Data integration has been one of the most popular buzzwords of 2021. One of the precursors for generating insights related to huge amounts of data vests on data integration. It is believed that the integrity of data ensures that companies are able to derive original and effective insights and analytics from this. As such, it becomes necessary to examine the entire process of data integration from various aspects. In this article, we aim to understand the nomenclature, classification and methods that help us in strategizing data integration using various tools and techniques.
Definition and nomenclature
Data integration can be referred to as the collection of segregated and unstructured data sets from various platforms so that it can be used to derive insights for effective business decisions. This means that data integration is the pathway that enables us to consolidate different types of data sets. These data sets then become the raw material for various types of business actions and business intelligence.
There are five types of categories that can be considered with respect to data integration. The first is called manual data integration and the second is called middleware data integration. The third category is called application based data integration. The fourth category is called uniform access data integration and the fifth category is called common storage data integration. Let’s analyze the various types of data integration categories in detail.
Details of classification
Manual data integration is carried out with the help of a custom code. As the name suggests, manual data integration does not require any sort of automation. Advantage of manual data integration is its lower cost and its greater customisation that it provides to the user. However, it gives less control to the developer and may be accompanied by different types of errors. It is also difficult to scale up manual integration because of intricacies linked with the coding process.
The main function of middleware based setup is to transfer data between various platforms thereby ensuring data integration. The advantage of middleware based data integration is that it allows flawless streaming as well as hassle free communication between different platforms.
The third type of data integration is based on software applications. With the help of this application, data can be collected from different sources and the cleansing process can be carried out with a lot of ease. It uses minimum resources and ensures a high compatibility between platforms. The disadvantage of this setup is that it is a little complicated in nature that makes data management difficult at times.
Uniform access integration is the fourth type of data integration process that comes with very low storage requirements. It gives a very simple visualization to different data sets and presents them in a uniform format.
The fifth type of data integration is called common storage integration. This data integration is also popular by the name warehouse data integration. It is similar to the process of uniform integration in various aspects. It allows the creation of a copy of various data sets in the data warehouse. As such, it increases the control of the user over data and helps in advanced data analytics and insights. The costs with respect to maintenance and management are higher in this type of data integration. This type of data integration is one of the most complex in nature and is suitable for businesses that want to carry out deeper analytics.
Strategizing data integration
There are a large number of factors that can be considered for strategizing the process of data integration. The first and foremost factor is the consideration of an effective strategy with respect to data governance. The choice of cloud services is also very important as such services need to be aligned with your business requirements. In addition to this, the choice of data integration provider is also important and a trade off needs to be done between services provided and costs incurred. Needless to mention, there are different types of systems that a business may want to update. This choice has to be made carefully keeping in view the cost considerations and immediate system update requirements.
Concluding remarks
In the future, the process of data integration would assume utmost importance as workstations would become more localized in nature and shift operations to the cloud environments. Different types of businesses would not only share their data but trade it as well. This is where the need of conceiving state-of-the-art data integration tools would arise. These data integration tools would need to cater to advanced analytics and enable businesses to derive effective insights.
