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?”
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 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).
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.
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 Excellerate 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.
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.
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.
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.
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
NEW DELHI : Matt Hicks is president and chief executive officer of US enterprise open source solutions company Red Hat Inc., which was acquired by International Business Machines Corp. (IBM) in July 2019 for $34 billion but runs as an independent unit. Hicks, who took charge from Paul Cormier (now chairman of Red Hat) this July, is an IBM veteran and has been at the forefront of cloud computing for over a decade. In a video interview from the US, he spoke about his relationship with Arvind Krishna, chairman and CEO of IBM, and Cormier, even as he shared Red Hat’s overall roadmap, plans for India, and tech trends. Edited excerpts:
Yes, you read that right. For much of the last couple of decades, it’s felt as if everyone has been talking about the impending demise of the mainframe, whilst simultaneously attempting to emulate as many as possible of its key operational characteristics.
Originally this emulation was via industry-standard servers, but in the last few years “the cloud” has taken up this challenge. It began with cloud computing promising the same level of scalability, flexibility and operational efficiency that mainframe systems have long provided, and on scalability going somewhat further. For a while these were more words than reality, but now cloud capabilities are (finally) getting close to what mainframe users have long taken for granted.
More recently, attention in cloud circles has turned to other – what we might regard as core – mainframe attributes such as security, privacy, resilience and failover. Whether you believe the marketing of cloud providers on this is up to you (as it is with any vendor marketing messages). But ensuring such things certainly requires very careful practicing of the service level guarantees and contractual small print.
Today much of the focus of cloud services has switched to support for specialist workloads, and again, we see cloud following in the footsteps of the mainframe by using dedicated offload engines designed to optimise workload performance, and in many cases to minimise software licensing costs as well. But it’s always seemed as if cloud has been in catch-up mode, and the mainframe has remained in the lead. Which leads to the question, has the cloud now caught up?
In many ways, the answer is “yes”, but this is a qualified yes. When it comes to scalability, throughput, operational efficiency, and arguably even resilience and failover, cloud has arguably caught up with the mainframe of the 1990s or early 2000s. But there are other factors to bear in mind as the mainframe has not stood still.
For example, it is fair to say that cloud providers have made great strides on security and privacy, but in reality the mainframe is still recognised as the gold standard, with security baked into every layer in the systems stack.
Then there are questions such as latency and data location. With the mainframe, there is no doubt where the data resides and who can access it. Managing these details and the associated operational policies has been part of the platform for over fifty years. When it comes to latency, the mainframe is probably sitting very close to the data you are working with, making latency as low as possible in terms of system response times, something reinforced when considering the system’s very powerful processors and sophisticated, mature partitioning capabilities.
And the mainframe environment is getting even stronger when you look at the announcements made at the latest launch of the IBM z16. These include quantum-safe cryptography to protect against the development of Quantum computers able to decrypt current encryption standards, on-chip AI acceleration to boost ML and AI execution, and flexible capacity combined with on-demand workload transfer across multiple locations to further reduce the chance of service disruption.
But there are places where things are arguably closer, one of which is in the area of workload optimisation, although the two environments are developing in different ways. For example, the mainframe strives to deliver a consistent environment that can handle a wide range of workloads, but managed through the same set of frameworks and tools. The cloud, on the other hand, allows you to spin up dedicated specialised environments, e.g. for AI or analytics.
Which leaves the question of where is “the cloud” ahead of the mainframe? The obvious place to start is in terms of the diverse geographic distribution of the major public clouds which spread across the globe with huge resources that no mainframe or mainframe cluster can match that. But this advantage is no longer quite so huge given that IBM will shortly be making “mainframe as a service” available from its IBM Cloud data centres around the world.
Not quite as a corollary, it is also fair to say that cloud was ahead for a while with regard to modern software delivery methods such as DevOps and the implementation of various agile delivery solutions. But we must recognise that it hasn’t taken long for the gap to close because the fundamental principles underlying things like DevOps, container, microservices, APIs, etc. have been intrinsic to the mainframe environment for decades, indeed pretty much since its beginning. In addition, IBM and the other software vendors in the mainframe ecosystem, such as Broadcom and BMC, have developed their offerings to such a degree that today there’s almost absolute parity.
In essence today’s mainframe environment is one where the latest generation of developers should not feel out of place. It uses the same standards-based, open tools they handle daily. And with the mainframe-as-a-service soon to be available, devs will be able to build code wherever they like and run it on the mainframe with a few clicks and no need to build a complex environment.
This is good news for the mainframe, but having the technological capabilities is less than half of the challenge. What’s really needed is for the mainframe to catch the eye of modern developers. IBM needs to ensure that developers understand that the mainframe is not a new and alien place, but instead is ready for them to exploit using the tools they are already comfortable with.
When you stand back and consider the modern mainframe, particularly the LinuxOne version and the new Z16, it’s pretty clear any claims of the mainframe being out of date or legacy stem from a fundamental lack of awareness. Indeed, the mainframe has continued to lead the way in many critical areas, delivering IT cost-effectively and securely at scale. The bottom line is, it’s not that the mainframe has been trying to keep up with industry developments, it’s that the mainframe is still very much leading the way.
We read in a library. Watson actually reads the library. (Illustration: James Boast)
We live in a world increasingly run by algorithms. They drive the lion’s share of all equity trades and automate complex hedges and derivatives. They make love matches, set retail prices, sequence traffic lights and air traffic, predict ticket sales by "reading" scripts, diagnose diseases, prescribe drug protocols and identify suspected terrorists for no-fly lists. At Google, they’re learning to drive, which could eventually prevent tens of thousands of highway deaths every year.
Algorithms are critical to most of the things we now call smart, like cars and phones and grids. And they’ve made computers so smart we need a new word for them. About the only thing these “computers” have in common with what’s on your desk are silicon chips.
Their processors are called “neuromorphic” because they mimic the human nervous system, just as the latest “neural network” algorithm does. As a result, these “computers” can see, speak, listen, think and learn. Given certain questions and problems, particularly those relying on perfect memory and lightning speed with massive amounts of data, they are helping to make major advances in fields ranging from economics and engineering to medicine and basic science.
And now they’re getting around to lawyers.
Later this year, a small Canadian company is scheduled to introduce its “digital legal advisor” to the world’s law firms. Its name is ROSS (which actually doesn’t stand for anything). “The way you interact with ROSS is very simple,” says Jimoh Ovbiagele, one the co-founders. “You just ask it a question as you would ask another human being. This is very different from current legal research technologies where you type in keywords and it retrieves documents that have those keywords. ROSS doesn’t retrieve thousands of documents for you to sift through. It gives you an evidence-based response.”
In law firms, the word “research” is a vague umbrella term that refers to all the database mining, web surfing and trips to the law library to find the relevant legal background and precedents in a particular case. In general, it’s grunt work, and it often gets thrown, at least at first, to associates and paralegals. Traditionally, when it comes time for a client to pay, the time spent researching gets bundled in with the hours racked up by senior level attorneys. But that is starting to change.
“People are reassessing the structure in place which revolves around the billable hour,” says Andrew Arruda, another of the ROSS co-founders. “A lot of clients have refused to pay for the time spent doing research. They see it as part of what you should be doing anyway.” He and his co-founders built ROSS in part to pick up some of that work.
What do most people misunderstand about Watson?
Watson is not one system. It’s not one supercomputer. It’s a set of services in the cloud that can be used and can be trained. All these Watson systems are learning systems.
Our main view here is there are industries where people are overwhelmed with information and they can’t keep up with what’s happening. So they can be trained by different entrepreneurs or enterprises for whatever use they need them to do.
How big of an impact can Watson make?
Watson is going to transform industries and professions—all kinds of industries. Specifically, healthcare was one we said, legal was two, and education was three—all of those industries being big data intensive, substantial industries.
So how is IBM getting that transformation underway?
We have a set of Watson APIs we put on a developer cloud. And those are services that people can build other applications on, whether they’re in veterinary medicine or in charitable giving or in legal. We’ll see lots of people that will do those. We have over 6,000 app that are under development on the Watson developer cloud.
We’ll combine both the types of services that are available on that developer cloud as well as things that we still have from the lab where we spent over 6 billion dollars a year on research.
Can you describe what Watson actually does?
All of these different services start to mimic the way that people interact with the world, whether it’s understanding sight or language or personality. And they learn.
How do they learn?
Watson always trains with experts so that it gets up to speed. Then people who are using it, when they see answers from Watson, they can say they think it’s right, they think it’s wrong, [or] they think it’s kind of right. But they get to provide their point of view so that the system can use it to improve.
We want these learning systems to be able to learn from the people that are teaching them. They train and teach it so that it then specifically applies to them and it’s unique to them. So, two systems that start off with the same generic service can be trained to be dramatically different by the time they’re done.
And they couldn’t have done it without Watson, the most famous thinking apparatus of them all—the one whose father beat Gary Kasparov at chess and who itself four years ago bested the winningest Jeopardy contestants in the history of the show.
Since that remarkable public victory, Watson has travelled to Africa to act as a financial, agricultural and medical advisor to an entire continent of business partners, and more recently to Japan, where it will learn the Japanese language for the first time. It has helped banks around the world create new services, and it’s now being taught by world-renowned experts in the field of oncology how to match patients to the most effective treatment plans.
It was a commercially available Watson API that begat ROSS.
Although it is commonly thought of as a monolithic supercomputer, the Watson technology is a cognitive system that does not actually exist as a single entity. Through its developer cloud, IBM has already made available 13 different Watson APIs, each adept at a different specialty that developers can build on to create a cloud service. There’s one that’s a master of communication styles. There’s one that recognizes and analyzes visual input. There’s one that’s really good at weighing the pros and cons of a complex situation.
According to John Gordon, vice president of innovation for the IBM Watson Group, more than 6,000 beta apps have been built off the Watson Developer Cloud. The API that ROSS uses is called “Question and Answer.” Its strengths are very similar to those that helped Watson obliterate its competition on Jeopardy. It can understand a complex question, look through a vast database of stored documents and then give you, in simple, natural language, the right answer (or, in Jeopardy’s case, the right question).
Even so, it was up to the Ross team to help train Watson about legal principles or case law. “It was almost like an infant,” says Ovbiagele. “It didn’t know anything, so what we were tasked with doing was providing Watson information and teaching it how to read that information including laws, legislation and court decisions.”
“The really amazing part about it is that it also learns through use,” says Arruda. Every time it answers a question, ROSS asks for feedback on its performance. Over time, as experts chime in on how it’s doing, ROSS’s answers become more representative of the answers you would have gotten from the human professionals themselves. This is one of the primary features of all Watson progeny.
Of course, one question burns bright in the minds of latest law school grads—is Watson here to take away all the entry-level positions? Richard Susskind, an author who has been thinking, writing and talking about these issues for his entire career, repeatedly admonishes people in the legal profession not to try holding onto their grunt work.
“The law is no more there to provide a living for our lawyers than ill health is there to provide a living for doctors,” he recently told an audience at the University of Southampton. “It’s not the purpose of the law to keep lawyers in business.”
“In medical and healthcare, we looked at what an oncologist would need to do to find data that helps them make a good decision,” says IBM’s Gordon. “They don’t want the systems to make decisions. They want the system to help wade through all the data that exists and put the relevant pieces in front of them so they can make better decisions. And we think that same model that worked very effectively in healthcare passes over into law.”
At the moment IBM is looking for the right partners and institutions that will give Watson the best legal education. In the future, you can expect to see autonomous legal aides that go beyond just answering simple questions, digital paralegals that can actually help lawyers devise new strategies for their cases.
But first, says Gordon, Watson must go back to school.
‘Given its ability to boost innovation, productivity, resilience, and help organizations scale, IT has become a high priority in a company’s budget. As such, there is every reason to believe technology spending in the B2B space will continue to surpass GDP growth,’ says IBM CEO Arvind Krishna.
A strengthening IT environment that is playing into IBM AI and hybrid cloud capabilities means a rosy future for IBM and its B2B business, CEO Arvind Krishna told investors Monday.
Krishna, in his prepared remarks for IBM’s second fiscal quarter 2022 financial analyst conference call, said that technology serves as a fundamental source of competitive advantage for businesses.
“It serves as both a deflationary force and a force multiplier, and is especially critical as clients face challenges on multiple fronts from supply chain bottlenecks to demographic shifts,” he said. “Given its ability to boost innovation, productivity, resilience, and help organizations scale, IT has become a high priority in a company’s budget. As such, there is every reason to believe technology spending in the B2B space will continue to surpass GDP growth.”
[Related: IBM STARTS ‘ORDERLY WIND-DOWN’ OF RUSSIA BUSINESS]
That plays well with IBM’s hybrid cloud and AI strategy where the company is investing in its offerings, technical talent, ecosystem, and go-to-market model, Krishna said.
“Demand for our solutions remains strong,” he said. “We continued to have double-digit performance in IBM Consulting, broad-based strength in software, and with the z16 [mainframe] platform launch, our infrastructure business had a good quarter. By integrating technology and expertise from IBM and our partners, our clients will continue to see our hybrid cloud and AI solutions as a crucial source of business opportunity and growth.”
Krishna said hybrid clouds are about offering clients a platform to straddle multiple public clouds, private clouds, on-premises infrastructures, and the edge, which is where Red Hat, which IBM acquired in 2019, comes into play, Krishna said.
“Our software has been optimized to run on that platform, and includes advanced data and AI, automation, and the security capabilities our clients need,” he said. “Our global team of consultants offers deep business expertise and co-creates with clients to accelerate their digital transformation journeys. Our infrastructure allows clients to take full advantage of an extended hybrid cloud environment.”
As a result, IBM now has over 4,000 hybrid cloud platform clients, with over 250 new clients added during the second fiscal quarter, Krishna said.
“Those who adopt our platform tend to consume more of our solutions across software, consulting, and infrastructure, [and] expanding our footprint within those clients,” he said.
IBM is also benefitting from the steady adoption by businesses of artificial intelligence technologies as those businesses try to process the enormous amount of data generated from hybrid cloud environments all the way to the edge, Krishna said. An IBM study released during the second fiscal quarter found that 35 percent of companies are now using some form of AI with automation in their business to address demographic shifts and move their employees to higher value work, he said.
“This is one of the many reasons we are investing heavily in both AI and automation,” he said. “These investments are paying off.”
IBM is also moving to develop leadership in quantum computing, Krishna said. The company currently has a 127-qubit quantum computer it its cloud, and is committed to demonstrate the first 400-plus-qubit system before year-end as part of its path to deliver a 1,000-plus-qubit system next year and a 4,000-plus-qubit system in 2025, he said.
“One of the implications of quantum computing will be the need to change how information is encrypted,” he said. “We are proud that technology developed by IBM and our collaborators has been selected by NIST (National Institute of Standards and Technology) as the basis of the next generation of quantum-safe encryption protocols.”
IBM during the quarter also move forward in its mainframe technology with the release of its new z16 mainframe, Krishna said.
“The z16 is designed for cloud-native development, cybersecurity resilience, [and] quantum-safe encryption, and includes an on-chip AI accelerator, which allows clients to reduce fraud within real-time transactions,” he said.
IBM also made two acquisitions during the quarter related to cybersecurity, Krishna said. The first was Randori, an attack surface management and offensive cybersecurity provider. That acquisition built on IBM’s November acquisition of ReaQta, an endpoint security firm, he said.
While analysts during the question and answer part of Monday’s financial analyst conference call did not ask about the news that IBM has brought in Matt Hicks as the new CEO of Red Hat, they did seem concerned about how the 17-percent growth in Red Had revenue over last year missed expectations.
When asked about Red Hat revenue, Krishna said IBM feels very good about the Red Hat business and expect continued strong demand.
“That said, we had said late last year that we expect growth in Red Hat to be in the upper teens,” he said. “That expectation is what we are going to continue with. … Deferred revenue accounts for the bulk of what has been the difference in the growth rates coming down from last year to this year.”
IBM CFO James Kavanaugh followed by saying that while IBM saw 17 percent growth overall for Red Hat, the company took market share with its core REL (Red Hat Enterprise Linux) and in its Red Hat OpenShift hybrid cloud platform foundation. Red Hat OpenShift revenue is now four-and-a-half times the revenue before IBM acquired Red Hat, and Red Hat OpenShift bookings were up over 50 percent, Kavanaugh said.
“So we feel pretty good about our Red Hat portfolio overall. … Remember, we‘re three years into this acquisition right now,” he said. “And we couldn’t be more pleased as we move forward.”
When asked about the potential impact from an economic downturn, Krishna said IBM’s pipelines remain healthy and consistent with what the company saw in the first half of fiscal 2022, making him more optimistic than many of his peers.
“In an inflationary environment, when clients take our technology, deploy it, leverage our consulting, it acts as a counterbalance to all of the inflation and all of the labor demographics that people are facing all over the globe,” he said.
Krishna also said IBM’s consulting business is less likely than most vendors’ business to be impacted by the economic cycle as it involves a lot of work around deploying the kinds of applications critical to clients’ need to optimize their costs. Furthermore, he said. Because consulting is very labor-intensive, it is easy to hire or let go tens of thousands of employees as needed, he said.
For its second fiscal quarter 2022, which ended June 30, IBM reported total revenue of $15.5 billion, up about 9 percent from the $14.2 billion the company reported for its second fiscal quarter 2021.
This includes software revenue of $6.2 billion, up from $5.9 billion; consulting revenue of $4.8 billion, up from $4.4 billion; infrastructure revenue of $4.2 billion, up from $3.6 billion; financing revenue of $146 million, down from $209 million; and other revenue of $180 million, down from $277 million.
On the software side, IBM reported annual recurring revenue of $12.9 billion, which was up 8 percent over last year. Software revenue from its Red Hat business was up 17 percent over last year, while automation software was up 8 percent, data and AI software up 4 percent, and security software up 5 percent.
On the consulting side, technology consulting revenue was up 23 percent over last year, applications operations up 17 percent, and business transformation up 16 percent.
Infrastructure revenue growth was driven by hybrid infrastructure sales, which rose 7 percent over last year, and infrastructure support, which grew 5 percent. Hybrid infrastructure revenue saw a significant boost from zSystems mainframe sales, which rose 77 percent over last year.
IBM also reported revenue of $8.1 billion from sales to the Americas, up 15 percent over last year; sales to Europe, Middle East, and Africa of $4.5 billion, up 17 percent; and $2.9 billion to the Asia Pacific area, up 16 percent.
Sales to Kyndryl, which late last year was spun out of IBM, accounted for about 5 percent of revenue, including 3 percent of IBM’s Americas revenue.
IBM also reported net income for the quarter on a GAAP basis of $1.39 billion, or $1.53 per share, up from last year’s $1.33 billion, or $1.47 per share.
The MarketWatch News Department was not involved in the creation of this content.
Jul 26, 2022 (Heraldkeepers) -- New Jersey, United States- This report assesses the Big Data in the Financial Service market according to a collection of perspectives, including market size, market status, market examples, and measures. It in like manner recollects brief information for competitors and expresses growth opportunities with key market drivers. The report contains a comprehensive market examination isolated by associations, area, type, and application. As they fight with mechanical advances, steadfastness, and quality difficulties, new dealers in the business face extreme resistance from spread out in general suppliers.
The assessment will give answers to stresses concerning current market designs, contention, and opportunity cost, from that point, the sky’s the cutoff. We need to give our clients research on the Big Data in the Financial Service Market that ganders at the market from 2022 to 2030. More information is one of the goals. The chief portion of the survey is focused on describing the business for the thing or organization that is the subject of the Big Data in the Financial Service Market assessment. The record will then research the elements that are hindering and propelling present-day improvement.
The worldwide Big Data in the Financial Service market is expected to grow at a booming CAGR of 2022-2030, rising from USD billion in 2021 to USD billion in 2030. It also shows the importance of the Big Data in the Financial Service market main players in the sector, including their business overviews, financial summaries, and SWOT assessments.
Big Data in the Financial Service Market Segmentation & Coverage:
Big Data in the Financial Service Market segment by Type:
Software & Service, Platform
Big Data in the Financial Service Market segment by Application:
Banks, Insurers, Personal, Other
The years examined in this study are the following to estimate the Big Data in the Financial Service market size:
History Year: 2015-2019
Base Year: 2021
Estimated Year: 2022
Forecast Year: 2022 to 2030
Cumulative Impact of COVID-19 on Market:
Gathering, transportation and methodologies, and retail and customer stock have been the hardest hit. Despite how essential things are disallowed from the lockdown, the shortfall of work to chip away at creation lines, supply chains, and transportation has destroyed the availability of major things. With the improvement of COVID-19 over the world, Big Data in the Financial Service market relationship with individuals who travel universally a large part of the time can take advantage of fundamental exchanges.
Get a demo Copy of the Big Data in the Financial Service Market Report: https://www.infinitybusinessinsights.com/request_sample.php?id=875477
The assessment will assist with showcasing individuals in making future business approaches and perceiving Big Data in the Financial Service market contention. The survey consolidates an all-out market division examination considering creators, regions, types, and applications. The market study consolidates snippets of data for a couple of geographies, including the United States, Europe, China, Japan, Southeast Asia, India, and Central and South America.
The Key companies profiled in the Big Data in the Financial Service Market:
The study examines the Big Data in the Financial Service market’s competitive landscape and includes data on important suppliers, including Microsoft, Teradata, IBM, SAP, Amazon (AWS), Oracle, Accenture (Pragsis Bidoop), Google, Adobe, Cisco,& Others
Table of Contents:
List of Data Sources:
Chapter 2. Executive Summary
Chapter 3. Industry Outlook
3.1. Big Data in the Financial Service Global Market segmentation
3.2. Big Data in the Financial Service Global Market size and growth prospects, 2015 – 2026
3.3. Big Data in the Financial Service Global Market Value Chain Analysis
3.3.1. Vendor landscape
3.4. Regulatory Framework
3.5. Market Dynamics
3.5.1. Market Driver Analysis
3.5.2. Market Restraint Analysis
3.6. Porter’s Analysis
3.6.1. Threat of New Entrants
3.6.2. Bargaining Power of Buyers
3.6.3. Bargaining Power of Buyers
3.6.4. Threat of Substitutes
3.6.5. Internal Rivalry
3.7. PESTEL Analysis
Chapter 4. Big Data in the Financial Service Global Market Product Outlook
Chapter 5. Big Data in the Financial Service Global Market Application Outlook
Chapter 6. Big Data in the Financial Service Global Market Geography Outlook
6.1. Big Data in the Financial Service Industry Share, by Geography, 2022 & 2030
6.2. North America
6.2.1. Market 2022 -2030 estimates and forecast, by product
6.2.2. Market 2022 -2030, estimates and forecast, by application
6.2.3. The U.S.
220.127.116.11. Market 2022 -2030 estimates and forecast, by product
18.104.22.168. Market 2022 -2030, estimates and forecast, by application
22.214.171.124. Market 2022 -2030 estimates and forecast, by product
126.96.36.199. Market 2022 -2030, estimates and forecast, by application
6.3.1. Market 2022 -2030 estimates and forecast, by product
6.3.2. Market 2022 -2030, estimates and forecast, by application
188.8.131.52. Market 2022 -2030 estimates and forecast, by product
184.108.40.206. Market 2022 -2030, estimates and forecast, by application
6.3.4. the UK
220.127.116.11. Market 2022 -2030 estimates and forecast, by product
18.104.22.168. Market 2022 -2030, estimates and forecast, by application
22.214.171.124. Market 2022 -2030 estimates and forecast, by product
126.96.36.199. Market 2022 -2030, estimates and forecast, by application
Chapter 7. Competitive Landscape
Chapter 8. Appendix
How might you calculate what the Big Data in the Financial Service market will make all through the accompanying two or three years?
What is the expected bearing of the Big Data in the Financial Service Market with respect to volume and worth during the measured time span?
What effect will macroeconomic powers have accessible later on?
What are the principal Big Data in the Financial Service market drivers?
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