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Healthcare Data Integration Continues to Challenge Entities

The 2017 Gartner Magic Quadrant for Data Integration Tools highlights the challenges healthcare organizations face with storing and processing data from disparate sources.

Gartner says that healthcare data integration continues to evolve and challenge healthcare organizations.

Source: Thinkstock

By Elizabeth O'Dowd

- Healthcare data integration continues to gain importance as organizations seek to use their data for analytics.

Entities deploy many disparate devices and solutions that transmit structured and unstructured data. That data needs to be transformed and presented to clinicians in a format they can use to help treat patients.

Gartner’s latest Magic Quadrant for Data Integration Tools highlighted the need for organizations to focus on the data transformation needs of their data, and build future proof data integration tools that can handle the constant growth of analytics data.

Data integration combines data from different sources and transforms unstructured data into meaningful data that can be used to gain intelligence.

“Data integration tools are expected to collect, audit and monitor information regarding the deployed data integration services and processes in the organization,” explained the report. “This ranges from use cases for simple reporting and manual analysis to the inclusion of recommendations and even automated performance optimization.”

“While primarily focused on management tasks, the ability to profile new data assets and recognize their similar nature and use cases as compared to other data currently integrated is growing in importance,” report authors continued. “Small devices that roam and attach to data portals will also become prevalent. The requirement for metadata capabilities will become the center of all integration approaches.”

Gartner outlined the specific scenarios where data integration is needed, which includes:

Data acquisition for business intelligence (BI), analytics and data warehousing: Extracting, transforming, and merging data to deliver it for analytics.

Sourcing and delivery of application and master data in support of application data management and master data management (MDM): Enabling the connectivity and integration of data that represents business entities such as customers, products, or employees.

Data consistency between operational applications: Ensuring that data is consistent across all applications such as SaaS and Internet of Things (IoT) applications.

Interenterprise data sharing: Interoperability and data sharing requires the data to be integrated so it can be easily exchanged.

Populating and managing data in a data lake: Ensuring that the data in the data lake is collected and stored in a consistent manner so it can be processed more easily for future refinement and analytics.

Data migration: Data needs to be integrated before it is migrated for the migration to be done correctly and the data to be moved to the correct location with the correct values.

The report also highlighted the evolution of the data integration market, and how organizations are seeking tools that don’t require data to be siloed before it is integrated. The biggest challenge facing data integration is that organizations are collecting data much faster than their ability to analyze and use that data.

“During the past three decades, information technology has swung back and forth between abundant capacity for processing, storage, memory and even networks (and being forgiving of poor design) versus data volumes and process demands that overwhelm any planned capacity,” said Gartner analysts. “The pendulum has begun to swing back to a position where capacity is no longer abundant when compared to data volume and availability.”

“Cloud providers are now encountering the same connectivity and management issues that on-premises providers encountered a decade ago.”

The lack of space causes organizations to seek flexible data integration solutions that can mix approaches. Entities are facing rapid increase of different data types challenges current data integration solutions.

The report found that smaller and more targeted data integration solutions are filling in the gaps that broader solutions are not able to fulfill.

“Since data integration is one constant in the IT universe, implementers do not always seek a more complete solution because integrating multiple integration tools is the basis of the practice, just like integrating data,” the report explained. “This trend is what drives the concept of roles and more-active metadata analysis to be built into the tools.”

Data integration is becoming more complicated as the volume of data and the disparate data sources continue to flood data storage requiring processing. Healthcare organizations are seeking flexible solutions that can quickly process and integrate data so it can actively be accessed and used to treat patients.