Data is omnipresent. It is available across multiple applications, data warehouses, databases, and even the public Cloud in every organization. Data belongs to various groups within an organization and is commonly shared across teams and applications.
As an organized sports team has a clear division of responsibilities, each with a dedicated role to play and win; organizations should ensure all departments have specific roles and coordinated effort from functional units to get the most out of their resources and get things done right. To make sure everything is cooperating effectively, companies need to work towards improving their data integration architecture. This will help them keep track of what is going on and share information in real-time, which gives them better insight into how things are progressing and where there might be opportunities for improvement.
What is Data Integration Architecture?
Data integration architecture is the engine driving the business data ecosystem, where people can focus on generating customer value. Too often, users spend time searching for data rather than using it to create new products or find ways to increase sales. A Data Integration Platform supports critical functions of an enterprise by allowing users to consolidate data from multiple sources into a single platform, transforming information into actionable knowledge, and seamlessly sharing that data across the organization for business decision making.
Why is Data Integration Architecture Important?
It’s important to create a data integration architecture to help you integrate whole data and normalize it to support faster decision support and innovation. Your company depends on the analytics and insights gleaned from all sorts of data. Having a dependable data integration architecture in place is so important when supporting these business functions.
Creating a data integration architecture does not mean creating a framework that combines all of your enterprise’s information source into one system, like a giant database or big data analytics
There are more issues like storing, managing and analyzing complex and large data in banks and financial institutions. Through data analytics, organizations can solve these issues. Financial organizations have realized the importance of data analysis and are slowly adopting these changes to improve accuracy and efficiency.
Typically, there are multiple databases in financial industries that store the data. The banking data is complex and spread across many systems. It is challenging to unify the data into a single data warehouse from multiple systems. Banking professionals use data integration architecture or data warehouses to simplify and standardize the way they collate the data and create a single database.
Instead, it means understanding how different systems and tools across your organization communicate to share accurate and relevant information across the company. Data integration architecture helps define how relevant information can be shared between internal departments and external business partners through compatible technologies – usually ensuring that companies avoid ineffective redundancies and achieve better functionality and streamlined teamwork across the board.
Factors to be Considered
As analysts pursuing business intelligence, you must know how challenging it can be to find the method of data integration that will most ensure access and availability and flexibility for analysis.
Consider the following:
- How many different data sources do you need to integrate?
- Your data set’s size and format.
- Your source data’s reliability.
Data integration should be considered by companies to embark on achieving their goals, which may take a combination of different methods and tools to accomplish.
Types of Data Integration
As analysts, make sure to consider multiple types of data integration methods for your business. It’s crucial to find the method that best suits the insights you need as a business, as well as what you’ll be using your data for.
Data consolidation is a method of acquiring data from different sources and usually requires specialized software with a query interface to combine data from multiple sources into a single database.
Data propagation is a method of integration that duplicates data stored in source data warehouses. This can be used to transfer data to local access databases based on propagation rules.
Federating data means connecting various pieces of information so they can be viewed centrally. Data federation is a technology that allows companies to link together data from multiple sources using a kind of ‘bridge.’
Data Integration Techniques
There are several data integration approaches to choose from, each with its own set of capabilities, functions, benefits, and limitations.
- Manual Data Integration: The process of locating information, accessing different interfaces directly, comparing, cross-referencing, and combining it yourself to get the insight you need is a manual data integration.
- Application-based integration: Application-based integration is the process of accommodating individual applications, each with their unique purpose, to work in conjunction.
- Middleware Data Integration: It serves as a “layer” between two dissimilar systems, allowing them to communicate. For example – The architecture in Finacle 10x is SOA, which has middleware that integrates with CRM to offer a 360-degree view of customers and learn about the customer experience.
- Uniform access integration: Uniform access integration is a type of integration that focuses on developing a uniform translation process that presents information obtained from multiple sources in the best way possible. It does this without having to move any information – data remains in its original location.
How Data Integration improves performance and customer experience
Understanding your customer, their needs, and their purchasing preferences are essential parts of any successful business. With the amount of data about your customer available to you right at your fingertips, it’s becoming easier for any entrepreneur to build a successful customer-driven strategy. However, with most data now being stored digitally, the challenge today is to quickly assess and apply all this large amount of data with limited resources!
There is a lot of data that’s floating around for you to take into account. With so many numbers and figures to consider, it can sometimes become difficult to determine which information is helpful and which isn’t. Luckily, having a customer data integration tool can help you better understand your consumer base by providing valuable insight and ways to reach those consumers as well as manage those.
Data integration architecture is the process of combining data from different sources into a single system. This data is then structured to be used for a specific purpose, such as a marketing campaign or a manufacturing process. Data integration architecture uses tools and technology to combine data from multiple sources. This process can have several benefits, including improved performance and a better customer experience.