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Information Technology Analytics

IT Data Analytics is also known as “IT Operations Analytics” (ITOA). IT Data Analytics is the grouping of technologies that are utilized to identify complex relationships within the large amount of data within an organization's IT system.

It is used to allow for more intelligent and responsive decision making, improving IT service delivery. Given the sheer size of data within management and monitoring technologies, mathematical algorithms and other innovations are used to extract meaningful information. [i]

 

What IT analytics can do for your business? [ii]

  • Automatically analyze service IT operational data

  • Enable proactive IT with cognitive machine learning

  • Detect incidents to avoid service outages

  • Detect anomalies to diagnose IT concerns

  • Diagnose IT issues for key insights

  • Unlock insights for more proactive operations

  • Search terabytes of log data

  • Save time locating problems

  • Improve operations efficiency

  • Flexible pricing and deployment

 

Data Metrics/KPIs [iii]

Account create success, Account termination success, Active directory performance index, Alert-to-ticket ratio, Average data center availability, Call center PBX availability, Campus PBX availability, Customer connection effectiveness, Data center capacity consumed, Email client availability, Exchange server availability, Incidents from change, Internet proxy performance, Network availability - High availability sites, Network availability - Standard sites, Network manageability index, No problem found/duplicate tickets, Percentage of branch office backup success, Percentage of circuits exceeding target utilization, Percentage of IT managed servers patched at deadline, Percentage of production servers meeting software configuration standards, Percentage of security update restarts within maintenance window, Percentage successful remote access server (RAS) connections, Phone answer service level, Priority 1 and priority 2 network incidents meeting SLA, Product adoption status and compliance, Restore success rate, Server growth rate, Server manageability index, Service desk client satisfaction - Percentage dissatisfied, Service desk time to escalate, Service desk time to resolve, Storage utility service availability, Storage utility utilization, Virtual machine provisioning interval, Virtual server utility availability, Web server availability.

List of Reports/Analysis [iv]

  • Root Cause Analysis: such analysis can help find out unknown root causes of overall system behavior pathologies by detecting models, structures and patterns of IT infrastructure or application stack being monitored.

  • Service Impact Analysis: it determines and ranks the potential impact based on the outputs of root cause analysis, so that resources can be prioritized and allocated to fix underlying issues in the most timely and cost-effective means.

  • Proactive Control of Service Performance and Availability: it forecasts future system states and their corresponding effects.

  • Problem Assignment: it determines how a problem can be resolved or directs the analysis results to the right individuals or communities within a business to further solve the problem.

  • Complement Best-of-breed Analysis: it corrects or extends the outputs of other discovery-oriented tools to improve the fidelity of information used in operational tasks (e.g., service dependency maps, application runtime architecture topologies, network topologies).

  • Real-time Application Behavior Learning Tools: creates tools that learn and correlate application behaviors based on user and underlying infrastructure patterns, creates metrics of such correlated patterns and stores them for further analysis.

  • Dynamic Baseline Threshold Tools: it optimizes the behaviors of the underlying infrastructure and technological components, creates benchmarks for the specific environments and dynamically changes them according to the changing infra and user patterns without any manual intervention

 

Advanced (Intermediate) Techniques [v]

Information Security Scores: A regular security evaluation may be performed on platforms, services, processes and applications. These typically produce a score with three to five security levels that have clear definitions.​

IT Overhead: Total IT spend for a period expressed as a percentage of revenue. Useful for benchmarking against an industry or competitor.​

IT Risk Score: IT risk management evaluations that produce a risk score typically calculated as impact × probability.

IT Security Training Rate: Information security incidents are often influenced by human factors. As such, information security awareness training is a common practice that is tracked by percentage of employees who have completed training in the past year.

Legacy Rate: The percentage of platforms and systems that are currently considered legacy including factors such as out-of-support components. In some cases, an annual IT health check produces a more detailed risk assessment for each major application.

 

Application: Industry Examples [vi]

A range of start-ups – Cue, reQall, Donna, Tempo AI, MindMeld and Evernote – and big companies like Apple, Facebook, Google are working on what is known as predictive search — new tools that act as personal valets, anticipating what you need before you ask for it.

 

Google, for instance, is continuously changing the landscape of search with predictive analytics.

Google launched predictive search back in 2004 with Google Suggest, which was renamed Google AutoComplete in 2010. In 2010, Google Instant came on the scene, generating search results instantly as users’ type. Google’s Knowledge Graph in 2013 further enhanced predictive search by predicting what type of information a user is searching for when they search a celebrity name “Brad Pitt” and generates context specific content right alongside normal search results.

Google Now is the next generation of predictive search, serving as a valet or personalized assistant that can predict your needs, wants, and deep desires.  This is taking multiple buckets of data and intelligently connecting them to facilitate decisions…. everyday data supported decision making. For some, Google Now delivers important information about the traffic on your morning commute, your updated flight itinerary, and the results of last night’s hockey game on your phone, without you even asking.

How does Google Now work…. In order to provide relevant contextual info that relates to you and only you, Google uses your private data, accessing your location, Gmail, daily calendar, and other info in order to keep tabs on things like appointments, flight reservations and hotel bookings.  Or auto-suggesting restaurants from the Zagat’s guide to have dinner at.

Google Now is evolving and forms a key foundational element for “Ok Google” voice search and augmented reality (“Google Glass”).  For instance, you are running thru the airport wearing Google Glass, which uses its predictive powers to send a gate change or flight delay alert as a Glasshole arrives through the airport.

Having Android on every smartphone and their own Moto-X allows Google to do extremely creative things enabling more and more of the augmented reality revolution going forward.

 

 

 

[i] King, Anthony (2015).  It’s time for businesses to use IT Operations Analytics! Data Science Central. Retrieved from https://www.datasciencecentral.com/profiles/blogs/it-s-time-for-businesses-to-use-it-operational-analytics

[ii] IBM Operations Analytics. IBM. Retrieved from https://www.ibm.com/us-en/marketplace/it-operations-analytics

[iii] Example KPIs for Information Technology (IT) Departments. Scoreboard. Retrieved from https://kpidashboards.com/kpi/department/information-technology/

[iv] King, Anthony (2015).  It’s time for businesses to use IT Operations Analytics! Data Science Central. Retrieved from https://www.datasciencecentral.com/profiles/blogs/it-s-time-for-businesses-to-use-it-operational-analytics

[v] Spacey, John (2016). 50 Information Technology Metrics. Simplicable. Retrieved from https://simplicable.com/new/information-technology-metrics

[vi] Kalakota, Ravi (2015). Big Data Analytics Use Cases. Practical Analytics. Retrieved from https://practicalanalytics.co/2015/05/25/big-data-analytics-use-cases/