When it comes to financial industry technologies and challenges, Alan Peacock has witnessed and worked with far more than most people.
After graduating from college in the early 1980s, Peacock joined the Royal Bank of Scotland, eventually rising to become its CTO. Following that were CTO and CIO positions with Lloyds Banking Group, and five years as the global head of IT Infrastructure for HSBC, one of the world’s largest banks, where he led a significant transformation of the company’s IT infrastructure and service management capabilities.
In June 2021 Peacock became the general manager of Delivery and Operations for IBM Cloud. His deep skills and expertise are especially valuable to IBM Cloud clients, including 19 out of the top 20 Fortune 500 banks and financial industry organizations. His experience is also well-attuned to the IBM Cloud for Financial Services and its ecosystem of global financial institutions and 90+ technology partners and FinTech companies.
Also see: Top Cloud Companies
Some highlights of a exact interview with Alan Peacock:
Pund-IT: What is IBM Cloud doing that is different or better than other public cloud platforms?
Peacock: IBM Cloud’s focus on both creating a cloud that is focused on regulated industries along with building an expert team that combines top talent from within IBM and experts from industry (including banking and financial services) hugely resonated with me and other peers in the industry that I spoke to before accepting the role in IBM.
Having people in IBM who fully understand the challenges that our banking clients have to manage on a day-to-day basis builds confidence with our clients.
Pund-IT: What are some of the lessons and practices you were able to bring to IBM Cloud from working with other cloud vendors in your previous roles?
Peacock: I brought many lessons to IBM Cloud based on my experiences consuming services from other large cloud providers. While they all had some great engineering capabilities, I needed deep industry expertise – the unique understanding of what it takes to run systemically important critical banking services that require very high levels of resilience and availability.
This is a very important point: Cloud native architecture and design is very different from traditional, on-premises deployments. If done incorrectly, cloud migrations can create less-stable services than organizations experienced with on-premises systems.
This is where you need people who can help partner to re-engineer business applications to utilize the great capabilities of cloud technology and to help build more resilient services. And, of course, IBM can bring significant industry expertise to assist. We have people who have previously built and run scale global operations that meet the needs of regulated industries like banking and financial services.
Also see: Why Cloud Means Cloud Native
Pund-IT: How does your experience complement your work in IBM Cloud?
Peacock: Having an understanding of what it takes from a banking and financial services client perspective makes a huge difference. You understand the challenges our clients have in their day-to-day roles and understand how you can help.
There are many lessons and best practice operational disciplines that I built over many years in banking that I have brought to IBM Cloud. Many of the disciplines that I built over the years in large banks are just as critical for cloud providers.
These disciplines have the clients at their heart. That client focus is a real differentiator for IBM Cloud and sets us apart from others in the market.
Pund-IT: Many people consider cloud infrastructures to be essentially different from traditional data centers. Is that something you agree with? Are there practices or lessons in traditional data centers that could or should be applied to cloud? And vice versa?
Peacock: There are undoubtedly some differences but there are also some similarities. Clouds are still made up of data centers, with servers, storage, networking and software. However, one of the major differences is that, historically, business applications relied on IT infrastructure to provide resilience.
In cloud native deployments, improved resilience is also built into the application software layer and not just the infrastructure layer that was often the case on premise. This is a significant change that organizations on their cloud journey need to be aware of.
Pund-IT: Broadly speaking, what were your strategic imperatives for IBM Cloud?
Peacock: The following were the key priorities.
1. Setting the tone from the top was vital. Ensuring security and service stability were and still are our top priorities.
2. Being close to and listening to our clients’ feedback to guide our focus.
3. Focusing on operational excellence.
4. Ensuring that we continue to build new features aligned to our clients’ priorities.
5. Supporting all of the above was creating a culture that supports learning and continuous improvement.
Also see: Cloud Native Winners and Losers
Pund-IT: How did you go about addressing those points?
Peacock: You might call it an unceasing hyper focus on clarity and priorities. First, I established clarity of our mission and accountability, established a rounded set of baseline KPIs so that we could measure our improvement progress and therefore focus on areas where we needed to improve. It is then a case of relentless focused execution and discipline while creating the right culture of learning and innovation. As part of our mission and execution focus, operational excellence and continuous improvement are key elements.
Pund-IT: What are some of the results of those efforts? How long did it take to achieve tangible results?
Peacock: Improvements were rapid. To provide a few examples
Pund-IT: In practical terms, how does IBM Cloud compare to/contrast with other public cloud platforms? What are its greatest strengths?
Peacock: While providing a secure, stable resilient platform, IBM Cloud differentiates itself versus other general purpose CSPs due to its additional security, control and compliance capabilities over and above general mass market CSPs.
We have developed capabilities that provide the required controls necessary to meet financial services compliance and control requirements. We developed this set of controls in consultation with multiple global and regional banks using their existing control frameworks. We have created a best-of-breed cloud set of controls that accelerates and de-risks the migration of services to the cloud.
Many of these controls are codified to automate monitoring and compliance evidence capture automatically, significantly reducing the manual effort burden on customers to meet control monitoring, audit and regulatory requirements. As well as the control benefits, this creates significant cost efficiencies for organizations.
Also see: Best Machine Learning Platforms
Pund-IT: It sounds like what IBM Cloud has done for banks and other financial services organizations could be replicated for other vertical industries.
Peacock: Although developed for financial services, due to the regulated nature of the FS industry, the control framework also hugely helps other industries to have confidence in migrating workloads to the IBM Cloud with lower risk.
For example, IBM works with numerous industry-specific ISVs to certify that their SaaS products meet the necessary security, compliance and controls required to operate safely in the cloud. This helps to accelerate the adoption of SaaS products and helps clients gain benefits faster, while being comfortable with the Security and Compliance controls that are in place.
This significantly helps organizations that are concerned about 3rd and 4th party risk, and again is a major differentiator for the IBM Cloud versus other cloud providers. Also, through the input of more than 100 Global CIOs, CROs and CISOs who participate in the Financial Services Cloud Councils, IBM Cloud continues to enhance our control framework.
That control framework is something that no other CSP has and provides a significant accelerator for organizations that are nervous about security and compliance controls in general purpose CSPs.
Finally, as regulations change and as we continue to learn and collaborate with the Global and Regional Financial Services Cloud Councils that have been formed and the regulators around the world, we in IBM Cloud will continue to enhance our capabilities to meet the needs of our clients and differentiate ourselves from other CSPs.
Also see: Top Business Intelligence Software
IBM is moving beyond tier-I cities in India to find talent. With its biggest shift in hiring strategy, the tech company has gone beyond the metros to recruit from places such as Mysore, Coimbatore, Ahmedabad, Hyderabad and Kochi.
“We do have some other things coming in the future,” Nickle LaMoreaux, chief human resources officer, told Moneycontrol.
With many employees wanting to avoid the metro cities or having moved back home after the pandemic, IBM’s hiring strategy allows HR teams to focus on a bigger pool of talent, she said.
LaMoreaux explains what IBM looks for in a candidate when hiring.
What does IBM prefer in candidates?
Demand at IBM is still extremely strong in hardware, software, and consulting.
Other than core skill sets such as new programming languages, the number one thing that IBM looks for is continuous learning – the ability, drive and curiosity of candidates to learn.
“It’s especially true in the IT industry, where the half-life of skills is shrinking,” LaMoreaux said.
She said earlier, if one knew Java, COBOL or C++, the person would last for 30-40 years in their career. “That's not true anymore,” she said.
During interviews, IBM prefers candidates who have “substantial pieces of evidence” to showcase their skill sets.
“One is any prior experience that you have – maybe it’s a work product, maybe something you created,” LaMoreaux said. “I've seen some candidates study the company and bring a proposal to the interview.”
This can be anything candidates think the company should do differently.
“It gives the interviewer a deep understanding of ‘Wow, this person has taken the time, the effort to understand the company.’ It also demonstrates the point about continuous learning,” LaMoreaux said.
Situational questions for prospective managers
When hiring managers, IBM carries out behaviour-based interviews that are situational or role-playing, asking candidates what they would do in a given situation.
“These situational questions help us to understand candidates’ thought process, reasoning and whether the person can apply it to new situations that can’t be predicted,” LaMoreaux said. “Because in our jobs, particularly knowledge jobs in the IT industry, no two days look the same.”
It’s about skills
The drastic shortage of skills and pandemic-induced learning have led IBM to rethink some aspects of its talent strategy. In India, IBM now adopts a ‘skill-first’ approach, where college degrees don’t matter.
“The biggest example is Python, which is a relatively new skill in the data science field that 20 years ago, somebody would not have gone and gotten a degree in,” LaMoreaux said. “Now, why do I care if you learned Python at an IIT or in the military, or you taught yourself at night from your home? It’s all about whether you have the right skills and making sure that you’re not closing the aperture.”IBM implemented the ‘skill-first’ approach previously in the US in 2012, when it removed the four-year college degree requirement from about 50 percent of its jobs. Ten years later, 20 percent of the present IBM US workforce in hardware, software, and consulting does not have a college degree.
Forty years after it first began to dabble in quantum computing, IBM is ready to expand the technology out of the lab and into more practical applications — like supercomputing! The company has already hit a number of development milestones since it released its previous quantum roadmap in 2020, including the 127-qubit Eagle processor that uses quantum circuits and the Qiskit Runtime API. IBM announced on Wednesday that it plans to further scale its quantum ambitions and has revised the 2020 roadmap with an even loftier goal of operating a 4,000-qubit system by 2025.
Before it sets about building the biggest quantum computer to date, IBM plans release its 433-qubit Osprey chip later this year and migrate the Qiskit Runtime to the cloud in 2023, “bringing a serverless approach into the core quantum software stack,” per Wednesday’s release. Those products will be followed later that year by Condor, a quantum chip IBM is billing as “the world’s first universal quantum processor with over 1,000 qubits.”
This rapid four-fold jump in quantum volume (the number of qubits packed into a processor) will enable users to run increasingly longer quantum circuits, while increasing the processing speed — measured in CLOPS (circuit layer operations per second) — from a maximum of 2,900 OPS to over 10,000. Then it’s just a simple matter of quadrupaling that capacity in the span of less than 24 months.
To do so, IBM plans to first get sets of multiple processors to communicate with one another both in parallel and in series. This should help develop better error mitigation schemes and Excellerate coordination between processors, both necessary components of tomorrow’s practical quantum computers. After that, IBM will design and deploy chip-level couplers, which “will closely connect multiple chips together to effectively form a single and larger processor,” according to the company, then build quantum communication links to connect those larger multi-processors together into even bigger clusters — essentially daisy-chaining increasingly larger clumps of processors together until they form a functional, modular 4,000-qubit computing platform.
“As quantum computing matures, we’re starting to see ourselves as more than quantum hardware,” IBM researcher Jay Gambetta wrote on Wednesday. “We’re building the next generation of computing. In order to benefit from our world-leading hardware, we need to develop the software and infrastructure capable of taking advantage of it.”
As such, IBM released a set of ready-made primitive programs earlier this year, “pre-built programs that allows developers easy access to the outputs of quantum computations without requiring intricate understanding of the hardware,” per the company. IBM intends to expand that program set in 2023, enabling developers to run them on parallelized quantum processors. “We also plan to enhance primitive performance with low-level compilation and post-processing methods, like introducing error suppression and mitigation tools,” Gambetta said. “These advanced primitives will allow algorithm developers to use Qiskit Runtime services as an API for incorporating quantum circuits and classical routines to build quantum workflows.”
These workflows will take a given problem, break it down into smaller quantum and classical programs, chew through those processes in either parallel or series depending on which is more efficient, and then use an orchestration layer to “circuit stitch” all those various data streams back into a coherent result that classical computers can understand. IBM calls its proprietary stitching infrastructure Quantum Serverless and, per the new roadmap, will deploy the feature to its core quantum software stack in 2023.
“We think by next year, we’ll begin prototyping quantum software applications for users hoping to use Qiskit Runtime and Quantum Serverless to address specific use cases,” Gambetta said. We’ll begin to define these services with our first test case — machine learning — working with partners to accelerate the path toward useful quantum software applications. By 2025, we think model developers will be able to explore quantum applications in machine learning, optimization, finance, natural sciences, and beyond.”
“For many years, CPU-centric supercomputers were society’s processing workhorse, with IBM serving as a key developer of these systems,” he continued. “In the last few years, we’ve seen the emergence of AI-centric supercomputers, where CPUs and GPUs work together in giant systems to tackle AI-heavy workloads. Now, IBM is ushering in the age of the quantum-centric supercomputer, where quantum resources — QPUs — will be woven together with CPUs and GPUs into a compute fabric. We think that the quantum-centric supercomputer will serve as an essential technology for those solving the toughest problems, those doing the most ground-breaking research, and those developing the most cutting-edge technology.”
Together, these hardware and software systems will become IBM Quantum System Two with the first prototype scheduled to be operational at some point next year.
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While some investors are already well versed in financial metrics (hat tip), this article is for those who would like to learn about Return On Equity (ROE) and why it is important. We'll use ROE to examine International Business Machines Corporation (NYSE:IBM), by way of a worked example.
Return on equity or ROE is an important factor to be considered by a shareholder because it tells them how effectively their capital is being reinvested. Put another way, it reveals the company's success at turning shareholder investments into profits.
Check out our latest analysis for International Business Machines
Return on equity can be calculated by using the formula:
Return on Equity = Net Profit (from continuing operations) ÷ Shareholders' Equity
So, based on the above formula, the ROE for International Business Machines is:
29% = US$5.6b ÷ US$19b (Based on the trailing twelve months to June 2022).
The 'return' is the amount earned after tax over the last twelve months. So, this means that for every $1 of its shareholder's investments, the company generates a profit of $0.29.
By comparing a company's ROE with its industry average, we can get a quick measure of how good it is. Importantly, this is far from a perfect measure, because companies differ significantly within the same industry classification. As is clear from the image below, International Business Machines has a better ROE than the average (16%) in the IT industry.
That's clearly a positive. Bear in mind, a high ROE doesn't always mean superior financial performance. Aside from changes in net income, a high ROE can also be the outcome of high debt relative to equity, which indicates risk. To know the 2 risks we have identified for International Business Machines visit our risks dashboard for free.
Companies usually need to invest money to grow their profits. The cash for investment can come from prior year profits (retained earnings), issuing new shares, or borrowing. In the case of the first and second options, the ROE will reflect this use of cash, for growth. In the latter case, the use of debt will Excellerate the returns, but will not change the equity. Thus the use of debt can Excellerate ROE, albeit along with extra risk in the case of stormy weather, metaphorically speaking.
International Business Machines does use a high amount of debt to increase returns. It has a debt to equity ratio of 2.58. While no doubt that its ROE is impressive, we would have been even more impressed had the company achieved this with lower debt. Investors should think carefully about how a company might perform if it was unable to borrow so easily, because credit markets do change over time.
Return on equity is a useful indicator of the ability of a business to generate profits and return them to shareholders. Companies that can achieve high returns on equity without too much debt are generally of good quality. All else being equal, a higher ROE is better.
But ROE is just one piece of a bigger puzzle, since high quality businesses often trade on high multiples of earnings. Profit growth rates, versus the expectations reflected in the price of the stock, are a particularly important to consider. So I think it may be worth checking this free report on analyst forecasts for the company.
If you would prefer check out another company -- one with potentially superior financials -- then do not miss this free list of interesting companies, that have HIGH return on equity and low debt.
Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team (at) simplywallst.com.
This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.
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Microcontrollers, miniature computers that can run simple commands, are the basis for billions of connected devices, from internet-of-things (IoT) devices to sensors in automobiles. But cheap, low-power microcontrollers have extremely limited memory and no operating system, making it challenging to train artificial intelligence models on "edge devices" that work independently from central computing resources.
Training a machine-learning model on an intelligent edge device allows it to adapt to new data and make better predictions. For instance, training a model on a smart keyboard could enable the keyboard to continually learn from the user's writing. However, the training process requires so much memory that it is typically done using powerful computers at a data center, before the model is deployed on a device. This is more costly and raises privacy issues since user data must be sent to a central server.
To address this problem, researchers at MIT and the MIT-IBM Watson AI Lab developed a new technique that enables on-device training using less than a quarter of a megabyte of memory. Other training solutions designed for connected devices can use more than 500 megabytes of memory, greatly exceeding the 256-kilobyte capacity of most microcontrollers (there are 1,024 kilobytes in one megabyte).
The intelligent algorithms and framework the researchers developed reduce the amount of computation required to train a model, which makes the process faster and more memory efficient. Their technique can be used to train a machine-learning model on a microcontroller in a matter of minutes.
This technique also preserves privacy by keeping data on the device, which could be especially beneficial when data are sensitive, such as in medical applications. It also could enable customization of a model based on the needs of users. Moreover, the framework preserves or improves the accuracy of the model when compared to other training approaches.
"Our study enables IoT devices to not only perform inference but also continuously update the AI models to newly collected data, paving the way for lifelong on-device learning. The low resource utilization makes deep learning more accessible and can have a broader reach, especially for low-power edge devices," says Song Han, an associate professor in the Department of Electrical Engineering and Computer Science (EECS), a member of the MIT-IBM Watson AI Lab, and senior author of the paper describing this innovation.
Joining Han on the paper are co-lead authors and EECS PhD students Ji Lin and Ligeng Zhu, as well as MIT postdocs Wei-Ming Chen and Wei-Chen Wang, and Chuang Gan, a principal research staff member at the MIT-IBM Watson AI Lab. The research will be presented at the Conference on Neural Information Processing Systems.
Han and his team previously addressed the memory and computational bottlenecks that exist when trying to run machine-learning models on tiny edge devices, as part of their TinyML initiative.
A common type of machine-learning model is known as a neural network. Loosely based on the human brain, these models contain layers of interconnected nodes, or neurons, that process data to complete a task, such as recognizing people in photos. The model must be trained first, which involves showing it millions of examples so it can learn the task. As it learns, the model increases or decreases the strength of the connections between neurons, which are known as weights.
The model may undergo hundreds of updates as it learns, and the intermediate activations must be stored during each round. In a neural network, activation is the middle layer's intermediate results. Because there may be millions of weights and activations, training a model requires much more memory than running a pre-trained model, Han explains.
Han and his collaborators employed two algorithmic solutions to make the training process more efficient and less memory-intensive. The first, known as sparse update, uses an algorithm that identifies the most important weights to update at each round of training. The algorithm starts freezing the weights one at a time until it sees the accuracy dip to a set threshold, then it stops. The remaining weights are updated, while the activations corresponding to the frozen weights don't need to be stored in memory.
"Updating the whole model is very expensive because there are a lot of activations, so people tend to update only the last layer, but as you can imagine, this hurts the accuracy. For our method, we selectively update those important weights and make sure the accuracy is fully preserved," Han says.
Their second solution involves quantized training and simplifying the weights, which are typically 32 bits. An algorithm rounds the weights so they are only eight bits, through a process known as quantization, which cuts the amount of memory for both training and inference. Inference is the process of applying a model to a dataset and generating a prediction. Then the algorithm applies a technique called quantization-aware scaling (QAS), which acts like a multiplier to adjust the ratio between weight and gradient, to avoid any drop in accuracy that may come from quantized training.
The researchers developed a system, called a tiny training engine, that can run these algorithmic innovations on a simple microcontroller that lacks an operating system. This system changes the order of steps in the training process so more work is completed in the compilation stage, before the model is deployed on the edge device.
"We push a lot of the computation, such as auto-differentiation and graph optimization, to compile time. We also aggressively prune the redundant operators to support sparse updates. Once at runtime, we have much less workload to do on the device," Han explains.
A successful speedup
Their optimization only required 157 kilobytes of memory to train a machine-learning model on a microcontroller, whereas other techniques designed for lightweight training would still need between 300 and 600 megabytes.
They tested their framework by training a computer vision model to detect people in images. After only 10 minutes of training, it learned to complete the task successfully. Their method was able to train a model more than 20 times faster than other approaches.
Now that they have demonstrated the success of these techniques for computer vision models, the researchers want to apply them to language models and different types of data, such as time-series data. At the same time, they want to use what they've learned to shrink the size of larger models without sacrificing accuracy, which could help reduce the carbon footprint of training large-scale machine-learning models.
This work is funded by the National Science Foundation, the MIT-IBM Watson AI Lab, the MIT AI Hardware Program, Amazon, Intel, Qualcomm, Ford Motor Company, and Google.
IBM and Linux Foundation AI (LFAI) launched Machine Learning eXchange (MLX) as a one stop shop for trusted data and AI artifacts in open source and open governance.
MLX provides a collection of free, open source, state-of-the-art deep learning models for common application domains. The curated list includes deployable models that can be run as a microservice on Kubernetes or OpenShift and trainable models where users can provide their own data to train the models.
It provides developers and data scientists with automated demo pipeline code generation to execute registered models, datasets, and notebooks, and a pipelines engine powered by Kubeflow Pipelines on Tekton, the core of Watson Studio Pipelines.
It also provides a registry for Kubeflow Pipeline Components, dataset management by Datashim, and a serving engine by KFServing.
“Due to the large number of steps that need to be worked on in the Data and AI lifecycle, the process of building a model can be bifurcated amongst various teams and large amounts of duplication can arise when creating similar Datasets, Features, Models, Pipelines, Pipeline tasks, etc. This also poses a strong challenge for traceability, governance, risk management, lineage tracking, and metadata collection,” the contributors to the project said. “To solve the problems mentioned above, we need a central repository where all the different asset types like Datasets, Models, and Pipelines are stored to be shared and reused across organizational boundaries.”
The MarketWatch News Department was not involved in the creation of this content.
Oct 10, 2022 (Alliance News via COMTEX) -- Quadintel's exact global IBM Watson Services market research report gives detailed facts with consideration to market size, cost revenue, trends, growth, capacity, and forecast till 2030. In addition, it includes an in-depth analysis of This market, including key factors impacting the market growth.
The global IBM Watson Services market is anticipated to grow at a CAGR of around 32.5% over the period of next 5 years.
This study offers information for creating plans to increase the market’s growth and effectiveness and is a comprehensive quantitative survey of the market.
Download Free demo of This Strategic Report :-https://www.quadintel.com/request-sample/ibm-watson-services-market/QI046
For industry executives, marketing, sales, and product managers, consultants, analysts, and stakeholders searching for vital industry data in easily accessible documents with clearly presented tables and graphs, the research contains historical data from 2017 to 2020 and predictions through 2030.
A component of IBM Corporation, The IBM Watson is a cognitive computing platform which aids in efficiency and agility of businesses by incorporating AI and other related technologies with advanced hypothesis generation and analytical algorithms.
It integrates various cognitive techniques for facilitating construction of software by crafting dialogues and defining intents for simulating conversion. These services are employed for processing insights, relationships and patterns across un-structured images, social media, emails and others.
The Watson introduced to shape businesses more intelligent; is delivered as a Software-as-a-Service on cloud and can be called by its clients using a small code snippet embedded in their system.
The growth of this market is attributed towards major relying factors including the proliferating usage ofIBM Watson servicesin healthcare & analytics across various regions, the growing demand for cognitive insight & digital technology globally and the rising number of technological advancements in healthcare as well as medical devices substantially etc.
Additionally, the advent of technologies such as machine learning, artificial intelligence, cognitive computing, natural language processing (NLP), data mining, and advanced text analytics have changed the whole working scenario of the healthcare industry. From quicker decision making, assisting in disease diagnosis, optimizing patient selection for clinical trials with intelligence matching, screening of patients? structured & unstructured data, fast marketing of new drug, the technological platforms of IBM Watson have been effectively aiding in the operations of healthcare sector over the past few years, which is thereby opening enormous growth opportunities for the market players existing in the market and eventually assisting in the growth of the overall market considerably.
Moreover, IBM Watson Services are also in extensive use in the media and entertainment industry since the last years and is contributing in the fueling of the market growth comprehensively.
Furthermore, other factors such as the effective and process downtime features of IBM Watson, the proliferating demand for collection of patient data in healthcare facilities, the rapid emergence of innovative drugs, the growing revolution in the field of medical devices & healthcare facilities and the increasing importance of data generated from the patients further augment the growth of the market.
However, few factors pertaining to IBM Watson Services such as the lack of trained professionals, the unstructured and fragmented data structuring technology, the imperfections in AI methodologies, their inability of making connections with different corpora, language issues, concerns relating to maintenance, the high switching cost and time-intensiveness involved in installation and training of the process are major barriers which hamper the growth of this market.
Access full Report Description, TOC, Table of Figure, Chart, etc. @https://www.quadintel.com/request-sample/ibm-watson-services-market/QI046
IBM WATSON SERVICES MARKET SEGMENTATION:
Watson Knowledge Catalog
Watson AI Assistant
Watson IoT Platform
Watson Speech to Text (STT)
Watson Text to Speech (TTS)
Watson Language Services
Watson Visual Recognition
Watson Tone Analyzer
Watson Personality Insights
Watson Data Refinery
Watson Machine Learning
Watson Deep Learning
Watson Compare and Comply
By End User Industry:
Discrete & Process Manufacturing
Media & Entertainment
Transportation & Logistics
Travel & Tourism
Middle East & Africa
The North America region followed by the European region holds the largest share in the IBM Watson Services market. The region is also expected to bolster tremendous growth in the upcoming years owing to factors such as the introduction of the Watson development platform in region by IBM for various purposes, the acquisition of a leading digital marketing & creative agency based in the U.S., Resource/Ammirati by IBM with a goal to create transformative brand experiences, the surging application of IBM Watson APIs for providing interactive mobile experiences to consumers in the region and the successful development of the production capacities of industries by these services in the region etc. The major contributors to the region include U.S and Canada.
The Asia Pacific region is the fastest growing regional market for IBM Watson Services in the world and is projected to also grow robustly in the upcoming years as well. The growth in the region can be attributed to factors such as the growing adoption of technologies such as blockchain, cognitive computing and others in various industries for assisting in commercialization and rapid prototyping of the client?s solutions in the region and the expansion of IBM?s headquarters in the major economies of this region etc. Japan, South Korea, India and China are the major contributors to this region?s growth.
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FEW KEY PLAYERS IN IBM WATSON SERVICES MARKET:
KPMG International Limited
Tata Consultancy Services Limited
Datamato Technologies Private Ltd.
Mainline Information Systems Inc.
DXC Technology Limited Accenture Plc
Deloitte Touche Tohmatsu Ltd.
Tech Mahindra limited
In February 2021, Humana Inc. and IBM Watson Health announced a collaboration leveraging IBM?s conversational artificial intelligence (AI) solution to help provide a better member experience while providing greater clarity and transparency on benefits and other related matters for Humana Employer Group members. As part of the agreement, Humana will deploy IBM Watson Assistant for Health Benefits, an AI-enabled virtual assistant built in the IBM Watson Health cloud.
In February 2021, IBM and Palantir Technologies announced a new partnership consisting of IBM?s hybrid cloud data platform designed to deliver AI for business, with Palantir?s next generation operations platform for building applications. The product is expected to simplify how businesses build and deploy AI-infused applications with IBM Watson and help users access, analyze, and take action on the vast amounts of data that is scattered across hybrid cloud environments without the need for deep technical skills. The new product, Palantir for IBM Cloud Pak for Data, is planned to be mace available in March of 2021.
Request Full Report : -https://www.quadintel.com/request-sample/ibm-watson-services-market/QI046
We are the best market research reports provider in the industry. Quadintel believes in providing quality reports to clients to meet the top line and bottom line goals which will boost your market share in today's competitive environment. Quadintel is a 'one-stop solution' for individuals, organizations, and industries that are looking for innovative market research reports.
We will help you in finding the upcoming trends that will entitle you as a leader in the industry. We are here to work with you on your objective which will create an immense opportunity for your organization. Our priority is to provide high-level customer satisfaction by providing innovative reports that enable them to take a strategic decision and generate revenue. We update our database on a day-to-day basis to provide the latest reports. We assist our clients in understanding the emerging trends so that they can invest smartly and can make optimum utilization of resources available.
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The latest release from WMR titled Healthcare Operational Analytics Market Research Report 2022-2029 contains all relevant information and Growth Factors. Providing its clients with accurate data, it provides the market outlook and helps in the making of crucial decisions. The market is described in general terms, along with its definition, uses, advancements, and production technology. This market research report on Healthcare Operational Analytics keeps tabs on all emerging advancements and changes in the industry. It provides information on the challenges faced when starting a business and offers advice on how to deal with impending difficulties. Healthcare Operational Analytics Market Research with 100+ market data Tables, Pie Chat, Graphs & Figures is now released BY WMR.
Ask For demo Report: https://www.worldwidemarketreports.com/sample/817083
The research offers a thorough analysis of the market, taking into consideration important factors including projected sales, cost analysis, import/export, production and consumption trends, CAGR, gross margin, and supply and demand trends. Additionally, it highlights current technological improvements, product innovations, and R&D initiatives in the area.
Analysis By Key Players:
◘ Truven Health Analytics
◘ Verisk Analytics
Analysis By Type
◘ Supply chain analytics
◘ Human resource analytics
◘ Strategic analytics
Analysis By Application
The Healthcare Operational Analytics research report provides an overview of the market, covering definition, applications, product launches, developments, challenges, and geographical regions. Forecasts indicate that the industry will demonstrate high growth due to increased demand in many markets. The Healthcare Operational Analytics study offers an analysis of the market designs currently in use as well as other fundamental aspects.
If you have any queries related to the Healthcare Operational Analytics market report, you can ask our expert: https://www.worldwidemarketreports.com/quiry/817083
𝐓𝐡𝐢𝐬 𝐫𝐞𝐩𝐨𝐫𝐭 𝐚𝐢𝐦𝐬 𝐭𝐨 𝐩𝐫𝐨𝐯𝐢𝐝𝐞:
An examination of the dynamics, trends, and projections for the years 2022 through 2029, both qualitatively and quantitatively.
The ability of the customers and suppliers to make financially advantageous decisions and expand their businesses is explained by the use of analysis techniques like SWOT analysis and Porter’s five force analysis.
The detailed research of market segmentation helps in identifying the current market opportunities.
By collecting unbiased information under one roof, our Healthcare Operational Analytics report ultimately helps you save time and money.
𝐑𝐞𝐠𝐢𝐨𝐧-𝐖𝐢𝐬𝐞 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐭𝐡𝐞 Healthcare Operational Analytics Market:
◘ The Middle East and Africa (Turkey, GCC Countries, Egypt, South Africa)
◘ North America (United States, Mexico, and Canada)
◘ South America (Brazil etc.)
◘ Europe (Germany, Russia, UK, Italy, France, etc.)
◘ Asia-Pacific (Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)
𝐊𝐞𝐲 𝐈𝐧𝐝𝐢𝐜𝐚𝐭𝐨𝐫𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐞𝐝
Market Players & Competitor Analysis: The report covers the key players of the industry including Company Profile, Product Specifications, Production Capacity/Sales, Revenue, Price, and Gross Margin & Sales with a thorough analysis of the market’s competitive landscape and detailed information on vendors and comprehensive details of factors that will challenge the growth of major market vendors.
𝐑𝐞𝐠𝐢𝐨𝐧𝐚𝐥 𝐌𝐚𝐫𝐤𝐞𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬:
The report includes the & Regional market status and outlook Further the report provides breakdown details about each region & country covered in the report. Identifying its sales, sales volume & revenue forecast. With detailed analysis by types and applications.
Market key trends include Increased Competition and Continuous Innovations.
𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬 𝐚𝐧𝐝 𝐃𝐫𝐢𝐯𝐞𝐫𝐬:
Identifying the Growing Demands and New Technology
𝐏𝐨𝐫𝐭𝐞𝐫’𝐬 𝐅𝐢𝐯𝐞 𝐅𝐨𝐫𝐜𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬:
The report provides the state of competition in the industry depends on five basic forces: the threat of new entrants, the bargaining power of suppliers, the bargaining power of buyers, the threat of substitute products or services, and existing industry rivalry.
Healthcare Operational Analytics Market 𝐓𝐚𝐛𝐥𝐞 𝐎𝐟 𝐂𝐨𝐧𝐭𝐞𝐧𝐭:
1. Healthcare Operational Analytics Market Introduction
2. Executive Summary
2.1.Key Findings by Major Segments
2.2.Top strategies by Major Players
3. Global Healthcare Operational Analytics Market Overview
3.1.Healthcare Operational Analytics Market Dynamics
3.2.COVID-19 Impact Analysis in Global Healthcare Operational Analytics Market
3.4.Opportunity Map Analysis
3.5.PORTER’S Five Forces Analysis
3.6.Market Competition Scenario Analysis
3.7.Product Life Cycle Analysis
3.8.Manufacturer Intensity Map
3.9.Major Companies sales by Value & Volume
𝐓𝐡𝐞 𝐟𝐨𝐥𝐥𝐨𝐰𝐢𝐧𝐠 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐩𝐫𝐢𝐦𝐚𝐫𝐲 𝐫𝐞𝐚𝐬𝐨𝐧𝐬 𝐭𝐨 𝐩𝐮𝐫𝐜𝐡𝐚𝐬𝐞 𝐭𝐡𝐞 Healthcare Operational Analytics Market report:
The global Healthcare Operational Analytics market research analysis provides exact and thorough insightful insights on industry trends, allowing businesses to make useful and smart decisions to gain a competitive advantage over the competition.
It provides a complete analysis of the Healthcare Operational Analytics market as well as the most exact rising industry trends in the global Healthcare Operational Analytics market.
The global Healthcare Operational Analytics market is comprised of valuable suppliers, industry trends, and massive movement in demand from 2022 to 2029.
Finally, the global Healthcare Operational Analytics market report provides a systematic and descriptive analysis of the Healthcare Operational Analytics market, supported by historical and current information of key players and vendors, and all of the factors mentioned above, as well as potential future developments, to help in gaining critical insights regarding revenue, volume, and others, which could benefit clients in business-related decisions.
𝐁𝐮𝐲 𝐓𝐡𝐢𝐬 𝐏𝐫𝐞𝐦𝐢𝐮𝐦 𝐑𝐞𝐩𝐨𝐫𝐭 𝐔𝐩𝐭𝐨 𝟕𝟎% 𝐃𝐢𝐬𝐜𝐨𝐮𝐧𝐭 𝐚𝐭: https://www.worldwidemarketreports.com/promobuy/817083
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Batch 2 of the Chief Operations Officer Programme by IIM Lucknow is set to begin in December. This program, in collaboration with Emeritus — a platform aimed at providing quality online learning for in-demand skills — will be taught by the leading faculty at IIM Lucknow. This programme aims to upskill professionals toward leading operational excellence and business growth.
A PTI report stated that IIM Lucknow is ranked sixth among the top business schools in India as per NIRF 2022. The institute announced that the Chief Operations Officer Programme will be launched on December 30, 2022. The first batch of this 11-month programme — which received a rating of 4.4 out of 5 — trains business leaders and aspiring new COOs to gain the skills required to Excellerate their organisation’s operational efficiency resilience to disruption and scale sustainable business growth, stated the report.
According to a study by IBM Insight, 2021, about 81% of the COOs in the world rely on data to Excellerate their organisation’s operational efficiency. This data is based on customer demand, technology and data that is undergoing seismic shifts. Increasingly, COOs are at the forefront of their organisation's sprint toward transformation.
The PTI report also stated that the position of a COO in a company is essential for the growth of the business and the COO must find new ways to recalibrate their priorities and focus on cost control and efficiency.
This programme by the IIM Lucknow will train the students to take up mid and senior-level operational leaders and business heads who want to increase their existing knowledge and transition into COO roles. This training will allow these professionals to enhance their practical aligned and industry-aligned skills with a robust knowledge of both digital and engineering operations management roles. The sessions of this programme will be organised through an online session that is in collaboration with the Emeritus and five-day in-campus sessions in which the lead faculty of IIM Lucknow will be coaching. The PTI report stated that this will have a good impact as eight of ten IIM Lucknow professors were senior professionals and business leaders with over 10 years of experience in the field.
Commenting on the launch of the programme, Programme Directors, Dr Suresh K Jakhar, Associate Professor, Operations Management and Dr Himanshu Rathore, Assistant Professor, Operations Management, IIM Lucknow, said, “As traditional boundaries fade, a COO must go beyond operational excellence and become a company's strategic steward. In a modern world of complexity and rapid changes, there are two kinds of organisations.” He added, "As a Chief Operations Officer, how do you ensure your organisation is a thriver? How do you increase responsiveness, create new sources of value, and develop sustainable growth? We have designed the programme curriculum to provide the right balance between functional excellence and visionary growth,” as reported by PTI.
Mohan Kannegal, CEO of India and APAC Emeritus, added, “As per PWC's 25th CEO survey, Indian leaders are including new metrics in their companies' long-term corporate strategy. 81% include customer satisfaction metrics, while 75% focus on employee engagement metrics and 78% include automation and digitisation goals. This calls for nothing less than reimagining the way companies are run.”
The second batch commences on December 30 and has a programme fee of INR 4,30,000 + GST, with an early bird discount of INR 30,000 + GST for participants who apply by Monday, October 31, 2022.