What Is Data Integration?

What Is Data Integration?

Data Integration

Data integration is the collection of data from different business sources to ensure complete and accurate data. The integration allows consolidating data of different kinds (structured, unstructured, streaming, etc.). Thus, companies ensure that any business operation can be carried out, such as consulting databases or doing complex analytics.

According to an article published in the Harvard Business Review, only 3% of business data meets basic quality standards.

Data integration can be done manually, but this type of integration becomes unsustainable even for small businesses after reaching a certain volume. For this reason, many software manufacturers offer data integration platforms to facilitate this work. These softwares are Oracle Data Integration Suite, IBM Cloud Pack for Integration, and SAP Cloud Platform Integration Suite.

What Is Data Integration For?

With the advent of the internet and the rise of technology, data is not only increasingly voluminous, but it is also often scattered across different systems. The most common reasons why a company does data integration are the following:

1. Creating data lakes: Some companies want a data lake to store all their business data. The data within a data lake is in a natural, raw format, usually blobs of objects or files.

2. Master data management and data consistency: data integration is also widely used to ensure the connection of business entities and domains (such as customers, suppliers, staff, products, etc.). Integrating the data makes it possible to access the information and synchronize the processes and, thus, improve the master data management. It also increases consistency at the database level between applications.

3. Migration: When data migration occurs from one business solution to another, data integration occurs. The ETL process (data extraction, transformation and loading) thus is needed to be carried out in the new system.

4. Database replication (data replication): replication of the database is very important since this way, companies improve the availability, consistency and accessibility of data. If there is an incident in any database, the system will redirect the affected users to the other database that contains the replicated data.

5. Storage of data from different sources in a data warehouse or data centre: companies put their data in a data warehouse or data services to ensure interoperability between the company’s different systems. In this way, the company ensures synchronization of the data of the different systems, avoiding that employees have to insert the same data in different applications.

6. Prepare data for BI systems: the BI systems need to take data from sources with a specific format. For this reason, many companies make use of the data services of their BI solutions to ensure that the data is in the correct format. Some examples of these data services are Microsoft Dataverse, required for applications managed in PowerApps, such as PowerBI, or SAP Data Center, used by systems such as SAP BusinessObjects or SAP Analytics Cloud.

Challenges Of Data Integration

Data integration is a complicated process. Therefore, it is advisable to study why and how should Data integration be done. In addition, when a company considers doing data integration, it encounters the following challenges:

You Have To Do A Careful Design Of The Integration

When starting a data integration project, the first thing to do is a requirements analysis. The analysis of these requirements must be both: functional (why you want to do it, what objectives and results you want to obtain with the integration); and non-functional ones (how many users will make use of the integrated data, what is the maximum time it takes to process data, improve the policy of data security).

Implementation Is No Easy Task

On completing the requirements analysis, it is important to do a feasibility study to choose a data integration tool. The choice of tool will also depend on the purpose you want to achieve. For example, the company should not choose the same tool to obtain a more scalable ecosystem because the company is growing. What you want is a reduction in the cost of implementing a tool or license.

In addition, it is also often the case that some data is closely tied to the system in which it was created. This happens in systems that already have a certain time. Generally, this data is usually more difficult to integrate due to this unique link to your system, so it is not easy to extract for other company areas. Also, during implementation, in many cases, it will be necessary to perform data transformation work (data modelling) to fit the central data model, be it a data warehouse or data centre. 

Some aspects are semantic because error alerts can arise if embedded data is saved in an inappropriate format. For example, if the date is normally saved as mm/dd / yyyy and some employees write it as yyyy / mm / dd, the date may be traversed in the wrong format when the data is embedded.

Integration Requires A Large Investment.

Once the integration tool or platform is chosen, it must be considered that they are very difficult to use. That means the company will have to hire a specialist to do the integration work. Hiring staff of this type is not cheap at all. 

In addition, there will also be capital investment expenses (the cost of the integration tool, if it had to be purchased hardware or some other system to do the integration) and operating expenses. To these costs, we must also add hosting, maintenance, support and management of the necessary infrastructure.

Also Read: This Is How The Future Cyberattacks Will Be: They Will Have More Sophisticated Malware.

Ways To Carry Out Data Integration

Depending on the company’s needs and requirements, the firm should integrate the data. The most popular ways to integrate data are listed below:

  • Manual data integration: the person who will be in charge of the integration of the data will have to collect and clean the data from the different sources and then combine them within the same warehouse. This type of integration can only happen in very small companies with very little data. This is because it is time-consuming, so it turns out to be a bit inefficient, and also because human errors can be made when doing the integration. The language used to do this type of coding in SQL.
  • Integration with middleware (Middleware data integration): the middleware helps to normalize the data according to the target application to be used. This type of integration is often used when there is a legacy system (legacy system) since, due to its age, it does not usually fit in well with other systems. Therefore, using middleware, the integration of the data from this system in others is facilitated.
  • Application-based integration: this type of integration is only possible when the integration is to be carried out between a not very large number of applications. This is because the integration tool that does this type of integration will be software that locates, extracts and integrates the data from the different sources. Also, for this reason, the different sources must be compatible with each other since the transformation part of the data is not always included.
  • Uniform access integration: in this type of integration, the data is kept in the source of the data. However, an interface is created to appear consistent when the data is accessed from other sources. For example, this type of integration can be used in object-oriented database management systems because it creates an appearance of uniformity between different databases.
  • Common storage integration: This integration consists of copying the data from the different sources in a data warehouse or data service. In this way, a unified vision is achieved. Therefore, this form of integration is the opposite of uniform access integration since the data is saved as a copy in other systems, not only in each data source.

Some of these forms, such as integration from applications or shared storage, rely on ETL process tools to do data integration. This process consists of extracting the data from the source system, transforming it to be compatible with the system and according to the established business form, and, finally, the data is loaded in the destination system.

Also Read: Machine Learning: Curse Or Blessing For IT Security?


Tech Amazers

Tech Amazers is “Of the technocrats, By the technocrats and For the technocrats.” We endeavor to constantly provide our readers with the best information related to Technology, Business, Gadgets, and everything that interests you in the changing technological world.

Leave a Reply

Your email address will not be published. Required fields are marked *