Request a Software Demo

See why EMA says "FixStream is a vendor to watch very closely."

Please share your email to download the report

Download Document

Download Document

Download Document

Blog

How Automation is Transforming IT Service Management

IT Services – Focused on Business Needs

As technology has become more embedded into business processes, organizations have commonly embraced IT service management (ITSM) to improve customer service and help IT services align with business goals. ITSM is a set of policies, processes and procedures to manage the support of customer-oriented IT services throughout their lifecycle. ITSM activities, including problem/incident management, change management, and asset/configuration management, can be used to improve customer service and enable digital transformation initiatives.

With its emphasis on optimizing IT service operation and improvement, ITSM is essentially a framework for supporting business needs. As such, it should evolve and adapt in tandem with enterprise technology requirements. In fact, ITSM has become a key resource for transforming and modernizing IT services.  

ITIL vs ITSM – How Do They Differ

A framework of best practices for delivering effective IT services, the IT Infrastructure Library (ITIL) sounds a lot like ITSM, and they’re related but not the same thing. ITIL is just one of several popular frameworks within the ITSM discipline (other ITSM frameworks include COBIT, Six Sigma, and Microsoft Operations Framework). But as one of the most popular ITSM frameworks, organizations use ITIL defined processes and standards to optimize IT service management. ITIL 4, the most recent version introduced this year, emphasizes the value of automating processes, improving service management and integrating the IT department into the business.

ITIL 4

ITIL 4 is an evolution of ITIL v3 concepts, not a replacement according to Beyond20, a FixStream business partner and expert ITSM consulting firm. The table below, provided by Beyond20, includes a summary of three key differences between ITIL version 3 (also commonly referred to as the ITIL 2011 edition) and ITIL 4:

ITIL 4’s Guiding Principles are as follows:

  • Focus on value: Everything an organization does needs to map to value for stakeholders
  • Start where you are: Do not start from scratch and build something new without considering what is already available to be leveraged; investigate and observe the current state directly to ensure it is understood
  • Progress iteratively with feedback: Do not attempt to everything at once; continuously gather and use feedback—before, during, and after—each iteration to ensure activities are appropriate and focused on the right outputs, even when circumstances change
  • Collaborate and promote visibility: Working together across boundaries produces results that have greater buy-in, more relevance to objectives, and increased likelihood of long-term success; avoid hidden agendas, promote transparency, and share information to the greatest degree possible
  • Think and work holistically: No service, or element used to provide a service, stands alone; outcomes will suffer unless the organization works as a whole, not just on its parts
  • Keep it simple and practical: If a process, service, action, or metric fails to provide value or produce a useful outcome, eliminate it; use outcome-based thinking to produce solutions that deliver results
  • Optimize and automate: Resources of all types should be used to their best effect; eliminate anything that is truly wasteful and leverage technology to its greatest capability

A push for automation

The last point of the guiding principles is a focus on leveraging technology for optimization and automation. 

Why the focus on automation? The digital economy has changed business processes and priorities, and IT service management, including ITSM and ITIL, is changing as well. For example, ITSM/ITIL best practices are being adapted for cloud computing environments. As enterprise IT departments move from legacy systems to cloud-based solutions, they are also looking to automate ITSM processes, incorporating AI-powered functions such as machine learning and natural language processing.

And why not? ITSM automation can help organizations move toward a consistent IT service management practice, eliminating many redundant manual processes that add cost and increase the risk of human error. For example, automating help desk tasks such as ticket routing and change requests can improve accuracy and speed of response, as well as increase employee productivity and satisfaction. Other examples of AI-led ITSM use cases include automated problem-solving, infrastructure provisioning, self-service systems, finding/resolving threats with anomaly-detection algorithms, and better management of dynamic cloud configurations.

An Opportunity to Improve Business Processes

With all the benefits inherent in IT service automation, it’s tempting to jump right in and get started. But, according to experts, that might not be the best approach. ITSM automation is not about speeding up legacy IT services, it’s about improving service delivery processes and meeting the priorities and needs of your user communities. For example, you might consider new ways to structure your IT teams and work modes to facilitate collaboration across siloes and enable agility. 

An ITSM automation project is a great opportunity to modernize IT service delivery and strengthen the link between IT service providers and their cohorts. Before launching such a project, take time to finetune processes as needed to optimize workflow, remove redundant steps or complexities, and identify needed improvements. Otherwise, you’ll end up with the same outdated, faulty processes – just done more quickly. 

Beyond20 agrees with the need to proactively plan for an ITSM project, starting with a clear vision of the desired results. They recommend taking time to examine IT processes to ferret out hidden problems and implement best practices. Recognizing the complexities involved, Beyond20 offers a free ITSM Assessment Readiness Kit to help clients clarify objectives in order build an actionable roadmap for ITSM automation.

FixStream AIOps and ITSM Transformation

FixStream, a leader in AIOps solutions, sees the synergy with bringing ITOM domains closer to ITSM automation with AIOps capabilities to further enrich the core ITSM capabilities for root cause analysis, change management, asset management etc. AIOps solutions help with dynamic discovery and correlation of on-prem and public cloud application and infrastructure entities, correlation of massive amount of events and metrics collected from existing ITOM tools or infrastructure devices, and detecting the root cause of incidents, identification of impacts from changes and anomalies and feeding this insights into ITSM tools uplifts the value of ITSM automation framework to the next level. Without the insights from AIOps tools, it’s is difficult and almost impossible to implement ITSM automation due to the volume, varieties and velocity of dynamic information collected from modern hybrid IT.

Enzo Signore, CMO at FixStream, points out the value of AIOps-based automation in a recent article. He notes “it’s extremely hard to correlate events across the IT stack, and the amount of data involved makes it impossible for humans to do it. Companies need to automate the process to correlate event data across all the IT domains so that they can quickly locate the problem and avoid disasters. This is the foundation of AIOps solutions.”

Learn more about how FixStream’s new AIOps+ platform can help modernize and automate your IT Operations.

Not All AIOps Solutions are Created Equal

As our market evolves, a number of AIOps solutions have entered the market. It is hard to discern the differences between the offerings. This article helps you understand the core features that you should look for when evaluating these types of solutions. This article was previous published in DZone.

As enterprises accelerate the digital transformation of their business, they’ve increased their dependency on always-on, high-performing business processes. As such, it’s critical that these mission-critical applications perform optimally and are always available to users. To improve system availability and aid troubleshooting, organizations have turned to AIOps (Artificial Intelligence for IT Operations), a technology based on AI and Machine Learning, to automate the identification and remediation of numerous IT issues and automate day to day IT operations activities.

AIOps is helping organizations cope with the challenges of digital business transformation, siloed IT operations and the exponential growth of operational data such as logs, alerts, network faults and performance data generated by the typical digital enterprise. AIOps can use that data to understand dependencies between IT entities across domains, observe the health of critical IT assets, understand business impacts and improve visibility into root cause of system outages and slowdowns.

Harness the Power of Big Data with AIOps

Using big data analytics and machine learning algorithms, AIOps solutions can ingest and aggregate multiple streams of metrics, events and logs to filter through noise and uncover problems. They can provide IT departments with valuable insights into complex cloud-based and virtualized environments, system problems, and business-impacting issues. But the success of the AIOps platform depends on its access to cleanse the disparate data gathered from all across the hybrid IT ecosystem, using various data lineage and relationships. Without the complete set of data and the data relationships, the AIOps system can’t analyze and learn from it, and its success is limited.

Why is this? At its core, AIOps is data-driven, so it requires access to all relevant operations data, including unstructured machine data such as logs, metrics, streaming data, API outputs, and device data, and structured data such as databases. In order to eliminate false positives and accurately identify cause vs impacts, anomalies across related entities, AIOps solutions must utilize relationships across the entities in the machine learning algorithms.   

AIOps technology learns from the input data source to identify trends and patterns to provide an early warning whenever it discovers anomalies or reoccurrence of a known pattern indicating business impacting incident. By correlating and analyzing data from multiple enterprise application and infrastructure domains, the right AIOps solution can reveal trends and patterns within the “noise” of millions of system incident reports, highlighting potential risks and performance issues. It can also uncover patterns to show what has occurred, a boon to system diagnostics and predictive analytics.

Not all AIOps are created equal

AIOps is a multi-layered platform whose capabilities should include efficient data collection at big data scale, correlation across the collected data, machine learning, analytics and visualization. Most AIOps vendors focus on capabilities of data ingestion, and machine learning for noise reduction and root-cause failure analysis. However, when shopping for an AIOps platform, it helps to remember that AIOps solutions come with varying functionality and ability to manage data sources.                

Cross-Domain Data correlation provides valuable insight

Successful AIOps platforms need to be able to collect data from the entire multi-vendor and multi-domain environment, including network and storage solutions, containers, and public cloud. So, it’s important to select an AIOps platform that can ingest, correlate, and provide access to a broad range of historical and streaming data types. This will enable a broader analysis of trends and issues within the distributed hybrid IT ecosystem and avoid blind spots.

The AIOps solution’s value lies in its ability to ingest and correlate data across the siloed hybrid environment, helping organizations deal with the high volume, variety and velocity of data generated by today’s complex IT operations. Within the data, the AIOps platform can uncover patterns, which can then be used historically to identify the root causes of specific system issues in real time, or proactively to predict potential problems.

Data correlation capabilities have other benefits as well, such as revealing application dependencies and what specific resources each application requires. The AIOps platform may also be able to unite the power of machine learning and big data with domain knowledge to identify a multitude of data relationships and interdependencies. This insight can help IT managers better allocate resources, plan migrations, and purchase only what their cloud-based applications really need.

Enhance digital business operations with AIOps

Today’s modular, dynamic and distributed IT systems require a new multi-perspective approach to fully understand how they’re operating. One approach is to implement AIOps solutions capable of ingesting, correlating, and analyzing data from a number of sources and across IT siloes. This ability to consolidate and analyze system information gives IT teams the ability to more quickly diagnose system issues and resolve proactively before it impacts business

Business applications are the lifeblood of every digital enterprise. With more mission-critical applications based in the cloud, managers need better tools to understand and track how those applications are performing. Done right, an AIOps implementation can lead to a decreased mean time to remediation and better proactive problem solving. By increasing system availability and responsiveness, AIOps can enhance your digital business operations and improve profitability.

Rev Up Your Digital Transformation Engine with AIOps

Digital transformation is a mandate for the online business world, and it’s at the top of every CIO’s agenda. More than just an IT challenge, digital is changing economic fundamentals, business dynamics, and competition on a global scale.

To support the scope and scale of digital-driven change on the organization, IT executives are spending big money on solutions. Industry analyst firm IDC estimates spending will exceed $2 trillion in 2019, and 40 percent of all technology spending will be for digital transformation technologies. And for good reason. Sixty-four percent of IT executives surveyed expect to derive significant value from digital technologies over the next two years.

Yet, despite the investment, IDC also predicts that 75% of CIOs and their enterprises will fail to meet all of their digital objectives in 2018. There are many reasons why digital initiatives fail, but one reason is that they create a whole new set of issues for IT. The level of complexity created by digital has never been seen before, and it continually drains resources and budget.

IT is Drowning in Complexity

It’s easy to see why digital transformation is falling short. IT teams are bogged down by the sheer complexity of today’s hybrid, dynamic IT environments. Along with steep learning curves and lightning-fast changes to new technology, IT departments must cope with soaring consumer expectations, supporting legacy systems, lack of skilled talent, disparate tool sets, and opaque physical and virtual IT infrastructures. These factors complicate IT staff’s ability to oversee operations and optimize system performance.

To demonstrate how data complexity impairs IT operations, a recent IDC article describes how data analysts spend over 80% of their time on data collection, preparation, and governance, versus just 20% of their time on data analytics, where the real business value lies. The problem is compounded by the growth in data volumes as well as the increasing complexity of the data itself. The article points out possible solutions, noting that “machine learning has the potential to significantly change the way data is managed and automate many of the tasks related to data.”

Blinded by the Details

Despite continued investments in system resources, IT cannot holistically see what’s in their physical and virtual infrastructure. And without a clear view of the entire IT environment and the ability to make sense of the mountain of data being served up by various system tools, Ops teams can’t figure out what’s wrong – or how to fix it.

We have seen this challenge repeatedly at our prospective customers. Recently, we visited a company using 17 different tools to monitor infrastructure, applications and services. Each tool has its own portal, its own alert system, and is owned by a different department. There is no way to correlate the data generated from each of these disparate tools. When there is a critical alert, support staff must email back and forth between departments to gather information needed to identify root cause – MTTR can take hours while the business is at a standstill.

Transforming IT with AIOps

Traditional, domain-centric monitoring and IT operations management is no longer adequate in today’s dynamic virtualized environment. These older systems cannot correlate the onslaught of data various IT domains create, and they’re unable to provide the insights IT operations teams need to proactively manage their environments.

But you can rev up digital transformation initiatives with AIOps – Artificial Intelligence for IT Operations. These software systems combine big data and AI or machine learning technology to enhance and partially replace a broad range of IT operations processes and tasks, including availability and performance monitoring, event correlation and analysis, IT service management, and automation — dramatically simplifying IT operations.

AIOps automatically correlates the millions of data points across the entire stack, applying machine learning so IT can increase end-to-end application assurance and uptime. By correlating, visualizing and predicting issues across hybrid IT stacks, AIOps provides the insights IT teams need to proactively manage and support their digital environments. The improved visibility into the system components and their interdependencies helps organizations accelerate their technology migrations.

The business benefits of AIOps are clear. AIOps optimizes IT by automating 
root cause analysis, enhancing system performance and availability. It can dramatically simplify IT operations, improve efficiencies, and drive down costs. The technology can free up an amazing amount of resources, reducing waste to make operations more efficient. In turn, the organization can align IT and LOB to focus on business transformations that drive higher financial results.

This article was originally published in insideBigData.

How to Predict and Avoid IT Problems and Outages with AIOps

By Sameer Padhye

Originally Posted on NetworkComputing.com

To succeed as digital companies, enterprises need to reconsider their IT ops strategy, including how they think about application and network uptime. System downtime, although common, is no longer acceptable. With business-critical applications as indispensable as the electricity powering office environments, it’s crucial to avoid system outages and the associated business impact.

We all know the difficulties of monitoring dynamic and ever-changing IT environments. Traditional IT operations management processes and assets are ill-equipped to address the challenges of today’s multi-layered, disparate hybrid IT infrastructure, with its extensive set of applications and services (including third party/outsourced ones) and multiple actors. Outdated domain-centric tools force manual data processing by human IT specialists to correlate thousands and thousands of disparate data points, creating painful bottlenecks that prevent the rapid diagnosis and resolution of system issues.

Improve the visibility of your IT environment and its activities with AIOps

Digital applications generate a huge volume, variety and velocity of data. This flood of data generates a vast number of alerts that need to be analyzed and addressed, with only a few requiring actions. How can an IT team find relevant information in so much system noise?

What if there was a solution that could automate big data analytics analysis and build an accurate, real-time view of all the moving parts across your hybrid IT environment? With the insight provided, you could minimize false alarms/redundant events (system noise), identify anomalies, and more accurately identify probable causes of system incidents.

That solution is Artificial intelligence systems for IT operations (AIOps) solutions — software systems that combine big data analytics solutions, visualization, and AI/machine learning functionality to automate IT operational tasks such as performance monitoring and event data correlations. The term was coined by analyst firm Gartner in 2017, and they recommend AIOps to organizations as an enhancement to application performance monitoring (APM) and network performance monitoring and diagnostics (NPMD) tools.

How does it work? By correlating millions of data points across all IT domains, and applying machine learning to detect patterns, AIOps provides a consolidated overview and interpretation of what’s happening across the entire stack. IT ops team can then use the information to uncover and resolve the root causes of outages and performance issues so system availability is increased.

Augment your IT operations with AIOps for better system reliability

Because of its underlying importance to the enterprise, IT teams are under pressure to maintain system availability and performance. With the average cost of system downtime approaching $300,000-400,000 per hour, many enterprises and service providers are adopting solutions such as AIOps to avoid network/server disruptions and minimize their impact. The insight provided by AIOps can help IT teams do their job better and more efficiently.

It’s important to note here that AIOps systems aren’t necessarily meant to replace existing IT service management tools and personnel. Rather, AIOps can augment IT environments, serving as the glue that binds disparate systems together and helps IT teams make sense of the constant flow of data. The goal is to simplify and streamline IT operations management, improve system reliability, and automate tedious manual processes for faster problem resolution.

Many AIOps solutions can work with legacy IT resources and tools, integrating with existing business applications such as ERP and correlating information previously locked in siloes. By ingesting and consolidating information across the IT environment, an AIOps platform can provide an updated, accurate, synchronized view of IT operations. Staff can then spot and react to pertinent issues in real time.

Identify and Resolve IT Problems Before They Happen

Some AIOps platforms can also aid configuration planning, helping IT teams anticipate how system changes might impact the IT environment. Whether you’re planning a technology upgrade, migrating to the cloud, or installing patches, an AIOps platform can maintain an accurate and updated view into system assets, applications, dependencies, and the underlying infrastructure. This information can help you plan for and mitigate potential issues with the updates – before they cause an outage.

Conclusion: Better IT and Business Performance with AIOps

AIOps can ease the difficulty IT teams have in managing their increasingly complex IT environment and keeping it running at peak performance. By providing an end-to-end view across all domains, AIOps solutions can enable rapid data anomaly detection and investigation of IT incidents, quicker root cause analysis, and automated data analysis, enabling optimized IT systems uptime for better business results.

Avoid “IT Firefighting” With AIOps

By Bishnu Nayak — CTO, FixStream

Originally Posted on DZONE

In today’s 24×7 IT environments, nothing is more important than avoiding system outages and slowdowns that impact business. Without ready access to the desired applications, frustrated workers and customers are unable to complete their transactions. Business grinds to a halt, revenue is lost, and corporate reputation is damaged.

But manually detecting and diagnosing system glitches across a multi-layered, siloed infrastructure is time-consuming and cumbersome. Outdated domain-centric tools leave IT specialists unable to proactively troubleshoot and repair system issues. Your IT team can end up “fighting fires” rather than working on the important projects that add value to the business.

Solution: Predictive Analytics for Continuous Oversight of IT Operations

Predictive analytics, an emerging category of big data analytics, can help organizations predict future outcomes based on historical data. When reviewing the data, analysts can detect trends and patterns that may highlight risks, correlations, or current as well as future conditions.

Already used for applications such as inventory forecasts and customer service, predictive analytics can uncover abnormal trends, detect threats, and forecast issues before they impact operations and create emergencies. Examples include:

Multi-variate anomaly detection can identify anomalies in applications behaving abnormally. For example, utilization spikes on Monday are normal, while a similar surge on Sunday may indicate a security threat.

  • Capacity prediction — Don’t pay for unused servers or be caught short-footed by unanticipated server demand. Use analytics to forecast and optimize system resources usage, while minimizing your operational footprint.
  • Incident prediction — Predictive analytics, enhanced by data mining, can help analysts interpret the structured and unstructured data recorded in tickets. The results can be used to highlight and fix potential failures.

AIOps: Fixing IT Problems Before They Happen

Powered by Machine Learning, AIOps has advanced analytical capabilities to help IT organizations forecast and avoid system issues. Using its Artificial Intelligence capabilities, AIOps can be taught to observe and recognize patterns and anomalies over time. Then it can automatically analyze massive amounts of digital data, correlate leading indicators, and use historical behavior to help predict what could happen next. By delivering contextual operational insight, AIOps can help your team predict and prevent business outages before they cause actual problems.

AIOps can be taught to examine data trends and provide an early warning whenever it discovers possible issues. The application can detect trends and patterns within the “noise” of millions of system incident reports, highlighting potential risks and performance issues.

When system outages occur, the predictive insights from AIOps can speed up root-cause analysis and remediation. By quickly and accurately diagnosing the root cause, problems are fixed faster, often reducing downtime from hours to just minutes. The result is optimized system availability and enhanced business operations.

Conclusion

Depending on the platform, AIOps can quickly predict business application issues across an enterprise’s entire IT stack. Using its end-to-end view across all domains, AIOps helps customers rapidly identify issues and predict outages, so they can resolve problems proactively and avoid IT firefighting.

Stop IT “Brain Drain” with AIOps

By Enzo Signore, FixStream

Originally Posted on NSIGHTAAS

CIO’s face many challenges today – cybersecurity threats, limited budgets, and business transformation issues, to name a few. But is headcount the biggest worry?  According to a May 2018 Gartner survey, CEOs identified a lack of talent and workforce capability as the biggest inhibitor to digital business progress. There just aren’t enough trained IT professionals to go around, and the competition for available tech talent is fierce.

Hiring for artificial intelligence (AI) positions is especially difficult, as fewer than 10,000 people in the world are qualified to do state-of-the-art AI research and engineering. A recent article in the Silicon Valley Business Journal has highlighted just how significant the current AI talent shortage is. And a study by Ernst & Young  has revealed that over 50 percent of companies working with AI say the lack of qualified workers impacts business operations. “This year, as businesses strategized how to integrate AI into their operations, they were hampered by a shortage of experts with requisite knowledge of the technology,” Ernst & Young chief analytics officer Chris Mazzei said in a press release.

Other IT categories, including business intelligence and data analytics, artificial intelligence, and DevOps/agile processes, also face critical staffing shortages, according to CIO magazine. While recruiting outside talent is one way to fill these IT positions, another approach may be to develop internal workers. Many organizations find that the best way to fill job openings is to train existing IT staff in new job skills and in areas like data science and cloud security.

Get strategic about staff retention

Whether the decision is made to hire outside candidates or develop internal staff, filling IT job openings is just the first step. With so many talented people already employed, employee poaching is at an all-time high, making IT staff retention a big challenge as well. The tight job market forces recruiters to become aggressive, reaching out to workers who may not be considering a job change, but would do so for the right incentive.

After spending so much time and effort to hire/develop your IT staff, how can business operators keep these employees from walking out the door? With so many other suitors, how is it possible to stop the “brain drain” of IT workers?

One way to fight back is to keep current employees engaged with innovative projects that capture their interests and use their skills. These workers should be treated like the valuable assets they are. Their time and talent should not be wasted on menial work that can be better done by system tools. Investing in automation solutions and system upgrades that relieve IT workers of tedious system maintenance and testing duties will enable them to work on more valuable activities.

Modernize the organization to retain IT talent

Savvy CIOs realize that they will need to change their work culture if they want to retain talented employees. Implementing work-saving automation tools and cutting-edge technology can boost IT professionals’ morale and job performance. Freeing up the teams to work on the “fun stuff” — interesting, transformative projects with higher value to the business — will improve staff retention rates and deliver greater “bang for the buck” from investment in IT personnel resources.

There are several ways automation such as AI for IT Operations (AIOps) can change culture.

Enable staff to be more efficient – Many IT employees feel underutilized and bored with ongoing responsibilities like patch management and system maintenance, and have no time to work on innovative projects that benefit the business. Eliminate waste by automating IT inventory discovery. In most IT environments, staff wastes significant time and resources performing manual service management tasks since their CMDB is inaccurate and they do not have visibility into CI relationships. This is especifically challenging when the organization has deployed dynamic environments (virtualized, containers, cloud). To avoid repetitious, work waste, automate the discovery of IT assets.

Make teamwork a priority – Break down internal silos by mapping applications to infrastructure. Traditional silos caused by IT monitoring tools force the IT team to manually correlate processes across multiple domains. This means long conference calls, manual collection of large amounts of data, and finger pointing when performance issues arise. By mapping applications to infrastructure, its possible to break down these barriers and drive constructive teamwork.

Become predictive – Increase productivity by automating root cause analysis. Instead of repeating the same manual, error-prone and wasteful troubleshooting process that can last 4 hours on average and consume 11 full time employees (in 15 percent of cases, according to the Digital Enterprise Journal), why not predict when the next outage will occur?  This will not only increase the uptime and performance of the business application, but it will free up the IT staff to work on new projects.

By modernizing recruitment/retention practices, and the IT operations process itself, CIO’s can succeed in hiring and keeping valuable IT personnel. Using AIOps is one way to enhance staff productivity and effectiveness so that these workers become a source of innovation.

FixStream 2019 Predictions: Digital Transformation Powered by AIOps

By Bishnu Nayak, Chief Technology Officer, FixStream

Original Posted in VMBLOG.com

Digital Transformation Powered by AIOps

In a recent press release, industry analyst firm IDC made ten predictions for 2019 which center on the ongoing digitization of the global economy. Underpinned by 3rd Platform technologies such cloud, mobile, and big data analytics, IDC reminds us that digital innovation is transforming industries and regions, and it’s imperative that organizations adapt. As Frank Gens, senior vice president and chief analyst at IDC, pointed out in the press release. “The ability to accelerate digital innovation volume and pace will be the most critical new benchmark for organizations competing in the digital economy”.

The need to keep pace with this digital acceleration means that yesterday’s toolsets and practices-which depend on human skills and manual processes-are no longer effective. Today’s interconnected hybrid cloud IT environments are too complex and siloed, not to mention mission critical, for traditional approaches. Users have very high expectations of system performance, and uptime of critical business applications, so IT Operations need new tools and approaches.

Let’s look at one example to illustrate the challenge. ITSM functions such as change management, asset management, and incident management heavily depend on the accuracy and richness of the data maintained in the underlying CMDB. Yet, when modern data centers leverage emerging technologies such as virtualization, containers, micro services, and/or hybrid cloud to deliver digital services, traditional CMDB’s struggle to keep up. Changes across the infrastructure, such as the addition or deletion of VM’s or containers, or new micro service deployments, may or may not be recorded. Often, the CMDB can’t manage the frequency of changes, or maintain the dependencies and relationships across CIs in an accurate, complete and timely fashion.

Artificial Intelligence for IT Operations (AIOps), the next generation of IT operations analytics, can address this issue and more. AIOps solutions can more efficiently and accurately implement changes and handle incidents that impact critical business services. For example, AIOps can discover interdependencies and relationships of CI’s across the infrastructure and business applications, ensuring the CMDB stays accurate and current. This in turn enriches and optimizes the ITSM functions required for successful delivery and operations of digital services.

Business impacts from new changes can be easily determined and forecasted using AiOps solution enabling enterprises to quickly roll out updates such as new technology, system patches, upgrades/maintenance updates, or new vendor applications. Better yet, AIOps event correlation can quickly identify the root cause of applications negatively impacted by system change done incorrectly.

Industry analysts also see the valuable role that AIOps plays in digital transformation. A Sept. 2018 blog by EMA Vice President Dennis Drogseth reviews the results of a study the firm conducted on a range of AIOps topics. In the survey of 300 ITOps professionals, 87% revealed they are already using an AIOps platform, with another 13% in the planning or testing stage. 54% of respondents identified the CMDB as ‘extremely important’ to their analytic strategy. Drogseth explains that investment in AIOps solution can reinvigorate their CMDB/CMS and application/infrastructure dependency mapping technology areas.

Studies like this make us feel confident in predicting that, in 2019, more and more organizations will adopt AIOps to accelerate their digital business transformation.

Transforming IT Operations Through AIOps-Powered Data Correlation and Visualization

By Sameer Padhye

Originally Posted in Data Center Journal

The digital transformation across enterprises and industries has not only accelerated the pace of business. It’s also led to more organizations adopting more connected applications. That has created greater reliance on underlying enterprise networks on which these critical business applications run. The infrastructure is becoming more distributed, heterogeneous, intelligent, open, and virtualized to support the growth, agility, and scale of the business applications.

With pressure on to deliver solutions as quickly as possible, application architecture is changing to adopt newer technologies such as containers. Containers, with their ability to bundle applications and associated software libraries, enable developers to create “build once, run anywhere” code, for portable applications. It’s easy to understand their popularity. Over 2/3’s of organizations who adopt containers achieve greater developer efficiency, according to a Forrester study. That allows faster deployment of the application entities in multiple data center and cloud environments, which can scale dynamically. (In October 2017, DockerCon Europe reported that 24 billion containers have been downloaded.)

Diverse Systems Create Complexity – and Problems

Business network elements frequently come from a wide variety of hardware and software suppliers. And these networks are only becoming more diverse given the movement by business networking professionals to avoid vendor lock-in, embrace open architectures, and use best-of-breed solutions.

In this heterogenous dynamic application environment, changes can happen very abruptly and obliquely. Using legacy techniques to track the changes and correlate the events in this type of system environment is very challenging. Manual processing of massive amounts of data – which is dynamic across the stacks – to identify patterns, anomaly scenarios, and predict capacity requirements is almost impossible. So much system noise makes it extremely difficult to uncover and resolve the incidents that are impacting system performance. That, in turn, poses tremendous business risks and hinders business innovation.

Finding Insight in a Mountain of Data

It’s tedious and time-consuming for your IT Operations teams to comb through all that data to find useful insights that could improve system reliability and performance. Instead, consider an Artificial Intelligence platform for IT Operations (AIOps) solution that combines the power of machine learning with the ability to auto-discover and correlate entities across critical layers of digital business – business, application, and infrastructure.

AIOps-powered auto-discovery and machine learning can uncover, correlate and analyze all the data from multiple enterprise application and infrastructure domains quickly and accurately, providing visibility into application and infrastructure vulnerabilities. Using machine-learning algorithms to detect patterns and eventually predict potential outages, AIOps can help IT workers thwart system failures, security issues, and performance bottlenecks, so IT departments can enable business continuity and customer satisfaction.  

Augment your IT Staff with AIOps

Artificial intelligence and machine learning are not a replacement for people in this scenario. Rather, they help humans perform day to day IT operational tasks such as troubleshooting, capacity management, migration, and planning. Also, AIOps can offload many menial error-prone tasks from your IT employees, enabling them to focus on more strategic, higher-level activities that improve business operations.

“Most recent advances in AI have been achieved by applying machine learning to very large data sets,” notes McKinsey & Co. “Machine-learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt in response to new data and experiences to improve efficacy over time.”

Data Correlation Feeds Predictive Analytics

Machine learning can correlate and analyze data from multiple enterprise application and infrastructure domains, dealing with the volume, velocity, and varieties of data generated. It can uncover patterns to show what has occurred. It can use current conditions and past learning to spot exceptions and predict the future. Machine learning can even offer suggestions on what to do in various scenarios.

AIOps platforms leverage machine learning to deliver AI capabilities for IT operations. Here are some interesting use cases.

  • Multivariate anomaly detection can identify anomaly scenarios across various dependent entities. Such anomalies may signal that a planned or unplanned business event has taken place. For example, a multivariate anomaly group may represent an unplanned event like a DDoS cyberattack or a planned business effort such as Black Friday event.
  • A time-series sequential pattern detection algorithm can predict business outages triggered by events anywhere in the stack business functions are deployed.
  • It’s also possible to use AI and machine learning to predict when you’ll run out of capacity. For example, it could signal a potential lack of storage disk volume and excessive network bandwidth use of a router. Such information helps IT experts do proactive capacity planning to better meet business needs.

Improved System Performance

Machine learning automates IT operations and can notify operations teams of potential business outages before they happen. It also can detect security issues, identify infrastructure performance bottlenecks, and recommend capacity augmentation and optimization.

IT teams can then set systems to trigger actions for remediation. Executing remediation scripts or integrating with other orchestration and automation tools to take actions minimizes human tasks.

By proactively detecting and fixing system issues with AIOps, you can enable business continuity and assure customer satisfaction. In the age of digital transformation, such capabilities and AIOps solutions are an absolute must.

With machine learning, IT staff can continually and completely look for traffic exceptions. As a result, IT experts can be far more effective in preventing and quickly responding to cyberattacks. So, businesses can stay up and running, and stay out of the headlines.

AI – Driving Digital Transformation Forward

These are just a few reasons why AI and machine learning have become key components of digital transformation. And that’s only going to accelerate moving forward.

“During the next few years, the technologies associated with this [digital transformation] wave — including artificial intelligence, cloud computing, online interface design, the Internet of Things, Industry 4.0, cyberwarfare, robotics, and data analytics — will advance and amplify one another’s impact,” note PwC analysts Leslie H. Moeller, Nicholas Hodson, and Martina Sangin.

Many businesses are already on board with AI, and others are planning to implement it. Forrester Research says more than half of organizations already have implemented some form of an AI project. And it says another 20 percent are planning AI projects in the near future.

Machine Learning – the Air Traffic Control System for Your Data

Machine learning is to network operations as air traffic control is to airline operations. Consider that each hour of the day, there are about 5,000 airplanes flying in the sky just within the U.S.  With that much air traffic, using manual processes to track the planes as they move around would be nearly impossible and just plain dangerous. So instead, we use air traffic controllers to manage the chaos. The air traffic control system helps experts keep track of all the traffic (airplanes) among the different domains (various airports and airlines). By bringing together the various data points and presenting a complete view of what’s happening, air traffic control helps avoid mishaps and enables smoother traffic flow.

Machine learning likewise enables data correlation and analytics. That way, IT experts can keep the network and its applications running safely and on time. And that allows organizations to deliver better and safer customer experiences, make better use of their human and technological resources, and keep their applications and businesses moving forward.

2019 Predictions for AIOps

Many enterprises have found that Artificial Intelligence for IT operations (AIOps) platforms help them better manage their hybrid IT environment and improve system availability. Next-generation solutions – call it AIOps+ — now offer advanced capabilities such as automated pattern discovery and prediction, automated it infrastructure entity discovery and application mapping, and data and event correlation across the IT stacks. Based on these advanced features, we predict more firms will adopt AIOps+ in the coming year.

#1 – Enterprises will experiment with AIOps tools but will deploy AIOps+ solutions

While standalone AIOps tools can deliver useful capabilities such as anomaly detection and noise reduction across both legacy and digital environments, organizations may discover the promise of AIOps is difficult to achieve without understanding the business application context. Enter AIOps+ that works across the IT environment, ingesting and correlating massive amounts of data from a variety of sources, from business transactions to applications to it infrastructure. The full-stack data correlation delivered by AIOps+ will increase the accuracy of the machine learning predictions by enabling the ML algorithms to work on real-time, application specific events.

#2 – AIOps+ will help organizations succeed with digital transformations

While traditional IT environments can accept MTTR in the range of hours, modern organizations that are increasing their reliance on digital processes, will not survive unless they can depend on hybrid IT environments with significantly higher uptime performance. Imagine an on-line reservation system, a patient onboarding application, or an eCommerce site – what would be the business impact if they were down for hours or day? AIOps+ solutions provide the advanced real-time/predictive analytics needed for IT teams to make sense of vast sets of event data and drive faster IT incident response. By extracting meaningful insights from business transaction metrics, application flows and incident data, IT can quickly determine the root cause of an issue and even predict when the next outage will occur. Industry analyst Gartner sees an increasing role for artificial intelligence systems  in the next few years as IT departments struggle to support their sprawling digital environments with limited staff.

#3 – MSPs will start deploying AIOps+ to increase their revenues and efficiency

Given the pressing need for 24/7 system availability and reliability, AIOps+ is becoming the future of the agile, digital enterprise. Clients depend on their MSPs to meet their service level agreements (SLA’s) and maintain system availability at all time. However, as more mission-critical applications migrate to the cloud, the siloed, fragmented nature of distributed hybrid IT operations can hinder the incident diagnosis/resolution process. Performing root-case analysis and system diagnostics is complicated by several factors such as: where is the problem located and who owns it; is the issue related to an application or infrastructure failure; and how is the problem impacting system performance?  AIOps+ can provide the end-to-end visibility of distributed IT environment and real time data analysis that MSP’s need to automate root cause analysis and quickly resolve service performance issues.

#4 – Enterprises will appoint centralized teams to take responsibility for AIOps+ deployments

Today, very few organizations have a centralized team responsible for IT operations. Since today’s tools are siloed, IT organizations have structured themselves in the same manner with compute, network, applications and storage analysts each focused on their niche, each using a disparate tool. They still are not using cross-functional tools that correlate data across each silo. In isolation, it may not look as if there is an issue however when the data is correlated, issues emerge. With AIOps+ solutions, organizations gain access to powerful new capabilities. Building a centralized team ensures that the organizations gets a holistic view of the entire hybrid IT environment.

#5 – Consolidation in the AIOps market will start to occur, with those offering AIOps+ gaining ground

Many AIOps vendors have emerged in the market – and not all are worth the hype. Today, there are big and small AIOps providers with varying capabilities. As the AIOps market matures, we foresee a supplier shakeout, with those offering limited AIOps capabilities not surviving. Rather, we think successful vendors will be those offering robust AIOps+ solutions with advanced features such full stack correlation, agentless auto-discovery, automated remediation/restoration, data anomaly detection, and seamless integration with ITSM tools.

#6 – Traditional siloed ITOM/ITOA budgets will freeze and be reallocated to AIOps+

To keep up with the demands of digital business, organizations need to rethink their approach to managing silo hybrid IT environments and system maintenance. As the pain of operational volume, value, variety and velocity of big data analytics grows, organizations need an effective alternative to disparate application monitoring tools and siloed solutions. We predict forward-thinking organizations will reallocate budgets to AIOps+ solutions to better manage, plan, and troubleshoot issues across the entire IT infrastructure monitoring in real-time.

#7 – An ecosystem of solutions (built by SI/VAR/MSPs) will emerge to enrich existing AIOps+ platforms

The best AIOps solutions can seamlessly integrate with other IT assets, ERP applications, and multiple data sources. For example, FixStream’s new release integrates with ServiceNow and Cherwell ITSM/CMDB platforms, New Relic APM, SolarWinds and ManageEngine monitoring tools, etc. delivering functions such as auto-ticketing and change management, and creating an integrated solution greater than the sum of its parts. Taking it a step forward, we see a community of value-added resellers (VARs), system integrators (SI’s) and MSP’s emerging to create customized solutions for AIOps+ platforms that enhance its capabilities, perhaps to meet the needs of a specific industry or to incorporate advanced security features.

Conclusion

In 2019, we think more IT organizations will recognize how AIOps+ solutions can detect, predict and resolve business issues across an enterprise’s entire hybrid IT environment. For example, the latest AIOps+ offering from FixStream enables customers to detect patterns with 90%+ probability and reduce MTTR to just minutes, so IT operations can achieve appropriate service levels and meet customer expectations.

For more information on how FixStream AIOps+ can help you modernize IT ops this year, view our video.

What’s the Real Cost of Data Center Downtime?

We all know that IT downtime is expensive and damaging to organizations and their productivity. Yet, despite ongoing investments in technology, system outages still bedevil many enterprises, including cloud-based environments. In 2018, for example, cloud outages at companies such as Microsoft, Amazon Web Services, and Visa reminded us that even well-maintained IT environments are vulnerable to system disruptions. An extreme example is the 2017 Delta Airlines 5-hour outage that caused the cancellation of 280 flights and cost the company $150 million dollars.

With numerous IT assets spread across on-premise and cloud environments, downtime today can cause a lot more harm than mere customer inconvenience. System outages can directly impact productivity, throughput, profitability and customer attrition. You only need to experience one system outage to realize how much harm it can cause to a company’s reputation and standing in the marketplace. But rather than considering IT downtime as an inevitable cost of doing business, senior IT managers can proactively take steps to minimize their occurrences and impact. And quantifying the cost of system outages can justify spending to remediate them.

How Much do System Outages Really Cost?

Catastrophic airline outages aside, just how costly is downtime to the typical enterprise? There are direct costs for system diagnosis and repair, as well as indirect costs such as the loss of organizational productivity and damage to corporate reputation. Costs may also include damage to (or loss of) mission-critical data and other assets, legal and regulatory impact, and repair costs for core business processes and systems. Add in the lost revenue related to business disruptions and missed sales opportunities, and you can see how even one hour of downtime can cost several hundred thousand or million dollars.

To tally up estimated costs, analyst firms have surveyed clients who experience system downtime and can quantify its impact. Cost estimates will vary by industry, size of the organization, and region. For example, according to Statista, in 2017/2018 the average cost of server downtime was approximately $300,000-400,000 per hour, while 44% of survey responders reporting costs of $1M/per hour or more. And those costs, and associated business impact, would be higher if you experience an unplanned outage during peak traffic time.

The more complex, virtualized and interconnected system environments become, the longer it takes to diagnose and resolve unplanned outages. The siloed, disparate nature of most IT hybrid system infrastructures have caused Mean-Time-To-Recovery (MTTR) rates to escalate along with costs. In fact, ITIC’s Reliability and Hourly Cost of Downtime Trends Survey confirmed that 81% of organizations report the cost of unplanned downtime typically exceeds $300,000/hour, with monetary costs exceeding millions of dollars per minute in extreme cases.

The ITIC study also shows that downtime costs vary between industries and enterprise size. For example, large enterprises with over 1,000 employees could see the costs associated with a single of hour of downtime to exceed $5 Million in nine specific industries, including Banking/Finance; Government; Healthcare; Manufacturing; Media & Communications; Retail; Transportation and Utilities.

Total Cost of System Downtime by Industry

(summary Ponemon Institute study)

  • Financial Services $994,000
  • Healthcare $918,000
  • eCommerce $909,000
  • Industrial $761,000
  • Retail $758,000
  • Hospitality $514,000
  • Public Sector $476,000

Take a look at the following charts (Ponemon Institute study). The first chart shows reveals the relatively consistent breakdown of cost categories associated with business disruption. The second chart illustrates that the average shutdown duration hasn’t changed in the last 6 years.

Why System Outages Happen

A 2018 study by Information Technology Intelligence Consulting points to human error and security issues topping the list of causes for unplanned downtime, with network interruptions another contributing factor, and outdated processes can lengthen the time it takes to resolve outages. Pinpointing the root cause of system outages is complicated by manual, time-consuming correlation of massive amounts of siloed operational data. Proactive planning, automation, and better resources can help minimize human error and hardware/software failures. For example, automated real-time correlation of data between business, application, and infrastructure components can help predict potential system issue so they can be resolved before impacting the business.

Proactively Manage Assets to Minimize System Downtime

To prevent downtime, you must be able to effectively monitor and maintain your assets in real time, and arm your IT staff with tools to help make sense of IT complexity. Artificial Intelligent systems for IT Operations (AIOps) can transforms IT Ops by significantly reducing human errors and the tedious repetition of cumbersome manual processes. By providing real-time full-stack data correlation and visualization of the entire system environment IT staff can gain actionable insights to optimize system performance and meet customer expectations. Viewing and understanding application dependencies can also help employees forecast the potential impact of system changes before they are implemented. This allows you to carefully plan transitions and migrations so they don’t affect the performance of business-critical applications.

AIOps can reduce system downtime by increasing application assurance and uptime. To see how FixStream AIOps can help you improve system availability and reliability, download our free eBook.

Submit to Download

>