Digital Intelligence at .conf2013 – A Reflection
Last week, I had the opportunity to look at the proposed sessions for .conf2014 – Splunk’s annual user conference. I was very impressed by the variety of topics and the interesting use cases that many of the customers have proposed. Well I can’t share details about the topics, however I can promise that it is going to be simply amazing!
While we are on it, let’s talk about some of the sessions that were focused on digital intelligence at .conf2013. We had great use cases from ADP, Lincoln Financial Group, and in-depth discussion about data capture and tag management from the CTO of Tealium. Lincoln Financial Group shared how they are improving user experience by combining …
Introducing Weblog Add-on
Another exciting day at Splunk and another great product release! I am thrilled to announce the release of Weblog Add-on. During .conf2011, we announced beta release of Splunk App for Web Intelligence. We learned quite a bit from this beta release. After over 7500 downloads of the Web Intelligence beta App, we decided to close the beta and work on a product that closely aligns to the customer needs. Weblog Add-on has couple of key features:
1) Field Extraction: Easy to map fields from Apache or IIS weblogs. This includes both standard fields and ability to create and map custom fields. No need to write code in configuration files to map fields.
2) Event-Type Library: Making event-types from Web Intelligence …
Further Simplifying Big Data Analytics
In the past we’ve talked about simplifying big data analytics and the 80:20 rule for data analysis. Most organizations spend 80% of analytics efforts running and optimizing the business and 20% on advanced analytics, which includes advanced data mining, algorithm development and advanced predictive modeling.
Hadoop has seen very good adoption for big data analytics, specifically batch analytics for large datasets, and many organizations have initiatives to use it for advanced analytics and optimizing the business. Unfortunately, those organizations are struggling to derive value from their Hadoop implementations. They’re finding that analysis takes too long and requires specialized talent. Another issue is that getting data into Hadoop is difficult, getting meaningful analysis even more challenging.
In the past few months, …