Name: DevOps & AI: Do we still need ITIL Change Management?
Start: 2020-12-09T16:00:00Z
End: 2020-12-09T16:00:54.000Z
Location: BrightTALK
Rating: 0

Numerify provides IT leaders with a comprehensive system of intelligence for end-to-end visibility into their Plan-Build-Run activities. The suite of analytical applications span project planning, software development, and service and asset management. The company’s cloud-based platform includes full data orchestration, IT-specific business models, and a complete spectrum of analytical capabilities. Numerify provides IT business analytics applications to leading organizations, including companies ranked in the top 5 across 10 major industries. For more information, visit www.numerify.com or follow @numerify.

In today’s volatile environment, your customers, partners, and employees are more dependent than ever on your ability to deliver reliable IT services. Investing in AIOps can be transformative in improving the quality and availability of services through more effective decision-making and analytics-driven automation.

While most IT organizations focus on using AIOps to improve incident MTTD (Mean Time to Detection) and MTTR (Mean Time To Resolution), that is really only the tip of the iceberg of the value they could realize.

E.g., are your IT Change and Service Management teams using the wealth of historical insights in ITSM, APM, DevOps, and other systems to avoid major incidents in the future? What best practices can help development teams create change that doesn’t cause business disruption? 

AIOps delivers precise insights that allow your teams to not only resolve incidents faster but also help avoid incidents altogether. Join our special guest speaker Dennis Drogseth, VP of Research at Enterprise Research Associates, and Amit Shah, Director of Product Marketing at Digital.ai, to learn: 

- About the current AIOps market landscape
- AIOps use cases that have driven customer value
- How to align AIOps use cases with your goals and initiatives
- Case studies of Digital.ai customers using AIOps capabilities today

How to Use AIOps to improve stability and accelerate innovation

If your Change Management processes and teams are like most, your reaction to the recent crisis was probably a change freeze. Many were forced down this path due to an inability to reliably assess the risk of individual changes. This is understandable – Gartner estimates that Change accounts for 80% of major incidents. However, the backlog of deferred changes only continues to grow, while your customers need you to innovate and take advantage of the opportunities the crisis brings.

How can AI-powered analytics help you evolve your Change Management from reactively shutting down to proactively mitigating risk? E.g., a Change Risk Prediction engine could help you not only flag risky changes but also identify actions to mitigate or eliminate risk upstream in development processes as well as downstream in infrastructure.

Learn how to continuously adapt to new challenges while improving the quality, availability, and performance of mission-critical services. Join guest speaker Charles Betz, Forrester Principal Analyst, and Amit Shah, Numerify Product Director, to learn how:

- Recent Forrester survey results show how your peers have been changing their approach to Change Management
- The Change Advisory Board’s role must evolve in a DevOps world
- Numerify’s Change Management Maturity model can guide your transition from reactive to proactive
- Fortune 500 companies across various industries are adopting AI for a data driven approach to change

Change Management: From Reactive to Proactive with AI

Join Configuration, Change and Release Management expert Ben Sapp as he answers your questions about how to make it a reality.

We'll start this live session with Ben's 5 FAQ asked questions around Configuration, Change and Release Management, and then we'll answer the audience's burning questions around this facet of IT Service Management.

Ask the Expert: Making Configuration, Change & Release Management a Reality

Continuous Service Improvement (CSI) programs are vital to preventing outcomes such as low customer satisfaction scores and long incident resolution times. However, with so many potential opportunities for improvement, where should your teams focus their energy?

This webinar will take you through the journey of Rogers Communications, one of the leading telecommunications and media companies in Canada. They use data and AI-powered analytics to drive a CSI program that has produced significant improvements. By providing the ability to get real-time answers to questions about IT Operations, the Rogers team was able to reduce major incidents, MTRS, and ultimately customer impact.

Join the webinar to learn from the experiences of Connel Valentine, Senior Manager for Reporting & Analytics at Rogers Communications, and Srikant Gokulnatha, Chief Product Officer at Numerify. Topics of discussion will include:

- Accelerate operational improvements by incorporating analytics into your ITSM processes

- Drive the transformation of IT Operations teams to a data-driven culture

- Evolve the role of reporting analysts from data preparation to finding insights

- Numerify use cases for taking IT Operations to the next level with AIOps for ITSM

How AIOps helps Rogers Communications deliver better IT Operations

In the era of DevOps, IT leaders are under constant pressure to be more responsive to changing business needs. However, the cost of failed IT changes can be high. While you may know that some deployed changes will fail, what you often don’t know is which ones — until it’s too late. In fact, a recent Gartner study estimates that 80% of major incidents are change-related. Finally, new approaches are breaking through, based on a rigorous application of Machine Learning (ML) and predictive analytics.

So how can you reduce change risk while retaining the agility of DevOps?

Learn how to apply AI / ML techniques to increase your systems’ stability while delivering the speed of innovation your customers expect. 

In this webinar Amit Shah, Numerify Product Director, will share how you can:

• Use Machine Learning to predict risky changes and facilitate root cause analysis both upstream and downstream of change
• Find and eliminate the systemic causes of change failure across people, process, and technology
• Continuously monitor existing risk factors and get early warning indicators for new risk factors
• Learn from case studies of organizations that have successfully implemented these techniques

How to use AI to reduce change risk while retaining the agility of DevOps

Automation and AI in ITSM have come a long way. The improvements they’ve made to parts of ITSM - including to workflows, change management, pushing updates and ticketing - have had a significant impact on customer and employee experience and satisfaction.

So what’s next for AI, machine learning and automation in ITSM? And how is it changing employee service management? Join this expert panel as they share insights, advice and use cases about areas of ITSM that are being revolutionized by intelligent technologies, including:

- Improved IT operational efficiencies
- Increased cost savings
- Faster incident and problem resolution 
- Better release management 
- Higher customer/end-user satisfaction

[Panel] Exploring the Future of Automation and AI in ITSM

It is Monday morning, and once again, everyone in the organization drops what they are doing to take on unplanned work due to another major incident. Sound familiar? If so, then your organization is struggling with the challenges related to systemic problems caused by technical debt, un-stable snowflake environments, and issues related to process capability.

To reverse this trend, organizations need to go on the offensive by moving from a reactive Incident approach to proactive problem identification and management. However, to do this successfully, Lean teaches that you need data since you cannot avoid a problem you cannot see.

Join Troy DuMoulin, VP of Research and Development at Pink Elephant, and Amit Shah, Director of Product Marketing at Numerify, as they discuss the critical success factors for enabling Proactive Problem Management and how data, analytics, and machine learning can enable organizations to avoid major incidents and reduce un-planned work. Topics that will be covered include:

- Reactive versus proactive Problem Management 
- Major Incident reviews and trend analysis 
- Using root cause analysis techniques to improve incident prediction
- Using risk management to manage problem backlogs
- Using AI/ML techniques to create early warning systems

A New Guide to Enabling Proactive Problem Management & Reducing Major Incidents

How can you leverage Natural Language Processing (NLP) techniques to make sense of the human-generated incident data?

There is a treasure trove of insights in the text descriptions entered into your incident tickets regarding common symptoms, root causes, and resolutions. However, with thousands of incidents generated weekly, it can be hard to manually extract these insights and make them available to your Incident teams.

Join Abhijeet Joshi, Senior Product Manager, and Joe Foley, Director of Business Analytics, to learn how organizations like yours:

- Achieve Smart Incident Management with AI-Powered Analytics
- Leverage NLP to help make sense of large volumes of unstructured text descriptions
- Identify related incidents using Incident Topic Clustering
- Benefit from using Topic Clustering for Smart Incident Management

How to Leverage NLP for Smarter Incident Management

Every IT organization would like to reduce the number of incidents they experience. The rewards for achieving this are substantial: fewer business disruptions, lower IT costs, and the ability to concentrate talent on growth and innovation.
 
However, with your organization experiencing ever-increasing complexity, where do you start? In this on-demand webinar, Joe Foley, Director of IT Business Analytics Services, and Jim Henaghan, Solutions Consultant, speak about the experiences of companies like yours. Topics that will be covered include:
 
- Proactive analytics-driven incident reduction strategies that target the highest impact problem hot spots
- How Machine Learning models can help predict incidents and reduce incident volume
- Using analytics to bring together siloed teams and standardize on best practice processes
- How text analytics techniques can help extract insights trapped in unstructured data

Top 5 Best Practices to Reduce Incident Volume

Many IT operations teams focus on reducing the amount of time it takes to restore business services in the aftermath of a major incident. However, the very systems and processes that help detect and respond to a major incident also contain clues that could have helped predict and prevent the major incident from ever occurring. The benefits of being more proactive are obvious; the hours it takes to restore service can be very costly from a customer and business process disruption perspective.

Join Amit Shah, Director of Product Marketing and Ben Sapp, Insights Architect as they share best practices on how to use predictive analytics and AI for major incident management. During this webinar you’ll learn how to:

- Use availability, problem, and incident trends to predict major incidents
- Create an early warning system for major incidents using AI/ML techniques
- Prioritize risk mitigation activities by quantifying expected business impact
- Identify and operationalize opportunities to prevent service impact and improve restoration time

How to Improve Major Incident Management Using Predictive Analytics and AI

In today’s volatile environment, your customers, partners, and employees are more dependent than ever on your ability to deliver reliable digital services. Investing in AI-powered capabilities that improve the quality, availability, and performance of these mission-critical services is now all the more important, and technological innovations are making such capabilities easier to adopt. 

IT Operations teams have always struggled to identify and fix complex problems quickly, in part because they often cannot correlate data from multiple monitoring systems, e.g., is the root cause of a major incident related to a recent change? If so, which of the 100+ changes made in the past week is the culprit? 

How can IT Change and Service Management processes use the wealth of historical insights in ITSM, APM, DevOps, and other systems to avoid major incidents in the future? 

AIOps (AI for IT Operations) delivers precisely these insights allowing your teams to not only resolve incidents faster but also help avoid incidents altogether. Watch this webinar co-hosted by our guest speaker Rich Lane, Senior Analyst at Forrester Research, and Ted Sapountzis, VP of Marketing at Numerify, to learn: 

- What is AIOps and how can it help you improve service reliability, agility, and the employee experience 
- How AIOps can not only automate decision making but also augment the capabilities of your IT Service and Change Management teams 
- How Fortune 500 organizations are applying these principles today 
- How to get started, even if you don’t have data scientists in your organization

How AIOps helps IT Change and Service Management be more reliable and nimble

It should be the goal of every IT organization to reduce the number of incidents they experience in 2020. But with organizations experiencing ever-increasing complexity, where do you start with trying to reduce IT Incidents? It can be hard to know with service delivery fragmented across multiple systems, processes, geographies, and providers. These silos of teams and data lead to a lack of visibility, process inefficiencies, and knowledge gaps.

Join Numerify Director of IT Business Analytics Joe Foley as he shares AI-powered, analytics-driven best practices and strategies that will help you reduce incident volume and improve customer satisfaction in 2020 and beyond.

Topics Joe will cover include:

• Proactive analytics-driven incident reduction strategies that target the highest impact problem hot spots
• How Artificial Intelligence & Machine Learning models can help predict incidents and reduce incident volume
• Using analytics to bring together siloed teams and standardize on best practice processes
• How text analytics techniques can help extract insights trapped in unstructured data

How to Use AI & Analytics to Reduce IT Incident Volume in 2020 & Beyond

With so much business and technology change surrounding IT Service Management - as well as the impact of COVID-19 - what does the future of ITSM hold?

Join this expert panel as we take a closer look at the areas in which ITSM is evolving and morphing, as well as those where its roots in people, process and technology are proving to be steadfast supporters in a time of immense upheaval.

Topics of discussion will include:
- What are ITSM professionals currently focusing on?
- Which technological advancements are helping ITSM better meet business demands?
- How have - or haven’t - ITSM best practices changed?

- Moderated by Alex Hilton - Chief Executive at Cloud Industry Forum
- Panelists include: Ben Sapp - Insights Solutions Architect at Numerify, Steve Groom - Founder and CEO at Vissensa, and Ian Moyse - EMEA Sales Director at Natterbox

[Panel] What Does the Future of ITSM Hold?

As you transition to a highly distributed workforce, it is more important than ever to adapt your IT Service Management processes to new and unforeseen demands. However, in the face of current volatility, most IT teams are focused on maintaining the system stability needed to retain customers and keep distributed employees productive.

At the same time, IT teams themselves are struggling to adapt to working within a distributed team, making it difficult for IT leaders to swarm resources to the highest impact problems. Not to mention, new service request and incident patterns are stressing the ability of IT teams to deliver their services in a reliable and timely fashion.

How can IT business analytics help you evolve from reactive to proactive ITSM and Change management?

AI-powered analytics can help you continuously adapt to new challenges while improving the quality, availability, and performance of mission-critical services. Join Amit Shah, Director of Product at Numerify, to learn how:

- Fortune 500 companies across various industries are adapting to an increasingly distributed workforce

- Predicting change risk using AI/ML can provide organizations the confidence they need to continue innovating in the face of volatility

- AI-powered techniques like Natural Language Processing can help you find signals within human entered text in Incident and Change data

- Get started, even if you don’t have access to data scientists in your organization

How to adapt your IT Service & Change Management for a distributed workforce

Are you trying to adapt your incident management processes to respond to the current crisis? With so many employees working remotely, your business depends on reliable IT applications and services to keep your operations functioning, your employees productive, and your customers happy.  At the same time, incident responders must deal with the inevitable surge of incidents that such a sudden and drastic change in your organization’s operating model will bring.

During this period of disruption, organizations are relying on their IT departments and suppliers to deploy digital engagement platforms and applications at increasing speed, while at the same time requiring stable operations and increased availability.  At the same time, while high availability is an expectation of digital services, it is a reality of life that, in the face of volatility, complex systems have inherent flaws that will inevitably cause service disruption.

How can ITIL 4 High Velocity IT paired with AI-powered analytics help you continuously adapt and respond to any crisis?

Join our special guest Troy DuMoulin, VP R&D at Pink Elephant and member of the global ITIL 4 architect team and co-author on the ITIL 4 Foundation publication, and Joe Foley, Insights Architect at Numerify as they discuss the latest techniques and experiences of organizations like yours. 

Join the webinar to learn how to:
 - Leverage “ITIL 4.0 High Velocity IT” principles that can help manage volatility with a high level of resilience
 - Use AIOps techniques in incident and problem management to improve service resiliency
 - Use Natural Language Processing to keep your incident responders productive and reduce minor incident volume
 - Mitigate the risk of major incidents & better predict change failure with Machine Learning

Driving resiliency & high velocity in IT Operations through AI-powered Analytics

In the era of DevOps, application development and IT Operations leaders face ever-increasing demands to be more agile. Unfortunately, traditional IT Change Management processes can be more of a bottleneck than an enabler of agility. Case in point: if your Change Management processes and teams are like most, your reaction to the recent crisis was probably a change freeze.

So, is it time to finally ditch ITIL Change Management and fully embrace DevOps? This is probably the wrong question to ask. A better question would be: how can AI-powered analytics, like Change Risk Prediction, bridge the gap between ITIL and DevOps?

Learn how recent advances in AI-powered analytics can help you adopt ITIL 4 best practices, realize the benefits promised by DevOps, and retain the discipline of ITIL. Join guest speaker George Spalding, Pink Elephant Executive VP, and Joe Foley, Digital.ai Director and Insights Architect, to learn how:

- The shortcomings of traditional ITIL v3 make it incompatible with DevOps
- ITIL v4 and DevOps complement and enhance each other
- The Change Advisory Board’s role must evolve in a DevOps world
- AI and Machine Learning can help find and eliminate root causes of change failure at all stages of the DevOps ellipse

DevOps & AI: Do we still need ITIL Change Management?

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DevOps & AI: Do we still need ITIL Change Management?

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