You are currently browsing the category archive for the ‘Operational Intelligence’ category.

Fall is a busy time for software industry analysts. It’s a season filled with vendors’ user conferences and some industry conferences. Throughout the course of attending these events I’ve come to the realization that big vendors are often considered the Rodney Dangerfield of the software industry: They get no respect. What I mean by no respect is revealed in snarky social media comments, less enthusiastic coverage by tech media than smaller vendors get and a general sense that big vendors don’t do anything new with their development efforts. However, I suggest this is a shortsighted view of the software world. Smaller vendors serve a valuable function as a source of innovation for the industry, but they get a disproportionate share of attention. I suggest the big vendors deserve businesses’ attention, too, when they consider new software purchases.

If we define big vendors as those with at least US$1 billion in annual revenue, the list of analytics and data management software platform vendors includes companies such as IBM, Informatica, Microsoft, Oracle, SAP, SAS, Teradata and TIBCO. Each of these companies generates 10 to 100 times the revenue of even the most successful startup organizations. There are a handful of other large software platform vendors with revenue up to $1 billion such as Information Builders, MicroStrategy, Qlik, Splunk and Tableau. While the newer ones in this group still have some of the “glow” of their startup days, as a whole this group also suffers disrespect similar to the largest companies.

The fundamental problem is a mismatch in expectations. As an industry we should not generally expect groundbreaking innovations from the largest software companies. Sure, there are exceptions, but the focus of the largeventanaresearch_technologyinnovationawards_2016_white vendors’ research and development efforts is primarily on integrating various capabilities, often the result of an acquisition, and hardening those capabilities to stand up to mission-critical requirements. I recall working for a smaller “innovative” vendor that had hundreds of customers and tens of millions of dollars in revenue; the goal there with respect to workload management was to emulate one of the billion-dollar vendors above. It was considered “the gold standard.” So while the company had some innovative technology, we recognized that enterprises needed the features that larger, longer established vendors had been providing for years.

I’ve written about the interrelationship between large and small software vendors before as I described the software industry ecosystem. Small vendors often bring new technologies to market. Big vendors make things work, often in less obvious but also innovative ways. Both of these efforts are indispensable.

We kept this symbiosis in mind recently in completing our 2016 Ventana Research Technology Innovation Award Winners. In this list you will see a healthy representation of companies both large and small. Each has a role, so let’s give the big vendors some respect for the value that they provide.

Regards,

David Menninger

SVP & Research Director

Follow Me on Twitter @dmenningerVR and Connect with me on LinkedIn.

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 fromvr_big_data_analytics_04_types_of_big_data_for_analytics_updated 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.

At the event, Splunk announced general availability of Splunk Cloud and Splunk Enterprise 6.5. The company also announced new versions of Splunk IT Service Intelligence, Splunk Enterprise Security and Splunk User Behavior Analytics. These new versions incorporate machine learning capabilities to help organizations analyze the massive volumes of machine data they collect with more advanced analytics and in a more automated manner. Machine learning has become a hot topic lately; it was also a popular subject at Strata+Hadoop World, as I wrote recently.

The machine learning capabilities, which arose in part from Splunk’s July 2015 acquisition of Caspida, have been added to Splunk Cloud and Splunk Enterprise 6.5. Machine learning is a method used to develop predictive analytics without explicitly programming the models. In effect the algorithms are designed to sift through the data, learn from it and make predictions. With Version 6.5 Splunk also has simplified its data preparation capabilities and enhanced its user interface to appeal to more types of users. The company also offers tighter integration with Hadoop in this version.  Storing historical data in Hadoop can help lower costs, and the Hadoop data can be combined with data in Splunk Enterprise using the Splunk query capability for a single unified interface.

Splunk IT Service Intelligence (ITSI), an application built on the Splunk platform, provides a view of how critical IT services are operating as well as an environment in which to investigate and triage incidents when they occur. The latest release of ITSI, 2.4, includes machine learning capabilities to perform anomaly detection, identifying unusual system activity to help prevent outages and service degradations. The system can learn what the pattern of normal operations looks like and then establish thresholds for alerts that adapt to cyclical changes in usage. Adaptive alerts help reduce “alert fatigue” when so many alerts are issued that they overwhelm the recipients.

Splunk Enterprise Security (ES), a security information and event management (SIEM) application, provides real-time monitoring of security threats and an environment to support incident response teams. Splunk ES 4.5, the latest release, provides a similar adaptive alerting feature based on machine learning as described above. ES 4.5 now includes the Glass Tables feature that has been available in ITSI, which allows users to create custom visualizations and KPIs. Splunk User Behavior Analytics (UBA) complements ES by analyzing longer periods of history to create a profile of normal user behavior and comparing it with peers to provide more advanced detection of security threats. UBA 3.0 incorporates more than 40 machine learning models, which cover a combination of streaming and batch analytic scenarios. Splunk in 2015 received the Technology Innovation Award for CIO for its innovation in advancing cybersecurity through these products.

Splunk has followed a unique path. While a pioneer in the big data market, it built its products on a proprietary big data architecture rather than open source technologies as others did. In recent releases, however, it has broadened its support for Hadoop. Splunk focused on one subset of big data – machine data – and based much of its user interface around search. Rather than expand into the horizontal business intelligence market the company has chosen to tackle the IT service market and the SIEM market. This focus appears to have been successful so far. It’s hard to argue with its success. If you are looking for a way to manage and analyze the machine data in your organization, including IT service applications or enterprise security, I recommend you consider the offerings from Splunk.

Regards,

David Menninger

SVP & Research Director

Follow Me on Twitter @dmenningerVR and Connect with me on LinkedIn.

Follow on WordPress.com

Enter your email address to follow this blog and receive notifications of new posts by email.

Join 22 other followers

RSS David Menninger’s Analyst Perspective’s at Ventana Research

  • An error has occurred; the feed is probably down. Try again later.

David Menninger – Twitter

Top Rated

Blog Stats

  • 46,006 hits
%d bloggers like this: