Microservices Expo Authors: Liz McMillan, Elizabeth White, Yeshim Deniz, Ian Khan, Jason Bloomberg

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.

@MicroservicesExpo Stories
Without lifecycle traceability and visibility across the tool chain, stakeholders from Planning-to-Ops have limited insight and answers to who, what, when, why and how across the DevOps lifecycle. This impacts the ability to deliver high quality software at the needed velocity to drive positive business outcomes. In his general session at @DevOpsSummit at 19th Cloud Expo, Eric Robertson, General Manager at CollabNet, will discuss how customers are able to achieve a level of transparency that e...
@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.
24Notion is full-service global creative digital marketing, technology and lifestyle agency that combines strategic ideas with customized tactical execution. With a broad understand of the art of traditional marketing, new media, communications and social influence, 24Notion uniquely understands how to connect your brand strategy with the right consumer. 24Notion ranked #12 on Corporate Social Responsibility - Book of List.
In his keynote at 19th Cloud Expo, Sheng Liang, co-founder and CEO of Rancher Labs, will discuss the technological advances and new business opportunities created by the rapid adoption of containers. With the success of Amazon Web Services (AWS) and various open source technologies used to build private clouds, cloud computing has become an essential component of IT strategy. However, users continue to face challenges in implementing clouds, as older technologies evolve and newer ones like Docke...
The reason I believe digital transformation is not only more than a fad, but is actually a life-or-death imperative for every business and IT executive on the planet is simple: there will be no place for an “industrial enterprise” in a digital world. Transformation, by definition, is a metamorphosis from one state to another, wholly new state. As such, a true digital transformation must be the act of transforming an industrial-era organization into something wholly different – the Digital Enter...
Just over a week ago I received a long and loud sustained applause for a presentation I delivered at this year’s Cloud Expo in Santa Clara. I was extremely pleased with the turnout and had some very good conversations with many of the attendees. Over the next few days I had many more meaningful conversations and was not only happy with the results but also learned a few new things. Here is everything I learned in those three days distilled into three short points.
In his session at 19th Cloud Expo, Claude Remillard, Principal Program Manager in Developer Division at Microsoft, will contrast how his team used config as code and immutable patterns for continuous delivery of microservices and apps to the cloud. He will show the immutable patterns helps developers do away with most of the complexity of config as code-enabling scenarios such as rollback, zero downtime upgrades with far greater simplicity. He will also have live demos of building immutable pipe...
Application transformation and DevOps practices are two sides of the same coin. Enterprises that want to capture value faster, need to deliver value faster – time value of money principle. To do that enterprises need to build cloud-native apps as microservices by empowering teams to build, ship, and run in production. In his session at @DevOpsSummit at 19th Cloud Expo, Neil Gehani, senior product manager at HPE, will discuss what every business should plan for how to structure their teams to d...
When we talk about the impact of BYOD and BYOA and the Internet of Things, we often focus on the impact on data center architectures. That's because there will be an increasing need for authentication, for access control, for security, for application delivery as the number of potential endpoints (clients, devices, things) increases. That means scale in the data center. What we gloss over, what we skip, is that before any of these "things" ever makes a request to access an application it had to...
SYS-CON Events announced today that Transparent Cloud Computing (T-Cloud) Consortium 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. The Transparent Cloud Computing Consortium (T-Cloud Consortium) will conduct research activities into changes in the computing model as a result of collaboration between "device" and "cloud" and the creation of new value and markets through organic data proces...
In many organizations governance is still practiced by phase or stage gate peer review, and Agile projects are forced to accommodate, which leads to WaterScrumFall or worse. But governance criteria and policies are often very weak anyway, out of date or non-existent. Consequently governance is frequently a matter of opinion and experience, highly dependent upon the experience of individual reviewers. As we all know, a basic principle of Agile methods is delegation of responsibility, and ideally ...
The evolution of JavaScript and HTML 5 to support a genuine component based framework (Web Components) with the necessary tools to deliver something close to a native experience including genuine realtime networking (UDP using WebRTC). HTML5 is evolving to offer built in templating support, the ability to watch objects (which will speed up Angular) and Web Components (which offer Angular Directives). The native level support will offer a massive performance boost to frameworks having to fake all...
Today every business relies on software to drive the innovation necessary for a competitive edge in the Application Economy. This is why collaboration between development and operations, or DevOps, has become IT’s number one priority. Whether you are in Dev or Ops, understanding how to implement a DevOps strategy can deliver faster development cycles, improved software quality, reduced deployment times and overall better experiences for your customers.
Apache Hadoop is a key technology for gaining business insights from your Big Data, but the penetration into enterprises is shockingly low. In fact, Apache Hadoop and Big Data proponents recognize that this technology has not yet achieved its game-changing business potential. In his session at 19th Cloud Expo, John Mertic, director of program management for ODPi at The Linux Foundation, will explain why this is, how we can work together as an open data community to increase adoption, and the i...
All clouds are not equal. To succeed in a DevOps context, organizations should plan to develop/deploy apps across a choice of on-premise and public clouds simultaneously depending on the business needs. This is where the concept of the Lean Cloud comes in - resting on the idea that you often need to relocate your app modules over their life cycles for both innovation and operational efficiency in the cloud. In his session at @DevOpsSummit at19th Cloud Expo, Valentin (Val) Bercovici, CTO of So...
JetBlue Airways uses virtual environments to reduce software development costs, centralize performance testing, and create a climate for continuous integration and real-time monitoring of mobile applications. The next BriefingsDirect Voice of the Customer performance engineering case study discussion examines how JetBlue Airways in New York uses virtual environments to reduce software development costs, centralize performance testing, and create a climate for continuous integration and real-tim...
Virgil consists of an open-source encryption library, which implements Cryptographic Message Syntax (CMS) and Elliptic Curve Integrated Encryption Scheme (ECIES) (including RSA schema), a Key Management API, and a cloud-based Key Management Service (Virgil Keys). The Virgil Keys Service consists of a public key service and a private key escrow service. 

SYS-CON Events announced today that eCube Systems, the leading provider of modern development tools and best practices for Continuous Integration on OpenVMS, will exhibit at SYS-CON's @DevOpsSummit at Cloud Expo New York, which will take place on June 7-9, 2016, at the Javits Center in New York City, NY. eCube Systems offers a family of middleware products and development tools that maximize return on technology investment by leveraging existing technical equity to meet evolving business needs. ...

Let's just nip the conflation of these terms in the bud, shall we?

"MIcro" is big these days. Both microservices and microsegmentation are having and will continue to have an impact on data center architecture, but not necessarily for the same reasons. There's a growing trend in which folks - particularly those with a network background - conflate the two and use them to mean the same thing.

They are not.

One is about the application. The other, the network. T...

This is a no-hype, pragmatic post about why I think you should consider architecting your next project the way SOA and/or microservices suggest. No matter if it’s a greenfield approach or if you’re in dire need of refactoring. Please note: considering still keeps open the option of not taking that approach. After reading this, you will have a better idea about whether building multiple small components instead of a single, large component makes sense for your project. This post assumes that you...