Wrong data is the primary source of operational snafus as it causes severe consequences to its users. Similarly, it also generates economic damage.
For instance, when products are sent to a wrong address due to insufficient data, there is an additional expense, loss of sales opportunity, and fines payment.
Statistics carried out by IBM in 2016 shows that over $3.0 trillion expense was incurred due to data quality issues and data correction. It is vital to use the correct and integrated data when carrying out analysis and other business operations.
In this article, we would be discussing how to attain quality data through five basic integration methods, but first, what is Data Integration?
- 1 Data Integration
- 2 Methods of Data Integration
Data integration is the combination of data from diverse sources into a unified view. It comprises a series of operations that begins with the “ingestion process,” followed by other steps; ETL mapping, transformation, and cleansing. Integration of data gives room for practical business intelligence. The client initiates a request for data from the master server in a typical data integration process. The master server receives this information, extracts data from internal and external sources, consolidates it in a single data set, and serves the client.
How can you integrate the data? This question leads us to the central idea of this article.
Methods of Data Integration
When an organization takes data integration measures, it will enjoy various advantages like:
- Reduction in cost of bad data identification,
- Increase in analytic application accuracy,
- Sales boost
All this makes the organization attain a competitive edge over rivals, expanding business dashboards and tools.
Here are the tools and method for data integration
#1. Application-based Integration
The method does not require much input from the user as the software application does most of the tasks ranging from cleansing, locating, retrieving, and integrating data from disparate sources. This method ensures the smooth movement of data between different sources.
An application-based integration system stands out for the presence of a simplified process. The use of software applications removes all complications from the integration process and makes it accurate. It also involves the optimum use of resources in that the bulk of the work is automated. There is also an easy exchange of information as the application ensures that data transfer between systems happens without any hassle.
However, users should be prepared to deal with restricted access, as not everyone can use this technique. There could also be inconsistency in results because using this application varies from one organization to another, depending on several factors. MuleSoft development company provides one of the best integration software to connect applications, devices, and data for businesses.
#2. Uniform Access Integration
This technique ensures uniformity and presentation of data from disparate sets. The original location of the data is retained when using this method. This method stands out for reducing storage requirements as there is no need for extra data space. This technique creates basic data views, a uniform and simplified appearance of data, making it pleasant and easy to use. Data access is easy as one can access multiple systems at data sources at once.
However, users should be ready to deal with data probity challenges because data integrity could be easily compromised during multiple system access. There is also the possibility of strained systems as the host systems during multiple access often can’t handle that amount of data at once.
#3. Common Storage Integration
This technique creates a copy of the data and stores it in a warehouse (data warehouse); thus, you can attain versatility. Standard storage integration is one of the most common data integration techniques. Such a method stands out because it reduces users’ burden as queries get reduced. Decreasing questions technically means their responsibility reduces. They also enjoy a cleaner appearance of data as uniformity, and good data appearance is maintained when managers and analysts carry out several queries on the data set.
#4. MiddleWare Data Integration
This software layer brings a common platform for all forms of interactions in the firm, whether internal or external. The exchange applies in many forms like human-to-system, system-to-database, system-to-system, mobile-based interaction, etc.
In short, the idea behind middleware integration is to link two or more applications, which makes it great to connect critical external and internal systems. Many firms have transformed their IT system into a general-purpose functionality block with this system, which brings about a swift and better change in business. The data streaming and access between the system during integration is enhanced. However, there is the issue of restricted access as well.
#5. Manual Data Integration Method
Finally, we have the manual approach in which a user will have to collect and gather data from various sources. It doesn’t end as the user will have to clean up the data and send it to the required database. This approach stands out due to the low cost of maintenance and the freedom it gives to users. Users will have to deal with scaling difficulties because manually changing the codes during more extensive project scaling takes time. There is also a high tendency for error due to manual operation.
While organizations keep creating data that they can’t analyze quickly, integration aids in closing the gap. Using the right data integration technique results in more innovative ideas in your business and helps you make the right decision for your organization.