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6 top challenges to successful data integration
Data variety, velocity, veracity, volume and sources are among the top factors in whether an organization can successfully integrate disparate data.
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Why is data integration so hard?
The scope of data integration has significantly changed over the past decade. This is due to the increase in sheer number of data sources, hybrid environments, constantly evolving APIs and new, disruptive data types. To provide a unified view of data for ad hoc analysis and business intelligence, most companies are combining data across several disparate sources. In its new study, "2018 Data Connectivity Annual Report," Progress examines the top factors influencing data integration success.
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Data spread across an increasing number of data sources
Each organization has a unique set of APIs, and 47 percent of survey respondents pointed to integrating all these sources as their most challenging task.
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Integrating cloud data with on-premises data
Many respondents agree that the biggest challenge is incorporating all relevant data across an ever-increasing number of cloud, database with on-premises database, cited by 44 percent.
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Data veracity
Data veracity includes data inconsistency, data uncertainty, ambiguous data, incomplete data, etc., and was cited by 36 percent.
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Data volume
The rapid advancement of social media and IOT contribute greatly to soaring volume of data circulating in the networks, especially with the rise number of connected devices. And 35 percent are worried about the volume of data they’re trying to handle.
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Data velocity
Data velocity includes batch, near real-time, real-time, streaming, etc., and was cited by 32 percent.
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Data variety
Data variety includes structured, unstructured, semi-structured, and was cited by 31 percent.