|By Jyoti Bansal||
|April 2, 2014 10:30 AM EDT||
Data is dumb but we can't seem to get enough of it these days. An entire industry has evolved to make sense of the massive amounts of data being generated every day. Massive data collection by itself does not guarantee the context required to solve business and IT problems... If we are smart about data, however, it will lead us in the right direction.
Today's businesses are defined by the software they run on, enabling them to innovate and create new services faster. Apple re-invented the music industry through software; Netflix changed the way we consume movies through software; grocery shopping, taxi-hire, parcel-logistics, car rental, travel booking... these are all industries that have been completely disrupted by software.
As more and more organisations depend upon the software they run on, the consumer demand for superior services, faster innovation, and improved performance continues to accelerate. As a result the software required to compete in this unforgiving market only continues to increase in its complexity.
It was only a few short years ago that when I wanted to book my family vacation, we all made the trip down to the local travel agent and sat around a desk while the agent tapped away on her green-screen searching for available flights and hotels. The process of booking our hotels has obviously changed dramatically, and so have the applications that support the process.
For example, below is a screen shot of the Business Transaction flow through a global travel site when a user makes a simple search for a hotel. All the user does is select a destination, some dates and maybe one or two preferences, but the resultant transaction kicks off more than 200 service requests before the results are returned to the user they can make their selection of preferred hotel.
This rise in application complexity often means we generate increasingly more structured and unstructured data from our applications such as log files, email, alerts, infrastructure stats, network stats etc. Close to 200 billion emails will be sent in 2014, I wonder how many of those emails are false alerts from monitoring tools?
I have been helping companies build monitoring strategies for nearly a decade, and the problem I used to regularly face was dealing with organisations that just didn't have enough data. IT departments didn't have the required information to be able to quickly diagnose and remediate problems when they occurred.
As organisations continue to have a greater dependency on the software they run on and application complexity continues to increase, there is a danger we swing the other way by collecting and storing too much data to be able to make sense of it in a timely manner. My colleague Jim Hirschauer often refers to this as the "home hoarders effect" drawing parallels with people who simply never throw anything out from their homes. The bigger the piles get the harder it is to find what you need, and at some point that pile is going to topple over.
Keep what you need
There are lots of good reasons to keep data, but "just in-case we need it" is not a good reason. It is important to think about why the data is being captured and stored and how it will help solve problems in the future. By keeping the relevant data and throwing away the clutter, you become more efficient and effective in troubleshooting and resolving problems.
But data alone isn't good enough to solve your business and IT problems. Data in and of itself is one-dimensional and dumb. Data doesn't tell you when there are problems, it doesn't tell you when business is going well, it doesn't tell you anything meaningful without some help and a lot more context. Let's explore an example to illustrate my point.
Let's say we have a couple of data points about a person. The data points are...
Heart Rate = 150 bpm
Blood Pressure = 200 over 100
Now tell me, is this person performing well? With these data points we have no idea. We need more data. The table below shows a list of some possible data points we can collect.
Notice the last attribute in the table. The activity provides us with the context we need to focus on the proper data points to figure out if the person is performing well or not. Here are some more relevant data points...
Distance Run = 100 meters
Time = 9.58s
Now can we determine if the person is performing well or not? I'm not much of a track and field aficionado so I have no idea if this is a good performance or not. I need point of comparison to determine if 9.58 seconds in the 100-meter dash is good. So here is our baseline for comparison sake...
100-Meter World Record Time = 9.69s
Well it looks like the person was performing really well. They set a world record in the 100-meter dash. All of the data points individually didn't tell us anything. We required correlation (context) and analytics (comparison to baseline) in order to turn data into information. I like to refer to this concept as creating Smart Data.
Smart Data Defined
Smart Data is actionable, intelligent, information.
Smart Data is created by performing correlation and analytics on data sets. AppDynamics correlates end user business transaction details with completion status (success, error, exception), response times, and all other data points measured at any given time. It automatically analyzes the entire data set to provide information from which to draw conclusions and take the appropriate action. This information is called Smart Data.
Being smart about your monitoring data collection allows you to isolate and resolve problems much faster, and with a much lower cost of ownership and overhead.
I have recently been working with a company to replace their legacy monitoring tool with AppDynamics. The environment they are monitoring is a good size, consisting of about 1,200 servers and their main application processes approximately 300,000 transactions per minute. They invested in a monitoring tool to help manage the performance of their applications which captured and stored all the data they could "just in-case" it was needed. Unfortunately this approach required an additional 92 servers to be provisioned for the monitoring tool itself, which consumed approximately 80TB of data storage per year. The increasing investments this customer needed to make in hardware, storage, people, and maintenance was too much to manage for them and they decided to look for a different approach.
AppDynamics "smart data" approach to analytics means this particular customer now only requires two reporting servers and the storage requirements were reduced to just 1TB per year. Collecting only the data required to make smart decisions gave them both a 98% reduction in hardware costs and more effective analytics in the process.
Adding business context
Smart data is not just about resolving problems faster though. In October last year we introduced Real-time Business Metrics and described how AppDynamics customers can use Real-time Business Metrics to extract and present business metrics directly from within their applications. These business metrics provide business context enabling customers to turn smart data into actionable information. Our customers, for example, can see the exact revenue impact of performance problems, the end user experience during an app upgrade, or the real-time impact of marketing campaigns. Smart analytics not only clearly show the business benefits of making immediate improvements, they help direct where limited resources should be invested for further business and application improvement going forward.
AppDynamics is focused on delivering actionable intelligence to sell problems for IT operations and development teams as well as business owners. To learn more about Real-time Business Metrics read here.
The post A New Breed of Actionable Analytics written by Tom Levey appeared first on Application Performance Monitoring Blog from AppDynamics.
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