Big Data & Biotech (Questions/Answers)

Should BioTech Keep Big Data On The Ground Or In The Cloud?

Is Big Data really safer in the cloud, and is the cloud more cost-effective? 

The latest chatter – everywhere – is about the cloud, moving to the cloud, cloud storage, cloud solutions…you get the idea. What exactly is the cloud and what makes the idea of moving data – especially Big Data – to the cloud such an appealing prospect for BioTech businesses all over the globe?

The appeal of cloud computing has roots in the birth of wireless technology. Modern Wi-Fi technology, as we know it, emerged in the late 1990’s and the deceptively simple concept hasn’t changed much since: Wi-Fi and compatible devices connect to a wireless network through an access point – the more common being a wireless router – which transmits radio waves. That’s not to say the technology hasn’t advanced; how wireless technology is applied has evolved, it’s just that consumers don’t see a major difference in how we connect.

As use increased, bandwidth needed to expand to support throughput. Basically, the law of supply and demand was coming into play: more demand was leading to more supply. The solution? New wireless technology introduced faster transmission speeds to double the bandwidth on a network.

What’s the difference? Bandwidth is the maximum amount of data that can be supported by a network, while throughput is the actual amount of data that is supported by a network. The difference between the two amounts is caused by connectivity factors, like when a user experiences buffering during a streaming video.

Faster wireless speeds led to increased amounts of data transmissions over wireless networks, and by the early 2000’s, virtual private networks were common office installations. Connecting devices wirelessly to each other to centralize content, communication, and collaboration through a system that had become known as “the cloud” through a metaphorical reference to describe how elements communicated wirelessly but within a defined boundary – a network. The biggest concern with early cloud network designs was data encryption and network security, and technology organizations were quick to step in and create an industry for cloud computing.

The biggest use of the cloud varies today, but data storage is a major component. If the data generated by running a business could be stored, saved, and shared using a simple hard drive on a desktop computer or laptop, the industry would barely be necessary. Businesses generate and store a lot of data, and the hardware required to support the storage space typically involves servers. These servers take up space, require nonstop power sources and maintenance, including consistent back-ups: in short, these servers cannot fail.

Speaking of a lot of data, there is now a term of massive quantities of data – Big Data. Big Data isn’t really defined by a set size, but rather by an evolving set of factors that define its complexity and diversity.

Providers have emerged within the cloud computing industry in response to the need for Big Data storage, as well as peripheral technology. launched Amazon Web Services in 2006, with its Elastic Compute Cloud (EC2) a pivotal element of the platform. Google and IBM both launched competing service platforms, and rounding out the market was Microsoft with Microsoft Azure in 2010, all aiming to provide a full suite of cloud-based storage and productivity apps.

Big Data and Cloud Storage. What’s The Best Choice?

Big Data is big business, and a closer look at four key areas will help you choose the right path.

#1 – Infrastructure

What does your network infrastructure include? The average network consists of a variety of devices and peripherals, including:

  • Servers
  • Software
  • A data center
  • Desktop workstations
  • Legacy PBX systems or VoIP telephony
  • Internet connectivity

More IT firms are offering cloud-based infrastructure as a service (IaaS), helping their customers reduce complexity and costs by migrating infrastructure components to the cloud, automating operations processes, and shifting entire operations to a distributed data model, where not all storage units are attached to one processor. This decentralized distribution helps to improve performance compared to on-site data storage, with decreased demand where all users would otherwise access one centralized storage unit. No time for “buffering” here!

Migrating from these individual elements into cloud-based services, where possible, offers significant savings. Cloud-based solutions are a fraction of the cost of the required investments for ongoing hardware or software maintenance or upgrades, or the full-time staff needed to support these elements.

#2 – Management

First, there was software-as-a-service (SaaS), then infrastructure-as-a-service (IaaS), and we can expect database-as-a-service (DaaS) offerings become the favorite solution in the BioTech industry. You have the scientists, you have the data, and you need someone to help manage all your data. Amazon and Microsoft already offer cloud-based productivity applications that include database solutions, but Big Data needs more than just a storage facility. The infrastructure design is just the first step. You need the infrastructure, and you need it managed in a way that it’s readily available, maintained in a protected manner to ensure quality – nobody likes “dirty data”!

#3 – Volume

Big Data sets are far too large or complex for traditional technology. One of the most significant sources of data is the mobile web, with global mobile Internet and social media traffic. Google, Facebook, and Instagram are proof enough that a strategy for storing, managing, and analyzing data is fundamental to an operational strategy. Technology has enabled the collection of this massive data quantity, but storage and manipulation of Big Data are expensive and time consuming by traditional technology standards.

Cloud-based storage has many benefits, but the focus for volume with Big Data is ease of scalability. Scalability is an important factor for any cloud-based storage solution, but with Big Data somewhat redefines the need. Scalability and volume impact the availability of data, and the previously mentioned decentralization and performance improvements for cloud-based storage are a clear benefit.

#4 – Data Analytics

Accessibility and quality are critical for business intelligence, informatics, and analytics applications. Big Data is defined by the value that can be extracted through analytics and presents a unique set of challenges due to its sheer volume. Structured, semi-structured, unstructured – big data comes in all shapes and from a variety of sources, with the greatest challenges being the collection, flow, and analytics.

Big Data isn’t just big business, Big Data is Big Business, and the cloud helps organizations like BioTech firms position their Big Data as a strategic advantage, simplifying the collection and analysis process for a competitive edge.

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