Global Big Data Analytics To Grow At A CAGR Of Over 19% By The End Of 2032

CITY, Country, 2023-Jan-04 — /EPR Network/ —

A new report collated by Fact.MR predicts the global big data analytics in healthcare market to exhibit a high double-digit CAGR throughout the forecast period from 2017 to 2026. Healthcare market by the end of 2026.

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Big Data Analytics to Boost Precision Medicine Research

Precision medicine has the potential to move from one-size-fits-all approaches to patient-specific therapies by using massive amounts of data collected from tools such as genomics, mobile biometric sensors and smartphone apps. . Health data enables physicians to build predictive models and better patient profiles to more effectively predict, diagnose and treat various diseases. Additionally, partnerships and collaborations between healthcare organizations and researchers have led to the development of data pools that can be used to build more personalized healthcare models. Such new capabilities are still in their infancy, and it is hoped that big data capabilities and policies will expand to ensure that patient data continues to inform health research.

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Big Data Analytics Helps Healthcare Organizations Reduce Costs

Several healthcare organizations are already using big data analytics, with the majority believing that big data analytics will reduce their annual costs by 25% or more over the next few years. One of the few ways big data analytics can reduce costs is by reducing hospital readmission rates. Big data analytics approaches are being adhered to by healthcare organizations seeking relationships consistent with previously unpredictable or understandable readmissions. Once these relationships are identified, healthcare organizations can set protocols for treating patients and prevent readmissions. In addition, big data analytics will allow us to forecast operating room demand, optimize staffing, streamline patient care, and create measures to improve the pharmaceutical supply chain.

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6 key takeaways from Fact.MR’s report on big data analytics in the healthcare market

• Asia Pacific Excluding Japan (APEJ) is not only the most lucrative market for big data analytics in healthcare, but is also expected to continue to expand rapidly. North America and Europe are also projected to grow in revenue at a similar CAGR through 2026, and will remain favorable regions for market expansion. Latin American and Japanese markets will reflect relatively higher CAGR than North America and Europe, but explain relatively lower revenues during the forecast period.
• Healthcare providers are expected to continue to dominate spending in global big data analytics in the healthcare market. However, from 2017 to 2026, revenue from healthcare payers will grow at a significantly higher CAGR than revenue from healthcare providers.
• CRM analytics is expected to continue to be the preferred tool for big data analytics in healthcare, followed by financial analytics and production reporting. Visual analytics revenue will grow fastest through 2026 based on tool type.
• Access to operational information is expected to remain the largest application of big data analytics in healthcare in terms of revenue.
• While on-premises deployments continue to be demanded by the market, revenues from cloud-based deployments are expected to grow relatively quickly through 2026.
• Healthcare settings are expected to be the most highly valued in this competitive market. In the current market scenario, vendors are competing to outperform and are more focused on providing innovative healthcare services. Major companies featured in Fact.MR’s report include Denodo Technologies Inc., Alteryx, SAP SE, SAS Instiute, Infosys, Cisco Systems Inc., Zephyr Health, Cerner Corp, Oracle Corp, MEDai’s Health, McKesson, Microsoft Corp. , and OptumHealth Care Solutions. .

Market classification

region	North America Latin America Europe Japan APEJMEA
spender type	Healthcare PayerHealthcare Provider
tool type	Data warehousing analysis Financial analysis Production reporting CRM analysis Predictive analysis Visual analysis Risk management analysis Supply chain analysis Test analysis
Application type	Access to clinical informationAccess to operational informationAccess to transactional dataOther applications
Deployment type	cloud-based on-premises

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