Glossary of terms commonly used in Incident Response, Observability, and AIOps

Andy Thurai

Vice President and Principal Analyst
Constellation Research

About Andy Thurai Andy Thurai is an accomplished IT executive, strategist, advisor, enterprise architect and evangelist with more than 25 years of experience in executive, technical, and architectural leadership positions at companies such as IBM, Intel, BMC, Nortel, and Oracle. Andy has written more than 100 articles on emerging technology topics for publications such as Forbes, The New Stack, AI World, VentureBeat, DevOps.com, GigaOm and Wired. Andy’s fields of interest and expertise include AIOps, ITOps, Observability, Artificial Intelligence, Machine Learning, Cloud, Edge, and other enterprise software. His strength is selling technology to the CxO audience with a value proposition rather than the usual technology sales pitch. Find more details and samples of Andy’s work on his…...

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The following are the common terms used in Incident Management, AIOps, and Observability practice areas:

Alert Fatigue

Alert fatigue is a phenomenon that occurs when the on-call personnel and incident responders receive an overwhelming number of alerts or notifications (either in volume or in frequency), causing them to ignore, dismiss, or become indifferent and insensitive to some of the highly sensitive and critical notifications/alerts as there are too many causing fatigue. This might result at times either in support personnel missing the right alert thereby not taking the right action or taking an inappropriate action that might make matters worse.

To mitigate this, many enterprises use AIOps or Incident Management solutions that help reduce or group the number of redundant, irrelevant, or non-critical alerts either by,

1. Prioritize the alerts so only highly critical alerts will make it to the on-call support personnel.
2. Group the alerts so any alerts, or notifications related to a specific event will be grouped in one single bunch for analysis.
3. Noise reduction, or dynamic filtering, by reducing the irrelevant alerts and focusing only on the critical alerts that need to be attended immediately.
4. False alarms.

These effective solutions can reduce burnouts of on-call personnel and SREs and help them more efficient with them resolving critical incidents faster.

ChatOps

ChatOps is a collaborative communication tool and process that is commonly used in incident management. The physical war rooms have evolved into virtual collaboration channels. Generally, as soon as an incident is identified and acknowledged, one of the first steps is step is to create a collaboration channel. This place centralizes all communications and assets about incidents. It also holds information about incident progress, status, plans, and resolution (if any), which allows anyone involved to get the status and necessary information in real-time. Anyone who needs to know the information or who can assist in solving the incident can be invited to the collaboration channel as needed. Oftentimes, on-call systems, alert/notification tools, and chatbots are often included in this category as well.

Incident

An incident is defined as unplanned downtime, or interruption, that either partially or fully disrupts a service by offering a lesser quality of service to the users. If the incident is major, then it is a crisis or major incident. When it starts to affect the quality of service delivered to customers, it becomes an issue, because most service providers have service-level agreements (SLAs) with their consumers that often have penalties built in. The longer the incident remains unsolved, the more it costs an organization.

They expect and prepare for major digital incidents and handle them well when they happen. They use a mix of open-source, commercial, and homegrown tools that blend well. Most of those organizations also successfully implement the following processes.

Incident Acknowledgement

Once an incident alert/notification is generated, it needs to be acknowledged by someone either from the support or SRE team or from the service owner. An acknowledgment of an incident is not a guarantee that the incident will be fixed soon. However, this is an early indication of how alert the incident teams are and how soon they can get to incidents. While a user has taken the responsibility for the incident, this doesn’t mean it has been escalated to the right user, yet. This acknowledgment mechanism is very common in most on-call alerting/notification tools. If there is no acknowledgment, the on-call tool will continue to escalate or try to find the right person until the incident is acknowledged.

Incident Commander

An Incident Commander (IC), or the Incident Manager, is a member of the IT team responsible for managing a coordinated critical incident response especially if the incident is considered an emergency or a crisis situation. An IC has ultimate control and final say on all incident decisions. He or she is also responsible for inviting the right personnel and escalating the incident to necessary teams as necessary and ultimately responsible for efficient and quick resolution of an incident.

Incident Lifecycle

The incident lifecycle is the duration of the incident from the occurrence to the time it is resolved. While the post-mortem analysis and fixing the underlying issue so the incident never repeats again is not part of the incident lifecycle, it is an important adjacent step that must be performed to avoid the recurrence of events.  If a specific incident occurs regularly and a possible solution is known, it should be automated as well which is not part of the incident lifecycle but will help reduce the lifecycle of future incidents.

MTTA (Mean Time To Acknowledge)

Mean time to acknowledge is the measure of how long it takes to acknowledge an incident. This time shows the efficiency and responsiveness of the responders and gives confidence to the customers that an enterprise is aware that the services are down and they are working on it. As soon as the first acknowledgment is done, the status update must be updated as well – such as status pages, email alerts, pager notifications, etc.

At a high level, MTTA is calculated by dividing the total time it has taken to acknowledge all incidents by the number of total incidents over the sampling period. 

MTTI (Mean Time To Innocence)

Mean Time to Innocence is a metric that is used to prove that someone is not guilty or associated with an incident. When an incident happens it has become a common practice to invite everyone that is deemed remotely associated with the incident to the incident collaboration channel. This results in a lot of wasted time and resources. Most times it becomes difficult to identify the root cause or solve the incident efficiently because there are “too many cooks in the kitchen,” each offering different advice, knowledge, and wisdom that is neither useful nor relevant.

Many organizations also are measuring mean time to innocence (MTTI) thereby letting the teams/personnel who are not directly responsible or cannot offer help in solving the incident leave the incident collaboration channel. This allows the innocent parties to continue to be productive in their regular job rather than waste time figuring out how to resolve the unplanned downtime that is unrelated to them, and about which they have no knowledge.

However, care should be taken while measuring this metric and having the team participate in this practice. Oftentimes, the teams involved will start to blame each other in order to prove their innocence. Either the guilty party becomes defensive or totally denies their responsibility. By trying to shift blame to others, the need for a collaborative mindset and keeping customers and solving unplanned outage focus can get lost while the blame game happens.

MTTR (Mean Time To Resolution)

Mean time to resolve is the average time it takes to resolve an incident. The resolution is defined as the combination of identification of the incident, identification of the root cause, and fixing the incident. In other words, the time it takes to bring the service back to the mode of its normal operation. Resolving the current incident doesn’t guarantee such events won’t happen in the future. 

An Update on IBM Cloud for the CIO

Dion Hinchcliffe

Vice President and Principal Analyst
Constellation Research

Title: About Dion Hinchcliffe Dion Hinchcliffe is an internationally recognized business strategist, bestselling author, enterprise architect, industry analyst, and noted keynote speaker. He is widely regarded as one of the most influential figures in digital strategy, the future of work, and enterprise IT. He works with the leadership teams of large enterprises as well as software vendors to raise the bar for the art-of-the-possible in their digital capabilities. He is currently Vice President and Principal Analyst at Constellation Research. Dion is also currently an executive fellow at the Tuck School of Business Center for Digital Strategies. He is a globally recognized industry expert on the topics of digital transformation, digital workplace, enterprise collaboration, API…...

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Recently I had the opportunity to take a deep-dive on IBM Cloud to get a sense of it's current capabilities. My takeaway is that the platform has matured well and has come a long way since its early days, when it was known as Bluemix, and was a scrappy contender among the fast-growing hyperscalers.

Somewhere along the way, IBM Cloud continued its journey but didn't quite get the same attention from developers and organizations as the dazzling new Internet cloud firms. That's unfortunate, because IBM Cloud has evolved considerably into an enterprise cloud contender of significance. I make the case below that it's appropriate for most CIOs and IT execs to conduct a revisit of the platform and its current capabilities.

Before we take a deeper dive into its differentiation and potential, no re-introduction and catch-up on IBM Cloud would be complete without noting that IBM brings modern cloud services to organizations with the fully expected set of IBM's most-renowned and best classic product sensibilities. These are deep industry-expertise, overall stability, an understanding of the unique needs of large enterprises, and a profound respect for security and compliance. In fact, IBM Cloud has some of the most extensive cloud compliance certifications in the industry.

Sizing Up IBM Cloud Today

Here are some of the more notable achievements of IBM Cloud over the last few years:

• IBM's Cloud began offering containers with its IBM Cloud Kubernetes Service (IKS) in 2017, starting the service on its vital modern cloud native journey
• In 2018, IBM announced an industry-first: A managed Kubernetes service on bare metal, for maximum performance, security, and control
• In 2019, right after the Red Hat acquisition, IBM launched a managed Red Hat OpenShift on IBM Cloud
• In 2021, IBM was the first to build an industry cloud, with IBM Cloud for Financial Services
• IBM has now pledged it will achieve net zero greenhouse gas emissions by 2030, helping CIOs meet their own net zero goals
• More recently, IBM released GA2 of IBM Cloud Satellite, its answer to the multi-cloud/cross-cloud imperative, to run IBM Cloud workloads on any public cloud

IBM Cloud has also grown quite a bit in the last decade, currently sporting a number of vital proof points of its evolving and maturing footprint:

• IBM Cloud has 17 major cloud service categories, with almost 200 individual services across AI/ML, analytics, blockchain, compute, databases, integration, IoT, networking, quantum computing, storage, security, and storage
• Customers of IBM Cloud can now run their workloads in over 46 data centers across 9 regions and 27 availability zones on 5 continents
• IBM Cloud's customer base now includes tens of thousands of businesses from startups to Fortune 500 firms, with extensive adoption notably in financial services, manufacturing, travel, hospitality, construction, healthcare, and education

Comparing IBM Cloud to the Hyperscalers

When comparing IBM Cloud with the big cloud hyperscalers like AWS, Azure, and Google Cloud, there are several important differentiations to consider. One of the most significant differentiators is IBM's focus on hybrid cloud solutions. IBM has a long history of providing enterprise-grade IT solutions, and their cloud offerings are no exception. IBM Cloud is designed to work seamlessly with on-premises infrastructure, providing a consistent experience across the entire IT environment. This is particularly important for businesses that have invested heavily in on-premises hardware and software, up to and including mainframes, and are looking to leverage the cloud for additional capacity or functionality. IBM's hybrid cloud approach enables businesses to move workloads between their on-premises infrastructure and the cloud without having to completely overhaul their IT infrastructure, especially if they are significant IBM customers already.

Another differentiation of IBM Cloud is their focus on data security and compliance. IBM has a wealth of experience in providing enterprise-grade security solutions, and this expertise is evident in their cloud offering. IBM Cloud provides robust security features, including identity and access management, network security, and data encryption. Additionally, IBM Cloud complies with a wide range of industry-specific regulations and standards, such as HIPAA, GDPR, and PCI DSS, making it an ideal choice for businesses operating in heavily regulated industries as well as the public sector. It also has many national and regional certifications as well.

IBM Cloud also offers a range of industry-specific solutions, including Watson Discovery and IBM Cloud Financial Services. These solutions leverage IBM's expertise in financial domains and regulated industry to provide businesses with powerful tools for analyzing data, making informed decisions, and running the business in the cloud. IBM Cloud also has solutions for retail, government, health, academia, and gaming.

Finally, IBM Cloud's pricing model is another differentiation from the cloud hyperscalers. While the hyperscalers typically offer a pay-as-you-go pricing model, IBM Cloud offers more flexible pricing options, including reserved instances and dedicated hosts. This can be particularly advantageous for businesses that have predictable workloads or that require dedicated infrastructure. This options can be particularly appealing to CIOs as the operational costs of cloud have been growing considerably in recent years.

Enterprise Developer Attraction

Much is made of the developer interest in the hyperscalers, but IBM Cloud is unique in that IBM has one of the largest and most engaged developer communities in enterprise IT in my long experience. There are a great many business developers on the "IBM track" around the world, and they remain interested in developing new skills to stay caught up the evolving IBM Cloud story.

ISVs and VARs also have experienced developers who want access to IBM's global customer base and can use their experience in both legacy IBM technology and the latest IBM Cloud developments to create compelling new solutions in the market. I still attend enthusiastic developer conferences for IBM legacy tech, like DB2, which remains very popular in many quarters around the world. For organizations that have these developers, IBM Cloud can propel them to do adopt, build skills, and innovate. Then there are developers that actually prefer alternatives other than the main ones, especially when they might have capabilities or engineering qualities not found in the other clouds. 

IBM Cloud and the CIO Perspective

These days I run into CIOs fairly often that have signed large all-in cloud contracts with one of the Big Three, who then soon find they are completely beholden to a cloud giant, with all their eggs in one basket, with little flexibility in pricing or control over putting workloads where they might most make overall sense. IBM Cloud can serve as a strong and capable fourth alternative that can act both as a hedge to the hyperscalers and as a strong core cloud partner with the many unique strengths and characteristics explored above.

In short, IBM Cloud isn't just for IBM customers, but orgs that need serious enterprise-class cloud with most flexibility, deep understanding of the needs of large and sophisticated organizations, modern cloud-native features, and the global footprint they need for education, support, and compliance. In short, I find that IBM Cloud is the most significant enterprise cloud that many CIOs still don't put on their shortlist, when they probably should keep their options open to more qualified cloud alternatives. In my analysis, IBM Cloud is a capable option for IT departments as a cloud provider as well as for maximizing their cloud options, choices, and needed capability/vendor mix.

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What to Expect from Generative AI in Analytics and Business Intelligence

Doug Henschen

Former Vice President and Principal Analyst
Constellation Research

Doug Henschen is former Vice President and Principal Analyst where he focused on data-driven decision making. Henschen’s Data-to-Decisions research examines how organizations employ data analysis to reimagine their business models and gain a deeper understanding of their customers. Henschen's research acknowledges the fact that innovative applications of data analysis requires a multi-disciplinary approach starting with information and orchestration technologies, continuing through business intelligence, data-visualization, and analytics, and moving into NoSQL and big-data analysis, third-party data enrichment, and decision-management technologies. Insight-driven business models are of interest to the entire C-suite, but most particularly chief executive officers, chief digital officers,…...

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I'm wrapping up research for a fresh Market Overivew report on analtyics and business intelligence (BI) products. As in other arenas, the timliest, most potentially game-changing trend in this space is the introduction of Generative AI capabilities. We've already seen announcements from Microsoft, Salesforce/Tableau and ThoughtSpot, and we'll undoubtedly see more. Here's a quick rundown on what to expect.

First off, it's noteworthy that everything is in private preview or limited public preview at this time, which should tell you something. Even a Natural Language to DAX code feature that Microsoft announced for Power BI way back in 2021 is still not technically generally available. On March 16 the company announced Microsoft 365 Copilot, which brings brings new generative AI capabilites to Microsoft productivity apps, Power Apps and Power Automate (though no new features specific to Power BI). I'm expecting to see a bevy of Power BI-related news during the Microsoft Build event in May. 

On March 7, Salesforce introduced Einstein GPT in preview. Einstein GPT will be open to using multiple large language models, including OpenAI’s GPT-3, as well as Anthropic, Cohere and perhaps others. Salesforce has demoed use cases for sales, service and marketing and has discussed possible analytical use cases, including better NL query and explanations and better data story telling and sentiment analysis. Release dates are not available and I wouldn't anticipate general availability until the second half of 2023.

ThoughSpot dove into the generative AI world with its March 7 announcement of ThougthSpot Sage. Sage combines ThoughtSpot’s search experience with GPT-3 and, in future, possibly other large language models. ThoughtSpot says the integration offers advantages over generative AI alone because GPT-3 on its own has limitations, such as lacking the context of business context, not handling complex, multi-dimensional schema well, getting confused when there are lots of columns, not handling temporal functions well and, without specific training, generating generic SQL rather than platform-specific SQL. Sage, which is in preview but expected in 1H 2023, is said to overcome these limitations. ThoughtSpot says Sage will improve the company’s NL query and NL explanations and will be able to generate new search data models based on NL input.    

Among other vendors, I've seen multiple SiSense blogs on generative AI, but no formal product announcement. I've had conversations with several other vendors who say they're working on the technology, but aren't ready to reveal their plans.

To sum things up in my report, I created the graphic above to explain the types of capabilities to expect. In my view, generative AI has the potential to transform many aspects of analytics/BI products and related administrative and analysis tasks. Again, all the generative AI features that have been announced in the analytics and BI space remain in preview, so caution is advised. Vendors are universally insisting that data-privacy concerns have been addressed and that humans remain in the loop to review AI-generated text or code before it is used. My concern would be that humans will review suggested content for a day, a week or maybe even a month, but will then just fall into the habit of hitting send/share on a chart or natural language explanation. If it's generated code, at least it will (presumably) face the usual QA of rigorous dev and test processes.

Another question that has yet to be answered is just how expensive generative capabilities will be. Large language models are notoriously resource intensive, particularly in the training phase. Customers will want to know which LLMs vendors are using, where those models are running, how their own data is used and, perhaps most importantly, how much it will cost if they turn on generative AI features and see a flood of adoption? Perhaps the cost can be justified if they are seeing a dramatic increase in productivity and breakthrough outcomes?

I'm updating my Cloud-Based Analytics and BI ShortList and I'm publishing out first ever Embedded Analytics ShortList in conjunction with this upcoming report. I'm holding off on an update of our Augmented BI and Analytics ShortList, last published in August 2022, precicely because there are so many generative AI capabilities now in preview that will need a thorough vetting. Hopefully at least a few of them will be generally available in time for our Q3 2023 update. 

News Analysis: Adobe Firefly - A Generative AI Offering For Creators

R "Ray" Wang

Principal Analyst and Founder
Constellation Research

R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...

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Adobe Delivers Generative AI With Adobe Firefly

At Adobe Summit on March 21, 2023, Adobe announced and delivered its generative AI offering known as Adobe Firefly. Adobe Firefly (In beta), is a collection of generative AI models built for creative applications.  The offering joins the platform’s AI services. The first Firefly model will focus on image generation and text effects, trained on the hundreds of millions of Adobe stock images, openly licensed content, and public domain content where copyright has expired. Adobe plans to integrate Firefly into their Creative Cloud, Document Cloud, and Adobe Express product lines.

While Firefly is intended to be made up of multiple models, the first model is trained on Adobe Stock images openly licensed content and public domain content, where copyright has expired, and is designed to generate images safe for commercial use. Future models will target additional use cases and content types, potentially leveraging other technology and training data.

Adobe feels, this approach to training differentiates Firefly models from other stable diffusion models where rights, copyrights, and ethical boundaries of an artist’s individual style have come into question. Adobe has also announced new Sensei GenAI services that will continue to expand across Adobe Experience Cloud, deploying multiple LLMs including the Microsoft Azure OpenAI and FLAN-T5 models.

A CRTV Interview With Adobe's Ely Greenfield on Adobe Firefly

Adobe's Takes A Comprehensive Approach To Empowering Creators

What sets Adobe Firefly apart from other offerings is the integration into content workflows such as image creation and text effects.  The goal - deliver generative AI capabilities wherever content is created and modified.  Moreover, the focus on safe for commercial use ensures that Firefly won't generate content based on other individual's or brand's IP.  This eliminates potential legal issues down the road.

In addition, Adobe's Content Authenticity Initiative (CAI)'s provenance technology creates transparency for digital content via Content Credentials. Creators can use Content Credentials to attach important information to a piece of content using meta data to include their name, date, and what tools were used to create it. That information travels with the content wherever it goes so that by the time people see it, they can experience content and context together.  They can also add tags to ensure "Do Not Train" as well.

On the monetization front, Adobe intends to take a compensation-forward approach with Adobe Stock contributors. Creators who contribute content for training will benefit from the revenue Firefly generates once Firefly is out of beta. Creators may be able to license and monetize their own style and design in the future.

Advancements In Generative AI Have Rapidly Improved

While generative AI has been around for some time, ChatGPT  has captured the hearts and minds of the general population in highlighting tangible possibilities of what AI can accomplish both in the consumer and enterprise world.  In fact, Generative AI has the ability to create chat responses, designs, and other new content including deep fakes and synthetic data.  Neural network techniques such as generative adversarial networks (GANs), variational auto encoders (VAEs), and transformers work together to create original content based on prompts.

On the languages side, GPTs or what’s known as a generative pre-trained transformer, generate conversational text using deep learning.   The pre-training capability allows the AI to take the model from one machine learning task to train another model.  These models are then pre-trained on large corpus of text.  Transformers, a type of neural network, maps the relationships among all the data sources such as text and sentence patterns.

For images, diffusion models allow images to be created from text prompts. Using random noise applied to a set of training images, the diffusion models allow one to remove noise and create a desired image.  Common approaches include DALL-E also from OpenAI, Dreambooth by Google, , Imagen, Lensa, Midjourney, and Stable Diffusion.

The more organizations interact with these AI systems, the quicker the AI systems will improve their rate of learning.

The Bottom Line: Expect Exponential Improvements In AI Driven Creativity And Productivity

Generative AI will improve content velocity and improve the ability to power content supply chains with improved collaboration in the delivery of precise, high quality content.  Creators can easily expand their capabilities from one medium to another while making variations to their work.  Moreover, the establishment of creator marketplaces for monetization will augment creativity not automate it.

The Lost Art of Being a Supervisor

Liz Miller

Vice President & Principal Analyst
Constellation Research

About Liz Miller: Liz Miller is Vice President and Principal Analyst at Constellation, focused on the org-wide team sport known as customer experience. While covering CX as an enterprise strategy, Miller spends time zeroing in on the functional demands of Marketing and Service and the evolving role of the Chief Marketing Officer, the rise of the Chief Experience Officer, the evolution of customer engagement, and the rising requirement for a new security posture that accounts for the threat to brand trust in this age of AI. With over 30 years of marketing experience, Miller offers strategic guidance on the leadership, business transformation, and technology requirements to deliver on today’s CX strategies. She has worked with global marketing organizations to transform everything from…...

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Being a supervisor—a manager, a team leader, whatever title that comes with the territory—isn’t the same as it was just a few short years ago. This is especially true in contact, service, and communications centers.

Supervisors Have It Rough

After years of enterprises planning for digital transformation and technology innovations that could turn the contact center into a more connected, omnichannel, omnipresent engagement hub, the COVID-19 pandemic accelerated that pathway to change. Carefully laid-out five-year plans were tossed out the window. Suddenly the teeming floors of contact centers were empty, with workers sent home armed with new laptops and headsets. Everyone, from C-suite leaders to customers calling in, had to trust in technology (and each other), assuming the way we all worked “before” would translate into how work needed to happen in the most insane times.

But something interesting happened in this acceleration of digital transformation: We forgot about the art of being a supervisor. While tools and systems were put into place to allow agents to be highly productive and effective, and executive dashboards and analytics systems were implemented to give senior leadership visibility across the board, tools and technologies for supervisors made a strange shift from tools built to support to tools built to surveil.

We forgot that supervisors don’t just appear—they become—and, more importantly, they grow on the job. They become great supervisors because they understand the role, responsibilities, and requirements of being an agent while simultaneously increasing their own understanding of the business and the impact they make in their leadership role. They are constantly learning and adjusting on the job, coaching individuals and facilitating change across teams while still retaining that skill and empathy that made them terrific agents in the first place. What supervisors had not been expected to be, until now, were technologists and digital transformation experts.

While agents were pushed to transition their workspaces and styles in the shift to working from home, supervisors had to shift right along with them, relearning the act of being a successful agent-from-anywhere while also relearning how to inspire, motivate, and lead under new and often challenging circumstances. Supervisors added technical support and change management to their already-filled leadership cards as entire business models pivoted to a new normal. And then, once everyone was settled into a groove of working from home and engagement-everywhere, the world shifted again with the emergence of new hybrid work models that would once again ask supervisors to stay multiple steps ahead of the pack.

In the name of digital transformation, new tools were introduced, bringing the power of data, analytics, and workflows to the fingertips of every agent, lead, supervisor, and C-suite leader. Agents could now engage with customers in a far more effective, efficient, and productive way as years of separated and segmented screens and tools began to consolidate into a single, easy-to-consume and even customized layer.

Similarly, tools for the C-suite were introduced to give top-level visibility across business strategy key performance indicators (KPIs), showcasing how workforce and workflow automations could impact growth. Toolkits powered by artificial intelligence (AI) invited leaders to ask new questions and interrogate results in new ways, flexibly giving new insights and context that could accelerate decision velocity.

Supervisors got more tools, more windows, and more screens.

Trained to Toggle, Not Transform

Today’s tools are unintentionally reactive, alerting supervisors to issues or the emergence of negative outcomes. While agents are empowered to proactively engage with customers to solidify relationships and deliver optimized experiences, supervisors are left to play whack-a-mole, juggling everything from agent performance data to workforce management, scheduling, and real-time conversation analytics—and all of this is before tackling coaching and flexing the very skills that promoted these leaders from agent to manager or supervisor.

Today’s modern, cloud-native, data-rich communications platforms offer an opportunity to break this cycle for supervisors and reinvigorate the art of supervising the front line of customer experience delivery. Thanks to the composability that cloud architectures provide, supervisors can be met with more flexible and contextual workspace canvases that are personalized to their priorities. In the same way that cloud solutions have delivered exceptional customizations to take the chore out of work for the contact center agent, this same user-centricity is required for leaders.

So how do we start to address the gap that exists between the demands of the modern contact center supervisor and the tools and resources available to these growth leaders? What should a supervisor’s experience look or feel like?

The Top 5 Requirements of a Supervisor’s Workspace Experience

Intuitive: Supervisors shouldn’t have to moonlight as dashboard architects. They also shouldn’t need a Ph.D. in user experience (UX) design to create digital workspaces that centralize the insights, information, and visibility they need to drive performance and move their teams forward.

Extensible: The capacity to extend visibility or enhance capabilities should be at the control of the individual entrusted with the leadership of a team or a function. With libraries of tools, widgets, or templates, supervisors should have the opportunity to enrich and extend as quickly as they centralize and control.

Composable: Composability isn’t just about the capacity to move modules or widgets around a page: It is as much about the microservices architecture underpinning the system that allows for data and intelligence to freely flow to exactly where a user wants and needs it. For the contact center supervisor, that translates into the freedom to view data based on current priorities—priorities that can shift month to month. It also translates into an almost infinite extensibility of where data and intelligence can come from, be it from within the organization or from third-party sources that can all work in concert to accelerate decision velocity.

Intelligent: AI and machine learning (ML) models have been regularly applied to bubble up recommendations and next-best actions, but in supervisor modules they are all too often deployed to react to negative sentiments or scenarios. It is time for AI to take on a far more critical task: proactive experiences, for the agent and the customer. Much like assisted or guided interactions for agents, supervisors should have access to smart coaching and performance recommendations that drive faster decisions and accelerate prioritization. Workspaces should be contextually aware, able to flex and automatically adjust based on priorities and patterns.

Personal: Every team member, manager, and supervisor is unique, meaning their tools and workspaces should be personalized to how they work and how they lead. Thanks to data and libraries being decoupled from presentation layers and experiences, what is right for one manager doesn’t have to be right for the next, allowing for independent controls and modular interfaces that pull data and intelligence directly from a wide array of sources, including third-party sources.

Too Much at Stake

This may seem like a tall order—fully composable, contextual, and intensely personalized workspaces purpose-built for the modern supervisor. But when you consider just how much rides on the success of our supervisors and leaders, raising the expectation bar for the tools and systems we put in place for our leaders shouldn’t feel unreasonable. It should be an expectation. After all, there is an art to being (and staying) a successful supervisor. The real question should be, are we willing to risk losing something that directly impacts and shapes customer outcomes?

Analysis: Huawei's Journey of Digital Transformation and Sustainability

Dion Hinchcliffe

Vice President and Principal Analyst
Constellation Research

Title: About Dion Hinchcliffe Dion Hinchcliffe is an internationally recognized business strategist, bestselling author, enterprise architect, industry analyst, and noted keynote speaker. He is widely regarded as one of the most influential figures in digital strategy, the future of work, and enterprise IT. He works with the leadership teams of large enterprises as well as software vendors to raise the bar for the art-of-the-possible in their digital capabilities. He is currently Vice President and Principal Analyst at Constellation Research. Dion is also currently an executive fellow at the Tuck School of Business Center for Digital Strategies. He is a globally recognized industry expert on the topics of digital transformation, digital workplace, enterprise collaboration, API…...

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The Chinese technology giant, Huawei, has had quite an odyssey the last few years, finding itself in geopolitical struggles that have significantly challenged several of its businesses, most notably its smartphones and carrier equipment divisions. However, as I've watched the company's journey over the last decade, it's clear that it has also been making long-term bets in adjacent businesses and industries. 

I remember seeing Huawei demonstrating remarkably sophisticated and well-thought out blueprints, often with matching solutions, for smart cities, connected factories, autonomous transportation, and other large scale digital transformation back in 2016, when it estimated that $15 trillion in unrealized economic gains were lying in wait for those with the vision to act on on the opportunities. Collectively, Huawei considers the pervasive digitlization of everything as its global industry vision (GIV), which it calls Intelligent World.

It is increasingly in these domains that the company seeks to move "up the stack" from low-level 5G infrastructure and communications devices, into the much higher potential and more value-add transformation of businesses, institutions, cities, and industries with its growing array of technology solutions.

In my talks with many Huawei executives in Barcelona last week, it's clear the company is quite serious about achieving these goals over time, and to do so while also achieving goals in sustainability using green ICT. In order to seize them as fully as possible, the company is also making massive investments into these areas. Thus Huawei now ranks among the very highest spenders on research and development in the world, spending $22.4 billion over the last year and a whopping $132.5 billion over the last decade, as it seeks to enter major new markets and industries. The relative success of these investments is a fascinating question and so Huawei brought a very special customer to us (see below) to demonstrate major traction while also citing that nearly 200 Fortune 500 companies have previously chosen Huawei to help them with their digital transformation.

A Snapshot of Huawei at Mobile World Congress 2023

In was with this backdrop that I was invited to see Huawei's latest progress in these endeavors in Barcelona earlier this month. What followed was a fascinating story told in three vignettes, during mini-events that Huawei held adjacent to the massive Mobile World Congress 2023 (MWC) confab at the huge trade fair and exhibition center, Fira de Barcelona.

Huawei also had the largest exhibit area of the MWC show, taking over half of an entire hall at the fairgrounds, and using the space to tell the stories of the many digital innovations and blueprints that it had, including an entire section devoted to Industrial Digital Transformation, which focused on the digital re-imagining of sectors including energy, manufacturing, education, and healthcare.

Huawei Day 0 Summit: Green ICT Development

However, before MWC even began, Huawei hosted its 12th annual Huawei Day 0 Summit. which it holds the day befoe the larger mobile event begins. The theme for this year's Day 0 event was Green ICT Development, basically a tightly orchestrated set of talks from various telecom leaders on sustainability as well as some environmental, social, and governance (ESG) issues. (For American readers, ICT stands for Information and Communications Technology, and similar in focus to information technology, or IT.) The mobile industry has a tremendous energy and environmental footprint, and this day was dedicated to seeing what the progress was.

Li Peng, VP and President of the Huawei Carrier Group kicked off the Day 0 Summit, noting that the company was focusing on "creating indicators to measure and improve energy efficiencies" in the mobile industry. Then Massamba Thioye, Project Executive of United Nations Framework Convention on Climate Change’s Global Innovation Hub mades an address. 

He noted that getting green ICT solutions to market means making knowledge about them far more discoverable and available. Interestingly, he also said they are creating a “deep search engine” to connect  climate change disruption tech to ICT buyers. Then, he painted a clear version for what the UNFCCC was attempting to achieve in the mobile area (and everywhere else): "A world that is generative rather than extractive. A world where a flourishing life is available to all people. Radical collaboration is required for a digital green transition." Finally, Thioye wrapped up with a key point: “We have to decarbonize the value chain of the ICT sector.”

Next, Steven Moore, the head of climate action for GSMA, the mobile carrier industry's main association, then presented an update on what the industry’s Climate Action Task Force has been doing to achieve green ICT and sustainable development. He points out that the source of energy is just one part of the green ICT development equation. "Efficiency a critical factor as well. The core goal is 0.24 kWh per GB of data to 0.17 KWh." This will be achieved by replacing high consumption legacy hardware with more efficient hardware as quickly as is feasible. He also cites Verizon has doing a particularly notable job of using green energy. In addition to nearly a quarter of their energy consumption coming from renewable sources today, they have added over 2 gigawatts of renewable energy capacity recently. It's an encouraging story that is early proof that decarbonation of the mobile industry is actually happening.

The takeaway was cautiously optimistic, but every speaker stressed that dramatic increases action are urgently needed. The needle is moving, but not fast enough, "radical collaboration is now needed between organizations and nations. But steadily improving data from across the industry makes the progress measureable and specific needs for action clearer.

Digital Transformation of the Mining Industry

Another notable session that Huawei arranged adjacent to MWC, was about the digital transformation of mining in Botswana. This is the story of Debswana, the world’s leading producer of diamonds, so a particularly notable proof point of Huawei penetrating into the mining industry. The project first started operation in December 2021. The mining firm now uses connected 5G networks, devices, and monitoring/analytics from Huawei, which has been key to the digitization of their business. Huwai says the Jwaneng mine is the world's first 5G-oriented smart diamond mine. 

“We have a specific vision, where we want to move from middle income country to a high income country", said  Thulagano M. Segokgo, Minister of Communications, Knowledge and Technology of Botswana, "This digitisation agenda is very, very critical to to us be achieving that we need to grow our economy."

“We have collision avoidance systems. We have fatigue management systems. They allow us to plan a very safe mine", noted Debswana's Head of Information Management, Molemisi Nelson Sechaba. He said they wish to get to zero injuries using the connected technologies that they now have in place from Huawei.

After the panel from Debswana and Huawei presented their story of connected 5G smart mining, I asked Debswana's Sechaba why they chose Huawei for digital transformation of their business, over more well-known companies within the mining industry.. He was immediately forthright, and said  they chose them because of 1) how well Huawei listens to them, 2) the quality of their products , 3) follow-through, 4) approachability, and 5) ability to work through issues.

Photo: I ask the Debswana ICT Executive About Selecting Huawei for the Digital Transformation of Mining at MWC 2023. Credit: Arnold Aranez

Since Huawei is not well-known for its deep experience with the #mining industry, the company's Jun Xu, president of Huawei Cloud, explain. As part of its major foray into digital transformation, company wanted to get its connected technologies (5G, Big Data, and AI) used in Industry 4.0 applications.

Finally, there was the important debate of what to do as people are steadily replaced with machines. The company and the country of Botswana have both clearly given this careful thought, noting they are seeking to make impact minimal. They must build ICT skills in local works, since high tech creates "major new employment opportunities."

It's no accident that Huawei chose to profile this particular case study, given it's prominance on the African continent and in the mining industry, both. Huawei seeks to grow in new markets and new industries, and the success of this effort is a key proof point to show they can enter new spaces with apparent ease and create happy customers. In my analysis, this is going to be key for Huawei to rapidly gain credibility as it evolves to expand its business into major new markets.

Roundtable on 5G Business Success

Yet it is 5G and what comes next after it -- namely 5.5G and 6G -- that remains one of Huawei's core businesses, which is increasingly powering a technology world that is going ever more wireless. To that end, the company facilitated a private media roundtable with analysis and influencers. The participants were Paul Scanlan, President Advisor of Huawei's Carrier Business Group, and Arun Sundarajajan of the Stern Business School at New York University.

The subject was the major shifts in the 5G industry that were triggered by the pandemic and increasing usage of bandwidth by the big hyperscalers. Scanlan, who is very well known in 5G circles, observed that 5.5G is an attempt to better "aggregate all the spectrum" to accomdate the growth of uplink, specifically adapting to "the big shift from central Big Tech media to user content [the explosion of Teams/Zoom calls] and security cams, and richer support for an ecosystems of devices." But it was pandemic specifically that "ushered in vast amounts of indoor uplink demand that the early 5G networks just weren’t built for." That's about to be addressed in 5.5G and will lead to more succcess for the costly rollouts that have taken place around the world.

Sundarajajan then noted that the maturing of 5G services has created opportunities to attract many new customers who about to get connected, “we can now reach 100s of millions of people who may not have been an attractive enough market to create that kind of content for in the first place.”

There was the obligatory discussion about ChatGPT and the network effects that drive, which was part of whole challenge of scale convo at MWC: “The layer people that are missing is that you're not just taking all public data, and dumping it into a large language model", said Sundarajajan, "there’s also a layer on top. Reinforcement training is now creating proprietary new data at scale."

Scanlan finished with key points about how 5G must now be used more strategically to transform businesses today, and not just focus on the" the savings of millions of dollars by not cabling everything with proprietary closed networks." He made the point that "it's also the added value of changing your business. And that's where the real difficulty happens. [The customers] have the technology, but [they] don't yet have the mindset. So for me, this is really a story about mindset. It's about training, it's about paradigms, it's about business models, it's about upskilling it's about seeing things differently, so we don't pave the cow path". 

Huawei's Future Journey and Evolution

It was clear from everything on display at their vast pavision in Hall 1 at MWC to the carefully choreographed events on sustainability, digital transformation, and making 5G foundational to industry transformation that the Huawei is thinking very big and seeking to become a strategic partner to enterprises around the world as they remake their industries for today's modern digital capabilities. While they will continue to focus on 5G, they will also be delivering more and more transformational industry solutions in everything from smart cities and intelligent enterprises to transportation, mining, education, and healthcare.

I spoke privately at a reception after these events to Paul Scanlan and asked him if he thought they have pivoted towards this new vision in light of their challenges, and he said definitely not. This has been the plan all along. If I review the 20 year planning models that I know that Huawei uses and the evidence I saw back in 2016, I would have to concur that it's very likely this is part of Huawei overall plan for evolution and growth. Telecom, smart devices, and low level carrier services were never going to fulfill their ultimate ambitions. Furthermore, their enormous R&D investments over the last decade began well before recent bumps in the road, and is yet another proof point. It will be fascinating to watch Huawei work within its geopolitcal constraints as it continues to enter these new markets and industries. One overarching point is clear to me, however: The company is absolutely committed to achieving its ambitious long term goals for becoming a global transformative technology leader.

Related Reading

My full Twitter coverage (photos/videos) of Mobile World Congress 2023 and adjacent Huawei events

A Portrait of Huawei: What Digital Leaders Should Know

Digital Transformation Target Platforms ShortList

An Oracle Netsuite Roadmap for the CIO and CFO

AWS re:Invent 2022: Perspectives for the CIO

CGTN's Mike Walter and Dion Hinchcliffe on Huawei Revenue

New Release: Q1 2023 Constellation ShortList™ Portfolio Updates - Week Two

Hannah Mason

Marketing Director
Constellation Research

Hannah Mason is a marketing director at Constellation Research. She oversees brand positioning, creative direction, and content development across digital platforms, events, and campaigns. Her work spans branding and design—including graphics, sales collateral, and event presentations—along with website content management, newsletters, and Constellation Insights. Mason also manages surveys and produces Constellation’s video collateral, including ConstellationTV. Her role ensures that the Constellation brand is communicated with clarity, creativity, and consistency across all channels. Contact Hannah at [email protected]. ...

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Elle DeRosa

Director of Marketing, Communications & Operations
Constellation Research

Elle is the Director of Marketing at Constellation Research and producer of the weekly enterprise tech show, DisrupTV. She leads marketing and communications efforts across all avenues while also managing an array of programs including the SuperNova Awards, Business Transformation 150, AX100, AI150, S50 and ShortLists. She has previous experience in public relations and has a strong passion for the marketing world. Contact Elle at [email protected] ...

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Today, we launched the final set of updates to our Constellation ShortList™ portfolio, including 25 new and updated lists.

Each technology vendor on this list has been chosen based on their products and services offering. Our analysts consider technology investment, use cases, strategic vision, customer value, executive leadership and price when anointing a vendor to the ShortList™.

Check out the 25 new and updated lists:

• Blockchain Technology Platforms
• Cloud-Based Planning and Performance Management Platforms
• Contact Center as a Service (CCaaS)
• Customer Loyalty and Retention
• Digital Adoption Platforms
• Digital Asset Management (DAM) for DX
• Digital Canvas Workboards
• Enterprise Cloud Finance
• Enterprise Low-Code Tools and Platforms
• Field Service Management
• Global IaaS for Next-Gen Applications
• Healthcare ERP
• Hybrid-Cloud and Multicloud Analytical Data Platforms
• Learning Marketplaces
• Master Data Management
• Microsoft End-to-End Service Providers
• P2C Management
• PaaS Suites
• Product-Centric Cloud ERP
• Quantum Computing Platforms
• Quantum Computing Software Platforms
• Sales Engagement Platforms
• Sales Force Automation
• Talent Management Suites
• Unified Communications as a Service (UCaaS)

This program is part of our open research library. You can download and view each list and the criteria for free. If you missed last week’s updates, be sure to check them out here. To engage us in a rapid vendor selection process, please contact [email protected]

We will update the rest of the portfolio in Q3 2023. Some lists may get updated twice a year depending on market changes and based on each analyst’s discretion for each area. If you see a list that wasn’t updated this quarter, it will be updated later this year.

For more information, visit https://www.constellationr.com/shortlist