CQRS Pattern, enable the decoupling of data ingestion and consumption by means of asynchronous communication, which optimizes queries and improves data flow, consistency and scalability over time.
Specifically, the Digital Integration Hub ensures that requests insist on a near-real-time replica of SoR data, and that data is structured per channel need, with performances, scalability, and availability depending on the DIH data decoupling layer.
