- The amount of data produced by healthcare organizations is growing at an exponential rate and entities need to prepare their HIT infrastructure to handle the continued influx of data. Big data has become more prominent in healthcare and it needs to be stored appropriately so it can be accessed quickly and so entities can benefit from collecting this data.
Big data includes large data sets that can be analyzed to reveal patterns and trends. Big data is essential to healthcare analytics because it can reveal patterns that can help better diagnose diseases and reveal behavior that may influence a patient’s health.
More digital tools are being brought into health IT ecosystems for both patients and clinicians to use. Digital tools such as wearable monitors, applications, and EHRs all collect and produce data. More medical images are also being produced, which take up a tremendous amount of space.
Human genome sets alone are hundreds of gigabytes and sequence data is doubling every seven to nine months.
Data sets are in the terabyte range and are expected to reach petabyte scales. There is more data being collected and used for patient care and it needs to be accommodated in a way that is scalable and cost effective.
Knowing how to approach storing healthcare big data without investing in too many physical servers is one of its biggest challenges. Many organizations cannot afford the cost or do not have the space to spare to add enough traditional servers to keep up with the demand of big data.
However, organizations can look into several methods to ease the amount of upfront investment needed to access storage tools and to get the most out of on-premises servers.
The first method is looking into the public cloud because it can be both a temporary and permanent solution for handling this influx of data.
Public cloud is one of the most scalable data storage solutions. Storage space can be added or dropped as the size of an organization changes. This makes public cloud popular for temporary projects as well as data migration.
Public cloud is often paid for by a monthly subscription fee that entities can easily modify depending on how much space is needed.
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The fee-for-service model caters to organizations that cannot afford their own on-premises storage space or private cloud platform. Organizations that are expanding their IT infrastructure, testing, or migrating applications can benefit greatly from the public cloud’s flexibility.
The public cloud can be used in conjunction with other on-premises and cloud solutions such as hybrid cloud or multicloud environments.
Organizations can also choose to host their data on-premises but different kinds of servers need to be deployed that are faster and take up less space.
For example, rack servers are more scalable than dedicated tower servers because they contain racks where more hardware can be placed. Rack servers also don’t take up as much space or require the same cooling energy costs, which makes them ideal for smaller organizations that want to host their datacenter on premise but don’t have much space or resources.
Many rack servers use flashed-based array, which brings down operating costs even more because of how they’re built. Flash-based arrays use solid-state drives (SSD), meaning they do not have fans or get as hot as traditional datacenter hardware. This also helps bring down maintenance costs significantly.
Organizations investing in more efficient servers can also take advantage of hyperconverged infrastructure (HCI).
Hyperconvergence virtualizes elements of datacenter infrastructure, including storage, networking, processing, and memory. The entire infrastructure is managed from a single place, which gives IT administrators more visibility and control over the entire environment.
By connecting and consolidating different parts of IT infrastructure through HCI, organizations can focus on modernizing applications and building better tools for users instead of spending unnecessary time and resources maintaining IT systems individually.
Getting started with hyperconvergence can also be more affordable because the cost up front is less because entities can buy servers in increments as needed instead of taking on a larger deployment that may be more expensive and harder to manage.
The storage aspect of HCI can make storing big data sets easier and it can also make accessing this data sets for analytics much smoother.
As organizations continue their digital transformations, they need to consider that every digital tool and piece of data produced needs to be stored. Without future-proof, scalable, and cost effective storage solutions, organizations cannot properly take advantage of big data analytics.