|By Rick Morrison||
|December 5, 2012 06:30 AM EST||
There is no doubt that Big Data holds infinite promise for a range of industries. Better visibility into data across various sources enables everything from insight into saving electricity to agricultural yield to placement of ads on Google. But when it comes to deriving value from data, no industry has been doing it as long or with as much rigor as clinical researchers.
Unlike other markets that are delving into Big Data for the first time and don't know where to begin, drug and device developers have spent years refining complex processes for asking very specific questions with clear purposes and goals. Whether using data for designing an effective and safe treatment for cholesterol, or collecting and mining data to understand proper dosage of cancer drugs, life sciences has had to dot every "i" and cross every "t" in order to keep people safe and for new therapies to pass muster with the FDA. Other industries are now marveling at a new ability to uncover information about efficiencies and cost savings, but - with less than rigorous processes in place - they are often shooting in the dark or only scratching the surface of what Big Data offers.
Drug developers today are standing on the shoulders of those who created, tested and secured FDA approval for treatments involving millions of data points (for one drug alone!) without the luxury of the cloud or sophisticated analytics systems. These systems have the potential to make the best data-driven industry even better. This article will outline key lessons and real-world examples of what other industries can and should learn from life sciences when it comes to understanding how to work with Big Data.
What Questions to Ask, What Data to Collect
In order to gain valuable insights from Big Data, there are two absolute requirements that must be met - understanding both what questions to ask and what data to collect. These two components are symbiotic, and understanding both fully is difficult, requiring both domain expertise and practical experience.
In order to know what data to collect, you first must know the types of questions that you're going to want to ask - often an enigma. With the appropriate planning and experience-based guesses, you can often make educated assumptions. The trick to collecting data is that you need to collect enough to answer questions, but if you collect too much then you may not be able to distill the specific subset that will answer your questions. Also, explicit or inherent cost can prevent you from collecting all possible data, in which case you need to carefully select which areas to collect data about.
Let's take a look at how this is done in clinical trials. Say you're designing a clinical study that will analyze cancer data. You may not have specific questions when the study is being designed, but it's reasonable to assume that you'll want to collect data related to commonly impacted readings for the type of cancer and whatever body system is affected, so that you have the right information to analyze when it comes time.
You may also want to collect data unrelated to the specific disease that subsequent questions will likely require, such as information on demographics and medications that the patient is taking that are different from the treatment. During the post-study data analysis, questions on these areas often arise, even though the questions aren't initially apparent. Thus clinical researchers have adopted common processes for collecting data on demographics and concomitant medications. Through planning and experience, you can also identify areas that do not need to be collected for each study. For example, if you're studying lung cancer, collecting cognitive function data is probably unrelated.
How can other industries anticipate what questions to ask, as is done in life sciences? Well, determine a predefined set of questions that are directly related to the goal of the data analysis. Since you will not know all of the questions until after the data collection have started, it's important to 1) know the domain, and 2) collect any data you'll need to answer the likely questions that could come up.
Also, clinical researchers have learned that questions can be discovered automatically. There are data mining techniques that can uncover statistically significant connections, which in effect are raising questions that can be explored in more detail afterwards. An analysis can be planned before data is collected, but not actually be run until afterwards (or potentially during), if the appropriate data is collected.
One other area that has proven to be extremely important to collect is metadata, or data about the data - such as, when it was collected, where it was collected, what instrumentation was used in the process and what calibration information was available. All of this information can be utilized later on to answer a lot of potentially important questions. Maybe there was a specific instrument that was incorrectly configured and all the resulting data that it recorded is invalid. If you're running an ad network, maybe there's a specific web site where your ads are run that are gaming the system trying to get you to pay more. If you're running a minor league team, maybe there's a specific referee that's biased, which you can address for subsequent games. Or, if you're plotting oil reserves in the Gulf of Mexico, maybe there are certain exploratory vessels that are taking advantage of you. In all of these cases, without the appropriate metadata, it'd be impossible to know where real problems reside.
Identifying Touch Points to Be Reviewed Along the Way
There are ways to specify which types of analysis can be performed, even while data is being collected, that can affect either how data will continue to be collected or the outcome as a whole.
For example, some clinical studies run what's called interim analysis while the study is in progress. These interim analyses are planned, and the various courses that can be used afterwards are well defined, but the results afterward are statistically usable. This is called an adaptive clinical trial, and there are a lot of studies that are being performed to determine more effective and useful ways that these can be done in the future. The most important aspect of these is preventing biases, and this is something that has been well understood and tested by the pharmaceutical community over the past several decades. Simply understanding what's happening during the course of a trial, or how it affects the desired outcome, can actually bias the results.
The other key factor is that the touch points are accessible to everybody who needs the data. For example, if you have a person in the field, then it's important to have him or her access the data in a format that's easily consumable to them - maybe through an iPad or an existing intranet portal. Similarly, if you have an executive that needs to understand something at a high level, then getting it to them in an easily consumable executive dashboard is extremely important.
As the life sciences industry has learned, if the distribution channels of the analytics aren't seamless and frictionless, then they won't be utilized to their fullest extent. This is where cloud-based analytics become exceptionally powerful - the cloud makes it much easier to integrate analytics into every user's day. Once each user gets the exact information they need, effortlessly, they can then do their job better and the entire organization will work better - regardless of how and why the tools are being used.
Augmenting Human Intuition
Think about the different types of tools that people use on a daily basis. People use wrenches to help turn screws, cars to get to places faster and word processers to write. Sure, we can use our hands or walk, but we're much more efficient and better when we can use tools.
Cloud-based analytics is a tool that enables everybody in an organization to perform more efficiently and effectively. The first example of this type of augmentation in the life sciences industry is alerting. A user tells the computer what they want to see, and then the computer alerts them via email or text message when the situation arises. Users can set rules for the data it wants to see, and then the tools keep on the lookout to notify the user when the data they are looking for becomes available.
Another area the pharmaceutical industry has thoroughly explored is data-driven collaboration techniques. In the clinical trial process, there are many different groups of users: those who are physically collecting the data (investigators), others who are reviewing it to make sure that it's clean (data managers), and also people who are stuck in the middle (clinical monitors). Of course there are many other types of users, but this is just a subset to illustrate the point. These different groups of users all serve a particular purpose relating to the overall collection of data and success of the study. When the data looks problematic or unclean, the data managers will flag it for review, which the clinical monitors can act on.
What's unique about the way that life sciences deals with this is that they've set up complex systems and rules to make sure that the whole system runs well. The tools associated around these processes help augment human intuition through alerting, automated dissemination and automatic feedback. The questions aren't necessarily known at the beginning of a trial, but as the data is collected, new questions evolve and the tools and processes in place are built to handle the changing landscape.
No matter what the purpose of Big Data analytics, any organization can benefit from the mindset of cloud-based analytics as a tool that needs to consistently be adjusted and refined to meet the needs of users.
Ongoing Challenges of Big Data Analytics
Given this history with data, one would expect that drug and device developers would be light years ahead when it comes to leveraging Big Data technologies - especially given that the collection and analytics of clinical data is often a matter of life and death. But while they have much more experience with data, the truth is that life sciences organizations are just now starting to integrate analytics technologies that will enable them to work with that data in new, more efficient ways - no longer involving billions of dollars a year, countless statisticians, archaic methods, and, if we're being honest, brute force. As new technology becomes available, the industry will continue to become more and more seamless. In the meantime, other industries looking to wrap their heads around the Big Data challenge should look to life sciences as the starting point for best practices in understanding how and when to ask the right questions, monitoring data along the way and selecting tools that improve the user experience.
The IT industry is undergoing a significant evolution to keep up with cloud application demand. We see this happening as a mindset shift, from traditional IT teams to more well-rounded, cloud-focused job roles. The IT industry has become so cloud-minded that Gartner predicts that by 2020, this cloud shift will impact more than $1 trillion of global IT spending. This shift, however, has left some IT professionals feeling a little anxious about what lies ahead. The good news is that cloud computin...
Mar. 30, 2017 08:30 AM EDT Reads: 1,687
Lots of cloud technology predictions and analysis are still dealing with future spending and planning, but there are plenty of real-world cloud use cases and implementations happening now. One approach, taken by stalwart GE, is to use SaaS applications for non-differentiated uses. For them, that means moving functions like HR, finance, taxes and scheduling to SaaS, while spending their software development time and resources on the core apps that make GE better, such as inventory, planning and s...
Mar. 30, 2017 06:30 AM EDT Reads: 1,033
After more than five years of DevOps, definitions are evolving, boundaries are expanding, ‘unicorns’ are no longer rare, enterprises are on board, and pundits are moving on. Can we now look at an evolution of DevOps? Should we? Is the foundation of DevOps ‘done’, or is there still too much left to do? What is mature, and what is still missing? What does the next 5 years of DevOps look like? In this Power Panel at DevOps Summit, moderated by DevOps Summit Conference Chair Andi Mann, panelists l...
Mar. 30, 2017 05:00 AM EDT Reads: 10,115
Without a clear strategy for cost control and an architecture designed with cloud services in mind, costs and operational performance can quickly get out of control. To avoid multiple architectural redesigns requires extensive thought and planning. Boundary (now part of BMC) launched a new public-facing multi-tenant high resolution monitoring service on Amazon AWS two years ago, facing challenges and learning best practices in the early days of the new service.
Mar. 30, 2017 04:00 AM EDT Reads: 3,339
DevOps is often described as a combination of technology and culture. Without both, DevOps isn't complete. However, applying the culture to outdated technology is a recipe for disaster; as response times grow and connections between teams are delayed by technology, the culture will die. A Nutanix Enterprise Cloud has many benefits that provide the needed base for a true DevOps paradigm.
Mar. 30, 2017 01:30 AM EDT Reads: 2,675
The rise of containers and microservices has skyrocketed the rate at which new applications are moved into production environments today. While developers have been deploying containers to speed up the development processes for some time, there still remain challenges with running microservices efficiently. Most existing IT monitoring tools don’t actually maintain visibility into the containers that make up microservices. As those container applications move into production, some IT operations t...
Mar. 30, 2017 01:30 AM EDT Reads: 3,243
DevOps tends to focus on the relationship between Dev and Ops, putting an emphasis on the ops and application infrastructure. But that’s changing with microservices architectures. In her session at DevOps Summit, Lori MacVittie, Evangelist for F5 Networks, will focus on how microservices are changing the underlying architectures needed to scale, secure and deliver applications based on highly distributed (micro) services and why that means an expansion into “the network” for DevOps.
Mar. 30, 2017 01:00 AM EDT Reads: 8,389
DevOps is often described as a combination of technology and culture. Without both, DevOps isn't complete. However, applying the culture to outdated technology is a recipe for disaster; as response times grow and connections between teams are delayed by technology, the culture will die. A Nutanix Enterprise Cloud has many benefits that provide the needed base for a true DevOps paradigm. In his Day 3 Keynote at 20th Cloud Expo, Chris Brown, a Solutions Marketing Manager at Nutanix, will explore t...
Mar. 29, 2017 04:00 PM EDT Reads: 3,305
As Enterprise business moves from Monoliths to Microservices, adoption and successful implementations of Microservices become more evident. The goal of Microservices is to improve software delivery speed and increase system safety as scale increases. Documenting hurdles and problems for the use of Microservices will help consultants, architects and specialists to avoid repeating the same mistakes and learn how and when to use (or not use) Microservices at the enterprise level. The circumstance w...
Mar. 29, 2017 03:00 PM EDT Reads: 4,534
SYS-CON Events announced today that Auditwerx will exhibit at SYS-CON's 20th International Cloud Expo®, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Auditwerx specializes in SOC 1, SOC 2, and SOC 3 attestation services throughout the U.S. and Canada. As a division of Carr, Riggs & Ingram (CRI), one of the top 20 largest CPA firms nationally, you can expect the resources, skills, and experience of a much larger firm combined with the accessibility and attent...
Mar. 29, 2017 02:30 PM EDT Reads: 898
SYS-CON Events announced today that HTBase will exhibit at SYS-CON's 20th International Cloud Expo®, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. HTBase (Gartner 2016 Cool Vendor) delivers a Composable IT infrastructure solution architected for agility and increased efficiency. It turns compute, storage, and fabric into fluid pools of resources that are easily composed and re-composed to meet each application’s needs. With HTBase, companies can quickly prov...
Mar. 29, 2017 02:15 PM EDT Reads: 3,399
Everyone wants to use containers, but monitoring containers is hard. New ephemeral architecture introduces new challenges in how monitoring tools need to monitor and visualize containers, so your team can make sense of everything. In his session at @DevOpsSummit, David Gildeh, co-founder and CEO of Outlyer, will go through the challenges and show there is light at the end of the tunnel if you use the right tools and understand what you need to be monitoring to successfully use containers in your...
Mar. 29, 2017 01:45 PM EDT Reads: 2,011
What if you could build a web application that could support true web-scale traffic without having to ever provision or manage a single server? Sounds magical, and it is! In his session at 20th Cloud Expo, Chris Munns, Senior Developer Advocate for Serverless Applications at Amazon Web Services, will show how to build a serverless website that scales automatically using services like AWS Lambda, Amazon API Gateway, and Amazon S3. We will review several frameworks that can help you build serverle...
Mar. 29, 2017 01:30 PM EDT Reads: 2,339
Buzzword alert: Microservices and IoT at a DevOps conference? What could possibly go wrong? In this Power Panel at DevOps Summit, moderated by Jason Bloomberg, the leading expert on architecting agility for the enterprise and president of Intellyx, panelists peeled away the buzz and discuss the important architectural principles behind implementing IoT solutions for the enterprise. As remote IoT devices and sensors become increasingly intelligent, they become part of our distributed cloud enviro...
Mar. 29, 2017 12:15 PM EDT Reads: 8,040
@DevOpsSummit has been named the ‘Top DevOps Influencer' by iTrend. iTrend processes millions of conversations, tweets, interactions, news articles, press releases, blog posts - and extract meaning form them and analyzes mobile and desktop software platforms used to communicate, various metadata (such as geo location), and automation tools. In overall placement, @DevOpsSummit ranked as the number one ‘DevOps Influencer' followed by @CloudExpo at third, and @MicroservicesE at 24th.
Mar. 29, 2017 10:45 AM EDT Reads: 10,587
By now, every company in the world is on the lookout for the digital disruption that will threaten their existence. In study after study, executives believe that technology has either already disrupted their industry, is in the process of disrupting it or will disrupt it in the near future. As a result, every organization is taking steps to prepare for or mitigate unforeseen disruptions. Yet in almost every industry, the disruption trend continues unabated.
Mar. 29, 2017 09:00 AM EDT Reads: 991
In his General Session at 16th Cloud Expo, David Shacochis, host of The Hybrid IT Files podcast and Vice President at CenturyLink, investigated three key trends of the “gigabit economy" though the story of a Fortune 500 communications company in transformation. Narrating how multi-modal hybrid IT, service automation, and agile delivery all intersect, he will cover the role of storytelling and empathy in achieving strategic alignment between the enterprise and its information technology.
Mar. 29, 2017 08:00 AM EDT Reads: 7,636
While DevOps most critically and famously fosters collaboration, communication, and integration through cultural change, culture is more of an output than an input. In order to actively drive cultural evolution, organizations must make substantial organizational and process changes, and adopt new technologies, to encourage a DevOps culture. Moderated by Andi Mann, panelists discussed how to balance these three pillars of DevOps, where to focus attention (and resources), where organizations might...
Mar. 29, 2017 06:30 AM EDT Reads: 6,319
Microservices are a very exciting architectural approach that many organizations are looking to as a way to accelerate innovation. Microservices promise to allow teams to move away from monolithic "ball of mud" systems, but the reality is that, in the vast majority of organizations, different projects and technologies will continue to be developed at different speeds. How to handle the dependencies between these disparate systems with different iteration cycles? Consider the "canoncial problem" ...
Mar. 29, 2017 06:00 AM EDT Reads: 9,174
In his session at 20th Cloud Expo, Scott Davis, CTO of Embotics, will discuss how automation can provide the dynamic management required to cost-effectively deliver microservices and container solutions at scale. He will discuss how flexible automation is the key to effectively bridging and seamlessly coordinating both IT and developer needs for component orchestration across disparate clouds – an increasingly important requirement at today’s multi-cloud enterprise.
Mar. 29, 2017 06:00 AM EDT Reads: 2,930