From the Blogosphere
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
By: Richard Park
Feb. 2, 2013 04:00 PM
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.
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