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How to Overcome Data Integration Challenges
Many business leaders rely heavily on data to make critical decisions. Many businesses are looking for new advanced methods to obtain.

By
Apac CIOOutlook | Friday, December 31, 2021
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The process of data integration is not a one-time event. As more relevant data becomes available, one can update one's integration solution and adapt it to one's new tasks and requirements.
Fremont, CA: Many business leaders rely heavily on data to make critical decisions. Many businesses are looking for new advanced methods to obtain more accurate data. This is where data enrichment comes in. Companies can gain more useful insights by combining data from multiple sources into a single dataset.
Adoption of data integration is quite costly and usually accompanied by a variety of difficulties. With these tips, however, one will be able to easily overcome any data integration challenge.
How best practices can help
Finding a Data Integration Solution That Brings Long-Term Advantages
The process of data integration is not a one-time event. As more relevant data becomes available, one can update one's integration solution and adapt it to one's new tasks and requirements. Data integration projects that are well-planned and executed yield long-term tangible results. This is also beneficial in avoiding potential data integration issues, such as having too much irrelevant and low-quality data.
Identifying Business Requirements First
Before committing to any integration platform, spend some time identifying one's company's requirements. This will assist one in developing a relevant approach to meet those needs. Determining the goals of the data integration will assist one in avoiding the use of excessive data and locating the most accurate and relevant data for better insights.
Tracking System to Know When Something Fails
Check that one's integrations have tracking features so that one can easily detect any problems. This will allow one to respond quickly and fix failures before they cause larger problems. This will also aid in avoiding the collection of irrelevant and low-quality data, which may have a negative impact on final insights.