IntelliMagic Vision and Splunk: The Best of Both Worlds for your Infrastructure Data Intelligence

By Brett AllisonIntelliMagic Vision and Splunk

Many companies are investing in Splunk® as a modern way to analyze machine-generated big data. Splunk provides a data analytics infrastructure that can load data from any source. Splunk is a versatile platform for data mining, and it provides a development platform to create reports and dashboards from raw data. In theory it is an excellent place to integrate all of your IT infrastructure and application data, but this is not without its challenges.

Splunk is optimized to parse and process unstructured log files and lets the user create any type of report. But since it does not contain built-in knowledge of your IT infrastructure, deriving deep insights in your data is largely up to the user. As a Splunk user, it is up to you to provide context and insights into your data. If you use Splunk to read infrastructure measurement data from your distributed infrastructure, or raw SMF and RMF data from your z/OS mainframe, it will not provide any interpretation out of the box.

This is where IntelliMagic Vision comes in: with its deep knowledge of storage, SAN, and mainframe infrastructures, it is designed to bring out both in-depth insights and relationships within your data. By turning raw data into rich information based on embedded expert knowledge, IntelliMagic Vision enriches your data which can then be sent to Splunk if so desired.

This way you can benefit from the enhanced data to create in-depth and meaningful insight within Splunk.

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Making Use of Artificial Intelligence for IT Operations Analytics / AIOps

Brent PhillipsBy Brent PhillipsArtificial Intelligence for IT Operations Analytics

Enterprise computing systems and storage operations teams have a difficult job: manage the IT infrastructure so that application availability is always efficiently maintained. But this is virtually impossible due to the complexity and disparity of the meta-data and reporting tools for all the various infrastructure components. A lack of information is not the problem, rather the great need is to derive meaningful intelligence out of all the information.

But the cloud, for example, will not work for all applications due to performance and security requirements. And outsourcing doesn’t make infrastructure performance problems go away, in fact it can make them harder to resolve. So most enterprise organizations will still benefit from and require deep infrastructure performance analysis capabilities.

In recent years, a new class of products initially called IT Operations Analytics (ITOA) have come on the market with the design objective of providing a single interface into all the data generated from disparate devices, and more importantly, helping interpret what it really means for performance, availability, and efficiency.

The idea is to employ the computer to do more of the work of deriving meaningful intelligence out of all the data. If designed correctly, this is a type of artificial intelligence which is done by the machine and enables human IT operations teams to be more effective. In 2017 Gartner coined the term AIOps which is a nice nomenclature for the capability.

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The High Cost of “Unpredictable” IT Outages and Disruptions

Curtis RyanBy Curtis RyanHigh Costs of IT Outages

It is no secret that IT service outages and disruptions can cost companies anywhere from thousands up to millions of dollars per incident – plus significant damage to company reputation and customer satisfaction. In the most high profile cases, such as recent IT outages at Delta and Southwest Airlines, the costs can soar to over $150 million per incident (Delta Cancels 280 Flights Due to IT Outage). Quite suddenly, IT infrastructure performance can become a CEO level issue (Unions Want Southwest CEO Removed After IT Outage).

While those kinds of major incidents make the headlines, there are thousands of lesser known, but still just as disruptive to business, service level disruptions and outages happening daily in just about every sizeable enterprise.

The costs of these often daily occurring incidents, like an unexpected slowdown in response time of a key business application during prime shift, can have a significant cumulative financial impact that may not be readily visible in the company’s accounting system.

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