Gartner Says Data Growth Demands Consolidation in IT Operations Analytics Tool Functionality
Recently Gartner published a report explaining how Data Growth Demands a Single, Architected IT Operations Analytics Platform. The research brief by Gartner Research VP Will Cappelli is based on Gartner client inquiries, based on the topic of IT Operations Analytics spending.
In the report, he describes the factors that have come together to grow demand for IT operations analytics. He notes that "Gartner estimates that worldwide spending in this market subsector will surpass $800 million in 2013, which is a $500 million increase from the $300 million spent in 2012. Furthermore, this more than 100% growth rate is expected to continue through 2014."
The report raises questions about how competently IT operations analytics platforms perform in the face multiple (and, possibly, conflicting) database structures, requiring a more detailed examination of precisely what data types and algorithms are supported by ITOA tools.
To this he says "Most ITOA vendors specialize in one or two of the five inference enablement technologies. Discussions with Gartner clients indicate increasing dissatisfaction with such specialization and the expectation that strategic ITOA suppliers will commit themselves now to deliver functionality in all five areas."
How are IT Operations Analytics changing the IT operations landscape?
IT Operations ChallengeCappelli explains that "The volume and variety of data required to monitor and manage complex IT systems has grown exponentially, which increases the need for new technologies to ingest, store and analyze the data." In fact, most IT operations are overwhelmed by the volume, velocity and variety of change and configuration data. And most of them lack insight or actionable information, making change and configuration problems a chronic challenge for IT departments.
ITOA Delivers Inferences about IT Operations
Capelli explains that "The primary goal of ITOA platforms is to deliver inferences about IT operations data to its users — an inference being an explicit addition to the information present in the data itself, generated by deductive and inductive processes applied to the data."
Traditional IT management tools were not designed to deal with the complexity and dynamics of the modern data center. These tools have not been automated to collect data down to granular details, analyzing all changes and consolidating information to extract meaningful information from the sea of raw change and configuration data. Without systems to manage and organize this growth, IT will drown in its own data. By taking a different perspectives on the abundant data and complexity confronting operations teams, IT operation analytics tools use mathematical algorithms and other innovations to extract meaningful information from the sea of raw change and configuration data.
ITOA Differs From Traditional Tools
Cappelli qualified that "ITOA platforms differ from traditional event correlation and analysis systems, performance monitoring systems and business service management platforms, which, although rich in functionality and capable of delivering great value, deduplicate, aggregate and visually present IT operations data without inferential addition."
Monitoring tools like APM or BSM solutions gather a lot of information, coming from a variety of sources: logs, application performance availability data, change and configuration data, and transaction data. Yet, overwhelmed by piles of distracting data, IT operations cannot find the information that could provide valuable insights. This makes separating what is useful from what is not like the "needle in a haystack" problem: you know it's there but you just can't find it.
ITOA Platforms Will Be Self-Contained
The report describes the development of ITOA, saying that "ITOA platform should also be able to capture data directly from the IT components themselves. One consequence of this principle is that users envision an ITOA platform built from four basic subsystems:
- A data collector subsystem
- A high volume, highly distributed database management system (DBMS)
- An analytics engine matrix system
- A presentation layer"
With data coming from an ever-growing range of sources, the thrust of the report focused on how "at least five distinct approaches to analyzing IT operations data are available on the market. For optimal results, they need to be combined."
The report describes these data that will need to be analyzed:
- Machine data: computation path tracking data generated by IT computing systems as they transform inputs into outputs.
- Wire data: contained in the headers and payloads of packets and their associated flow data as traffic moves from one node to another across a distributed IT system.
- Data collected from the increasing amount of instrumentation and agentry being built directly into hardware and software components.
- Data contained in human-authored documents describing aspects of IT components, architectures and installations.
- The output of legacy operations management systems, primarily performance and event-monitoring systems.