Over the past months, we at Greyhound Research have received a ton of requests from our end-user clients to offer more product deep-dives. Essentially, share insights that go beyond the usual 30,000 feet conversations and focus on products/suites that IT teams are currently exploring or using. In other words, insights sans management jargon and marketing hype. Hence we decided to launch Product Spotlight, a dialogue series wherein we host the real brains who are architecting products.
In this 1st exchange (E1) of Product Spotlight, we host Morgan Timpson, Product Manager, IBM’s Cloud Pak for Watson AIOps. In his current role, Morgan helps IT teams across the globe apply AI to their operations and works extensively with IBM’s AI technologies and strategy across multiple industries, including Banking, Telecommunications, Insurance, and Retail.
P.S.: As the name states, this dialogue series is focused on a product/suite on which our analysts receive enquiry calls from end-user clients. We host the Product Managers and quiz them on features, roadmap and more.
Greyhound Research:IBM has, over the past year or so, made a considerable investment in IT Ops by way of investments in Instana, Turbonomic and Watson AIOps. In addition, IBM also has valuable assets from Red Hat, including Advanced Cluster Management. Would you please help clarify how these add value to each other and, more importantly, how IT orgs can fully use each of these products in real life?
IBM: IBM is quite excited by the opportunity that AIOps represents. Based on the feedback so far, that excitement is shared by our customers and partners, especially given our recent acquisitions of Instana and Turbonomic.
At a high level, you can break down IBM’s current AIOps portfolio as follows:
Instana: Providing clients rich observability data through a powerful Application Performance Monitoring (APM) solution.
Turbonomic: Helping client organizations grow faster by efficiently allocating and leveraging essential technology resources.
IBM Cloud Pak for Watson AIOps: Using AI, ML and automation to drive more effective and efficient incident management processes for IT teams.
Red Hat: Through the flexibility provided by the OpenShift platform and through tools like Advanced Cluster Management and Ansible (among others), Red Hat is a valuable addition to the overall IBM DevOps and AIOps proposition.
Stepping back from the dynamics and architecture of any one solution and how it pairs with other solutions, the key takeaway is that IBM has positioned and invested heavily to help clients at any step in their journey to AIOps and is doing so in a way that allows clients to move at their preferred pace.
Clients are looking to:
1/ Reduce the number of IT Ops / AIOps tools they need to manage, administer, and pay for to get those benefits
2/ Get the benefits of AIOps – fewer user-impacting outages, more automation to drive resource and stability benefits, the use of AI to unearth insights in their observability and performance data
Vendors like IBM need to assemble platforms that bring together various essential IT Ops functions and allow for flexibility in how customers want to apply AIOps.Tweet
Greyhound Research:One of the critical issues for most IT ops teams today is the event flood and related incident reports. Would you please elaborate on IBM Cloud Pak for Watson AIOps event localization and blast radius capabilities and how far it improves incident diagnosis and isolation?
IBM:Clients using our localization and blast radius capabilities have seen 20% to 70% reductions in Mean-Time-To-Detect (MTTD), driving sharp improvements to the overall incident management process.
The key to these improvements is rich topology data gathered by IBM Cloud Pak for Watson AIOps.
The data helps ITOps teams quickly identify failures or downstream concerns; the granularity and historical data IBM Cloud Pak for Watson AIOps provides helps teams locate issues even in complex use-cases. For example, when containers have been spun-up and spun down in seconds, teams need “what happened” context to understand brief failures that hamper application performance.
Focusing on event flooding specifically, IBM Cloud Pak for Watson AIOps aggregates data from across different ITOps data sources (ex: Logs, Events, Alerts, others) to group signals all related to the same incident. This helps the organization accelerate the response to incidents, as all information is in a centralized location, instead of being slowly and manually “collected” bit by bit through various tools.
Greyhound Research:While data aggregation and visibility help the IT Ops teams, it’s the remedy and action that causes great heartburn. Would you please point more specifically to the use of NLP for the next-best-action suggestion and training of the in-house team for event resolution?
IBM:Natural Language Processing (NLP) is used, alongside structured information about the current ongoing incident, to help ITOps teams identify specific actions taken in prior incidents to drive resolution.
A key point to note here is that IBM Cloud Pak for Watson AIOps is not just recommending related tickets. The solution is consuming the information in prior incident tickets and recommending specific prior actions to the user. That user can act on the immediate recommendation if they are ready to move forward or learn more by reading the ticket in detail. This saves valuable time off the Mean-Time-To-Response (MTTR) process while keeping humans “in the loop” for important decisions.
NLP is also applied in a variety of other ways within our AIOps solution.
For example, IBM Cloud Pak for Watson AIOps also uses NLP in our Change Risk capability, more oriented toward application development teams. This essentially uses NLP (among other data) to provide risk scores to application developers before pushing code into production to reduce the likelihood of future outages.
Greyhound Research:In addition to ROI, an important metric that the board truly cares about is cybersecurity and related risk posture. Does AIOps help measure this since it already has a variety of data points across the stack?
IBM:Our focal solution for all cybersecurity use-cases is IBM Cloud Pak for Security. That said, our AIOps portfolio can help clients better use and understand ITOps data, which in turn can also be used in cybersecurity use-cases. For example, clients using IBM Cloud Pak for Watson AIOps have found cybersecurity issues via the solution’s log anomaly detection capabilities.
Building on my earlier comment, the IBM solution also provides code vulnerability risk scores via our Change Risk capability, though the risk being identified is narrowly focused.
Greyhound Research:Another critical issue for most organisations today is the silos between their digital workflow systems like ServiceNow and IT Ops tools. They often either depend on manual efforts to resolve tickets or are in the early stages of integration. Would you please point to the 3rd party integrations currently available with Cloud Pak for Watson AIOps?
IBM:IBM Cloud Pak for Watson AIOps has more than 200 different integrations across a range of different 3rd party tools, data sources, and functions, as well as relevant IBM solutions. This breadth gives us the flexibility to help our customers and users progress in their journey to AIOps with the tools they are ready to move forward with – instead of imposing new data types or processes.
Taking ServiceNow as an example, integrations exist with their ITSM and ITOM solutions, covering a number of use-cases. Briefly calling back to the prior NLP question, NLP in IBM Cloud Pak for Watson AIOps can be applied on ServiceNow ITSM tickets to more intelligently recommend the next best actions or push data into ServiceNow tickets to act as a source of “ground-truth” for clients examining their prior incidents.
Greyhound Research:As it’s natural for any such engagement, there’s a certain level of preparedness that orgs need to have before they can fully make use of the capabilities of such tools. Can you please share how do you work with client orgs to help prepare them?
IBM:For most AIOps efforts, work is usually required to understand the relevant environment or application and the related tools and data. Given the importance of AI in our solution, data is of particular concern (fairly standard for any AI-driven use-case or engagement).
IBM supports our clients by offering our expertise and by making AIOps easier for both nuanced users and newcomers to work with.
For example, the IBM Cloud Pak for Watson AIOps includes AI model management capabilities to help clients manage their data and AI more easily. The solution also includes pre-trained models to help clients get to value quickly, and IBM provides data scientists support via our AIOps Elite team.
Greyhound Research:It will also help if you can please help clarify the type of data feeds that IBM Cloud Pak for Watson AIOps aggregates and the kind of use-cases they help deliver value. In addition, are there are any third-party tools you use for specialized areas such as container security or log ingestion? Would you please help elaborate?
IBM:IBM Cloud Pak for Watson AIOps consumes data from a wide range of data sources and types, including logs, tickets, alerts, events, metrics, CI/CD data (among others).
Per an earlier comment, with more than 200 different integrations, IBM has invested in leveraging the data and tools our clients already use. For example, we can recognize and group anomalies in log data with anomalies in events or monitor data to provide end-users with a complete view of an outage or failure. We can also bring these data together with topology or trace data to help users visualize outages and understand an IT incident’s potential secondary or tertiary impacts.
For specific capabilities outside of the scope of the platform, IBM often uses partner solutions.
For example, Humio (recently acquired by CrowdStrike) has been a great partner and delivers rich logging capabilities to many customers.
Greyhound Research:Lastly, since we are talking about multiple different tools here, please share the kind of customers each will cater to? Is it fair to assume that Instana and Turbonomic will be easier and quicker to be used even by smaller orgs? And the complete Cloud Pak for Watson AIOps is more fitting for larger orgs with a more complex and mature technology setup? To be specific, it’ll be a natural upgrade for those with tools like Netcool and Tivoli?
IBM:Each solution has different values and use-cases, so segmenting by customer size can be helpful but can miss some valuable context.
Pre-IBM acquisition, Instana and Turbonomic both saw material enterprise usage; IBM, therefore, believes that larger enterprise clients can see material value by leveraging these capabilities alongside the value they expect.Tweet
In turn, IBM Cloud Pak for Watson AIOps has seen adoption among larger or enterprise clients today.
However, as we continue to make the solution more consumable and flexible, we expect to grow awareness with a wider community of organizations. Together across the IBM Cloud Pak for Watson AIOps, Instana and Turbonomic capabilities, IBM can help customers fully realize the benefits of AIOps across incident management, observability, and performance optimization.
Greyhound Research: Thanks again for your time. This has truly been a wonderful conversation.
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