I recently met with Dr. Nick Fuller, Vice President, Distributed Cloud, at IBM Research for a discussion about IBM’s long-range plans and strategy for artificial intelligence and machine learning at the edge.

Dr. Fuller is responsible for providing AI and platform–based innovation for enterprise digital transformation spanning edge computing and distributed cloud management. He is an IBM Master Inventor with over 75 patents and co-author of 75 technical publications. Dr. Fuller obtained his Bachelor of Science in Physics and Math from Morehouse College and his PhD in Applied Physics from Columbia University.

Edge In, not Cloud Out

In general, Dr. Fuller told me that IBM is focused on developing an "edge in" position versus a "cloud out" position with data, AI, and Kubernetes-based platform technologies to scale hub and spoke deployments of edge applications.

A hub plays the role of a central control plane used for orchestrating the deployment and management of edge applications in a number of connected spoke locations such as a factory floor or a retail branch, where data is generated or locally aggregated for processing.

“Cloud out” refers to the paradigm where cloud service providers are extending their cloud architecture out to edge locations. In contrast, “edge in” refers to a provider-agnostic architecture that is cloud-independent and treats the data-plane as a first-class citizen.

IBM's overall architectural principle is scalability, repeatability, and full stack solution management that allows everything to be managed using a single unified control plane.

IBM’s Red Hat platform and infrastructure strategy anchors the application stack with a unified, scalable, and managed OpenShift-based control plane equipped with a high-performance storage appliance and self-healing system capabilities (inclusive of semi-autonomous operations).

IBM’s strategy also includes several in-progress platform-level technologies for scalable data, AI/ML runtimes, accelerator libraries for Day-2 AI operations, and scalability for the enterprise.

It is an important to mention that IBM is designing its edge platforms with labor cost and technical workforce in mind. Data scientists with PhDs are in high demand, making them difficult to find and expensive to hire once you find them. IBM is designing its edge system capabilities and processes so that domain experts rather than PhDs can deploy new AI models and manage Day-2 operations.

Why edge is important

Advances in computing and storage have made it possible for AI to process mountains of accumulated data to provide solutions. By bringing AI closer to the source of data, edge computing is faster and more efficient than cloud. While Cloud data accounts for 60% of the world’s data today, vast amounts of new data is being created at the edge, including industrial applications, traffic cameras, and order management systems, all of which can be processed at the edge in a fast and timely manner.

Public cloud and edge computing differ in capacity, technology, and management. An advantage of edge is that data is processed and analyzed at / near its collection point at the edge. In the case of cloud, data must be transferred from a local device and into the cloud for analytics and then transferred back to the edge again. Moving data through the network consumes capacity and adds latency to the process. It’s easy to see why executing a transaction at the edge reduces latency and eliminates unnecessary load on the network.

Increased privacy is another benefit of processing data at the edge. Analyzing data where it originates limits the risk of a security breach. Most of the communications between the edge and the cloud is then confined to such things as reporting, data summaries, and AI models, without ever exposing the raw data.

IBM at the Edge

In our discussion, Dr. Fuller provided a few examples to illustrate how IBM plans to provide new and seamless edge solutions for existing enterprise problems.

Example #1 – McDonald’s drive-thru

Dr. Fuller’s first example centered around Quick Service Restaurant’s (QSR) problem of drive-thru order taking. Last year, IBM acquired an automated order-taking system from McDonald's. As part of the acquisition, IBM and McDonald's established a partnership to perfect voice ordering methods using AI. Drive-thru orders are a significant percentage of total QSR orders for McDonald's and other QSR chains.

McDonald's and other QSR restaurants would like every order to be processed as quickly and accurately as possible. For that reason, McDonald's conducted trials at ten Chicago restaurants using an edge-based AI ordering system with NLP (Natural Language Processing) to convert spoken orders into a digital format. It was found that AI had the potential to reduce ordering errors and processing time significantly. Since McDonald's sells almost 7 million hamburgers daily, shaving a minute or two off each order represents a significant opportunity to address labor shortages and increase customer satisfaction.

Example #2 – Boston Dynamics and Spot the agile mobile robot

According to an earlier IBM survey, many manufacturers have already implemented AI-driven robotics with autonomous decision-making capability. The study also indicated that over 80 percent of companies believe AI can help Boost future business operations. However, some companies expressed concern about the limited mobility of edge devices and sensors.

To develop a mobile edge solution, IBM teamed up with Boston Dynamics. The partnership created an agile mobile robot using IBM Research and IBM Sustainability Software AI technology. The device can analyze visual sensor readings in hazardous and challenging industrial environments such as manufacturing plants, warehouses, electrical grids, waste treatment plants and other hazardous environments. The value proposition that Boston Dynamics brought to the partnership was Spot the agile mobile robot, a walking, sensing, and actuation platform. Like all edge applications, the robot’s wireless mobility uses self-contained AI/ML that doesn’t require access to cloud data. It uses cameras to read analog devices, visually monitor fire extinguishers, and conduct a visual inspection of human workers to determine if required safety equipment is being worn.

IBM was able to show up to a 10X speedup by automating some manual tasks, such as converting the detection of a problem into an immediate work order in IBM Maximo to correct it. A fast automated response was not only more efficient, but it also improved the safety posture and risk management for these facilities. Similarly, some factories need to thermally monitor equipment to identify any unexpected hot spots that may show up over time, indicative of a potential failure.

IBM is working with National Grid, an energy company, to develop a mobile solution using Spot, the agile mobile robot, for image analysis of transformers and thermal connectors. As shown in the above graphic, Spot also monitored connectors on both flat surfaces and 3D surfaces. IBM was able to show that Spot could detect excessive heat build-up in small connectors, potentially avoiding unsafe conditions or costly outages. This AI/ML edge application can produce faster response times when an issue is detected, which is why IBM believes significant gains are possible by automating the entire process.

IBM market opportunities

Drive-thru orders and mobile robots are just a few examples of the millions of potential AI applications that exist at the edge and are driven by several billion connected devices.

Edge computing is an essential part of enterprise digital transformation. Enterprises seek ways to demonstrate the feasibility of solving business problems using AI/ML and analytics at the edge. However, once a proof of concept has been successfully demonstrated, it is a common problem for a company to struggle with scalability, data governance, and full-stack solution management.

Challenges with scaling

“Determining entry points for AI at the edge is not the difficult part,” Dr. Fuller said. “Scale is the real issue.”

Scaling edge models is complicated because there are so many edge locations with large amounts of diverse content and a high device density. Because large amounts of data are required for training, data gravity is a potential problem. Further, in many scenarios, vast amounts of data are generated quickly, leading to potential data storage and orchestration challenges. AI Models are also rarely "finished." Monitoring and retraining of models are necessary to keep up with changes the environment.

Through IBM Research, IBM is addressing the many challenges of building an all-encompassing edge architecture and horizontally scalable data and AI technologies. IBM has a wealth of edge capabilities and an architecture to create the appropriate platform for each application.

IBM AI entry points at the edge

IBM sees Edge Computing as a $200 billion market by 2025. Dr. Fuller and his organization have identified four key market entry points for developing and expanding IBM’s edge compute strategy. In order of size, IBM believes its priority edge markets to be intelligent factories (Industry 4.0), telcos, retail automation, and connected vehicles.

IBM and its Red Hat portfolio already have an established presence in each market segment, particularly in intelligent operations and telco. Red Hat is also active in the connected vehicles space.

Industry 4.0

There have been three prior industrial revolutions, beginning in the 1700s up to our current in-progress fourth revolution, Industry 4.0, that promotes a digital transformation.

Manufacturing is the fastest growing and the largest of IBM’s four entry markets. In this segment, AI at the edge can Boost quality control, production optimization, asset management, and supply chain logistics. IBM believes there are opportunities to achieve a 4x speed up in implementing edge-based AI solutions for manufacturing operations.

For its Industry 4.0 use case development, IBM, through product, development, research and consulting teams, is working with a major automotive OEM. The partnership has established the following joint objectives:

• Increase automation and scalability across dozens of plants using 100s of AI / ML models. This client has already seen value in applying AI/ML models for manufacturing applications. IBM Research is helping with re-training models and implementing new ones in an edge environment to help scale even more efficiently. Edge offers faster inference and low latency, allowing AI to be deployed in a wider variety of manufacturing operations requiring instant solutions.
• Dramatically reduce the time required to onboard new models. This will allow training and inference to be done faster and allow large models to be deployed much more quickly. The quicker an AI model can be deployed in production; the quicker the time-to-value and the return-on-investment (ROI).
• Accelerate deployment of new inspections by reducing the labeling effort and iterations needed to produce a production-ready model via data summarization. Selecting small data sets for annotation means manually examining thousands of images, this is a time-consuming process that will result in - labeling of redundant data. Using ML-based automation for data summarization will accelerate the process and produce better model performance.
• Enable Day-2 AI operations to help with data lifecycle automation and governance, model creation, reduce production errors, and provide detection of out-of-distribution data to help determine if a model’s inference is accurate. IBM believes this will allow models to be created faster without data scientists.

Maximo Application Suite

IBM’s Maximo Application Suite plays an important part in implementing large manufacturers' current and future IBM edge solutions. Maximo is an integrated public or private cloud platform that uses AI, IoT, and analytics to optimize performance, extend asset lifecycles and reduce operational downtime and costs. IBM is working with several large manufacturing clients currently using Maximo to develop edge use cases, and even uses it within its own Manufacturing.

IBM has research underway to develop a more efficient method of handling life cycle management of large models that require immense amounts of data. Day 2 AI operations tasks can sometimes be more complex than initial model training, deployment, and scaling. Retraining at the edge is difficult because resources are typically limited.

Once a model is trained and deployed, it is important to monitor it for drift caused by changes in data distributions or anything that might cause a model to deviate from original requirements. Inaccuracies can adversely affect model ROI.

Day-2 AI Operations (retraining and scaling)

Day-2 AI operations consist of continual updates to AI models and applications to keep up with changes in data distributions, changes in the environment, a drop in model performance, availability of new data, and/or new regulations.

IBM recognizes the advantages of performing Day-2 AI Operations, which includes scaling and retraining at the edge. It appears that IBM is the only company with an architecture equipped to effectively handle Day-2 AI operations. That is a significant competitive advantage for IBM.

A company using an architecture that requires data to be moved from the edge back into the cloud for Day-2 related work will be unable to support many factory AI/ML applications because of the sheer number of AI/ML models to support (100s to 1000s).

“There is a huge proliferation of data at the edge that exists in multiple spokes,” Dr. Fuller said. "However, all that data isn’t needed to retrain a model. It is possible to cluster data into groups and then use sampling techniques to retrain the model. There is much value in federated learning from our point of view.”

Federated learning is a promising training solution being researched by IBM and others. It preserves privacy by using a collaboration of edge devices to train models without sharing the data with other entities. It is a good framework to use when resources are limited.

Dealing with limited resources at the edge is a challenge. IBM’s edge architecture accommodates the need to ensure resource budgets for AI applications are met, especially when deploying multiple applications and multiple models across edge locations. For that reason, IBM developed a method to deploy data and AI applications to scale Day-2 AI operations utilizing hub and spokes.

The graphic above shows the current status quo methods of performing Day-2 operations using centralized applications and a centralized data plane compared to the more efficient managed hub and spoke method with distributed applications and a distributed data plane. The hub allows it all to be managed from a single pane of glass.

Data Fabric Extensions to Hub and Spokes

IBM uses hub and spoke as a model to extend its data fabric. The model should not be thought of in the context of a traditional hub and spoke. IBM’s hub provides centralized capabilities to manage clusters and create multiples hubs that can be aggregated to a higher level. This architecture has four important data management capabilities.

1. First, models running in unattended environments must be monitored. From an operational standpoint, detecting when a model’s effectiveness has significantly degraded and if corrective action is needed is critical.
2. Secondly, in a hub and spoke model, data is being generated and collected in many locations creating a need for data life cycle management. Working with large enterprise clients, IBM is building unique capabilities to manage the data plane across the hub and spoke estate - optimized to meet data lifecycle, regulatory & compliance as well as local resource requirements. Automation determines which input data should be selected and labeled for retraining purposes and used to further Boost the model. Identification is also made for atypical data that is judged worthy of human attention.
3. The third issue relates to AI pipeline compression and adaptation. As mentioned earlier, edge resources are limited and highly heterogeneous. While a cloud-based model might have a few hundred million parameters or more, edge models can’t afford such resource extravagance because of resource limitations. To reduce the edge compute footprint, model compression can reduce the number of parameters. As an example, it could be reduced from several hundred million to a few million.
4. Lastly, suppose a scenario exists where data is produced at multiple spokes but cannot leave those spokes for compliance reasons. In that case, IBM Federated Learning allows learning across heterogeneous data in multiple spokes. Users can discover, curate, categorize and share data assets, data sets, analytical models, and their relationships with other organization members.

In addition to AI deployments, the hub and spoke architecture and the previously mentioned capabilities can be employed more generally to tackle challenges faced by many enterprises in consistently managing an abundance of devices within and across their enterprise locations. Management of the software delivery lifecycle or addressing security vulnerabilities across a vast estate are a case in point.

Multicloud and Edge platform

In the context of its strategy, IBM sees edge and distributed cloud as an extension of its hybrid cloud platform built around Red Hat OpenShift. One of the newer and more useful options created by the Red Hat development team is the Single Node OpenShift (SNO), a compact version of OpenShift that fits on a single server. It is suitable for addressing locations that are still servers but come in a single node, not clustered, deployment type.

For smaller footprints such as industrial PCs or computer vision boards (for example NVidia Jetson Xavier), Red Hat is working on a project which builds an even smaller version of OpenShift, called MicroShift, that provides full application deployment and Kubernetes management capabilities. It is packaged so that it can be used for edge device type deployments.

Overall, IBM and Red Hat have developed a full complement of options to address a large spectrum of deployments across different edge locations and footprints, ranging from containers to management of full-blown Kubernetes applications from MicroShift to OpenShift and IBM Edge Application Manager.

Much is still in the research stage. IBM's objective is to achieve greater consistency in terms of how locations and application lifecycle is managed.

First, Red Hat plans to introduce hierarchical layers of management with Red Hat Advanced Cluster Management (RHACM), to scale by two to three orders of magnitude the number of edge locations managed by this product. Additionally, securing edge locations is a major focus. Red Hat is continuously expanding platform security features, for example by recently including Integrity Measurement Architecture in Red Hat Enterprise Linux, or by adding Integrity Shield to protect policies in Red Hat Advanced Cluster Management (RHACM).

Red Hat is partnering with IBM Research to advance technologies that will permit it to protect platform integrity and the integrity of client workloads through the entire software supply chains. In addition, IBM Research is working with Red Hat on analytic capabilities to identify and remediate vulnerabilities and other security risks in code and configurations.

Telco network intelligence and slice management with AL/ML

Communication service providers (CSPs) such as telcos are key enablers of 5G at the edge. 5G benefits for these providers include:

• Reduced operating costs
• Improved efficiency
• Increased distribution and density
• Lower latency

The end-to-end 5G network comprises the Radio Access Network (RAN), transport, and core domains. Network slicing in 5G is an architecture that enables multiple virtual and independent end-to-end logical networks with different characteristics such as low latency or high bandwidth, to be supported on the same physical network. This is implemented using cloud-native technology enablers such as software defined networking (SDN), virtualization, and multi-access edge computing. Slicing offers necessary flexibility by allowing the creation of specific applications, unique services, and defined user groups or networks.

An important aspect of enabling AI at the edge requires IBM to provide CSPs with the capability to deploy and manage applications across various enterprise locations, possibly spanning multiple end-to-end network slices, using a single pane of glass.

5G network slicing and slice management

Network slices are an essential part of IBM's edge infrastructure that must be automated, orchestrated and optimized according to 5G standards. IBM’s strategy is to leverage AI/ML to efficiently manage, scale, and optimize the slice quality of service, measured in terms of bandwidth, latency, or other metrics.

5G and AI/ML at the edge also represent a significant opportunity for CSPs to move beyond traditional cellular services and capture new sources of revenue with new services.

Communications service providers need management and control of 5G network slicing enabled with AI-powered automation.

Dr. Fuller sees a variety of opportunities in this area. "When it comes to applying AI and ML on the network, you can detect things like intrusion detection and malicious actors," he said. "You can also determine the best way to route traffic to an end user. Automating 5G functions that run on the network using IBM network automation software also serves as an entry point.”

In IBM’s current telecom trial, IBM Research is spearheading the development of a range of capabilities targeted for the IBM Cloud Pak for Network Automation product using AI and automation to orchestrate, operate and optimize multivendor network functions and services that include:

• End-to-end 5G network slice management with planning & design, automation & orchestration, and operations & assurance
• Network Data and AI Function (NWDAF) that collects data for slice monitoring from 5G Core network functions, performs network analytics, and provides insights to authorized data consumers.
• Improved operational efficiency and reduced cost

Future leverage of these capabilities by existing IBM Clients that use the Cloud Pak for Network Automation (e.g., DISH) can offer further differentiation for CSPs.

5G radio access

Open radio access networks (O-RANs) are expected to significantly impact telco 5G wireless edge applications by allowing a greater variety of units to access the system. The O-RAN concept separates the DU (Distributed Units) and CU (Centralized Unit) from a Baseband Unit in 4G and connects them with open interfaces.

O-RAN system is more flexible. It uses AI to establish connections made via open interfaces that optimize the category of a device by analyzing information about its prior use. Like other edge models, the O-RAN architecture provides an opportunity for continuous monitoring, verification, analysis, and optimization of AI models.

The IBM-telco collaboration is expected to advance O-RAN interfaces and workflows. Areas currently under development are:

• Multi-modal (RF level + network-level) analytics (AI/ML) for wireless communication with high-speed ingest of 5G data
• Capability to learn patterns of metric and log data across CUs and DUs in RF analytics
• Utilization of the antenna control plane to optimize throughput
• Primitives for forecasting, anomaly detection and root cause analysis using ML
• Opportunity of value-added functions for O-RAN

IBM Cloud and Infrastructure

The cornerstone for the delivery of IBM's edge solutions as a service is IBM Cloud Satellite. It presents a consistent cloud-ready, cloud-native operational view with OpenShift and IBM Cloud PaaS services at the edge. In addition, IBM integrated hardware and software Edge systems will provide RHACM - based management of the platform when clients or third parties have existing managed as a service models. It is essential to note that in either case this is done within a single control plane for hubs and spokes that helps optimize execution and management from any cloud to the edge in the hub and spoke model.

IBM's focus on “edge in” means it can provide the infrastructure through things like the example shown above for software defined storage for federated namespace data lake that surrounds other hyperscaler clouds. Additionally, IBM is exploring integrated full stack edge storage appliances based on hyperconverged infrastructure (HCI), such as the Spectrum Fusion HCI, for enterprise edge deployments.

As mentioned earlier, data gravity is one of the main driving factors of edge deployments. IBM has designed its infrastructure to meet those data gravity requirements, not just for the existing hub and spoke topology but also for a future spoke-to-spoke topology where peer-to-peer data sharing becomes imperative (as illustrated with the wealth of examples provided in this article).

Wrap up

Edge is a distributed computing model. One of its main advantages is that computing, and data storage and processing is close to where data is created. Without the need to move data to the cloud for processing, real-time application of analytics and AI capabilities provides immediate solutions and drives business value.

IBM’s goal is not to move the entirety of its cloud infrastructure to the edge. That has little value and would simply function as a hub to spoke model operating on actions and configurations dictated by the hub.

IBM’s architecture will provide the edge with autonomy to determine where data should reside and from where the control plane should be exercised.

Equally important, IBM foresees this architecture evolving into a decentralized model capable of edge-to-edge interactions. IBM has no firm designs for this as yet. However, the plan is to make the edge infrastructure and platform a first-class citizen instead of relying on the cloud to drive what happens at the edge.

Developing a complete and comprehensive AI/ML edge architecture - and in fact, an entire ecosystem - is a massive undertaking. IBM faces many known and unknown challenges that must be solved before it can achieve success.

However, IBM is one of the few companies with the necessary partners and the technical and financial resources to undertake and successfully implement a project of this magnitude and complexity.

It is reassuring that IBM has a plan and that its plan is sound.

Paul Smith-Goodson is Vice President and Principal Analyst for quantum computing, artificial intelligence and space at Moor Insights and Strategy. You can follow him on Twitter for more current information on quantum, AI, and space.

Note: Moor Insights & Strategy writers and editors may have contributed to this article.

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Last week, after IBM’s report of positive quarterly earnings, CEO Arvind Krishna and CNBC’s Jim Cramer shared their frustration that IBM’s stock “got clobbered.” IBM’s stock price immediately fell by10%, while the S&P500 remained steady (Figure 1)

While a five-day stock price fluctuation is by itself meaningless, questions remain about the IBM’s longer-term picture. “These are great numbers,” declared Krishna.

“You gave solid revenue growth and solid earnings,” Cramer sympathized. “You far exceeded expectations. Maybe someone is changing the goal posts here?”

The Goal Posts To Become A Digital Winner

It is also possible that Krishna and Cramer missed where today’s goal posts are located. Strong quarterly numbers do not a digital winner make. They may induce the stock market to regard a firm as a valuable cash cow, like other remnants of the industrial era. But to become a digital winner, a firm must take the kind of steps that Satya Nadella took at Microsoft to become a digital winner: kill its dogs, commit to a mission of customer primacy, identify real growth opportunities, transform its culture, make empathy central, and unleash its agilists. (Figure 2)

Since becoming CEO, Nadella has been brilliantly successful at Microsoft, growing market capitalization by more than a trillion dollars.

Krishna’s Two Years As CEO

Krishna has been IBM CEO since April 2020. He began his career at IBM in 1990, and had been managing IBM’s cloud and research divisions since 2015. He was a principal architect of the Red Hat acquisition.

They are remarkable parallels between the careers of Krishna and Nadella.

· Both are Indian-American engineers, who were born in India.

· Both worked at the firm for several decades before they became CEOs.

· Prior to becoming CEOs, both were in charge of cloud computing.

Both inherited companies in trouble. Microsoft was stagnating after CEO Steve Ballmer, while IBM was also in rapid decline, after CEO Ginny Rometty: the once famous “Big Blue” had become known as a “Big Bruise.”

Although it is still early days in Krishna’s CEO tenure, IBM is under-performing the S&P500 since he took over (Figure 3).

More worrying is the fact that Krishna has not yet completed the steps that Nadella took in his first 27 months. (Figure 1).

1. Jettison Losing Baggage

Nadella wrote off the Nokia phone and declared that IBM would no longer sell its flagship Windows as a business. This freed up energy and resources to focus on creating winning businesses.

By contrast, Krishna has yet to jettison, IBM’s most distracting baggage:

· Commitment to maximizing shareholder value (MSV): For the two prior decades, IBM was the public champion of MSV, first under CEO Palmisano 2001-2011, and again under Rometty 2012-2020—a key reason behind IBM’s calamitous decline (Figure 2) Krishna has yet to explicitly renounce IBM’s MSV heritage.

· Top-down bureaucracy: The necessary accompaniment of MSV is top-down bureaucracy, which flourished under CEOs Palmisano and Rometty. Here too, bureaucratic processes must be explicitly eradicated, otherwise they become permanent weeds.

· The ‘Watson problem’: IBM’s famous computer, Watson, may have won ‘Jeopardy!’ but it continues to have problems in the business marketplace. In January 2022, IBM reported that it had sold Watson Health assets to an investment firm for around $1 billion, after acquisitions that had cost some $4 billion. Efforts to monetize Watson continue.

· Infrastructure Services: By spinning off its Cloud computing business as a publicly listed company (Kyndryl), IBM created nominal separation, but Kyndryl immediately lost 57% of its share value.

· Quantum Computing: IBM pours resources into research on quantum computing and touts its potential to revolutionize computing. However unsolved technical problems of “decoherence” and “entanglement” mean that any meaningful benefits are still some years away.

· Self-importance: Perhaps the heaviest baggage that IBM has yet to jettison is the over-confidence reflected in sales slogans like “no one ever got fired for hiring IBM”. The subtext is that firms “can leave IT to IBM” and that the safe choice for any CIO is to stick with IBM. It’s a status quo mindset—the opposite of the clients that IBM needs to attract.

2. Commit To A Clear Customer-Obsessed Mission

At the outset of his tenure as CEO of Microsoft, Nadella spent the first nine months getting consensus on a simple customer-driven mission statement.

Krishna did write at the end of the letter to staff on day one as CEO, and he added at the end:“Third, we all must be obsessed with continually delighting our clients. At every interaction, we must strive to offer them the best experience and value. The only way to lead in today’s ever-changing marketplace is to constantly innovate according to what our clients want and need.” This would have been more persuasive if it had come at the beginning of the letter, and if there had been stronger follow-up.

What is IBM’s mission? No clear answer appears from IBM’s own website. The best one gets from About IBM is the fuzzy do-gooder declaration: “IBMers believe in progress — that the application of intelligence, reason and science can Boost business, society and the human condition.” Customer primacy is not explicit, thereby running the risk that IBM’s 280,000 employees will assume that the noxious MSV goal is still in play.

3. Focus On Major Growth Opportunities

At Microsoft, Nadella dismissed competing with Apple on phones or with Google on Search. He defined the two main areas of opportunity—mobility and the cloud.

Krishna has identified the Hybrid Cloud and AI as IBM’s main opportunities. Thus, Krishna wrote in his newsletter to staff on day one as CEO: “Hybrid cloud and AI are two dominant forces driving change for our clients and must have the maniacal focus of the entire company.”

However, both fields are now very crowded. IBM is now a tiny player in Cloud in comparison to Amazon, Microsoft, and Google. In conversations, Krishna portrays IBM as forging working partnerships with the big Cloud players, and “integrating their offerings in IBM’s hybrid Cloud.” One risk here is whether the big Cloud players will facilitate this. The other risk is that IBM will attract only lower-performing firms that use IBM as a crutch so that they can cling to familiar legacy programs.

4. Address Culture And The Importance Of Empathy Upfront

At Microsoft, Nadella addressed culture upfront, rejecting Microsoft’s notoriously confrontational culture, and set about instilling a collaborative customer-driven culture throughout the firm.

Although Krishna talks openly to the press, he has not, to my knowledge, frontally addressed the “top-down” “we know best” culture that prevailed in IBM under his predecessor CEOs. He has, to his credit, pledged “neutrality” with respect to the innovative, customer-centric Red Hat, rather than applying the “Blue washing” that the old IBM systematically applied to its acquisitions to bring them into line with IBM’s top-down culture, and is said to have honored its pledge—so far. But there is little indication that IBM is ready to adopt Red Hat’s innovative culture for itself. It is hard to see these two opposed cultures remain “neutral” forever. Given the size differential between IBM and Red Hat, the likely winner is easy to predict, unless Krishna makes a more determined effort to transform IBM’s culture.

5. Empower The Hidden Agilists

As in any large tech firm, when Nadella and Krishna took over their respective firms, there were large hidden armies of agilists waiting in the shadows but hamstrung by top-down bureaucracies. At Microsoft, Nadella’s commitment to “agile, agile, agile” combined with a growth mindset, enabled a fast start.. At IBM, if Krishna has any passion for Agile, it has not yet shared it widely.

Bottom Line

Although IBM has made progress under Krishna, it is not yet on a path to become a clear digital winner.

And read also:

Is Your Firm A Cash-Cow Or A Growth-Stock?

Why Companies Must Learn To Discuss The Undiscussable

IBM is acquiring Databand.ai, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures, and poor quality. The acquisition further strengthens IBM's software portfolio across data, AI, and automation to address the full spectrum of observability.

Databand.ai is IBM's fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities.

Databand.ai's open and extendable approach allows data engineering teams to easily integrate and gain observability into their data infrastructure.

This acquisition will unlock more resources for Databand.ai to expand its observability capabilities for broader integrations across more of the open source and commercial solutions that power the modern data stack.

Enterprises will also have full flexibility in how to run Databand.ai, whether as-a-Service (SaaS) or a self-hosted software subscription.

The acquisition of Databand.ai builds on IBM's research and development investments as well as strategic acquisitions in AI and automation. By using Databand.ai with IBM Observability by Instana APM and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations.

"Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don't have access to the data they need in any given moment, their business can grind to a halt," said Daniel Hernandez, general manager for data and AI, IBM. "With the addition of Databand.ai, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale."

The acquisition of Databand.ai further extends IBM's existing data fabric solution by helping ensure that the most accurate and trustworthy data is being put into the right hands at the right time—no matter where it resides.

Headquartered in Tel Aviv, Israel, Databand.ai employees will join IBM Data and AI, further building on IBM's growing portfolio of Data and AI products, including its IBM Watson capabilities and IBM Cloud Pak for Data. Financial details of the deal were not disclosed. The acquisition closed on June 27, 2022.

For more information about this news, visit www.ibm.com.

The MarketWatch News Department was not involved in the creation of this content.

Aug 09, 2022 (The Expresswire) -- The global Cybersecurity Market report focused on a thorough examination of the industry's current and future prospects. This report is a solidification of primary and secondary research, which gives market size, offers elements, and conjecture for different fragments and sub-portions thinking about the large scale and miniature ecological variables. To calculate the growth rates for each category and sub-segment, an in-depth examination of past trends, projected trends, demographics, technological improvements, and regulatory requirements for the Cybersecurity market was conducted.

The MarketWatch News Department was not involved in the creation of this content.

This press release was orginally distributed by SBWire

New Jersey, USA — (SBWIRE) — 07/12/2022 — The latest study released on the Global Customer Analytics Applications Market by AMA Research evaluates market size, trend, and forecast to 2027. The Customer Analytics Applications market study covers significant research data and proofs to be a handy resource document for managers, analysts, industry experts and other key people to have ready-to-access and self-analyzed study to help understand market trends, growth drivers, opportunities and upcoming challenges and about the competitors.

Key Players in This Report Include:
Mixpanel (California),Kissmetrics (United States),Woopra (United States),Zoho pagesense (India),Crazyegg (California),Adobe Inc. (United States),IBM Corp. (United States),Sprout Social (United States),Brightedge (California),Tableau Software (United States),RapidMiner (United States),Qlik (United States),Sisense (United States),SAS (United States),Domo Inc. (United States),Clicktale (Israel)

Download trial Report PDF (Including Full TOC, Table & Figures) @ https://www.advancemarketanalytics.com/sample-report/111631-global-customer-analytics-applications-market

Definition:
Customers analytics application is specialized by apps which are used to gain insight into the customer experience, understand customer behavior and help tailor marketing campaigns to specific customer segments. This software is helpful to provide businesses with crucial data about their marketing efforts and customer behavior.

Market Trends:
– Analyzing online behavior to increase sales and big data
– Increasing penetration of voice-enabled smart devices, in-home automation systems, and wearables devices has a key trend of the market

Market Drivers:
– Increasing preferences of better understanding of a customer's buying habits and lifestyle
– Increased customer response to promotions, strengthens customers loyalty that boosts the sales revenue of the market

Market Opportunities:
– Increasing sales opportunities

The Global Customer Analytics Applications Market segments and Market Data Break Down are illuminated below:
by Type (Descriptive Analytics, Predictive Analytics, Prescriptive Analytics), Software (Analytical Customer Analytics Software, Operational Customer Analytics Software, Collaborative Customer Analytics Software), Platform (Mobile adoption, Customer retention, User engagement, In-app purchases), End-User (Marketing & Sales, Customer Service, IT, Others)

Global Customer Analytics Applications market report highlights information regarding the current and future industry trends, growth patterns, as well as it offers business strategies to helps the stakeholders in making sound decisions that may help to ensure the profit trajectory over the forecast years.

Have a query? Market an enquiry before purchase @ https://www.advancemarketanalytics.com/enquiry-before-buy/111631-global-customer-analytics-applications-market

Geographically, the detailed analysis of consumption, revenue, market share, and growth rate of the following regions:
– The Middle East and Africa (South Africa, Saudi Arabia, UAE, Israel, Egypt, etc.)
– North America (United States, Mexico & Canada)
– South America (Brazil, Venezuela, Argentina, Ecuador, Peru, Colombia, etc.)
– Europe (Turkey, Spain, Turkey, Netherlands Denmark, Belgium, Switzerland, Germany, Russia UK, Italy, France, etc.)
– Asia-Pacific (Taiwan, Hong Kong, Singapore, Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia).

Objectives of the Report
– -To carefully analyze and forecast the size of the Customer Analytics Applications market by value and volume.
– -To estimate the market shares of major segments of the Customer Analytics Applications market.
– -To showcase the development of the Customer Analytics Applications market in different parts of the world.
– -To analyze and study micro-markets in terms of their contributions to the Customer Analytics Applications market, their prospects, and individual growth trends.
– -To offer precise and useful details about factors affecting the growth of the Customer Analytics Applications market.
– -To provide a meticulous assessment of crucial business strategies used by leading companies operating in the Customer Analytics Applications market, which include research and development, collaborations, agreements, partnerships, acquisitions, mergers, new developments, and product launches.

Buy Complete Assessment of Customer Analytics Applications market Now @ https://www.advancemarketanalytics.com/buy-now?format=1&report=111631

Major highlights from Table of Contents:
Customer Analytics ApplicationsMarket Study Coverage:
– It includes major manufacturers, emerging player's growth story, and major business segments of Customer Analytics Applications market, years considered, and research objectives. Additionally, segmentation on the basis of the type of product, application, and technology.
– Customer Analytics Applications Market Executive Summary: It gives a summary of overall studies, growth rate, available market, competitive landscape, market drivers, trends, and issues, and macroscopic indicators.
– Customer Analytics Applications Market Production by Region Customer Analytics Applications Market Profile of Manufacturers-players are studied on the basis of SWOT, their products, production, value, financials, and other vital factors.
– Key Points Covered in Customer Analytics Applications Market Report:
– Customer Analytics Applications Overview, Definition and Classification Market drivers and barriers
– Customer Analytics Applications Market Competition by Manufacturers
– Impact Analysis of COVID-19 on Customer Analytics Applications Market
– Customer Analytics Applications Capacity, Production, Revenue (Value) by Region (2022-2027)
– Customer Analytics Applications Supply (Production), Consumption, Export, Import by Region (2022-2027)
– Customer Analytics Applications Manufacturers Profiles/Analysis Customer Analytics Applications Manufacturing Cost Analysis, Industrial/Supply Chain Analysis, Sourcing Strategy and Downstream Buyers, Marketing
– Strategy by Key Manufacturers/Players, Connected Distributors/Traders Standardization, Regulatory and collaborative initiatives, Industry road map and value chain Market Effect Factors Analysis.

Browse Complete Summary and Table of Content @ https://www.advancemarketanalytics.com/reports/111631-global-customer-analytics-applications-market

Key questions answered
– How feasible is Customer Analytics Applications market for long-term investment?
– What are influencing factors driving the demand for Customer Analytics Applications near future?
– What is the impact analysis of various factors in the Global Customer Analytics Applications market growth?
– What are the recent trends in the regional market and how successful they are?

Thanks for studying this article; you can also get individual chapter wise section or region wise report version like North America, Middle East, Africa, Europe or LATAM, Asia.

Contact US:
Craig Francis (PR & Marketing Manager)
AMA Research & Media LLP
Unit No. 429, Parsonage Road Edison, NJ
New Jersey USA – 08837
Phone: +1 (206) 317 1218
[email protected]

For more information on this press release visit: http://www.sbwire.com/press-releases/customer-analytics-applications-market-to-witness-huge-growth-by-2027-woopra-crazyegg-ibm-1360455.htm

IBM recently announced it has acquired Databand.ai, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures and poor quality — before it impacts their bottom-line. Today's news further strengthens IBM's software portfolio across data, AI and automation to address the full spectrum of observability and helps businesses ensure that trustworthy data is being put into the right hands of the right users at the right time.

Databand.ai is IBM's fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities. IBM has acquired more than 25 companies since Arvind Krishna became CEO in April 2020.

As the volume of data continues to grow at an unprecedented pace, organizations are struggling to manage the health and quality of their data sets, which is necessary to make better business decisions and gain a competitive advantage.

Data observability takes traditional data operations to the next level by using historical trends to compute statistics about data workloads and data pipelines directly at the source, determining if they are working, and pinpointing where any problems may exist. When combined with a full stack observability strategy, it could help IT teams quickly surface and resolve issues from infrastructure and applications to data and machine learning systems.

Databand.ai's open and extendable approach allows data engineering teams to easily integrate and gain observability into their data infrastructure. This acquisition would unlock more resources for Databand.ai to expand its observability capabilities for broader integrations across more of the open source and commercial solutions that power the modern data stack. Enterprises would also have full flexibility in how to run Databand.ai, whether as-a-Service (SaaS) or a self-hosted software subscription.

The acquisition of Databand.ai builds on IBM's research and development investments as well as strategic acquisitions in AI and automation. By using Databand.ai with IBM Observability by Instana APM and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations.

"Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don't have access to the data they need at any given moment, their business could grind to a halt," said Daniel Hernandez, general manager for Data and AI, IBM. "With the addition of Databand.ai, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale."

Headquartered in Tel Aviv, Israel, Databand.ai employees will join IBM Data and AI, further building on IBM's growing portfolio of Data and AI products, including its IBM Watson capabilities and IBM Cloud Pak for Data. Financial details of the deal were not disclosed. The acquisition closed on June 27, 2022.

A new research center for artificial intelligence and machine learning has sprung up at the University of Oregon, thanks to a collaboration between IBM and the Oregon Advanced Computing Institute for Science and Society. The Oregon Center for Enterprise AI eXchange (CE-AIX) leverages the university's high-performance computing technology and enterprise servers from IBM to create new training opportunities and collaborations with industry.

"The new lab facility will be a valuable resource for worldwide universities and enterprise companies wanting to take advantage of IBM Enterprise Servers POWER9 and POWER10 combined with IBM Spectrum storage, along with AIX and RHEL with OpenShift," said Ganesan Narayanasamy, IBM's leader for academic and research worldwide.

Narayanasamy said the new center extends state-of-the-art facilities and other Silicon Valley-style services to researchers, system developers, and other users looking to take advantage of open-source high-performance computing resources.  The center has already helped thousands of students gain exposure and practice with its high-performance computing training, and it is expected to serve as a global hub that will help prepare the next generation of computer scientists, according to the center's director Sameer Shende.

"We aim to expand the skillset of researchers and students in the area of commercial application of artificial intelligence and machine learning, as well as high-performance computing technologies," Shende said.

Thanks to a long-term loan agreement with IBM, the center has access to powerful enterprise servers and other capabilities. It was envisioned to bring together data scientists from businesses in different domains, such as financial services, manufacturing, and transportation, along with IBM research and development engineers, IBM partner data scientists, and university students and researchers.

The new center also has the potential to be leveraged by everyone from global transportation companies seeking to design more efficient trucking routes to clean energy firms looking to design better wind turbines based on models of airflow patterns. At the University of Oregon, there are potential applications in data science, machine learning, environmental hazards monitoring, and other emerging areas of research and innovation.

"Enterprise AI is a team sport," said Raj Krishnamurthy, an IBM chief architect for enterprise AI and co-director of the new center. "As businesses continue to operationalize AI in mission-critical systems, the use cases and methodologies developed from collaboration in this center will further promote the adoption of trusted AI techniques in the enterprise."

Ultimately, the center will contribute to the University of Oregon's overall research excellence, said AR Razdan, who serves as the university's vice president for research and innovation.

"The center marks another great step forward in [the university's] ongoing efforts to bring together interdisciplinary teams of researchers and innovators," Razdan said.

This post was created by IBM with Insider Studios.

IBM today announced a new server -- a powerful, version of the IBM zEnterprise System that's the most scalable mainframe ever – to extend the mainframe's innovation and unique qualities to more organizations, especially companies and governments in emerging markets in Asia, Africa and elsewhere.

The new IBM zEnterprise 114 mainframe server follows the introduction of the zEnterprise System for the world's largest banks, insurance companies and governments in July 2010.  The new server, which allows mid-sized organizations to enjoy the benefits of a mainframe as the foundation for their data centers, costs 25%1 less and offers up to 25%2 percent more performance than its predecessor, the System z10 BC.  Clients can consolidate workloads from 40 Oracle server cores on to a new z114 with just three processors running Linux3. Compared to the Oracle servers the new z114 costs 80% less with similar dramatic savings on floor space and energy3.

At a starting price of under $75,000 -- IBM's lowest ever price for a mainframe server -- the zEnterprise 114  is an especially attractive option for emerging markets experiencing rapid growth in new services for banking, retail, mobile devices, government services and other areas.  These organizations are faced with ever-increasing torrents of data and want smarter computing systems that help them operate efficiently, better understand customer behavior and needs, optimize decisions in real time and reduce risk.

IBM also introduced new features that allow the zEnterprise System to integrate and manage workloads on additional platforms.  New today is support for select System x blades within the zEnterprise System. These System x blades can run Linux x86 applications unchanged, and in the future will be able to run Windows applications.   With these capabilities, the zEnterprise System including the new z114 can help simplify data centers with its ability to manage workloads across mainframe, POWER7 and System x servers as a single system.  Using the zEnterprise Blade Center Extension (zBX), customers can also extend mainframe qualities, such as governance and manageability, to workloads running across multiple platforms.

Smaller firms like PSP --a provider of credit card processing services--turned to a mainframe for the first time to consolidate multiple racks of HP servers on to a single IBM Business Class mainframe with just 2 processors. Additional available capacity already built into their entry level mainframe server is designed to meet their rapid growth projection needs without increasing their IT footprint.

IBM System z servers are also making inroads in emerging markets like Africa. Governments and businesses in Cameroon, Senegal and Namibia have all recently purchased new IBM mainframe servers.

zEnterprise 114

With the z114 clients can start with smaller configurations and access additional capacity built into the server as needed without increasing the data center footprint or systems management complexity and cost.  The new z114 can also consolidate morean th300 HP Proliant servers running Oracle workloads.4

The z114 is powered by up to 14 of the industry's most sophisticated microprocessors of which up to 10 can be configured as specialty engines. These specialty engines, the System z Application Assist Processor (zAAP), the System z Integrated Information Processor (zIIP), and the Integrated Facility for Linux (IFL), are designed  to integrate new Java, XML, and Linux applications and technologies with existing workloads, as well to optimize system resources and reduce costs on the mainframe.  For example, using a fully configured machine running Linux for System z, clients can create and maintain a Linux virtual server in the z114 for as little as $500 per year.

The z114 offers up to an 18%6 improvement on processing traditional z/OS workloads and a 25%7 improvement on microprocessor intensive workloads compared to the z10 BC.

The z114 runs all the latest zEnterprise operating systems including the new z/OS V 1.13 announced today.   This new version adds new software deployment and disk management capabilities.  It also offers enhanced autonomics and early error detection features as well as the latest encryption and compliance features extending the mainframe's industry leading security capabilities.

Hybrid Computing

In a move that will further simplify data center management and reduce costs, IBM is also announcing the ability to integrate and manage workload on select IBM System x servers running Linux as part of the zEnterprise System8.  Support for Microsoft Windows on select System x servers will follow.

This capability is delivered through the IBM zEnterprise Unified Resource Manager and the IBM zEnterprise BladeCenter Extension (zBX), which allows customers to integrate the management of zEnterprise System resources as a single system and extend mainframe qualities, such as governance and manageability, to workloads running on other select servers.

The zEnterprise System can now integrate and manage workloads running on tens of thousands of off-the-shelf applications on select general purpose IBM POWER7-based and System x blades as well as the IBM Smart Analytics Optimizer to analyze data faster at a lower cost per transaction and the IBM WebSphere DataPower XI50 for integrating web based workloads.

Up to 112 blades can be integrated and managed as part of zBX.  Different types of blades and optimizers can be mixed and matched with in the same BladeCenter chassis.

New Financing Options

IBM Global Financing offers attractive financing options for existing IBM clients looking to upgrade to a z114 as well as clients currently using select HP and Oracle servers.

For current System z clients, IBM Global Financing (IGF) can buyback older systems for cash and upgrade customers to the z114 on a Fair Market Value (FMV) lease, which offers a predictable monthly payment. IGFalso will “Sweep the Floor” of existing HP Itanium or Oracle Sun Servers with the purchase of a z114. IGF will remove and recycle these older systems in compliance with environmental laws and regulations and pay clients the fair market value of HP and Oracle-Sun servers. IGF is also offering a 6 month deferral of any hardware, software, services or any combination for clients who wish to upgrade now, but pay later.

IGF is also offering a 0% financing for 12 months on any IBM Software, including IBM middleware for the z114 such as Tivoli, WebSphere, Rational, Lotus and Analytics products.

IBM-owned, Melbourne-based cloud integrator SXiQ has completed migration services for Bega Cheese as part of its $560 million acquisition of rival dairy giant Lion Dairy & Drinks.

Bega bought Lion Dairy & Drinks in late 2020, which owns brands, such as Dairy Farmers, Yoplait, Big M, Dare, Masters, Juice Brothers, Daily Juice,  and Farmers Union iced coffee.

Bega Cheese chief information officer Zack Chisholm said in a statement that the Vegemite owner required Lion Dairy & Drinks’ applications, data and processes to be transitioned into its existing and expanded infrastructure.

Chisholm said that infrastructure migration and transition of 31 physical sites performing production, distribution and administration duties, had to be completed within a 12-month window with minimal disruption to the operations of both businesses. 

Bega Cheese’s IT team partnered with SXiQ on end-state design, migration planning and execution for several parts of the project - such as migration of applications, databases and associated backups, implementation of prod and non-prod AWS accounts and a landing zone to house all LD&D workloads. The work also included implementation of a continuous integration and continuous deployment toolset and workflow built on CloudFormation, Ansible, Jenkins and GitLab.

SXiQ said it also assisted Bega Cheese’s IT team with cloud cost optimisation strategies for efficient consumption of cloud resources to support the newly acquired business, and uplifting the cloud ops team to ensure Bega Cheese IT incorporated true DevSecOps into its core capability to support the new platform.

SXiQ chief executive officer John Hanna said, “our experts executed deep analysis, strategic thinking, and detailed planning to ensure the successful migration of Lion to Bega Cheese’s existing infrastructure.”

“By uplifting infrastructure, cloud management tooling and practices, SXiQ has enhanced management of Bega Cheese’s cloud assets, improving consistency, security and reducing time to deploy cloud infrastructure in the future.” 

Global tech giant IBM acquired SXiQ late last year for an undisclosed sum to bolster its Consulting’s Hybrid Cloud Services business’ Amazon Web Services and Microsoft Azure consulting capabilities.