zAnalytics – What it means to me
SandeepPerumbuduri 270003HW8Q Visits (4976)
A system programmer received a call that one of the applications on a transaction server is having some problem. The system programmer logs on to a data analyzer, quickly glances at the analyzed reports, understands what might be going wrong and fixes it. This seems to be a first reaction when a customer notices an issue in their workshop.
There seems to be an exponential growth in the data generated worldwide. About 90% of data was generated in the last two years alone. It is also significant to know that 70% of the unstructured data in 2013 was transaction data.
Most of the enterprise applications run on a mainframe. Due to the mainframe's high reliability, scalability and high security, most of the financial industries prefer to use a mainframe. This raises the following questions.
We need a method to analyze the huge set of data generated in the financial industries that will help both the business and the individuals. Hence the importance of zAnalytics.
zAnalytics is a method of analyzing the unstructured data to establish a relationship in the information, find patterns and trends that would help understand the issue on hand. This may mean we have per-existing conditions to check, or make the program smart to enable learning through the historical data.
There are program that can help us gather log data from CICS, DB2, MQ, or IMS and a server will help to analyze the data to present a consolidated and annotated view of the log data. This might help a client in understanding and debugging a problem in few minutes rather than in a few hours or sometimes in days. Then there are programs that will collect the unstructured data and learn from it. This knowledge can then be used to predict future anomalies or issues.
Why should you let the programs analyze the data for you?
Not all organizations will have the resources and time to conduct a manual evaluation of data and debug the issue. It demands the right expertise, requires time to be set aside to understand the data and needs resources to help in debugging the problem. And it is of no surprise to know that an organization is 33% more likely to improve in business who focus on improving the analytic capabilities in their workshops.
I believe clients are realizing the power of data analytics, and so adopting the use of such analytic tools in their environment. Like I mentioned earlier, we have tools that analyze the data or predict future results. These existing applications can be used to build a solution where in we can predict an issue and automatically optimize the mainframe to address the challenge.
For information on how to search and diagnose a z/OS Application problem using IBM SmartCloud Analytics – Log Analysis, go through the following videos here