Database Insights – Process of Data Integration and Challenges in it
The modern-day business operation ecosystem is quickly getting piled up with really huge quantities of sorted and unsorted data. The number of various file types containing different information categories is now growing much faster than the digital ecosystem itself. However, this enormous growth may create an unprecedented burden on the IT staff who struggle to manage it, particularly as modern-day organizations want to leverage the analytical benefits of data using business intelligence systems.
There is plenty of data collection, storage, analysis, and integration tools available, which help address the challenges in business decision making and big data, etc. The majority of such applications come with in-built data integration tools. Still, it cannot solve the issues related to disparate systems and may ultimately create more workload for the IT professionals. But we can overcome these types of issues with a centralized management approach and job scheduling with which the database administrators can automate the system control processes. For this, it is also important to understand the major challenges the organizations may face and the most appropriate tools to address these.
The concept of data integration
Our discussion topic in this article, data integration, is the consolidating of data from various sources. Data integration is the prerequisite for any business intelligence-related processes, including reporting, analysis, forecasting, etc. It is noted that data integration is often confused with the process of application integration. Even though these are closely connected, there are some notable distinctions between these.
- In data integration, data from diversified sources get on to a centralized system like a data warehouse. Here, the location needs to be capable of handling a huge volume of various types of data. Data integration is essential to power analytical use cases.
- On the other hand, application integration is all about moving the data in and out between various applications in sync with the enterprise databases. Here, each application follows a particular way to accept and emit data, and these data packages move in smaller volumes.
- There is another concept called ETL, a short form of extract, transform, and load. This is data extraction from the source system by transforming it into another format or structure and then loading it to the destination storage. We can say that data integration and application integration, as we discussed above, are different types of ETL.
Let's further discuss in more depth data integration to help you. Using data integration can be a great choice for any business to streamline their process in the best way.
Challenges of data integration
It can be seen that organizations are now increasingly facing the challenge with enormously growing volumes of data and its variety. Data is considered the most important asset for business organizations, which is no longer coming in structured, neatly packaged formats. With an explosion in information, The U.S. Department of Transportation had released a comprehensive list of new-age DI challenges. You may seek the assistance of remote database service providers like RemoteDBA.com if you find it too challenging.
Heterogeneous nature of data
One major big data challenge for business enterprises is the volume of data and its quality, making it difficult to integrate. Along with these, IT professionals are also expected to deal with a mix of structured and unstructured data from various sources. This fact was confirmed by many surveys conducted by IBM and Intel among various sectors' IT managers. About 84 percent of the Intel survey respondents said that they are largely dealing with unstructured data lately.
Bad data quality
Another major factor that can hurt data integration is data quality, which can have a very adverse impact on the same. Bad data quality can affect compliance management and business process improvement. When it comes to integration, the data's accuracy is often the first consideration, and there are many factors and metrics to assess whether the information is good enough for the integration purpose. As per the standard guidelines in data quality management, you need to ensure the following aspects for smooth data integration.
Compromised data integration practices may harm these characteristics, which can have long-term adverse implications on enterprise database management. Even a single misstep, which you may not find significant now, may affect many other core metrics and end up in value deterioration.
It is also no secret that handling disparate data systems can be much time-consuming and cost-intensive. The additional hours of work and expert workforce involved can quickly burn out the budget allocation for such projects. While considering cost, it is important to note the costs of both planned and unplanned DI tasks to calculate the total cost of DI technology ownership.
DI software tools
The advanced data integration tools come with features like job scheduler. However, checking out if the job scheduling software may support cross-platform integration tasks or having centralized management can be time-consuming. So, for those who are planning to invest in DI tools, it is ideal for auditing the company's existing IT ecosystem to decide which solutions to adopt. For example, enterprises that frequently deal with more complex sets of data from disparate sources may focus on getting a robust functionality that can streamline the entire DI process and offer greater visibility of enterprise operations. Doing thorough planning and requirement analysis will help you through this phase to identify the must-have needs against the DI tools' offerings into consideration. Some such primary considerations to make based on your business priorities are:
- Real-time integration
- Data cleansing
- Post-failure integration recovery
- Middleware capacity
- Performance monitoring
- Data semantics etc.
It is the IT leaders' role in combination with the operations and management teams to identify the business priorities and objectives to define an appropriate data integration approach. A database administrator will not be able to take this decision along without in-depth knowledge of the business objectives and the project's future scope.