Identifying and executing an integration strategy in a large corporation where every department has specific technological independence is a challenge. It’s important to capitalize on best practices, implement common policies, and provide a homogenous service to clients – all while leaving autonomy to different entities to deliver the value promised to customers, make informed decisions, and create new revenue streams.
What Are the Key Components of Integration Strategy?
The goal of a particular data integration approach is to connect with partners and customers and deliver the value they’ve been promised more quickly and efficiently. Any strategy that fails to deliver to meet this goal and jeopardizes the current level of service is not successful.
When designing the appropriate strategy for the business, organizations such as banks and retailers need to ensure that their strategy has a defined goal. What’s more, they need to rely on a modern data integration solution. A modern data integration platform that uses automation and self-service fits the bill here.
A self-service-powered solution enables non-technical business users to implement data connections while freeing IT to focus on more high-value business priorities. At the same time, it uses automation that applies AI and machine learning to help organizations map and integrate customer data much more quickly, securely, and easily.
What Conditions Should Your Integration Solution Meet to Compete Better?
Any bank or retail unit must rely on a data integration solution that enables them to function collaboratively with existing management solutions or execution networks. What’s more, the data integration solution needs to have features mentioned to deliver value faster.
A modern data integration solution must fill the gaps in the following areas: automation, self-service, security, and partner management.
Automation
Organizations can rely on AI and machine-learning algorithms to map and integrate complex, bi-directional data streams more efficiently. These solutions use machine learning algorithms to learn from existing data maps to suggest and predict data mapping rules while authoring additional data maps. Now, because the decisions are made using previously gathered data with the help of machine learning algorithms, the chances of errors occurring, such as missing values, errors, and other issues, become minimal. In short, AI data mapping accurately maps disparate sources to the target fields, but it also maintains integrity to improve decision-making and change the way business is done.
Self-service
Self-service data integration solutions empower non-technical business users to build data connections much more quickly. Users can rely on pre-built application connectors, shared templates, AI-data mapping, and intuitive screens to implement data connections much more quickly, securely, and efficiently. At the same time, IT teams are free to focus on more high-value business priorities.
Security
Modern data integration solutions have an end-to-end encrypted environment to allow only authenticated business users to access and use data. This enables organizations to ensure data security and avoid data breaches and thefts.
Business Partner Management
Modern data integration solutions consolidate and exchange data with business partners in a secure and faster way. In other words, these solutions facilitate business partner management and deliver the value they’ve been promised.
Learn how Adeptia’s data integration solution enables banks and retailers to deliver value and grow revenue. Watch a demo!