5 Key Takeaways on the Road to Dominating Integration

Factors That Influence Successful Data Integration Project In the last ten years, data integration has experienced robust growth and change. At least 40% of data management executives place data integration in the same rank as other operational challenges. Today, it’s possible to exploit a multitude of integration techniques for different applications across different industries. Sadly, not many people in the data integration realm are able to fathom emerging integration techniques despite the availability of innovative industry tools. For your data integration endeavor to succeed, make sure that you rise above out of date techniques and the fallacies associated with data integration. With time, you need to ensure that data integration is a fundamental strategy regardless of the kind of business you operate. Your business will not know the real value of the data you have amassed if you don’t incorporate data integration best practices in the process. Nowadays, consolidating distinct data from various sources has become an uphill task. Companies are finding it challenging due to the huge volumes of data to be assimilated while more data sources continue to emerge. Even though successful data integration requires a robust platform, there critical factors that determine whether you achieve your goals. Before you initiate, it’s wise to know that your integration project will succeed only if you work with refined data quality. It’s true that the best data integration endeavors will fail if you start working with flawed data. If you want to avoid losses, it’s advisable to push for data quality before, during and after the data integration and implementation processes.
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If you ignore data quality, your project will not only fail, but it will result in poor user adoption. If your users notice that you have poor quality data within your system, they will lose confidence in it, and they are likely to go for alternatives. Data integration projects that deliver significant outcomes are as a result of a consolidated approach from the onset. If you are haphazard with the data integration project; it becomes difficult to master the desired view of big data from one point. It’s advisable to note that preventable errors along the way can result in massive losses and lack of efficiency.
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Some companies have taken up one-off integration options, but you need to consider scalability. If you work with scalability in mind, you will be able to amp up your data integration project when the system gets an upgrade. A business that chooses integration systems that are scalable will save money, and it will be ahead any time a change occurs. If you expect to succeed in the data integration endeavor, it’s imperative that the senior management offer unending support. When data integration best practices and proper support is in place, the project doesn’t have to be marred by delays and steep learning curves.