Welcome!

Big Data Journal Authors: Greg Schulz, Pat Romanski, Elizabeth White, Adrian Bridgwater, Liz McMillan

Related Topics: Big Data Journal, SOA & WOA, Virtualization, Web 2.0, Cloud Expo, Apache

Big Data Journal: Article

Babies, Big Data, and IT Analytics

Machine learning is a topic that has gone from obscure niche to mainstream visibility over the last few years

Machine learning and IT analytics can be just as beneficial to IT operations as it is for monitoring vital signs of premature babies to identify danger signs too subtle or abnormal to be detected by a human. But an enterprise must be willing to implement monitoring and instrumentation that gathers data and incorporates business activity across organizational silos in order to get meaningful results from machine learning.

Machine learning is a topic that has gone from obscure niche to mainstream visibility over the last few years. High profile software companies like Splunk have tapped into the Big Data "explosion" to highlight the benefits of building systems that use algorithms and data to make decisions and evolve over time.

One recent article on machine learning on the O'Reilly Radar blog that caught my attention made a connection between web operations and medical care for premature infants. "Operations, machine learning, and premature babies" by Mike Loukides describes how machine learning is used to analyze data streamed from dozens of monitors connected to each baby. The algorithms are able to detect dangerous infections a full day before any symptoms are noticeable to a human.

An interesting point from the article is that the machine learning system is not looking for spikes or irregularities in the data; it is actually looking for the opposite. Babies who are about to become sick stop exhibiting the normal variations in vital signs shown by healthy babies. It takes a machine learning system to detect changes in behavior too subtle for a human to notice.

Mike Loukides then wonders whether machine learning can be applied to web operations. Typical performance monitoring focuses on thresholds to identify a problem. "But what if crossing a threshold isn't what indicates trouble, but the disappearance (or diminution) of some regular pattern?" Machine learning could identify symptoms that a human fails to identify because he's just looking for thresholds to be crossed.

Mike's conclusion sums up much of the state of the IT industry concerning machine learning:

At most enterprises, operations have not taken the next step. Operations staff doesn't have the resources (neither computational nor human) to apply machine intelligence to our problems. We'd have to capture all the data coming off our servers for extended periods, not just the server logs that we capture now, but any every kind of data we can collect: network data, environmental data, I/O subsystem data, you name it.

As someone who works for a company that applies a form of machine learning (Behavior Learning for predictive analytics) to IT operations and application performance management, I read this with great interest. I didn't necessarily disagree with his conclusion but tried to pull apart the reasoning behind why more companies aren't applying algorithms to their IT data to look for problems.

There are at least three requirements for companies who want to move ahead in this area:

1. Establish maturity of one's monitoring infrastructure. This is the most fundamental point. If you want to apply machine intelligence to IT operations then you need to first add instrumentation and monitoring. Numerous monitoring products and approaches abound but you have to get the data before you can analyze it.

2. Coordinate multiple enterprise silos. Modern IT applications are increasingly complex and may cross multiple enterprise silos such as server virtualization, network, databases, application development, and other middleware components. Enterprises must be willing to coordinate between these multiple groups in gathering monitoring data and performing cross-functional troubleshooting when there are performance or uptime issues.

3. Incorporate business activity monitoring (BAM). Business activity data provides the "vital signs" of a business. Examples of retail business activity data include number of units sold, total gross sales, and total net sales for a time period. Knowing the true business impact of an application performance problem requires the correlation of business data. When an outage occurred for 20 minutes, how many fewer units were sold? What was the reduction in gross and net sales?

An organization that can fulfill these requirements is capable of achieving real benefits in IT operations and can successfully apply analytics. Gartner has established the ITScore Maturity Model for determining one's sophistication in availability and performance monitoring. Here is the description for level 5, which is the top tier:

Behavior Learning engines, embedded knowledge, advanced correlation, trend analysis, pattern matching, and integrated IT and business data from sources such as BAM provide IT operations with the ability to dynamically manage the IT infrastructure in line with business policy.

Applying machine learning to IT operations isn't easy. Most enterprises don't do it because they need to overcome organizational inertia and gather data from multiple groups scattered throughout the enterprise. For the organizations willing to do this, however, they will see tangible business benefits. Just as a hospital could algorithmically detect the failing health of a premature infant, an enterprise willing to use machine learning will visibly see how abnormal problems within IT operations can impact revenue.

More Stories By Richard Park

Richard Park is Director of Product Management at Netuitive. He currently leads Netuitive's efforts to integrate with application performance and cloud monitoring solutions. He has nearly 20 years of experience in network security, database programming, and systems engineering. Some past jobs include product management at Sourcefire and Computer Associates, network engineering and security at Booz Allen Hamilton, and systems engineering at UUNET Technologies (now part of Verizon). Richard has an MS in Computer Science from Johns Hopkins, an MBA from Harvard Business School, and a BA in Social Studies from Harvard University.

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.


Latest Stories from Big Data Journal
Enthusiasm for the Internet of Things has reached an all-time high. In 2013 alone, venture capitalists spent more than $1 billion dollars investing in the IoT space. With “smart” appliances and devices, IoT covers wearable smart devices, cloud services to hardware companies. Nest, a Google company, detects temperatures inside homes and automatically adjusts it by tracking its user’s habit. These technologies are quickly developing and with it come challenges such as bridging infrastructure gaps,...
Predicted by Gartner to add $1.9 trillion to the global economy by 2020, the Internet of Everything (IoE) is based on the idea that devices, systems and services will connect in simple, transparent ways, enabling seamless interactions among devices across brands and sectors. As this vision unfolds, it is clear that no single company can accomplish the level of interoperability required to support the horizontal aspects of the IoE. The AllSeen Alliance, announced in December 2013, was formed wi...
Goodness there is a lot of talk about cloud computing. This ‘talk and chatter’ is part of the problem, i.e., we look at it, we prod it and we might even test it out – but do we get down to practical implementation, deployment and (if you happen to be a fan of the term) actual cloud ‘rollout’ today? Cloud offers the promise of a new era they say – and a new style of IT at that. But this again is the problem and we know that cloud can only deliver on the promises it makes if it is part of a well...
There’s Big Data, then there’s really Big Data from the Internet of Things. IoT is evolving to include many data possibilities like new types of event, log and network data. The volumes are enormous, generating tens of billions of logs per day, which raise data challenges. Early IoT deployments are relying heavily on both the cloud and managed service providers to navigate these challenges. In her session at 6th Big Data Expo®, Hannah Smalltree, Director at Treasure Data, to discuss how IoT, B...
SYS-CON Events announced today that Connected Data, the creator of Transporter, the world’s first peer-to-peer private cloud storage device, will exhibit at SYS-CON's 15th International Cloud Expo®, which will take place on November 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA. Connected Data is the creator of Transporter, the world’s first peer-to-peer private cloud storage device. Connected Data is focused on providing elegantly designed solutions for consumers, professi...
Cisco on Wedesday announced its intent to acquire privately held Metacloud. Based in Pasadena, Calif., Metacloud deploys and operates private clouds for global organizations with a unique OpenStack-as-a-Service model that delivers and remotely operates production-ready private clouds in a customer's data center. Metacloud's OpenStack-based cloud platform will accelerate Cisco's strategy to build the world's largest global Intercloud, a network of clouds, together with key partners to address cu...
Technology is enabling a new approach to collecting and using data. This approach, commonly referred to as the “Internet of Things” (IoT), enables businesses to use real-time data from all sorts of things including machines, devices and sensors to make better decisions, improve customer service, and lower the risk in the creation of new revenue opportunities. In his session at Internet of @ThingsExpo, Dave Wagstaff, Vice President and Chief Architect at BSQUARE Corporation, will discuss the real...
I write and study often on the subject of digital transformation - the digital transformation of industries, markets, products, business models, etc. In brief, digital transformation is about the impact that collected and analyzed data can have when used to enhance business processes and workflows. If Amazon knows your preferences for particular books and films based upon captured data, then they can apply analytics to predict related books and films that you may like. This improves sales. T...
IoT is still a vague buzzword for many people. In his session at Internet of @ThingsExpo, Mike Kavis, Vice President & Principal Cloud Architect at Cloud Technology Partners, will discuss the business value of IoT that goes far beyond the general public's perception that IoT is all about wearables and home consumer services. The presentation will also discuss how IoT is perceived by investors and how venture capitalist access this space. Other topics to discuss are barriers to success, what is n...
When one expects instantaneous response from video generated on the internet, lots of invisible problems have to be overcome. In his session at 6th Big Data Expo®, Tom Paquin, EVP and Chief Technology Officer at OnLive, to discuss how to overcome these problems. A Silicon Valley veteran, Tom Paquin provides vision, expertise and leadership to the technology research and development effort at OnLive as EVP and Chief Technology Officer. With more than 20 years of management experience at lead...