Welcome!

@CloudExpo Authors: Pat Romanski, Liz McMillan, Elizabeth White, Kevin Jackson, Sanjay Zalavadia

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
"We work in the area of Big Data analytics and Big Data analytics is a very crowded space - you have Hadoop, ETL, warehousing, visualization and there's a lot of effort trying to get these tools to talk to each other," explained Mukund Deshpande, head of the Analytics practice at Accelerite, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York City, NY.
Cloud Expo, Inc. has announced today that Andi Mann returns to 'DevOps at Cloud Expo 2016' as Conference Chair The @DevOpsSummit at Cloud Expo will take place on November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. "DevOps is set to be one of the most profound disruptions to hit IT in decades," said Andi Mann. "It is a natural extension of cloud computing, and I have seen both firsthand and in independent research the fantastic results DevOps delivers. So I am excited t...
IoT offers a value of almost $4 trillion to the manufacturing industry through platforms that can improve margins, optimize operations & drive high performance work teams. By using IoT technologies as a foundation, manufacturing customers are integrating worker safety with manufacturing systems, driving deep collaboration and utilizing analytics to exponentially increased per-unit margins. However, as Benoit Lheureux, the VP for Research at Gartner points out, “IoT project implementers often ...
Unless your company can spend a lot of money on new technology, re-engineering your environment and hiring a comprehensive cybersecurity team, you will most likely move to the cloud or seek external service partnerships. In his session at 18th Cloud Expo, Darren Guccione, CEO of Keeper Security, revealed what you need to know when it comes to encryption in the cloud.
Edge Hosting has announced a partnership with and the availability of CloudFlare, a web application firewall, CDN and DDoS mitigation service. “This partnership enhances Edge Hosting’s world class, perimeter layer, application (layer 7) defensive mechanism,” said Mark Houpt, Edge Hosting CISO. “The goal was to enable a new layer of customer controlled defense and compliance through the application of DDoS filters and mitigations, the web application firewall (WAF) feature and the added benefit ...
Redis is not only the fastest database, but it is the most popular among the new wave of databases running in containers. Redis speeds up just about every data interaction between your users or operational systems. In his session at 19th Cloud Expo, Dave Nielsen, Developer Advocate, Redis Labs, will share the functions and data structures used to solve everyday use cases that are driving Redis' popularity.
"SpeedyCloud's specialty lies in providing cloud services - we provide IaaS for Internet and enterprises companies," explained Hao Yu, CEO and co-founder of SpeedyCloud, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York City, NY.
Your business relies on your applications and your employees to stay in business. Whether you develop apps or manage business critical apps that help fuel your business, what happens when users experience sluggish performance? You and all technical teams across the organization – application, network, operations, among others, as well as, those outside the organization, like ISPs and third-party providers – are called in to solve the problem.
Creating replica copies to tolerate a certain number of failures is easy, but very expensive at cloud-scale. Conventional RAID has lower overhead, but it is limited in the number of failures it can tolerate. And the management is like herding cats (overseeing capacity, rebuilds, migrations, and degraded performance). Download Slide Deck: ▸ Here In his general session at 18th Cloud Expo, Scott Cleland, Senior Director of Product Marketing for the HGST Cloud Infrastructure Business Unit, discusse...
Machine Learning helps make complex systems more efficient. By applying advanced Machine Learning techniques such as Cognitive Fingerprinting, wind project operators can utilize these tools to learn from collected data, detect regular patterns, and optimize their own operations. In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, discussed how research has demonstrated the value of Machine Learning in delivering next generation analytics to imp...
It's easy to assume that your app will run on a fast and reliable network. The reality for your app's users, though, is often a slow, unreliable network with spotty coverage. What happens when the network doesn't work, or when the device is in airplane mode? You get unhappy, frustrated users. An offline-first app is an app that works, without error, when there is no network connection. In his session at 18th Cloud Expo, Bradley Holt, a Developer Advocate with IBM Cloud Data Services, discussed...
The cloud promises new levels of agility and cost-savings for Big Data, data warehousing and analytics. But it’s challenging to understand all the options – from IaaS and PaaS to newer services like HaaS (Hadoop as a Service) and BDaaS (Big Data as a Service). In her session at @BigDataExpo at @ThingsExpo, Hannah Smalltree, a director at Cazena, provided an educational overview of emerging “as-a-service” options for Big Data in the cloud. This is critical background for IT and data profession...
"Tintri was started in 2008 with the express purpose of building a storage appliance that is ideal for virtualized environments. We support a lot of different hypervisor platforms from VMware to OpenStack to Hyper-V," explained Dan Florea, Director of Product Management at Tintri, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York City, NY.
In the world of DevOps there are ‘known good practices’ – aka ‘patterns’ – and ‘known bad practices’ – aka ‘anti-patterns.' Many of these patterns and anti-patterns have been developed from real world experience, especially by the early adopters of DevOps theory; but many are more feasible in theory than in practice, especially for more recent entrants to the DevOps scene. In this power panel at @DevOpsSummit at 18th Cloud Expo, moderated by DevOps Conference Chair Andi Mann, panelists discusse...
A strange thing is happening along the way to the Internet of Things, namely far too many devices to work with and manage. It has become clear that we'll need much higher efficiency user experiences that can allow us to more easily and scalably work with the thousands of devices that will soon be in each of our lives. Enter the conversational interface revolution, combining bots we can literally talk with, gesture to, and even direct with our thoughts, with embedded artificial intelligence, wh...
Connected devices and the industrial internet are growing exponentially every year with Cisco expecting 50 billion devices to be in operation by 2020. In this period of growth, location-based insights are becoming invaluable to many businesses as they adopt new connected technologies. Knowing when and where these devices connect from is critical for a number of scenarios in supply chain management, disaster management, emergency response, M2M, location marketing and more. In his session at @Th...
As organizations shift towards IT-as-a-service models, the need for managing and protecting data residing across physical, virtual, and now cloud environments grows with it. Commvault can ensure protection, access and E-Discovery of your data – whether in a private cloud, a Service Provider delivered public cloud, or a hybrid cloud environment – across the heterogeneous enterprise. In his general session at 18th Cloud Expo, Randy De Meno, Chief Technologist - Windows Products and Microsoft Part...
What does it look like when you have access to cloud infrastructure and platform under the same roof? Let’s talk about the different layers of Technology as a Service: who cares, what runs where, and how does it all fit together. In his session at 18th Cloud Expo, Phil Jackson, Lead Technology Evangelist at SoftLayer, an IBM company, spoke about the picture being painted by IBM Cloud and how the tools being crafted can help fill the gaps in your IT infrastructure.
In his keynote at 18th Cloud Expo, Andrew Keys, Co-Founder of ConsenSys Enterprise, provided an overview of the evolution of the Internet and the Database and the future of their combination – the Blockchain. Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life sett...
"delaPlex is a software development company. We do team-based outsourcing development," explained Mark Rivers, COO and Co-founder of delaPlex Software, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York City, NY.