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Although quite a basic method, data integration techniques such as this one require dedicated data engineering. While combining data and integrating data, the business employee in charge individually codes and manages data connections in real-time. It is crucial for this employee or data engineer to correctly clean and reorganise data as required. They must also manually transfer it to the allocated destination.
With the manual data integration process, you have complete control over data management and integration. If your business already has a data management team or data engineer on the team, then this will not incur any additional costs.
When this method of data integration is used, other staff members may find it difficult to conduct data analysis or access your data. Without hiring additional staff, scalability is harder and errors may arise in complex data connections. Data engineers are also required to manually and individually code different data sources to code connectors each time a new system must be accessed. Be careful to consider these points if you choose the manual integration approach over other data integration techniques.
One of the other data integration solutions is Middleware data integration which refers to the software that is used to transfer customer data (as well as other types of data). This data integration method transfers data from multiple applications to different databases. It is often automated and is able to perform data transformation that ensures that data is compatible with new systems. This is quite important if your business is going from an older data integration system to modern applications. By using this data integration technology, the network of information is made accessible to all employees in your organisation, which offers additional opportunities for streamlining your business processes.
Your network systems will communicate better while using Middleware data integration. Enterprise data will be automatically transformed and transferred consistently, facilitating all of your data integration projects.
This data integration solution requires a skilled developer to both maintain and install Middleware, potentially increasing operational costs. It is important to note that not all systems are compatible with Middleware.
A uniform data integration approach focuses on portraying data in a consistent format so that it is easier to use. It does so by keeping the source data in its original destination. Instead of combining data, data replication is put into motion as you view the data, but the data itself always stays in the original source systems.
Through the uniform data integration tool, you gain a unified view of the data – even though no data consolidation takes place. This allows numerous apps or systems to stay connected to a central source system. In addition, there is no high-storage requirement.
As you access data from multiple sources, this will result in frequent data access requests that can put an additional strain on data host systems and limit their functionality. Numerous data access points can also lead to compromised data integrity and data quality.
With business data becoming more abundant and complex, many businesses choose to take a common storage data integration approach. This approach is similar to the uniform access approach, as the information is transformed before being copied to a data warehouse. Due to this, your system only accesses one data source instead of hundreds of source systems. As the data is in one area and always accessible, you can run business intelligence tools and analytics whenever you need. Common storage integration is used for quite a few data warehouses, especially modern ones.
When this data integration process is used, there is less strain placed on data host systems through common data storage. This is because data sets are held in one destination due to data consolidation. The uniform data appearance allows you to streamline analytics as well. Overall, there are more opportunities available for efficient and effective analytics due to higher data integrity.
The data storage costs could potentially increase as the data volume increases. There are maintenance costs associated with this form unless you are using a third-party data warehousing solution.
If you choose to use application-based data integration software, then you will find that it effectively transforms, locates, and retrieves your data. It will also integrate data into your preferred destination area. This revolves around pre-built connections to wide data sources, automation, and connecting additional data sources if needed.
This offers a seamless data transformation and information exchange between multiple systems and destinations. It does not take a toll on resources due to automation and is scalable as the data you intake increases. It doesn’t always require technical experts and is generally quite simple to get around.
If you decide to use on-premise software, then this may require highly qualified technical experts to stay on-site which can add up to the overall costs of this data integration solution.
Feel free to connect with us today for help with using data integration tools and implementing data integration strategies.
To access their data in a single spot and promptly reach choices, firms must adopt a systematic approach to data integration. This article has outlined five distinct data integration approaches that companies can employ, each with advantages and disadvantages. The methods covered include application-based data integration, middleware data integration, universal data access integration, and common storage data integration.
Businesses must take into account variables including data quality, source systems, data integration technology, and data warehousing costs when selecting a data integration technique. To reduce errors, enhance decision-making, and improve data quality, it is crucial to choose the appropriate strategy.
To sum up, organizations should evaluate their data management systems to identify the optimum data integration strategy. Businesses can reach their goals for data integration and make educated decisions by consulting experts. Businesses can improve data quality, streamline analytics, and accomplish their goals by putting the correct data integration approach into practice. In this manner, data integration can assist companies in realizing the value of their data and achieving their objectives.
Businesses can take use of the technology integration services offered by us here at digitize.au to execute the best data integration strategies for your unique requirements. Businesses may increase data quality, streamline data management processes, and improve decision-making with our professional help.
To find out more about our technology integration services and to maximize the value of your data, contact us today!
At Digitize we are a focused team that invest constantly in improving so that we can provide the best capabilities to our clients. Our processes and teams are built around being flexible so we can deliver tailored solutions instead of trying to make existing solutions fit.
Each client has a dedicated account manager that will ensure you are always getting the best service possible.