As the world moves to the cloud, data integration becomes critical to an organization’s cloud strategy. Data forms the basis for implementing modern technologies like the Internet-of-Things (IoT), Artificial Intelligence/Machine Learning (AI/ML), and advanced analytics, to name a few. These technologies bring challenges for both data integration and data management. And because of this, data integration strategies will undoubtedly define the performance and experience of most business systems.
Multiple approaches can be taken for data integration, and different businesses will have different requirements. While use cases will help with establishing the right approach, the requirements, constraints, and end goals are often defined by the size of an organization. Let’s examine what requirements an organization may have, based on its size.
Data Integration Requirements Based on Business Size
Small businesses require a data strategy that delivers operational and competitive advantages, like automating data processes, reducing skill requirements and costs, and ensuring scalability. This is primarily to operate efficiently and grow rapidly with limited resources.
Medium-sized businesses aim to enable better decision-making by tracking business performance and using complex analytics. Data security and compliance requirements are also critical and require a resilient data integration strategy.
Large enterprises produce large quantities of operational data and can use it in many ways. However, enterprise-wide visibility, real-time analytics, and creating a rationalized, centralized data repository are primary drivers for enterprises.
All businesses can take advantage of improved data quality, seamless data exchange and communication, enhanced customer experience, simplification of data processing, and increased collaboration between teams.
Read how Mitra streamlined the customer experience for Cellcard.
To achieve this, there are various data integration approaches available. Some are explored below.
Types of Data Integration Approaches and Their Uses
Data integration approaches enable different forms of data operations, for example:
Below, we examine different data exchange capabilities and how to enable them with different data integration approaches.
Extract, Transform, Load (ETL)
ETL is the process by which data from multiple sources is combined into one large, central data repository. ETL extracts data from the source, transforms it into the required formats, including cleaning, filtering, and, if required, anonymizing, and finally loads it onto the target system or application.
ETL enables P2P and hub-and-spoke integrations but is unsuitable for event-driven integration, where the sourcing and transformation processes can slow down response times.
Enterprise Service Bus (ESB)
ESB uses a centralized data exchange system, like middleware, to provide flexible and comprehensive data exchange between systems. It can route messages, transform data, and mediate exchange based on predefined protocols and interfaces. While not compulsory, standardization of data achieves the best results with the ESB integration approach.
ESB can enable all forms of integration as long as a few conditions are met, like the standardization of data for faster event-driven exchange. It can use all forms of data and connect all data sources with their respective systems, but performance can be limited by the data itself.
Read about Mitra’s deployment of ESB as part of the Moneta Money Bank middleware upgrade.
Data Virtualization
Data visualization software aggregates data for virtual viewing through a dashboard or visualization tool. The software hides the complex processes involved in accessing disparate data types from different sources. It eliminates the need to move and replicate data by providing a virtual layer to connect all data to a single (virtual) source. Virtualization is useful for distributed data architectures where data is not consolidated into a single library.
Data virtualization is unsuitable for hub-and-spoke integration due to the conflict between centralization and decentralization. It is very useful for real-time, performance-driven applications and use cases in a multi-cloud or similar distributed system.
Application Programming Interfaces (APIs)
API-based integration is among the most popular integration approaches due to its secure exchange capabilities with external (eco)systems. APIs can be used to communicate and exchange data between applications within the system and expose them to external systems. They enable tools like real-time authentication, data retrieval, and manipulation.
APIs enable all forms of communication but are best used for event-driven scenarios. APIs are also widely used for communications between different ecosystems due to their secure exchange, wide compatibility, and scalability.
Read how Mitra increased performance and reduced downtime for a major Vietnamese Bank.
Devising a Data Integration Strategy
Any data integration strategy begins with analyzing the requirements and objectives of the business and what it requires from its data. It should also include future requirements for growth and technology adoption that may depend on their data integration maturity. Once you have this information, you can devise the right data integration strategy.
- Assess data sources and utilization – It is important to consider the current and future volume of data along with the variety and frequency of different use cases. Being future-ready is also critical, especially for technologies like IoT, AI, blockchain, etc., that heavily depend on data architecture.
- Evaluate integration approaches and forms – This is a complex and important step in devising your strategy. A combination of approaches will work best for most medium to large businesses to enable all the necessary functions of data processing.
- Data security and compliance requirements – Managing data security and risks need to be assessed with the data integration architecture, and different forms of data should be integrated accordingly. Regulations and compliance requirements may also limit the integration approaches indirectly and require thorough analysis.
- Cost considerations – Budgets and resources available are the primary limiting factors for selecting integration approaches. Depending on the required storage and processing power, organizations must balance the above requirements with the implementation and operating costs of different data integrations.
- Performance and scalability – As businesses continue collecting data, they may hit the limits of their performance. Similarly, more data requires more resources to manage it. These factors have additional costs that must be considered in any long-term strategy.
- Vendor selection – Depending on the approaches and their scope, organizations need to utilize different vendors’ tools, services, and platforms. Avoiding vendor lock-in is desirable, while compatibility, features, support, scalability, and costs all need to be evaluated.
- Implementation planning and timelines – Depending on the project’s complexity and the above factors’ limitations, an implementation strategy can be devised in multiple ways. A phase-wise approach is often the most suitable to ensure progress and plan further. Implementation plans also need to account for training, development, and organizational change management.
Data integration and management technologies have become integral to business enablement for most organizations. Data integration supports businesses of all sizes and requirements, as long as the right approach is adopted.
It is worth considering future requirements when devising a data integration strategy. While data is an enabler in itself, integrations that don’t work effectively can deter organizations from adopting the latest technologies or taking advantage of business opportunities. It makes sense for most organizations to look for platform-agnostic data solutions to achieve the best compatibility and avoid limitations like vendor lock-in.
Mitra Innovation is a premier cloud-first and security-focused solutions partner. Our business-centric approach has enabled our clients to use their data systems fully. We provide customized solutions that are future-ready and performance-oriented to rapidly improve your ROI. Talk to our experts and learn more about our integration services now.
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