Data integration fails because there is too much focus on point-to-point solutions. If you want your good DI solution to be great, you have to look at it with a business lens.
While the need for technology, the amount of data, and the speed for access of that data is constantly growing, it is challenging to view all of the various and disparate data sets in a modern organization.
Land yourself in one of our use cases and see how you fit into common reference architectures to simplify your problem and build a purpose-driven integration environment.
This research is designed for:
- Data analysts feeling the pains of poor data integration from inaccuracies and inefficiencies in reporting.
- Business analysts communicating the need for improved integration of data.
- Data architects looking to design and facilitate improvements in the holistic data environment.
- Data solutioners putting high-level architectural design changes into action.
- Understand what data integration is, and how it fits into your organization.
- Identify opportunities for leveraging improved integration for data-driven insights.
- Design a loosely coupled integration architecture that is flexible to changing needs.
- Determine the needs of the business for data integration and design solutions for the gaps that fit the requirements.
However, the only constant in the world is change. Changes in address, amounts, product details, partners, and more occur at a rapid rate. If your data is isolated, it will quickly become stale. Getting up-to-date data to the right place at the right time is where data integration comes in.
The four-phase methodology included in this blueprint incorporates a hybrid linear and iterative process that will help you break down data silos.
Phase 1: Gather business requirements
Phase 2: Analyze technical requirements
Phase 3: Design the solution
Phase 4: Build the solution
This methodology will provide you with the strategy needed to address the common challenges of data integration and create a loosely coupled integration architecture. By engaging the crucial roles and understanding what is needed from the business, and then from a technical perspective, you will generate integration solutions that serve the needs of the business in the present and that are scalable, agile, and responsive for changes in the future.
This research is created with reference to the Data Asset Management Association’s Book of Knowledge, Version 2 (DAMA DMBOK2).
- As organizations process more information at faster rates, there is increased pressure for faster and more efficient data integration.
- Data integration is becoming more and more critical for downstream functions of data management and for business operations to be successful. Poor integration holds back these critical functions.
- Investments in data integration can be a tough sell for the business and it is difficult to get support for data integration as a standalone project.
- Evolving business models and uses of data are growing rapidly at rates that often exceed the investments in data management and integration tools. As a result, there is often a gap between data availability and the business’ latency demands.
- Create a data integration solution that supports the flow of data through the organization and meets the organization’s requirements for data accuracy, relevance, availability, and timeliness.
- Build your data integration practice with a firm foundation in governance and reference architecture; use best-fit reference architecture patterns and the related technology and resources to ensure that your process is scalable and sustainable.
- The business’ uses of data are constantly changing and evolving, and as a result, the integration processes that ensure data availability must be frequently reviewed and repositioned in order to continue to grow with the business.
- Every IT project requires data integration. Regardless of the current problem and the solution being implemented, any change in the application and database ecosystem requires you to solve a data integration problem.
- Data integration problem solving needs to start with business activity. After understanding the business activity, move to application and system integration to drive the optimal data integration activities.
- Data integration improvement needs to be backed by solid requirements that depend on the use case. The use cases will help you identify your organization’s requirements and integration architecture for its ideal data integration solution.