I recently attended .conf2016, Splunk’s seventh annual user conference. Splunk created the market for analyzing machine data (shorthand for machine-generated data), which consists of log files and event data from various types of systems and devices. Our big data analytics benchmark research shows that these are two of the most common sources of big data that organizations analyze. This market has proven to be fertile ground for Splunk, growing steadily with revenues more than doubling over the previous two fiscal years. Machine data is also the backbone for the Internet of Things (IoT) and operational intelligence, which form the basis of forthcoming benchmark research from Ventana Research.
Splunk’s innovated ability to access and use machine data for targeted operational insights can help improve IT and enhance business operational efficiency. Its work to capitalize on big data was part of my last analysis, while my colleague Tony Cosentino looked at its focus on search and operational analytics. Splunk also was a recipient of the 2012 Ventana Research Technology Innovation Award for IT Performance for Splunk Enterprise.
Topics: Big Data, Splunk, Splunk Storm, IT Performance, Operational Performance, Business Analytics, Business Intelligence, Business Performance, Cloud Computing, Customer & Contact Center, Information Applications, Information Management, Machine data, Operational Intelligence
Splunk recently entered the financial markets as a publicly traded company (NASDAQ: SPLK) and also entered a new phase in its corporate growth. Splunk combines the power of search and discovery with analytics on data generated by IT systems, that they call machine data, and provide insight for a new generation of operational intelligence that helps everyone in IT including the CIO determine the efficiency of its systems that support business. The company has built a platform that can index data on a large scale (“big data”) for rapid analysis and search. They also through its analytics provide the ability to perform visual and data discovery which is critical to reduce the time to determining unknown issues in existing IT systems. This helps IT staff ascertain not just the performance but the efficiency of systems that operate on a 24-by-7 basis. Splunk’s software operates in real time, surpassing the traditional methods of applying business intelligence against a data warehouse – a practice that’s ineffective for use in IT, where time is not the CIO’s friend when it comes to understanding issues or opportunities for improvement. Splunk has grown rapidly, partly because it’s simple to download and try, and then to license for use in production. It has more than 3,300 licensed customers in 75 countries. The management team is led by CEO Godfrey Sullivan, who has experience and a track record at companies such as Hyperion.
Topics: Big Data, Social Media, Supply Chain Performance, Splunk, IT Performance, Operational Performance, Business Analytics, Business Collaboration, Business Intelligence, Business Mobility, Business Performance, Business Technology, Chief Information Officer, CIO, Cloud Computing, Customer & Contact Center, Financial Performance, Governance, Risk & Compliance (GRC), Hadoop, Information Applications, Information Management, Information Technology, IT Analytics, Location Intelligence, Machine data, Operational Intelligence
Splunk may be one of the biggest software companies you’ve never heard of. I’ve been following the seven-year-old company for over six months now and recently attended its second annual user conference. Splunk focuses on analyzing large volumes of machine-generated data in underlying applications and systems, which includes application and system logs, network traffic, sensor data, click streams and other loosely structured information sources. Many of these “big data” sources are the same sources analyzed with Hadoop, according to our recently published benchmark research. However, Splunk takes a different approach that focuses on performing simple analyses on this data in real time rather than the batch-based advanced analytics we see as the most common use for Hadoop.
Topics: Big Data, Predictive Analytics, Sales Performance, Social Media, Supply Chain Performance, IT Performance, Operational Performance, Business Analytics, Business Intelligence, Business Performance, Customer & Contact Center, IT Performance Management, Machine data, Operational Intelligence