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    Editor's Pick (1 - 4 of 8)
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    Useful Practices for Overcoming Data Integration Challenges

    Apac CIO Outlook | Friday, December 17, 2021
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    Companies should check that their integrations have tracking features so that they can easily detect any problems. This will allow them to respond quickly and fix failures before they cause larger problems.

    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. However, with these tips, one will be able to easily overcome any data integration challenge.

    Identifying Business Needs First

    Before committing to any integration platform, spend some time identifying a company's requirements. This will assist the company in developing a relevant approach to meet those needs. Determining the goals of the data integration will assist companies in avoiding the use of excessive data and locating the most accurate and relevant data for better insights.

    Tracking Systems to Know When Something Goes off Track

    Companies should check that their integrations have tracking features so that they can easily detect any problems. This will allow them 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.

    Finding a Data Integration Solution That Brings Long-Term Advantages

    The process of data integration is not a one-time event. As companies get more relevant data, they can update their integration solution, adapt it to their 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.

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