The federal government through the Ministry of Communications and Digital Economy, has signed a memorandum of understanding (MoU) with Tech giant, International Business Machines (IBM) West Africa.
The deal is for partnership and collaboration in the area of digital skills development in Nigeria.
The Minister of Communications and Digital Economy, Dr. Isa Pantami, who signed on behalf of the federal government, said the partnership would provide impetus to the digital, innovation and entrepreneurship skills of the economic development plan of President Muhammadu Buhari.
He described the partnership as a quantum leap in the digital economy strategy of the ministry.
The MoU, which was signed in Abuja recently, is scheduled to take off in February 2020.
The MoU provides a platform to empower Nigerian youths with digital literacy skills, enable innovation, design and development of indigenous solutions, self -sufficiency and make Nigeria a hub for critical skills for Africa and the world at large.
Under the partnership, and in line with the Digital Literacy initiative and drive of the Minister, IBM would through its Digital Nation Africa Initiative, provide free training to Nigerians for a period of 12 to 16 weeks, in diverse areas of Information Technology (IT).
The MoU seeks to create awareness and support in the development and use of digital tools and applications to Boost the delivery of government services; create a pool of Nigerians with digital skills validated by globally recognised certifications; bridge the gap between the academia and the industry through sensitisation on digital tools and skills; and lower the access barrier to digital tools for the citizens.
Addressing IBM representatives led by the Country General Manager, Pantami expressed satisfaction at the organisation’s response to the digital economy policy by, sufficiently keying in, to bridge the divide between the academia and the industry, education and entrepreneurship.
Pantami noted that, “to achieve a digital economy, digital skills are central, and this has been adequately captured in the second pillar of the Digital Economy Strategy Policy Document as approved and launched by the President on the 28th of November 2019.”
The minister further disclosed that the importance of broadband in the implementation of a digital economy is the life line to its success and this again has been reflected in the seventh pillar of the strategy document.
“The importance of broadband penetration in achieving a digital economy has given rise to the National Broadband Committee to ensure that we thoroughly address the impediments to broadband penetration and achieving a Digital Economy,” Pantami said.
The minister urged institutions of learning to provide priority to skills, especially digital skills over paper qualifications.
According to him, “Digital skills are more relevant in today’s world of emerging technologies, therefore we must encourage innovation and drive digital literacy and skills among the populace.”
In his remarks, the Country General Manager at IBM, Mr. Dipo Faulkner, said: “IBM works with governments and key Ministries to address the societal impact of digital technology, leveraging our investment in education with platforms such as IBM Digital Nation Africa. This new collaboration furthers our aims of scaling digital job skills across Africa.”
The “Cirrus” Power10 processor from IBM, which we codenamed for Big Blue because it refused to do it publicly and because we understand the value of a synonym here at The Next Platform, shipped last September in the “Denali” Power E1080 big iron NUMA machine. And today, the rest of the Power10-based Power Systems product line is being fleshed out with the launch of entry and midrange machines – many of which are suitable for supporting HPC and AI workloads as well as in-memory databases and other workloads in large enterprises.
The question is, will IBM care about traditional HPC simulation and modeling ever again with the same vigor that it has in past decades? And can Power10 help reinvigorate the HPC and AI business at IBM. We are not sure about the answer to the first question, and got the distinct impression from Ken King, the general manager of the Power Systems business, that HPC proper was not a high priority when we spoke to him back in February about this. But we continue to believe that the Power10 platform has some attributes that make it appealing for data analytics and other workloads that need to be either scaled out across small machines or scaled up across big ones.
Today, we are just going to talk about the five entry Power10 machines, which have one or two processor sockets in a standard 2U or 4U form factor, and then we will follow up with an analysis of the Power E1050, which is a four socket machine that fits into a 4U form factor. And the question we wanted to answer was simple: Can a Power10 processor hold its own against X86 server chips from Intel and AMD when it comes to basic CPU-only floating point computing.
This is an important question because there are plenty of workloads that have not been accelerated by GPUs in the HPC arena, and for these workloads, the Power10 architecture could prove to be very interesting if IBM thought outside of the box a little. This is particularly true when considering the feature called memory inception, which is in effect the ability to build a memory area network across clusters of machines and which we have discussed a little in the past.
We went deep into the architecture of the Power10 chip two years ago when it was presented at the Hot Chip conference, and we are not going to go over that ground again here. Suffice it to say that this chip can hold its own against Intel’s current “Ice Lake” Xeon SPs, launched in April 2021, and AMD’s current “Milan” Epyc 7003s, launched in March 2021. And this makes sense because the original plan was to have a Power10 chip in the field with 24 fat cores and 48 skinny ones, using dual-chip modules, using 10 nanometer processes from IBM’s former foundry partner, Globalfoundries, sometime in 2021, three years after the Power9 chip launched in 2018. Globalfoundries did not get the 10 nanometer processes working, and it botched a jump to 7 nanometers and spiked it, and that left IBM jumping to Samsung to be its first server chip partner for its foundry using its 7 nanometer processes. IBM took the opportunity of the Power10 delay to reimplement the Power ISA in a new Power10 core and then added some matrix math overlays to its vector units to make it a good AI inference engine.
IBM also created a beefier core and dropped the core count back to 16 on a die in SMT8 mode, which is an implementation of simultaneous multithreading that has up to eight processing threads per core, and also was thinking about an SMT4 design which would double the core count to 32 per chip. But we have not seen that today, and with IBM not chasing Google and other hyperscalers with Power10, we may never see it. But it was in the roadmaps way back when.
What IBM has done in the entry machines is put two Power10 chips inside of a single socket to increase the core count, but it is looking like the yields on the chips are not as high as IBM might have wanted. When IBM first started talking about the Power10 chip, it said it would have 15 or 30 cores, which was a strange number, and that is because it kept one SMT8 core or two SMT4 cores in reserve as a hedge against bad yields. In the products that IBM is rolling out today, mostly for its existing AIX Unix and IBM i (formerly OS/400) enterprise accounts, the core counts on the dies are much lower, with 4, 8, 10, or 12 of the 16 cores active. The Power10 cores have roughly 70 percent more performance than the Power9 cores in these entry machines, and that is a lot of performance for many enterprise customers – enough to get through a few years of growth on their workloads. IBM is charging a bit more for the Power10 machines compared to the Power9 machines, according to Steve Sibley, vice president of Power product management at IBM, but the bang for the buck is definitely improving across the generations. At the very low end with the Power S1014 machine that is aimed at small and midrange businesses running ERP workloads on the IBM i software stack, that improvement is in the range of 40 percent, provide or take, and the price increase is somewhere between 20 percent and 25 percent depending on the configuration.
Pricing is not yet available on any of these entry Power10 machines, which ship on July 22. When we find out more, we will do more analysis of the price/performance.
There are six new entry Power10 machines, the feeds and speeds of which are shown below:
For the HPC crowd, the Power L1022 and the Power L1024 are probably the most interesting ones because they are designed to only run Linux and, if they are like prior L classified machines in the Power8 and Power9 families, will have lower pricing for CPU, memory, and storage, allowing them to better compete against X86 systems running Linux in cluster environments. This will be particularly important as IBM pushed Red Hat OpenShift as a container platform for not only enterprise workloads but also for HPC and data analytic workloads that are also being containerized these days.
One thing to note about these machines: IBM is using its OpenCAPI Memory Interface, which as we explained in the past is using the “Bluelink” I/O interconnect for NUMA links and accelerator attachment as a memory controller. IBM is now calling this the Open Memory Interface, and these systems have twice as many memory channels as a typical X86 server chip and therefore have a lot more aggregate bandwidth coming off the sockets. The OMI memory makes use of a Differential DIMM form factor that employs DDR4 memory running at 3.2 GHz, and it will be no big deal for IBM to swap in DDR5 memory chips into its DDIMMs when they are out and the price is not crazy. IBM is offering memory features with 32 GB, 64 GB, and 128 GB capacities today in these machines and will offer 256 GB DDIMMs on November 14, which is how you get the maximum capacities shown in the table above. The important thing for HPC customers is that IBM is delivering 409 GB/sec of memory bandwidth per socket and 2 TB of memory per socket.
By the way, the only storage in these machines is NVM-Express flash drives. No disk, no plain vanilla flash SSDs. The machines also support a mix of PCI-Express 4.0 and PCI-Express 5.0 slots, and do not yet support the CXL protocol created by Intel and backed by IBM even though it loves its own Bluelink OpenCAPI interconnect for linking memory and accelerators to the Power compute engines.
Here are the different processor SKUs offered in the Power10 entry machines:
As far as we are concerned, the 24-core Power10 DCM feature EPGK processor in the Power L1024 is the only interesting one for HPC work, aside from what a theoretical 32-core Power10 DCM might be able to do. And just for fun, we sat down and figured out the peak theoretical 64-bit floating point performance, at all-core base and all-core turbo clock speeds, for these two Power10 chips and their rivals in the Intel and AMD CPU lineups. Take a gander at this:
We have no idea what the pricing will be for a processor module in these entry Power10 machines, so we took a stab at what the 24-core variant might cost to be competitive with the X86 alternatives based solely on FP64 throughput and then reckoned the performance of what a full-on 32-core Power10 DCM might be.
The answer is that IBM can absolutely compete, flops to flops, with the best Intel and AMD have right now. And it has a very good matrix math engine as well, which these chips do not.
The problem is, Intel has “Sapphire Rapids” Xeon SPs in the works, which we think will have four 18-core chiplets for a total of 72 cores, but only 56 of them will be exposed because of yield issues that Intel has with its SuperFIN 10 nanometer (Intel 7) process. And AMD has 96-core “Genoa” Epyc 7004s in the works, too. Power11 is several years away, so if IBM wants to play in HPC, Samsung has to get the yields up on the Power10 chips so IBM can sell more cores in a box. Big Blue already has the memory capacity and memory bandwidth advantage. We will see if its L-class Power10 systems can compete on price and performance once we find out more. And we will also explore how memory clustering might make for a very interesting compute platform based on a mix of fat NUMA and memory-less skinny nodes. We have some ideas about how this might play out.
Note: An asterick (*) indictates a new course that is being finalized for approval.
CDA 501/EAS 503 Introduction to Data Driven Analysis
This course introduces students to computer science fundamentals for building basic data science applications. The course has two components. The first part introduces students to algorithm design and implementation in a modern, high-level, programming language (currently, Python). It emphasizes problem-solving by abstraction. subjects include data types, variables, expressions, basic imperative programming techniques including assignment, input/output, subprograms, parameters, selection, iteration, Boolean type, and expressions, and the use of aggregate data structures including arrays. Students will also have an introduction to the basics of abstract data types and object-oriented design. The second part covers regression analysis and introduction to linear models. subjects include multiple regression, analysis of covariance, least square means, logistic regression, and nonlinear regression. The students learn to implement the regression models as a computer program and use the developed application to analyze synthetic and real world data sets.
CDA 502/MGS 613 Database Management Systems
This course provides basic understanding of relational databases including normalization, database schemas and relational algebra, create, update, query and delete tables using standard SQL statements, understand workflows such as ETL (extract, transform, and load) to aggregate data from multiple sources integrating it in databases and data warehouses use, manage and customize NoSQL databases including key value, wide column, document and graph stores as well as their application on non-tabular data, use, manage and customize graph databases and apply them to multi-dimensional datasets.
CDA 511 Introduction to Numerical Analysis
A first course on the design and implementation of numerical methods to solve the most common types of problem arising in science and engineering. Most such problems cannot be solved in terms of a closed analytical formula, but many can be handled with numerical methods learned in this course. subjects for the two semesters include: how a computer does arithmetic, solving systems of simultaneous linear or nonlinear equations, finding eigenvalues and eigenvectors of (large) matrices, minimizing a function of many variables, fitting smooth functions to data points (interpolation and regression), computing integrals, solving ordinary differential equations (initial and boundary value problems), and solving partial differential equations of elliptic, parabolic, and hyperbolic types. We study how and why numerical methods work, and also their errors and limitations. Students gain practical experience through course projects that entail writing computer programs.
CDA 531/MTH 511 Probability and Data Analysis
Topics include: review of probability, conditional probability, Bayes' Theorem; random variables and distributions; expectation and properties; covariance, correlation, and conditional expectation; special distributions; Central Limit Theorem and applications; estimations, including Bayes; estimators, maximum likelihood estimators, and their properties. Includes use of sufficient statistics to 'improve' estimators, distribution of estimators, unbiasedness, hypothesis testing, linear statistical models, and statistical inference from the Bayesian point of view.
CDA 541/STA 545 Statistical Data Mining 1
This course presents statistical models for data mining, inference and prediction. The focus will be on supervised learning, which concerns outcome prediction from input data. Students will be introduced to a number of methods for supervised learning, including: linear and logistic regression, shrinkage methods, lasso, partial least squares, tree-based methods, model assessment and selection, model inference and averaging, and neural networks. Computational applications will be presented using R and high dimensional data to reinforce theoretical concepts.
CDA 546/STA 546 Statistical Data Mining 2
This course presents the Topic of data mining from a statistical perspective, with attention directed towards both applied and theoretical considerations. An emphasis will be placed on unsupervised learning methods, especially those designed to discover and exploit hidden structures in high-dimensional data. subjects include: hierarchical and center based clustering, principal component analysis, data visualization, random forests, directed and undirected graphical models, and special considerations when n>>p. Computational applications to high-dimensional data will be presented using Matlab and R to illustrate methods and concepts.
CDA 542/CSE 574 Machine Learning
Humans have an uncanny ability to learn from their mistakes and adapt to new environments by relying on their past experience. Machine learning focuses on "How to write a computer program than can Boost performance through experience?" Machine learning has a huge number of practical applications, more so in the present era of Big Data, where staggering volumes of diverse data in almost every facet of society, science, engineering, and commerce, are presenting opportunities for valuable discoveries. For example, machine learning is being used to understand financial markets, impact of climate change on society, protein-protein interactions, diseases, etc. Machine learning also has far ranging applications such as self-driving cars to never ending language learning systems. This course will focus on understanding the mathematical and statistical foundations of machine learning. We will also cover the core set of techniques and algorithms needed to understand the practical applications of machine learning. The course will be an integrated view of machine learning, statistics (classical and Bayesian), data mining, and information theory. A basic understanding of probability, statistics, algorithms, and linear algebra is expected. Familiarity with Python is required for homework assignments and for understanding in-class demonstrations.
CDA 551/MGS 639 Cybersecurity Privacy and Ethics
Present-day terms, philosophies, technologies, and strategies that go into buttressing an organization’s cybersecurity posture. Managing the resources of a corporate information assurance program, while continually improving a risk footprint and response, is an underpinning of all subjects that will be covered. Students will critically examine concepts such as networking, system administration, and system security as well as identifying and applying basic security hardening techniques. Students will gain practical experience through a virtualized lab environment where they will build and secure a small corporate network.
*CDA 561 Major Applications - Health, Social, Finance, Science and Engineering
This course will provide students with an overview of data driven analytics in different industry sectors. The class will have a series of visiting lecturers with the faculty member teaching the class providing overview, continuity and grading of homework and term papers.
CDA 571/ Project Guidance
This course will provide students with a final integrative project experience. The class will require students to obtain an integrative project experience either in industry or at the university. In either case the students will use the skills acquired during the other classes in executing project goals. Students will provide short reports to supervising faculty to ensure that learning objectives are being met.
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Artificial intelligence (AI) is the most revolutionary, consequential technology to have come along in decades. And while that’s exciting, it’s also ominous. Get it wrong, and you’re sunk.
At the same time, AI poses such an implantation challenge that getting it wrong is more likely than not. It will touch every digital aspect of the enterprise eventually, which means it is rife with pitfalls, namely, in the integration, training and execution phases of the rollout and, for many it will lead to a wholesale reworking of processes and even the business model itself.
On the surface, it would appear that AI deployments are moving along swimmingly. IBM reports that more than a third of companies say they are using AI in their businesses in some way, and another 42 percent are in the exploratory stage. Nevertheless, things like costs, lack of expertise and the inability to develop workable models are hampering these efforts, and far too few organizations are concentrating on fundamental aspects of AI like building trust, removing bias and tracking performance.
It’s no wonder, then, that many organizations – even large ones with substantial resources – are caught in a kind of AI paralysis. Even those that have dipped their toes in these waters are findingfew concrete examples of success.
For this reason, AI advocates are starting to shift the focus away from all the magical things the technology can do to more practical matters like how to deploy it in an efficient, effective manner. One example is a new book by six leaders in the field called Demystifying AI for the Enterprise. The book lays out a framework to overcome key implementation issues, such as understanding AI’s limits and targeting it at key problems that it can solve. As well, organizations should understand that, unlike previous technologies, AI doesn’t come fully formed out of the box. It must be trained to a level of maturity before it can provide effective support to key processes. And perhaps most importantly, AI works best when it allows people to become better at what they do.
A closer look at organizations that have successfully deployed AI shows a number of common themes, most of which involve looking past the technology itself to the data environment as a whole. For example, Jonathon Wright, chief technology evangelist at Keysight Technologies, notes that developing explainable use cases ahead of time greatly improves deployment and implementation, as does an overarching strategy of using AI to augment existing processes and filling gaps in the human workforce. As well, avoid an all-at-once, forklift upgrade to AI and instead plan for a smooth, nondisruptive transition from AI-ready to AI-capable and finally AI-enabled.
Every journey begins with a first step, however, so getting AI right on the first try, even in a limited fashion, can go a long way toward fostering future success. Naturally, this means putting AI to work on easy, proven tasks, which the editors at eWeek have identified as chatbots, image classification and price prediction. Intelligent chatbots are already revolutionizing customer support across many organizations, and they are fairly easy to implement using natural language processing (NLP) and other proven means to mimic conversational speech.
Meanwhile, AI is highly adept at scanning images to a greater degree than previous technologies, and then identifying anomalies and automating classification. And since most prices are subject to a wide range of influences that can only be effectively tracked and measured in an intelligent fashion, AI has become the solution of choice in this area and is already producing significant contributions to revenues and profits.
It will be the rare organization that experiences no setbacks or hiccups in the process of putting AI into production environments, but that doesn’t mean steps should not be taken to keep failures to a minimum. And even failure can be beneficial if it leads to greater understanding of how the technology should work.
At this point, it is hard to see AI not producing the revolutionary changes to systems and processes in the enterprise. The only question is whether organizations will take the easy way to that point, or the hard way.
Press release content from Business Wire. The AP news staff was not involved in its creation.
DUBLIN--(BUSINESS WIRE)--Jul 14, 2022--
The “Global Cloud Computing Market, By Deployment Type, By Service Model, Platform as a Service, Software as a Service, By Industry Vertical & By Region - Forecast and Analysis 2022 - 2028” report has been added to ResearchAndMarkets.com’s offering.
The Global Cloud Computing Market was valued at USD 442.89 Billion in 2021, and it is expected to reach a value of USD 1369.50 Billion by 2028, at a CAGR of more than 17.50% over the forecast period (2022 - 2028).
Cloud computing is the delivery of hosted services over the internet, including software, servers, storage, analytics, intelligence, and networking. Software-as-a-Service (SaaS), Infrastructure-as-a-Service (IaaS), and Platform-as-a-Service (PaaS) are three types of cloud computing services (PaaS).
The expanding usage of cloud-based services and the growing number of small and medium businesses around the world are the important drivers driving the market growth. Enterprises all over the world are embracing cloud-based platforms as a cost-effective way to store and manage data. Commercial data demands a lot of storage space. With the growing volume of data generated, many businesses have moved their data to cloud storage, using services like Amazon Web Services, Microsoft Azure, and Google Cloud Platform.
The growing need to regulate and reduce Capital Expenditure (CAPEX) and Operational Expenditure (OPEX), as well as the increasing volume of data generated in websites and mobile apps, are a few drivers driving the growth of emerging technologies. Emerging technologies like big data, artificial intelligence (AI), and machine learning (ML) are gaining traction, resulting in the global cloud computing industry growth. The cloud computing market is also driven by major factors such as data security, Faster Disaster Recovery (DR), and meeting compliance standards.
Aspects covered in this report
The global cloud computing market is segmented on the basis of deployment type, service model, and industry vertical. Based on the deployment type, the market is segmented as: private cloud, public cloud, and hybrid cloud. Based on the service model, the market is segmented as: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Based on industry vertical, the market is segmented as: Government, Military & Defense, Telecom & IT, Healthcare, Retail, and Others. Based on region it is categorized into: North America, Europe, Asia-Pacific, Latin America, and MEA.
Key Market Trends
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PUB: 07/14/2022 06:38 AM/DISC: 07/14/2022 06:38 AM
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Jul 07, 2022 (AB Digital via COMTEX) -- The global Product Analytics Market size to grow from USD 9.6 billion in 2021 to USD 25.3 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 21.3% during the forecast period. Various factors such as growing need to Boost customer behavior management to deliver personalized recommendation of products, increasing demand for advanced analytics tools to ensure market competitiveness, and growing adoption of big data and other related technologies are expected to drive the adoption of product analytics solutions and services.
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COVID-19’s global impact has shown that interconnectedness plays an important role in international cooperation. As a result, several governments started rushing toward identifying, evaluating, and procuring reliable solutions powered by AI. Advanced analytics and AI are invaluable to organizations managing uncertainty in real-time, but most predictive models rely on historical patterns. The use of advanced analytics and AI has accelerated in the COVID-19 pandemic period. This has helped organizations engage customers through digital channels, manage fragile and complex supply chains, and support workers through disruption to their work and lives. At the same time, leaders have identified a major weakness in their analytics strategy: the reliance on historical data for algorithmic models. From customer behavior to supply and demand patterns, historical patterns, and the assumption of continuity are empowering the predictive models. Technology and service providers have been facing significant disruption to their businesses from COVID-19. It has become important for product managers to evaluate the critical ways in which the pandemic affects their teams so they can mitigate the negative effects and plan for recovery. Product managers serve at the intersection of different functions. They glue together product, engineering, and design. However, as the COVID-19 has been changing the product landscape, these relationships have gone remote and that is not the only problem teams are tackling. As many of the world’s major economies work to address the second wave of COVID-19, it would be an appropriate time to look at how the pandemic has changed product management. Hence, the COVID-19 pandemic has disrupted the global financial markets and has created panic, uncertainty, and distraction in the operations of global corporations.
Scope of the Report
Market size available for years
Base year considered
Product Analytics Market Size in 2026
USD 25.3 billion
Component, Mode (Tracking Data, Analyzing Data), End User (Sales & Marketing Professionals, Consumer Engagement), Deployment Mode, Organization Size, Vertical, & Region
North America, Europe, APAC, MEA, and Latin America
Google (US), IBM (US), Oracle (US), Adobe (US), Salesforce (US), Medallia (US), Veritone (US), LatentView Analytics (US), Mixpanel (US), Amplitude (US), Pendo (US), Kissmetrics (US), Gainsight (US), UserIQ (US), Copper CRM (US), Countly (UK), Heap (US), Plytix (Denmark), Risk Edge Solutions (India), Woopra (US), Piwik PRO (Poland), Smartlook (Czech Republic), LogRocket (US), Auryc (US), Quantum Metric (US), cux.io (Germany), Refiner (France), InnerTrends (England), GrowthSimple (US), OmniPanel (US), and Productlift (Canada)
The services segment to hold higher CAGR during the forecast period
Based on components, the product analytics market is segmented into solutions and services. The services segment has been further divided into professional and managed services. These services play a vital role in the functioning of product analytics solutions, as well as ensure faster and smoother implementation that maximizes the value of the enterprise investments. The growing adoption of product analytics solutions is expected to boost the adoption of professional and managed services. Professional service providers have deep knowledge related to the products and enable customers to focus on the core business, while MSPs help customers Boost business operations and cut expenses.
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As per Heap, product analytics is a robust set of tools that allow product managers and product teams to assess the performance of the digital experiences they build. Product analytics provides critical information to optimize performance, diagnose problems, and correlate a customer activity with a long-term value. The product analytics market comprises product analytics services and solutions embedded with advanced technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) and big data analytics.
Some of the key players operating in the product analytics market include Google (US), IBM (US), Oracle (US), Adobe (US), Salesforce (US), Medallia (US), Veritone (US), LatentView Analytics (US), Mixpanel (US), Amplitude (US), Pendo (US), Kissmetrics (US), Gainsight (US), UserIQ (US), Copper CRM (US), Countly (UK), Heap (US), Plytix (Denmark), Risk Edge Solutions (India), Woopra (US), Piwik PRO (Poland), Smartlook (Czech Republic), LogRocket (US), Auryc (US), Quantum Metric (US), cux.io (Germany), Refiner (France), InnerTrends (England), GrowthSimple (US), OmniPanel (US), and Productlift (Canada). These product analytics vendors have adopted various organic and inorganic strategies to sustain their positions and increase their market shares in the global product analytics market.
Oracle was incorporated in 1977 and is headquartered in California, US. The company is a global leader in delivering a broad spectrum of products, solutions, and services designed to meet the requirements of corporate IT environments, such as platforms, applications, and infrastructure. Oracle’s customers include businesses of various sizes, government agencies, educational institutions, and resellers. The company, directly and indirectly, sells its products and services through a worldwide sales force and Oracle Partner Network, respectively. It specializes in developing, manufacturing, and marketing hardware systems, databases, middleware software, and application software. It provides SaaS offerings that are designed to incorporate emerging technologies, such as IoT, AI, ML, and blockchain. It operates through three business segments: cloud and license, hardware, and services, in more than 175 countries and caters to 4,30,000 customers across banking, telecommunications, engineering and construction, financial services, healthcare, insurance, public sector, retail, and utilities verticals. Oracle offers Oracle Analytics Cloud, Oracle Analytics Server, Oracle fusion analytics, and Oracle Essbase in the product analytics market.
IBM is a multinational technology and consulting corporation founded in the year 1911 and is headquartered in New York, US. It offers infrastructure, hosting, and consulting services and operates through five major business segments: cloud and cognitive software, global business services, global technology services, systems, and global financing. IBM’s product portfolio comprises various segments, such as IoT, analytics, security, mobile, social, and Watson. It caters to various industry verticals that include aerospace and defense, education, healthcare, oil and gas, automotive, electronics, insurance, retail and consumer products, banking and finance, energy and utilities, life sciences, telecommunications, media and entertainment, chemicals, government, manufacturing, travel and transportation, construction, and metals and mining. The company has a robust presence in the Americas, Europe, the MEA, and Asia Pacific and clients in more than 175 countries. In the product analytics market, IBM offers IBM Cognos Analytics, IBM Planning Analytics, IBM Spectrum control, IBM Streaming Analytics, and IBM QRadar User Behavior Analytics (UBA).
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Jul 07, 2022 (Alliance News via COMTEX) -- Competitors in the Market, The leading prominent companies profiled in the global software as a service (SaaS) market are:, Accenture plc., Adobe Incorporated, Cisco Systems, Incorporated, Google LLC, IBM Corporation, Microsoft Corporation, Oracle Corporation, Salesforce.com, Incorporated, SAP SE, ServiceNow, Other Prominent Players
The global software as a service (SaaS) market size was US$ 144.17 billion in 2021. The global software as a service (SaaS) market size is forecast to reach US$ 703.19 billion by 2030, growing at a compound annual growth rate (CAGR) of 18.83% during the forecast period from 2022 to 2030.
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Software as a Service refers to a licensing and delivery model where software is licensed on a subscription basis and hosted centrally. It's also known as "on-demand" software. A SaaS solution is a cloud-based service accessed through an internet browser instead of downloading and installing software on a PC or business network. Accessibility, compatibility, and operational management are the main advantages of SaaS software. Furthermore, SaaS offers lower upfront costs than traditional get and installation models, making them more accessible to businesses of all sizes making it easier for smaller companies to disrupt existing markets while empowering suppliers.
Factors Influencing Market Growth
Growing smartphone usage and apps-based services are boosting the overall market's growth.
A rise in public and hybrid cloud adoption is propelling the growth of the global SaaS market.
Globally, the increasing trend of business outsourcing, coupled with the adoption of artificial intelligence (AI) and machine learning (ML) across industries like BFSI, healthcare, and IT & telecom, provide lucrative opportunities for global market growth in the future.
The lack of security of cloud data and the high cost associated with the implementation and maintenance of SaaS platform solutions may slow down the global market growth.
Impact Analysis of COVID-19
The COVID-19 pandemic had a positive impact on SaaS adoption globally. As a result of government lockdowns, companies focused on advanced technology such as artificial intelligence (AI), machine learning (ML), internet of things (IoT), cloud computing, and analytics across industries to implement contactless technology. Due to this factor, demand for SaaS-based software or services increased, which drove the growth of the SaaS market worldwide. The pandemic has also introduced considerable challenges for companies trying to implement key processes, report accurately with data spread over multiple locations, operate complex systems, and communicate effectively with teammates without the proper infrastructure. As a result, more companies were investing in SaaS platforms. As a result of SaaS's unlimited scalability and continual enhancement of functionality, it assists companies in achieving digital transformation, which boosts global market growth.
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North America dominated the SaaS Market in 2020, and it is forecast to continue dominating during the forecast period. In the region, a large number of organizations, including healthcare organizations, retail & consumer goods stores, and others, have invested heavily in cloud-based SaaS solutions. In addition, North American countries such as the US and Canada have major SaaS vendors. In order to strengthen their product offerings and grow their client base, market players are entering into partnerships with technology providers.
Scope of the Report
The global software as a service (SaaS) market segmentation focuses on Solution Type, Deployment Mode, Organization Size, Industry Vertical, and Region.
Segmentation based on Solution Type
Customer Relationship Management (CRM)
Enterprise Resource Planning (ERP)
Human Resource Management (HRM)
Supply Chain Management (SCM)
Segmentation based on Deployment Mode
Segmentation based on Organization Size
Small & Medium-sized Enterprises (SMEs)
Segmentation based on Industry Vertical
IT & Telecom
Retail & E-Commerce
Energy & Utility
Media & Entertainment
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Segmentation based on Region
Rest of Western Europe
Rest of Eastern Europe
Australia & New Zealand
Rest of Asia Pacific
Middle East & Africa (MEA)
Rest of MEA
Rest of South America
What is the goal of the report?
?The market report presents the estimated size of the ICT market at the end of the forecast period. The report also examines historical and current market sizes.
?During the forecast period, the report analyzes the growth rate, market size, and market valuation.
?The report presents current trends in the industry and the future potential of the North America, Asia Pacific, Europe, Latin America, and the Middle East and Africa markets.
?The report offers a comprehensive view of the market based on geographic scope, market segmentation, and key player financial performance.
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