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6 top challenges to successful data integration<\/h2>
Data variety, velocity, veracity, volume and sources are among the top factors affecting whether an organization can successfully integrate disparate data.<\/p>

Why is data integration so hard?<\/h2>
The scope of data integration has significantly changed over the past decade. This is because of the increase in the 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 organizations 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.<\/p>

Solving data spread from an increasing number of data sources<\/h2>
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.<\/p>

Integrating cloud data with on-premises data<\/h2>
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.<\/p>

Achieving data veracity<\/h2>
Data veracity includes solving data inconsistency, data uncertainty, ambiguous data, incomplete data and other uncertainties, and it was cited by 36 percent.<\/p>

Soaring data volume<\/h2>
The rapid advancement of social media and the Internet of Things contributes greatly to soaring volume of data circulating in networks, especially with the rising number of connected devices. And 35 percent of respondents said they are worried about the volume of data they\u2019re trying to handle.<\/p>

Escalating data velocity<\/h2>
Data velocity includes batch, near real-time, real-time, streaming and other high-speed data flows, and it was cited by 32 percent of respondents.<\/p>

Increasing data variety <\/h2>
Data variety includes structured, unstructured and semi-structured forms for data, and it was cited by 31 percent.<\/p>

","saveContent":"

6 top challenges to successful data integration<\/h2>
Data variety, velocity, veracity, volume and sources are among the top factors affecting whether an organization can successfully integrate disparate data.<\/p>

Why is data integration so hard?<\/h2>
The scope of data integration has significantly changed over the past decade. This is because of the increase in the 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 organizations 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.<\/p>

Solving data spread from an increasing number of data sources<\/h2>
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.<\/p>

Integrating cloud data with on-premises data<\/h2>
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.<\/p>

Achieving data veracity<\/h2>
Data veracity includes solving data inconsistency, data uncertainty, ambiguous data, incomplete data and other uncertainties, and it was cited by 36 percent.<\/p>

Soaring data volume<\/h2>
The rapid advancement of social media and the Internet of Things contributes greatly to soaring volume of data circulating in networks, especially with the rising number of connected devices. And 35 percent of respondents said they are worried about the volume of data they\u2019re trying to handle.<\/p>

Escalating data velocity<\/h2>
Data velocity includes batch, near real-time, real-time, streaming and other high-speed data flows, and it was cited by 32 percent of respondents.<\/p>

Increasing data variety <\/h2>
Data variety includes structured, unstructured and semi-structured forms for data, and it was cited by 31 percent.<\/p>

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6 top challenges to successful data integration<\/h2>Data variety, velocity, veracity, volume and sources are among the top factors affecting whether an organization can successfully integrate disparate data.<\/p>

Solving data spread from an increasing number of data sources<\/h2>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.<\/p>

Integrating cloud data with on-premises data<\/h2>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.<\/p>

Achieving data veracity<\/h2>Data veracity includes solving data inconsistency, data uncertainty, ambiguous data, incomplete data and other uncertainties, and it was cited by 36 percent.<\/p>

Escalating data velocity<\/h2>Data velocity includes batch, near real-time, real-time, streaming and other high-speed data flows, and it was cited by 32 percent of respondents.<\/p>

Increasing data variety <\/h2>Data variety includes structured, unstructured and semi-structured forms for data, and it was cited by 31 percent.<\/p>","saveContent":"

6 top challenges to successful data integration<\/h2>Data variety, velocity, veracity, volume and sources are among the top factors affecting whether an organization can successfully integrate disparate data.<\/p>

Solving data spread from an increasing number of data sources<\/h2>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.<\/p>

Integrating cloud data with on-premises data<\/h2>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.<\/p>

Achieving data veracity<\/h2>Data veracity includes solving data inconsistency, data uncertainty, ambiguous data, incomplete data and other uncertainties, and it was cited by 36 percent.<\/p>

Escalating data velocity<\/h2>Data velocity includes batch, near real-time, real-time, streaming and other high-speed data flows, and it was cited by 32 percent of respondents.<\/p>

6 top challenges to successful data integration<\/h2>
Data variety, velocity, veracity, volume and sources are among the top factors affecting whether an organization can successfully integrate disparate data.<\/p>

Solving data spread from an increasing number of data sources<\/h2>
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.<\/p>

Integrating cloud data with on-premises data<\/h2>
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.<\/p>

Achieving data veracity<\/h2>
Data veracity includes solving data inconsistency, data uncertainty, ambiguous data, incomplete data and other uncertainties, and it was cited by 36 percent.<\/p>

Escalating data velocity<\/h2>
Data velocity includes batch, near real-time, real-time, streaming and other high-speed data flows, and it was cited by 32 percent of respondents.<\/p>

Increasing data variety <\/h2>
Data variety includes structured, unstructured and semi-structured forms for data, and it was cited by 31 percent.<\/p>

","saveContent":"

6 top challenges to successful data integration<\/h2>
Data variety, velocity, veracity, volume and sources are among the top factors affecting whether an organization can successfully integrate disparate data.<\/p>

Solving data spread from an increasing number of data sources<\/h2>
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.<\/p>

Integrating cloud data with on-premises data<\/h2>
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.<\/p>

Achieving data veracity<\/h2>
Data veracity includes solving data inconsistency, data uncertainty, ambiguous data, incomplete data and other uncertainties, and it was cited by 36 percent.<\/p>

Escalating data velocity<\/h2>
Data velocity includes batch, near real-time, real-time, streaming and other high-speed data flows, and it was cited by 32 percent of respondents.<\/p>

6 top challenges to successful data integration

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