Leveraging Big Data Analytics for IT ROI
Editor’s note: This article on Big Data analytics written by Ed Hallock was originally published on Enterprise Systems Media.
What is Big Data? Big Data is a term that conjures up different thoughts and meanings for many IT professionals. For some it’s seen as the volume, velocity, and variety of data created within the IT infrastructure. It’s created in enormous volumes, at incredible rates, in many different formats. Many organizations struggle with how to store it, and more important, how to leverage it.
Big Data Analytics
Big Data analytics is the process of examining large and varied sets of data to uncover hidden patterns, unknown correlations, and other useful information that can help organizations make more informed business decisions. However, it can also include analytics that provide insights into how well segments of the organization are performing in support of the business. This can give organizations a better idea of how their IT infrastructure is performing and how well IT services are being provided in support of the business.
There are a number of different big data analytics platforms available in today’s market. We’ll take a brief look at two of those platforms.
Splunk Enterprise collects and indexes data regardless of the location or format of the data. Data can be collected and indexed from system logs, network devices, applications, web servers, and other machine data sources existing within the IT infrastructure.
Elastic was previously known as the “ELK” stack of open source technologies. ELK represented Elasticsearch, Logstash, and Kibana – the three primary components of the stack, which still exist as part of the Elastic platform today. Elasticsearch is the analytics engine, Logstash handles the data processing, and Kibana provides the capabilities to visualize data and navigate the Elastic Stack.
What IT Can Do With Big Data Analytics
IT Operations Analytics (ITOA) is a market for solutions that bring advanced analytical techniques to IT Operations Management use cases and data. ITOA solutions collect, store, analyze, and visualize IT operations data from other applications and IT Operations Management (ITOM) tools, enabling IT operations teams to perform faster root cause analysis, triage, and problem resolution. The IT infrastructure of most large organizations is comprised of different systems and platforms each with unique toolsets requiring domain specific skills to extract and leverage value from those tools. Most platforms will have operating system vendor supplied or independent vendor tools for monitoring the performance of systems and applications. Leveraging an analytics platform enables data coming from different systems and sources to be normalized for better and expanded use. Having the data from all different sources in a single platform enables correlation across technology silos within a system, as well as across different platform types.
Security Information and Event Management (SIEM)
SIEM technology aggregates and provides real-time analysis of security alerts using event data produced by security devices, network infrastructures, systems, and applications. A primary function of SIEM is to analyze security event data in real-time for internal and external threat detection to prevent potential hacks and data loss. Understanding potential security threats and preventing security breaches has become paramount to just about every IT organization.
Monitor IT Service Delivery
In today’s constantly on and continuously connected world, requests for IT services are coming from web applications and mobile devices 24 hours a day, seven days a week. This means that the IT infrastructure and its underlying applications must be responsive and meet the demands being put on the business by customers. Big Data Analytics better help an organization understand how their IT components, systems, and applications are performing. To truly understand response time and ability to meet service levels, a mapping of the individual IT components comprise the business service is required, along with the Key Performance Indicators for those components.
The Critical Mainframe Gap
Most of the platforms have built-in capabilities for getting logs and machine data from network devices and open system platforms, including Windows, UNIX, and Linux. However, there is a still a huge gap – the IBM z/OS mainframe. Many data sources are available within the mainframe that can be leveraged to provide insight into the operation health of the system and applications as well as providing visibility into security and compliance issues. Understanding the different sources, and formatting the data into a usable format within an analytics platform can be a very large task.
Return on Investment in Big Data Analytics for IT
Collecting and indexing log and machine data from any source to provide powerful search, analysis, and visualization capabilities results in a real return on investment in the following areas:
Enhanced Business Insights
Splunk and Elastic can provide organizations the insights to drive operational performance and business results that can impact the bottom line of businesses.
Detect Security Threats and Prevent Security Breaches
Preventing one security breach that could cost the organization millions of dollars in financial and brand impact can justify the entire investment.
Eliminate Redundant Tools
Eliminating the license and maintenance costs associated with unneeded tools can save the organization money.
Increase Staff Productivity
Using the analytics platform can make staff more efficient, freeing them up to do higher priority tasks.
Reduce Dependency on Subject Matter Experts
By making new staff productive more quickly, an IT organization will be able to free up existing subject matter experts to perform more critical work.
Provide Faster Problem Detection and Resolution
Faster problem solving means less impact to the business and less staff time spent doing route cause analysis and correction.
Analytics platforms have emerged, providing organizations with the facilities needed to transform Big Data into business intelligence. One of the primary beneficiaries of Big Data analytics is the IT department. New opportunities to address IT operations, security, compliance, IT service delivery, and more are being addressed by ITOA and SIEM solutions built on top of, or within, major analytics platforms.
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