Splunk integrates machine learning, adding packaged and custom algorithms to core products

New user interface intended to take Splunk to less technical end users

Real-time analytics software and cloud services company Splunk is adding machine learning capabilities to its four main software packages and services, while also rolling out a new user interface that, it claims, will make Splunk's analytics tools easier to use for less technical and occasional users.

The updates, announced at the company's seventh .conf2016 user conference in Orlando, Florida today are being rolled into Splunk Enterprise 6.5, Splunk IT Service Intelligence (ITSI), Splunk Enterprise Security and Splunk User Behavior Analytics (UBA). All are available either as on-premise software packages or on a subscription-based model in the cloud.

The company is incorporating a series of packaged algorithms to provide a number of new, automated functions, as well as the capabilities for users to build their own custom algorithms.

The move is part of a shift towards predictive analytics based on behavioural analysis, with the aim of enabling organisations to use machine learning tools to optimise their IT, security and business operations, with the machine learning providing more and more automated responses.

The company has suggested four key use cases for its machine learning tools:

• Security investigation and analysis, helping security operations teams to identify and resolve IT and security incidents by automatically detecting anomalies and patterns in data;
• Intelligent alerting, to help reduce 'alert fatigue' by identifying normal patterns for specific sets of circumstances - and hence abnormal patterns;
• Predictive actions, so that organisations can better anticipate the consequences of, for example, proactive maintenance that might otherwise disrupt operations; and,
• Business optimisation, to help organisations better forecast demand, manage inventory and react to changing business conditions via the analysis of historical data and models.

Splunk Enterprise 6.5, unveiled today, will include a guided workbench to create customer machine learning models. In addition, the company claims that it will simplify data preparation and enable organisations to expand analytics to a wider range of users with a new, more intuitive interface and table-data views that can be used by occasional, less technically advanced users, as well as specialists.

It will also offer tighter integration with Hadoop and organisations will now be able to roll historical data into Hadoop and utilise hybrid search to analyse all of their data in Splunk.

Likewise, Splunk ITSI 2.4, unveiled today too, also adds machine learning tools to enable users to apply machine learning to event data. It will include pre-built machine learning algorithms that can be dynamically applied to baselined 'normal' operational patterns, taking account of different thresholds at different times. The aim is to reduce false positives and 'alert fatigue'.

It will also prioritise incidents through event analytics, such as multivariate anomaly detection, supported with business and services context and, like Splunk Enterprise, offer a new interface that Splunk claims is easier to user. It will also reduce the need for costly customisations.

Splunk Enterprise Security 4.5 will also feature the new user interface, while Splunk UBA 3.0 will include the new machine learning tools, as well as additional data sources and content updates of use cases.

"Digital transformation has changed the way that organisations work," said Splunk CEO Doug Merritt. "The big secret is that all of the change is underpinned by machine data."

He continued: "Machine learning enables organisations to get deeper insights from their machine data and ultimately increases the opportunity our customers can gain from digital transformation.

"The enterprise machine data fabric is the foundation for managing and deriving insights from that data at scale - and only Splunk provides the end-to-end analytics platform and ecosystem to support it."

Splunk's technology is widely deployed in major organisations around the world to cover a diverse range of applications.

These include Valve Software, which uses Splunk to monitor the performance on its wildly popular PC gaming platform Steam; French banking giant BNP Paribas, which rolled out the software to senior business managers; and betting company Paddy Power Betfair, which uses Splunk across its organisation, from security monitoring to its latest customer big-data projects.