Welcome!

@CloudExpo Authors: Pat Romanski, Elizabeth White, Suresh Sambandam, Richard Hale, Liz McMillan

Related Topics: @CloudExpo, Java IoT, Microservices Expo, Open Source Cloud, Agile Computing, Apache

@CloudExpo: Article

The Cure for the Common Cloud-Based Big Data Initiative

Understanding how to work with Big Data

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.

More Stories By Rick Morrison

Rick Morrison is CEO and co-founder of Comprehend Systems. Prior to Comprehend Systems, he was the Chief Technology Officer of an Internet-based data aggregator, where he was responsible for product development and operations. Prior to that, he was at Integrated Clinical Systems, where he led the design and implementation of several major new features. He also proposed and led a major infrastructure redesign, and introduced new, streamlined development processes. Rick holds a BS in Computer Science from Carnegie Mellon University in Pittsburgh, Pennsylvania.

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


@CloudExpo Stories
Aspose.Total for .NET is the most complete package of all file format APIs for .NET as offered by Aspose. It empowers developers to create, edit, render, print and convert between a wide range of popular document formats within any .NET, C#, ASP.NET and VB.NET applications. Aspose compiles all .NET APIs on a daily basis to ensure that it contains the most up to date versions of each of Aspose .NET APIs. If a new .NET API or a new version of existing APIs is released during the subscription peri...
Security, data privacy, reliability, and regulatory compliance are critical factors when evaluating whether to move business applications from in-house, client-hosted environments to a cloud platform. Quality assurance plays a vital role in ensuring that the appropriate level of risk assessment, verification, and validation takes place to ensure business continuity during the migration to a new cloud platform.
SYS-CON Events announced today that 910Telecom will exhibit at the 19th International Cloud Expo, which will take place on November 1–3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. Housed in the classic Denver Gas & Electric Building, 910 15th St., 910Telecom is a carrier-neutral telecom hotel located in the heart of Denver. Adjacent to CenturyLink, AT&T, and Denver Main, 910Telecom offers connectivity to all major carriers, Internet service providers, Internet backbones and ...
Ovum, a leading technology analyst firm, has published an in-depth report, Ovum Decision Matrix: Selecting a DevOps Release Management Solution, 2016–17. The report focuses on the automation aspects of DevOps, Release Management and compares solutions from the leading vendors.
Continuous testing helps bridge the gap between developing quickly and maintaining high quality products. But to implement continuous testing, CTOs must take a strategic approach to building a testing infrastructure and toolset that empowers their team to move fast. Download our guide to laying the groundwork for a scalable continuous testing strategy.
Adding public cloud resources to an existing application can be a daunting process. The tools that you currently use to manage the software and hardware outside the cloud aren’t always the best tools to efficiently grow into the cloud. All of the major configuration management tools have cloud orchestration plugins that can be leveraged, but there are also cloud-native tools that can dramatically improve the efficiency of managing your application lifecycle. In his session at 18th Cloud Expo, ...
SYS-CON Events announced today that LeaseWeb USA, a cloud Infrastructure-as-a-Service (IaaS) provider, will exhibit at the 19th International Cloud Expo, which will take place on November 1–3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. LeaseWeb is one of the world's largest hosting brands. The company helps customers define, develop and deploy IT infrastructure tailored to their exact business needs, by combining various kinds cloud solutions.
Qosmos has announced new milestones in the detection of encrypted traffic and in protocol signature coverage. Qosmos latest software can accurately classify traffic encrypted with SSL/TLS (e.g., Google, Facebook, WhatsApp), P2P traffic (e.g., BitTorrent, MuTorrent, Vuze), and Skype, while preserving the privacy of communication content. These new classification techniques mean that traffic optimization, policy enforcement, and user experience are largely unaffected by encryption. In respect wit...
SYS-CON Events announced today the Kubernetes and Google Container Engine Workshop, being held November 3, 2016, in conjunction with @DevOpsSummit at 19th Cloud Expo at the Santa Clara Convention Center in Santa Clara, CA. This workshop led by Sebastian Scheele introduces participants to Kubernetes and Google Container Engine (GKE). Through a combination of instructor-led presentations, demonstrations, and hands-on labs, students learn the key concepts and practices for deploying and maintainin...
SYS-CON Events announced today that Venafi, the Immune System for the Internet™ and the leading provider of Next Generation Trust Protection, will exhibit at @DevOpsSummit at 19th International Cloud Expo, which will take place on November 1–3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. Venafi is the Immune System for the Internet™ that protects the foundation of all cybersecurity – cryptographic keys and digital certificates – so they can’t be misused by bad guys in attacks...
ReadyTalk has expanded the capabilities of the FoxDen collaboration platform announced late last year to include FoxDen Connect, an in-room video collaboration experience that launches with a single touch. With FoxDen Connect, users can now not only engage in HD video conferencing between iOS and Android mobile devices or Chrome browsers, but also set up in-person meeting rooms for video interactions. A host’s mobile device automatically recognizes the presence of a meeting room via beacon tech...
The cloud market growth today is largely in public clouds. While there is a lot of spend in IT departments in virtualization, these aren’t yet translating into a true “cloud” experience within the enterprise. What is stopping the growth of the “private cloud” market? In his general session at 18th Cloud Expo, Nara Rajagopalan, CEO of Accelerite, explored the challenges in deploying, managing, and getting adoption for a private cloud within an enterprise. What are the key differences between wh...
Deploying applications in hybrid cloud environments is hard work. Your team spends most of the time maintaining your infrastructure, configuring dev/test and production environments, and deploying applications across environments – which can be both time consuming and error prone. But what if you could automate provisioning and deployment to deliver error free environments faster? What could you do with your free time?
Ixia (Nasdaq: XXIA) has announced that NoviFlow Inc.has deployed IxNetwork® to validate the company’s designs and accelerate the delivery of its proven, reliable products. Based in Montréal, NoviFlow Inc. supports network carriers, hyperscale data center operators, and enterprises seeking greater network control and flexibility, network scalability, and the capacity to handle extremely large numbers of flows, while maintaining maximum network performance. To meet these requirements, NoviFlow in...
Choosing the right cloud for your workloads is a balancing act that can cost your organization time, money and aggravation - unless you get it right the first time. Economics, speed, performance, accessibility, administrative needs and security all play a vital role in dictating your approach to the cloud. Without knowing the right questions to ask, you could wind up paying for capacity you'll never need or underestimating the resources required to run your applications.
It’s 2016: buildings are smart, connected and the IoT is fundamentally altering how control and operating systems work and speak to each other. Platforms across the enterprise are networked via inexpensive sensors to collect massive amounts of data for analytics, information management, and insights that can be used to continuously improve operations. In his session at @ThingsExpo, Brian Chemel, Co-Founder and CTO of Digital Lumens, will explore: The benefits sensor-networked systems bring to ...
On Dice.com, the number of job postings asking for skill in Amazon Web Services increased 76 percent between June 2015 and June 2016. Salesforce.com saw its own skill mentions increase 37 percent, while DevOps and Cloud rose 35 percent and 28 percent, respectively. Even as they expand their presence in the cloud, companies are also looking for tech professionals who can manage projects, crunch data, and figure out how to make systems run more autonomously. Mentions of ‘data science’ as a skill ...
Extreme Computing is the ability to leverage highly performant infrastructure and software to accelerate Big Data, machine learning, HPC, and Enterprise applications. High IOPS Storage, low-latency networks, in-memory databases, GPUs and other parallel accelerators are being used to achieve faster results and help businesses make better decisions. In his session at 18th Cloud Expo, Michael O'Neill, Strategic Business Development at NVIDIA, focused on some of the unique ways extreme computing is...
Cloud analytics is dramatically altering business intelligence. Some businesses will capitalize on these promising new technologies and gain key insights that’ll help them gain competitive advantage. And others won’t. Whether you’re a business leader, an IT manager, or an analyst, we want to help you and the people you need to influence with a free copy of “Cloud Analytics for Dummies,” the essential guide to this explosive new space for business intelligence.
Manufacturers are embracing the Industrial Internet the same way consumers are leveraging Fitbits – to improve overall health and wellness. Both can provide consistent measurement, visibility, and suggest performance improvements customized to help reach goals. Fitbit users can view real-time data and make adjustments to increase their activity. In his session at @ThingsExpo, Mark Bernardo Professional Services Leader, Americas, at GE Digital, discussed how leveraging the Industrial Internet a...