|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.
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.
Jul. 1, 2015 07:15 PM EDT Reads: 2,534
"We have a tagline - "Power in the API Economy." What that means is everything that is built in applications and connected applications is done through APIs," explained Roberto Medrano, Executive Vice President at Akana, in this SYS-CON.tv interview at 16th Cloud Expo, held June 9-11, 2015, at the Javits Center in New York City.
Jul. 1, 2015 05:00 PM EDT Reads: 891
Containers have changed the mind of IT in DevOps. They enable developers to work with dev, test, stage and production environments identically. Containers provide the right abstraction for microservices and many cloud platforms have integrated them into deployment pipelines. DevOps and Containers together help companies to achieve their business goals faster and more effectively. In his session at DevOps Summit, Ruslan Synytsky, CEO and Co-founder of Jelastic, reviewed the current landscape of...
Jul. 1, 2015 05:00 PM EDT Reads: 2,233
"AgilData is the next generation of dbShards. It just adds a whole bunch more functionality to improve the developer experience," noted Dan Lynn, CEO of AgilData, in this SYS-CON.tv interview at 16th Cloud Expo, held June 9-11, 2015, at the Javits Center in New York City.
Jul. 1, 2015 04:09 PM EDT Reads: 649
The Internet of Things is not only adding billions of sensors and billions of terabytes to the Internet. It is also forcing a fundamental change in the way we envision Information Technology. For the first time, more data is being created by devices at the edge of the Internet rather than from centralized systems. What does this mean for today's IT professional? In this Power Panel at @ThingsExpo, moderated by Conference Chair Roger Strukhoff, panelists addressed this very serious issue of pro...
Jul. 1, 2015 03:30 PM EDT Reads: 1,087
"We provide a web application framework for building really sophisticated web applications that run on a browser without any installation need so we get used for biotech, defense, and banking applications," noted Charles Kendrick, CTO and Chief Architect at Isomorphic Software, in this SYS-CON.tv interview at @DevOpsSummit (http://DevOpsSummit.SYS-CON.com), held June 9-11, 2015, at the Javits Center in New York
Jul. 1, 2015 02:45 PM EDT Reads: 1,100
Discussions about cloud computing are evolving into discussions about enterprise IT in general. As enterprises increasingly migrate toward their own unique clouds, new issues such as the use of containers and microservices emerge to keep things interesting. In this Power Panel at 16th Cloud Expo, moderated by Conference Chair Roger Strukhoff, panelists addressed the state of cloud computing today, and what enterprise IT professionals need to know about how the latest topics and trends affect t...
Jul. 1, 2015 02:30 PM EDT Reads: 1,209
Explosive growth in connected devices. Enormous amounts of data for collection and analysis. Critical use of data for split-second decision making and actionable information. All three are factors in making the Internet of Things a reality. Yet, any one factor would have an IT organization pondering its infrastructure strategy. How should your organization enhance its IT framework to enable an Internet of Things implementation? In his session at @ThingsExpo, James Kirkland, Red Hat's Chief Arch...
Jul. 1, 2015 02:21 PM EDT Reads: 687
In the midst of the widespread popularity and adoption of cloud computing, it seems like everything is being offered “as a Service” these days: Infrastructure? Check. Platform? You bet. Software? Absolutely. Toaster? It’s only a matter of time. With service providers positioning vastly differing offerings under a generic “cloud” umbrella, it’s all too easy to get confused about what’s actually being offered. In his session at 16th Cloud Expo, Kevin Hazard, Director of Digital Content for SoftL...
Jul. 1, 2015 01:15 PM EDT Reads: 2,179
"A lot of the enterprises that have been using our systems for many years are reaching out to the cloud - the public cloud, the private cloud and hybrid," stated Reuven Harrison, CTO and Co-Founder of Tufin, in this SYS-CON.tv interview at 16th Cloud Expo, held June 9-11, 2015, at the Javits Center in New York City.
Jul. 1, 2015 12:54 PM EDT Reads: 649
One of the hottest areas in cloud right now is DRaaS and related offerings. In his session at 16th Cloud Expo, Dale Levesque, Disaster Recovery Product Manager with Windstream's Cloud and Data Center Marketing team, will discuss the benefits of the cloud model, which far outweigh the traditional approach, and how enterprises need to ensure that their needs are properly being met.
Jul. 1, 2015 12:15 PM EDT Reads: 2,062
The time is ripe for high speed resilient software defined storage solutions with unlimited scalability. ISS has been working with the leading open source projects and developed a commercial high performance solution that is able to grow forever without performance limitations. In his session at Cloud Expo, Alex Gorbachev, President of Intelligent Systems Services Inc., shared foundation principles of Ceph architecture, as well as the design to deliver this storage to traditional SAN storage co...
Jul. 1, 2015 12:00 PM EDT Reads: 2,022
"Plutora provides release and testing environment capabilities to the enterprise," explained Dalibor Siroky, Director and Co-founder of Plutora, in this SYS-CON.tv interview at @DevOpsSummit, held June 9-11, 2015, at the Javits Center in New York City.
Jul. 1, 2015 11:45 AM EDT Reads: 1,050
It is one thing to build single industrial IoT applications, but what will it take to build the Smart Cities and truly society-changing applications of the future? The technology won’t be the problem, it will be the number of parties that need to work together and be aligned in their motivation to succeed. In his session at @ThingsExpo, Jason Mondanaro, Director, Product Management at Metanga, discussed how you can plan to cooperate, partner, and form lasting all-star teams to change the world...
Jul. 1, 2015 11:30 AM EDT Reads: 2,261
SYS-CON Events announced today that JFrog, maker of Artifactory, the popular Binary Repository Manager, will exhibit at SYS-CON's @DevOpsSummit Silicon Valley, which will take place on November 3–5, 2015, at the Santa Clara Convention Center in Santa Clara, CA. Based in California, Israel and France, founded by longtime field-experts, JFrog, creator of Artifactory and Bintray, has provided the market with the first Binary Repository solution and a software distribution social platform.
Jul. 1, 2015 11:30 AM EDT Reads: 1,004
In his session at 16th Cloud Expo, Simone Brunozzi, VP and Chief Technologist of Cloud Services at VMware, reviewed the changes that the cloud computing industry has gone through over the last five years and shared insights into what the next five will bring. He also chronicled the challenges enterprise companies are facing as they move to the public cloud. He delved into the "Hybrid Cloud" space and explained why every CIO should consider ‘hybrid cloud' as part of their future strategy to achi...
Jul. 1, 2015 11:10 AM EDT Reads: 718
"We got started as search consultants. On the services side of the business we have help organizations save time and save money when they hit issues that everyone more or less hits when their data grows," noted Otis Gospodnetić, Founder of Sematext, in this SYS-CON.tv interview at @DevOpsSummit, held June 9-11, 2015, at the Javits Center in New York City.
Jul. 1, 2015 11:00 AM EDT Reads: 974
Internet of Things is moving from being a hype to a reality. Experts estimate that internet connected cars will grow to 152 million, while over 100 million internet connected wireless light bulbs and lamps will be operational by 2020. These and many other intriguing statistics highlight the importance of Internet powered devices and how market penetration is going to multiply many times over in the next few years.
Jul. 1, 2015 10:30 AM EDT Reads: 2,150
Internet of Things (IoT) will be a hybrid ecosystem of diverse devices and sensors collaborating with operational and enterprise systems to create the next big application. In their session at @ThingsExpo, Bramh Gupta, founder and CEO of robomq.io, and Fred Yatzeck, principal architect leading product development at robomq.io, discussed how choosing the right middleware and integration strategy from the get-go will enable IoT solution developers to adapt and grow with the industry, while at th...
Jul. 1, 2015 09:45 AM EDT Reads: 1,957
The most often asked question post-DevOps introduction is: “How do I get started?” There’s plenty of information on why DevOps is valid and important, but many managers still struggle with simple basics for how to initiate a DevOps program in their business. They struggle with issues related to current organizational inertia, the lack of experience on Continuous Integration/Delivery, understanding where DevOps will affect revenue and budget, etc. In their session at DevOps Summit, JP Morgenthal...
Jul. 1, 2015 09:32 AM EDT Reads: 678