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MapR
MapR is a Data Solution for various industries, government and the health sector.
At the core of MapR is the MapR Converged Data Platform (MCDP) that enables simultaneous analytics and applications, so governments can manage the magnanimity and complexity of data.
The platform converges all data into a data fabric that can store, analyze, process, apply, and manage as data happens. Using predictive scenario techniques, advanced analytics or anomaly detection, MCDP analyzes all gathered data in real time (e.g. weather data, traffic censors etc.) and thus enables efficient e-government.
Type of content:
Assets
Type of asset:
Use case
Platform
Big data potential
Yes
Policy domains:
Environment & Energy
Phase in the policy cycle:
Policy Design and Analysis
Open license availability
No
Ease of use
Low
Serves:
Addresses:
| SWOT Analysis for MapR |
||
|---|---|---|
| Helpful | Harmful | |
Internal |
Strengths• Supports not only the standard Hadoop HDFS and HBase APIs but also expands the platform to support POSIX NFS which enables easier integration and access.” • Combine, organize, and analyse a variety of structured and unstructured data in a single platform • Use real-time and predictive data to manage and optimize patient flow, safety, and experience. • Reduce Risks and Fraud: healthcare providers can analyze patient records and billing to detect anomalies such as a hospital's overutilization of services within short time periods, patients receiving healthcare services from different hospitals in different locations simultaneously, or identical prescriptions for the same patient being filled in multiple locations. • Improve Care Delivery • Effectively integrate and share medical data to provide personalized care faster. With solutions like Customer 360, healthcare providers gain a 360-degree view of the patient by storing, processing, and correlating all patient information while protecting privacy rights. • Supports both big data applications and data science applications due to many of the aforementioned capabilities. • Support a variety of open APIs. • Supports the AI software development lifecycle, exploration, training, deployment and putting the models into a production environment. Including full support for real-time event streaming and hot-swappable models. |
Weaknesses• Low ease of use • Training personnel • Data security • Lack of system Integration: Clinical, administrative, and financial systems are not linked, and as a result, many healthcare institutions are not yet maximizing their IT potential • Slow IT Adoption: Traditionally, healthcare has been slow to adopt IT and has lagged significantly behind other industries in the use of IT. |
External |
Opportunities• Hybrid Cloud, Multi-Cloud Environment • In the whole healthcare area, there is an increasing cost pressure and every gain in efficiency has to be utilized • Development of a data-driven way to link and optimize processes and tasks in the operating room area |
Threats• Lack of system Integration: Clinical, administrative, and financial systems are not linked, and as a result, many healthcare institutions are not yet maximizing their IT potential • Slow IT Adoption: Traditionally, healthcare has been slow to adopt IT and has lagged significantly behind other industries in the use of IT. • Economic and medical challenges • Data protection - Cyber-attack • Rapid changes in technology and IT systems |
