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Understanding complex events in today’s business world is critical. The explosion of data analytics—and the desire for insights and knowledge—represents both an opportunity and a challenge.

At the center of this effort is predictive analytics. The ability to transform raw data into insights and make informed business decisions is crucial. Today, predictive analytics plays a role in almost every corner of the enterprise, from finance and marketing to operations and cybersecurity. It involves pulling data from legacy databases, data lakes, clouds, social media sites, point of sale terminals and IoT on the edge.

It’s critical to select a predictive analytics platform that generates actionable information. For example, financial institutions use predictive analytics to understand loan and credit card applications, and even grant a line of credit on the spot. Operations departments use predictive analytics to understand maintenance and repairs for equipment and vehicles, and marketing and sales use it to gauge interest in a new product.

Top predictive analytics platforms deliver powerful tools for ingesting and exporting data, processing it and delivering reports and visualizations that guide enterprise decision-making. They also support more advanced capabilities, such as machine learning (ML), deep learning (DL), artificial intelligence (AI) and even digital twins. Many solutions also provide robust tools for sharing visualizations and reports. 

Types of Predictive Models

Predictive models are designed to deliver insights that guide enterprise decision-making. Solutions incorporate techniques such as data mining, statistical analysis, machine learning and AI. They are typically used for optimizing marketing, sales and operations; improving profits and reducing costs; and reducing risks related to things like security and climate change.

Also see: Best Data Analytics Tools 

Four major types of predictive models exist:

Regression models

A regression model estimates the relationship between different variables to deliver information and insight about future scenarios and impacts. It is sometimes referred to as “what if” analysis.

For example, a food manufacturer might study how different ingredients impact quality and sales. A clothing manufacturer might analyze how different colors increase or decrease the likelihood of a purchase. Models can incorporate correlations (relationships) and causality (reasons).

Classification models

These predictive models place data and information in categories based on past histories and historical knowledge. The data is labeled, and an algorithm learns the correlations. The model can then be updated with new data, as it arrives. These models are commonly used for fraud detection and to identify cybersecurity attacks.

Clustering models

A cluster model assembles data into groups, based on common attributes and characteristics. It often spots hidden patterns in systems. In a factory, this might mean spotting misplaced supplies and equipment and then using the data to predict where it will be during a typical workday. In retail, a store might send out marketing materials to a specific group, based on a combination of factors such as income, occupation and distance.

Time-Series models

As the name implies, a time-series model looks at data during a given period, such as a day, month or year. Using predictive analytics, it’s possible to estimate what the trend will be in an upcoming period. It can be combined with other methods to understand underlying factors. For instance, a healthcare system might predict when the flu will peak—based on past and current time-series models.

In more advanced scenarios, predictive analytics also uses deep learning techniques that mimic the human brain through artificial neural networks. These methods may incorporate video, audio, text and other forms of unstructured data. For instance, voice recognition or facial recognition might analyze the tone or expression a person displays, and a system can then respond accordingly.

Also see: Top Business Intelligence Software 

How to Select a Predictive Analytics Platform

All major predictive analytics platforms are capable of producing valuable insights. It’s important to conduct a thorough internal review and understand what platform or platforms are the best fit for an enterprise. All predictive analytics solutions generate reports, charts, graphics and dashboards. The data must also be embedded into automation processes that are driven by other enterprise applications, such as an ERP or CRM system.

Step 1: Understand your needs and requirements

An internal evaluation should include the types of predictive analytics you need and what you want to do with the data. It should also include what type of user—business analyst or data scientist—will use the platform. This typically requires a detailed discussion with different business and technical groups to determine what types of analytics, models, insights and automations are needed and how they will be used.

Step 2: Survey the vendor landscape

The capabilities of today’s predictive analytics platforms is impressive—and companies add features and capabilities all the time. It’s critical to review your requirements and find an excellent match. This includes more than simply extracting value from your data. It’s important to review a vendor’s roadmap, its commitment to updates and security, and what quality assurance standards it has in place. Other factors include mobile support and scalability, Internet of Things (IoT) functionality, APIs to connect data with partners and others in a supply chain, and training requirements to get everyone up to speed.

Step 3: Choose a platform

Critical factors for vendor selection include support for required data formats, strong data import and export capabilities, cleansing features, templates, workflows and embedded analytics capabilities. The latter is critical because predictive analytics data is typically used across applications, websites and companies.

A credit card check, for example, must pull data from a credit bureau but also internal and other partner systems. The APIs that run the task are critical. But there are other things to focus on, including the user interface (UI) and usability (UX). This extends to visual dashboards that staff uses to view data and understand events. It’s also vital to look at licensing costs and the level of support a vendor delivers. This might include online resources and communities as well as direct support.

Top Predictive Analytics Platforms

Here are 10 of the top predictive analytics solutions:

Google Looker

Key Insight: The drag-and-drop platform is adept at generating rich visualizations and excellent dashboards. It ranks high on flexibility, with support for almost any type of desired chart or graphics. It can generate valuable data for predictive analytics by connecting to numerous other data sources, including Microsoft SQL and Excel, Snowflake, SAP HANA, Salesforce and Amazon Redshift. It also includes powerful tools for selecting parameters, filtering data, building data-driven workflows and obtaining results. While the platform is part of Google Cloud and it is optimized for use within this environment, it supports custom applications.

Pros

  • The highly flexible platform runs on Windows, Macs and Linux. It offers strong mobile device support.
  • Generates strong data-driven workflows and supports custom applications.
  • Users supply the platform Excellent Marks for its interface and ease of use.

Cons

  • Lacks some customization features found in other analytics solutions.

IBM SPSS Modeler

Key Insight: The predictive analytics platform is designed to put statistical data to work across a wide array of industries and use cases. It includes powerful data ingestion capabilities, ad hoc reporting, predictive analytics, hypothesis analysis, statistical and geospatial analysis and 30 base machine learning components. IBM SPSS Modeler includes a rich set of tools and features, accessible through sophisticated dashboards.

Pros 

  • Offers excellent open-source extensibility through R and Python, along with support for numerous data sources, including flat files, spreadsheets, major relational databases, IBM Planning Analytics and Hadoop.
  • Visual drag-and-drop interface speeds tasks for data analysts and data scientists.
  • Works on all major operating systems, including Windows, Linux, Unix and Mac OS X.

Cons

  • Not as user friendly as other platform analytics platforms. Typically requires training to use it effectively.

Qlik Sense

Key Insight: Qlik Sense is a cloud-native platform designed specifically for business intelligence and predictive analytics. It delivers a robust set of features and capabilities, available through dashboards and visualizations. Qlik Sense includes AI and machine learning components; embedded analytics that can be used for websites, business applications and commercial software; and strong support for mobile devices. The solution supports hundreds of data types, and a broad array of analytics use cases.

Pros

  • Offers powerful drag-and-drop interface to enable fast modeling.
  • A “Smart Search” feature aids in uncovering and connecting complex data relationships.
  • Provides rich visualizations along with highly interactive and powerful dashboard features.

Cons

  • Can be pricy, particularly with additional add-ons.

Salesforce

Key Insight:  The widely used CRM and sales automation platform includes powerful analytics and business intelligence tools, including features driven by the company’s AI-focused Einstein Analytics. It delivers insights and suggestions through specialized AI agents. A centralized Salesforce dashboard offers charts, graphs and other insights, along with robust reporting capabilities. There’s also deep integration with the Tableau analytics platform, which is owned by Salesforce.

Pros

  • Powerful features and highly customizable views of data.
  • Integrates with more than 1,000 platforms. Strong data ingestion capabilities.
  • A generally intuitive UI and strong UX make the platform powerful yet relatively easy to use.

Cons

  • No trial or free version available.

SAP Analytics Cloud

Key Insight: Formerly known as BusinessObjects for cloud, SAP Analytics Cloud can pull data from a broad array of sources and platforms. It is adept at data discovery, compilation, ad-hoc reporting and predictive analytics. It includes machine learning and AI functions that can guide data analysis and aid in modeling and planning. A dashboard offers flexible options for displaying analytics data in numerous ways.

Pros

  • Accommodates large data sets from a diverse range of sources.
  • Offers highly customizable, interactive charts and other graphics.
  • Includes powerful ML and AI components.

Cons

  • Not as visually rich as other platforms.

SAS Visual Analytics

Key Insight: The low-code cloud solution is designed to serve as a “single application for reporting, data exploration and analytics.” It imports data from numerous sources; supports rich dashboards and visualizations, with strong drill-down features; includes augmented analytics and ML; and includes robust collaboration and data sharing features. The platform also includes natural language chatbots that aid business users and other non-data scientists in content creation and management.

Pros

  • Delivers fast performance, even with large data volumes.
  • Supports a wide array of predictive analytics use cases.
  • Includes smart algorithms that accommodate predictive analytics without the need for programming skills.

Cons

  • Expensive, particularly for smaller organizations.

Tableau Desktop

Key Insight: The enormous popularity of Tableau is based on the platform’s powerful features and its ability to generate a wide range of appealing and useful charts, graphs, maps and other visualizations through highly interactive real-time dashboards. The platform offers support for numerous predictive analytics frameworks, including regression models, classification models, clustering models, time-series models. Non data scientists typically find its user interface accommodating and easy-to-learn.

Pros

  • Excellent UI and UX. Most users find it easy to generate useful analytics models and visualizations.
  • Supports flexible, scalable and highly customizable predictive analytics use cases.
  • Tight integration with Salesforce CRM. Generates excellent visualizations.

Cons

  • Sometimes pulls considerable compute resources, including CPUs and RAM. It can also perform slowly on mobile devices.

Teradata Vantage

Key Insight: Teradata Vantage delivers powerful predictive analytics capabilities, including the ability to use data from both on-premises legacy hardware sources and multicloud public frameworks, including AWS, Azure and Google Cloud. The solution also works across virtually any data source, including data lakes and data warehouses. It supports sophisticated AI and ML functionality and includes no-code and low-code drag and drop components for building models and visuals. The company is especially known for its fraud prevention analytics tools, though it offers numerous other predictive tools and capabilities.

Pros

  • Highly flexible. Powerful SQL engine supports broad, deep and complex queries.
  • Accommodates huge workloads, almost any type of query, and delivers fast and efficient data processing.
  • Generates dynamic visualizations and an easy-to-use yet powerful interface.

Cons

  • Some users complain about a lack of documentation and training materials.

TIBCO Spotfire

Key Insight: TIBCO Spotfire offers a powerful platform for performing predictive analytics. It connects to numerous data sources and includes real-time feeds that can be highly filtered and customized. The solution is designed for both business users and data scientists. It includes rich visualizations and supports customizations through R and Python.

Pros:

  • TIBCO offers a strong API management framework.
  • Delivers rich visualizations and immersive interactive visual analytics.
  • Strong support for real-time decision making across multiple industries and use cases.

Cons:

  • Customizations can be time consuming and difficult.

ThoughtSpot

Key Insight: The analytics cloud delivers highly flexible self-service predictive analytics. The query engine is designed to search on virtually any data format and understand complex table structures over billions of rows. It offers powerful search and filtering capabilities that extend to natural language queries. ThoughtSpot also provides a powerful processing engine that generates a range of visualizations, including social media intelligence. The solution includes a machine learning component that ranks the relevancy of results.

 Pros

  • The platform gets Excellent Marks from users for its user interface and ease of use.
  • ThoughtSpot delivers a high level of flexibility, including an ability to search Snowflake, BigQuery and other cloud data warehouses in real time.
  • Natural language search reduces the need for complex SQL input.

Cons

  • Users complain that data source connectivity isn’t as robust as other analytics platforms.

Also see: Top Cloud Companies

Predictive Analytics Vendor Comparison Chart

Company Key Product Pros Cons
Google Looker Excellent drag-and-drop interface with appealing visualizations and a high level of flexibility Support is almost entirely online
IBM SPSS Modeler Excellent open-source extensibility; strong drag-and-drop functionality Not as user friendly as other analytics solutions
Qlik Qlik Sense Powerful and versatile platform with strong modeling features Some complaints about customer support
Salesforce Salesforce Highly customizable; strong integration with other platforms and data sources Expensive
SAP Analytics Cloud Supports extremely large datasets and has powerful capabilities Visualizations are not always as appealing as other platforms
SAS Visual Analytics High performance platform that supports numerous data types and visualizations May require customization
Tableau Tableau Desktop Outstanding UI and UX, with deep Salesforce/CRM integration Can drain CPUs and RAM
TIBCO Spotfire Powerful predictive analytics platform with excellent visual output Steep learning curve
Teradata Advantage Highly flexible with a powerful SQL engine; generates rich visualizations Some users complain about an aging UI.
ThoughtSpot ThoughtSpot Flexible, powerful capabilities with a strong UI and UX Visualizations sometimes lag behind competitors
Sun, 25 Sep 2022 06:33:00 -0500 en-US text/html https://www.eweek.com/cloud/predictive-analytics-solutions/
Killexams : IBM Study: Supply Chain Leaders Are Investing in AI and Automation to Navigate Supply Chain Uncertainties and Excellerate Sustainability
  • Almost half (47%) of surveyed CSCOs said they have introduced new automation technologies in the last two years.

  • Surveyed CSCOs ranked sustainability as their third biggest challenge in the next few years, trailing only supply chain disruptions and technology infrastructure.

  • "The Innovators" are modernizing their technology infrastructure – 56% of those respondents are currently operating on hybrid cloud, and 60% are investing in digital infrastructure to scale and deliver value.

ARMONK, N.Y., Sept. 20, 2022 /CNW/ -- A new IBM (NYSE: IBM) Institute for Business Value (IBV) study "Own Your Transformation" unveils how Chief Supply Chain Officers (CSCOs) are navigating significant supply chain challenges brought on by a global COVID-19 pandemic, inflation, climate change and geopolitical events, and how they plan to future-proof their supply chains.

The survey* of 1,500 CSCOs and Chief Operating Officers (COOs) reveals that they are increasing investments in automation, AI and intelligent workflows, ecosystems and sustainability, and are reimagining their supply chain operations.

"To effectively combat the unprecedented supply chain stressors like inflation, it's imperative that CSCOs focus on using analytics, AI and automation initiatives to build intelligent, resilient, and sustainable supply chains," said Jonathan Wright, IBM Consulting Global Managing Partner, Sustainability Services and Global Business Transformation. "Automation and AI can enable CSCOs and their organizations to collect data, identify risk, validate documentation, and provide audit trails, even in high inflationary periods, while also managing their carbon, waste, energy and water consumption."

Key study findings show that:

CSCOs are embracing AI and automation technologies to provide interconnectivity with partners and suppliers and to enable sustainable operations and predictability.

Sustainability is both a challenge and a force for change.

"The Innovators": 20% of respondents stand apart for accelerating their data-led innovation to prepare for a precarious future, and this group is already outperforming peers on key metrics including reporting 11% higher annual revenue growth. They are:

The full study is available at: 
https://www.ibm.com/thought-leadership/institute-business-value/c-suite-study/csco

*Methodology
In cooperation with Oxford Economics, the IBM Institute for Business Value surveyed 1,500 CSCOs and COOs from 35+ countries and 24 industries as part of the 26th edition of the IBM C-suite Study series. To simplify, we refer to the full population as CSCOs.

About the IBM Institute for Business Value 
For two decades, the IBM Institute for Business Value has served as the thought leadership think tank for IBM. What inspires us is producing research-backed, technology-informed strategic insights that help leaders make smarter business decisions. From our unique position at the intersection of business, technology, and society, we survey, interview, and engage with thousands of executives, consumers, and experts each year, synthesizing their perspectives into credible, inspiring, and actionable insights. To stay connected and informed, sign up to receive IBV's email newsletter at ibm.com/ibv. You can also follow @IBMIBV on Twitter or find us on LinkedIn at https://www.linkedin.com/showcase/ibm-institute-for-business-value/

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Mon, 19 Sep 2022 22:01:00 -0500 en-US text/html https://www.yahoo.com/now/ibm-study-supply-chain-leaders-100000598.html Killexams : Cloud Ecosystem Market Analysis, Research Study With Microsoft, HPE, IBM

New Jersey, United States, Oct. 11, 2022 /DigitalJournal/ The Cloud Ecosystem Market research report provides all the information related to the industry. It gives the markets outlook by giving authentic data to its client which helps to make essential decisions. It gives an overview of the market which includes its definition, applications and developments, and manufacturing technology. This Cloud Ecosystem market research report tracks all the exact developments and innovations in the market. It gives the data regarding the obstacles while establishing the business and guides to overcome the upcoming challenges and obstacles.

The cloud ecosystem is a term used to describe the complex system of interdependent components that work together to enable cloud services. The center of a cloud ecosystem is a public cloud provider. This can be an IaaS provider such as Amazon Web Services (AWS) or a SaaS provider such as Salesforce.

Get the PDF demo Copy (Including FULL TOC, Graphs, and Tables) of this report @:

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Competitive landscape:

This Cloud Ecosystem research report throws light on the major market players thriving in the market; it tracks their business strategies, financial status, and upcoming products.

Some of the Top companies Influencing this Market include:Microsoft, HPE, IBM, Adobe and VMware, Dell EMC, Cisco, Amazon/AWS, Salesforce,

Market Scenario:

Firstly, this Cloud Ecosystem research report introduces the market by providing an overview that includes definitions, applications, product launches, developments, challenges, and regions. The market is forecasted to reveal strong development by driven consumption in various markets. An analysis of the current market designs and other basic characteristics is provided in the Cloud Ecosystem report.

Regional Coverage:

The region-wise coverage of the market is mentioned in the report, mainly focusing on the regions:

  • North America
  • South America
  • Asia and Pacific region
  • Middle East and Africa
  • Europe

Segmentation Analysis of the market

The market is segmented based on the type, product, end users, raw materials, etc. the segmentation helps to deliver a precise explanation of the market

Market Segmentation: By Type

Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a service (SaaS),

Market Segmentation: By Application

Banking, Financial Services, and Insurance (BFSI), Telecommunications, IT and ITeS, Government and Public Sector, Retail and Consumer Goods, Manufacturing, Energy and Utilities, Media and Entertainment, Healthcare and Life Sciences, Others (education, travel and hospitality, and transportation and logistics),

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An assessment of the market attractiveness about the competition that new players and products are likely to present to older ones has been provided in the publication. The research report also mentions the innovations, new developments, marketing strategies, branding techniques, and products of the key participants in the global Cloud Ecosystem market. To present a clear vision of the market the competitive landscape has been thoroughly analyzed utilizing the value chain analysis. The opportunities and threats present in the future for the key market players have also been emphasized in the publication.

This report aims to provide:

  • A qualitative and quantitative analysis of the current trends, dynamics, and estimations from 2022 to 2029.
  • The analysis tools such as SWOT analysis and Porter’s five force analysis are utilized, which explain the potency of the buyers and suppliers to make profit-oriented decisions and strengthen their business.
  • The in-depth market segmentation analysis helps identify the prevailing market opportunities.
  • In the end, this Cloud Ecosystem report helps to save you time and money by delivering unbiased information under one roof.

Table of Contents

Global Cloud Ecosystem Market Research Report 2022 – 2029

Chapter 1 Cloud Ecosystem Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Cloud Ecosystem Market Forecast

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Killexams : Okta: The Market Leader In Identity Management At A Bargain Valuation
Okta sign, logo on headquarters building of identity and access management software company.

Okta sign, logo on headquarters building of identity and access management software company.

Michael Vi

Okta - Can this sow’s ear ever return to silk purse status?

Stocks have been cratering! IT stocks have been cratering more! Even shares of cyber-security companies have fallen with the HACK (HACK) ETF, which incorporates the major cyber-security vendors down by 30% on a year-to-date basis. But Okta (NASDAQ:NASDAQ:OKTA), the leader in the identity management category, has seen its shares fall far beyond average. Just how much? As of the close on Friday, Sept. 30th, the shares are down by no less than 76% since the start of the year, and by 81% since they made an all-time high less than a year ago. They were the worst performer on NASDAQ for the 3rd calendar quarter, falling 37% Compared to other leading cyber-security names such as CrowdStrike (CRWD), down 22% since the start of the year, Palo Alto (PANW), down 13% since the start of the year, and Zscaler (ZS) down 50% since the start of the year, the performance of Okta is far worse.

Okta, by comparison, is all about pivots and turning, and rectifying past miscues of various kinds.

I have recommended the shares of the leading cyber-security vendors as “safe havens” in the coming recession. The fact is that all of the 3 companies above have recently reported strong numbers and guided for acceleration in previously anticipated growth and margin performance. Cyber security is not optional, and cyber-criminal don’t recognize recessions. Threat surfaces keep enlarging, and the consequences of breaches continue to escalate.

I recommend the shares of Okta from a contrarian perspective. As a former owner of the shares, I think it is fair to say that the operational performance of the company has left a bad taste. The company executed a strategic merger, with AuthO and then basically fumbled the ball in terms of achieving sales synergies that were a key justification for the substantial purchase price. But all of that is now on view, and the shares reflect the disappointment surrounding the fumble, and no longer reflect the potential synergies and accretion from the merger. That, to my mind, is what makes a contrarian opportunity.

I don’t want to suggest that the cyber-security companies mentioned above are really quite analogs of Okta in terms of what they sell: CrowdStrike is still mainly a vendor of endpoint security although it recently has begun to sell an identity management module that has proven to be very successful. Zscaler sells zero trust protection for web based networks, while Palo Alto sells both a zero trust solution and next generation firewalls. None of those is identity management, and most users will need a combination of various solutions to develop reasonable protection against cyber-criminals. This is a very unforgiving market-the understatement of the year I suppose. Miscues are punished severely, while strong execution merely slows declines. Sadly, Okta has had its share of miscues including a couple of data breach and a flawed sales force integration strategy.

For some years I had been an Okta skeptic. Valuation and lack of profitability had kept me away. And I had wondered about the ability of a company to create a competitive moat around the identity management space. It’s an application that has been around a very long time. But then about a year ago, in the wake of Okta's merger with AuthO, a principal competitor with advanced technology in part of the identity management space, i.e customer identity management, and a different sales focus which was centered on the developer community, I thought I saw an opportunity. I really saw an iceberg, and abandoned my position, down sharply, but down far less than the loss could have been, at the start of this year. I had been concerned that the breach the company had suffered in January, 2022 had been poorly handled and was not satisfied with the Okta’s response. And then reports of sales force integration issues began to emerge, and those were finally confirmed during the company’s latest conference call. I have had to wonder just how it is that a founder led company could mishandle such a key undertaking, which had to be a priority.

As mentioned, and to be perfectly clear, I recommend the shares of Okta from a contrarian perspective. I think most of the problems the company has are on full view. And I believe most current investors are expecting that the company will reduce multi-year targets to some extent when it next reports results in early December. The company's latest guidance sets revenue growth goals at very modest levels. In this current quarter, its forecast is for sequential revenue growth of just greater than 2%-that metric was 11% in the same quarter of the prior year, followed by sequential growth of 5% in what will be a fiscal Q4 compared to 9% the prior year. Sequential revenue growth in the just reported quarter was actually 9%. The results of the quarter just reported were a 5% upside when compared to prior guidance for revenues, and non-GAAP profitability was noticeably improved as well compared to the prior forecast. The growth in RPO balances was below forecast, but apparently was mainly a function of duration, as the growth in cRPO, at 36% was certainly acceptable for that metric. That said, the cRPO balance rose just 6% sequentially, which was apparently below prior expectations.

This guidance would seem to incorporate the current state of both economic headwinds and the specific sales execution challenges that the company has acknowledged. While self-evidently, the company’s management structure has seen some upheaval, and sales turnover is elevated, with much sales force dysfunction, presumably that is why the shares are valued as they are.

There are many investment opportunities these days in the enterprise software space. Skepticism abounds about company forecasts. Just the other day, a forecast affirmation by Splunk (SPLK) brought on a relief rally, albeit of brief duration. The reason is that many commentators think that all forecasts are suspect and will have to be reduced. One well known hedge fund leader, Dan Niles, recently provided an interview forecasting that the estimates of software companies were still exposed and would see further cuts.. One economist, Nouriel Roubini, notorious or not, depending on the disposition of the reader, is forecasting a long and deep recession with the potential for a further 40% drop in the S&P.

At the moment, while Todd McKinnon, the company CEO remains in his position, Fred Kerrest, the company’s COO is taking a one year sabbatical. The company has realigned its product development efforts with the former AuthO CEO leading product development efforts for customer identity while the CRO leads the development of workforce identity cloud products. Lots of moving parts, and signs of organizational stress. That said, the company’s CFO, Brett Tighe has been with the company for more than 7 years, and before that he was with Salesforce (CRM) in senior financial roles for 11 years. And the company’s President of Field Operations, Susan St. Ledger has been in her position for about 2 years, while the company’s Chief Revenue Officer, Steve Rowland has been in his position for 18 months. . Ms. St. Ledger held a similar role at Splunk for 4 years, while Rowland, not terribly surprisingly, is also a Splunk alumni. Can this leadership team right this troubled ship?

Almost all companies make mistakes and miscues from time to time. The same obviously can be said about analysts except our mistakes are on full view every day from 4:01PM on and often earlier than that. Recently, the analyst at Guggenheim, John DiFucci upgraded the shares, while calling Okta, a company in disarray. Last week, the analyst at Cleveland Research, Ben Bollin, downgraded the shares from buy to hold. He feels that the company is facing more significant fundamental challenges, mainly competitive, and will have to reduce guidance more substantially than has already been the case. The company, during its most exact conference call, suggested it was revisiting its targets for FY ‘26 and I doubt that anyone either owning the shares or providing recommendations is doing so based on a target

My reason to revisit the investment thesis is simply that identity management is an enormous, and under-penetrated market, and Okta remains the leading participant in the space. And I have a strategic disposition to increase my portfolio weighting in the cyber-security space, as I believe it will be the most recession resistant segment within IT. There are certainly other choices in the cyber-security space these days other than the 3 companies I already own. Checkpoint (CHKP) has shown some signs of life in terms of its operational performance, and shares of Rapid7 (RPD), now substantially rerated, have interest. CyberArk (CYBR), is also a competitor with a very competitive solution in what is called privileged asset management. But it is infrequent that a software category leader also has the potential for really significant returns. But I think that Okta is one such company.

The questions, of course, are whether, and at what cadence, Okta’s leadership can restore its momentum and start to realize the opportunities inherent in the acquisition of AuthO and resume its share gains in the space. This is not a short-term project. Restoring a broken sales force, and crafting the right go-to-market strategy and messaging almost always takes longer than expected. But fortunately for Okta, cyber-security and identity management will be less affected by recessionary headwinds than most other segments of the IT space. That said, unlike most of the other cyber-security vendors, Okta did call out macro headwinds in its prepared remarks, although it is easy to question whether this was real, or an excuse for sales performance.

I am not going to uncover some existential valuation metric regarding Okta that hasn’t been analyzed and dissected many times already. After falling by 75%, even while continuing to grow, the EV/S ratio has dropped noticeably below average for the company growth cohort, even when haircutting the company’s expected growth rate to a cohort of around than 30%. But the company is projecting just a modest free cash flow margin, and that is a significant negative in the current investment environment.

Despite, or perhaps because of the valuation compression, the shares are not well loved by analysts. In addition to the Cleveland Research downgrade a couple of days ago, the MoffettNathanson analyst started coverage of the shares just a couple of days ago with a sell rating and a $71 price target. At the start of September, the Morgan Stanley analyst lowered his rating on the shares to hold, but set a $93 price target. One of the many reasons why I simply don’t set price targets on companies that I cover is that they frequently follow rather forecast stock prices. Of course, no one would reasonably have a price target 20%+ above a current valuation with a sell rating, and it more than a bit difficult to decipher the logic of a price target more than 60% above the current valuation with a hold rating. But one of the reasons I am reviewing Okta, and not something else, is that actually quantifying the outlook, and using some kind of DPV analysis, really does leave targets far, far above current levels. Therein lies the significant potential.

That said, other analysts do see some of the opportunities inherent in category leadership in a key part of the enterprise cyber-security paradigm. The Jefferies analyst, Joseph Gallo, described Okta shares as a “one-of-a-kind” buying opportunity with 50% upside. Of course the upside percentage is greater now, with the shares down more than 15% since the date of the research report.

I was recently accused of using touchy-feely analysis in my review of Adobe (ADBE). The problem with that recommendation, and this one as well, is that there isn’t some kind of specific valuation flag that says, “buy me.” One thing about buying wounded assets, as this one appears to be, is that forecasting the timing of a turn is basically impossible. When the sales force is fixed, and observers believe it to be fixed, the shares will most likely appreciate markedly in just a day or two. Much of that is because Okta is the kind of investment that can be very popular with hedge funds due to its size/liquidity and easy to understand functionality. Even now, over 80% of the shares are held by institutions. While that is a substantial percentage, there are no aggressive hedge funds listed as large holders, suggesting a potential significant source for share demand.

Another thing to note. Okta is not going to achieve a turn-around from its current condition in a quarter or two. The company has suffered from heightened sales turnover, basically because many previous AuthO sales contributors have felt that their sales targets were unattainable and that the comp plan was deeply flawed. About the most that one might expect in the short term is that the company stops digging. Fixing sales execution issues, which means dealing with lots of unhappy people, doesn’t happen at the flip of a switch. The odds are that Okta will right its ship. I have been surprised that a company of this stature, having a clear priority, which was to create a unified sales platform, failed to do so in a timely fashion. About the most I can say is that this is the opportunity to do so is what is being presented to subscribers/readers at this point.

Finally, I should mention Okta uses stock based compensation, and lots of it, and there are readers and commentators who refuse to consider companies using SBC. One of the things that need to be noted about SBC is how the calculation works out for that metric in particular quarters. SBC is calculated based on when an option is vested and not when an option is granted. Because of last year’s acquisition of AuthO, the level of options reaching their vesting conditions has increased. On the other hand, that increase is abating, and thus SBC expense fell year-on-year in the quarter recently reported, and it was flat sequentially. But it is still elevated at 38% of revenues, although the ratio will almost inevitably decline going forward, as the company has moderated its hiring plans. The reality is that SBC is tightly correlated with the number of hires, so a decline in hiring, as the CFO foreshadowed in the latest conference call, will lead to lower SBC. Since I do not use GAAP estimates or data in addressing valuation, it is necessary to adjust estimates and projections for dilution. This company does forecast outstanding shares on both a quarterly and an annual basis. Based on trends, I use annual dilution of 3.5% in calculating valuation metrics.

Macro headwinds: Just how much of the guidance shortfall is a function of a deteriorating economy

There is obviously lots to consider when a company which has achieved an organic growth rate in the high 30% range (actually in the mid 40% range-all organic-last quarter) for some time, sees growth atrophying to the mid-20% range in a single quarter. Looking at both Glassdoor reviews and reviews from Best Place to Work, Okta’s evaluations are fairly typical, maybe a bit above average for an relatively large enterprise software company.

While Okta’s management did call out macro headwinds in its prepared remarks on this latest conference call as one reason for its reduced guide, management went on to say that the preponderance of its guide down was a function of sales integration, and the concomitant issue of elevated sales turnover. At some level, I would suggest that the scenario being portrayed is quite similar to many of the problems that had upended the Alteryx (AYX) growth outlook before the company took some hard steps and remediated the problems that had crippled its growth.

I think at this point almost all investors are aware of demand headwinds for most IT vendors. The company wound up reducing its guidance; in terms of revenues, the guide down for the last 6 months of the year was about 2% ($17 million). It might be noted that because this guide down was 2% and not some considerably greater number, the company’s forecast for its full year non-GAAP operating loss didn’t change, although this was more a factor of the Q2 beat on that metric than any dramatic change in the trajectory of opex. The company suggested that the macro headwinds being experienced weren’t all that substantial, at least at the time that guidance was provided. The company is forecasting that it cRPO balance will increase by 3% sequentially, and that is where the issues of sales force integration and macro headwinds are most visible. While cRPO has its own limitations as a proxy for current bookings, it is the best metric available to portray the strength of demand growth, and the forecast reflects a sharp slowdown in expected sales performance.

Although it is difficult to really know, I imagine many investors believe that there are additional shoes to drop with regards to Okta’s guidance. And despite the published consensus that calls for 28% growth in what will be FY ’24, I doubt that many analysts covering the company really expect that kind of performance, although I think the likely trajectory of expected margin improvement as shown by the 1st call consensus will prove to be too conservative. Obviously the question was raised during the conference call. The answer isn’t particularly surprising:

Josh Tilton

Hey, guys. Thanks for squeezing me in. Just a quick one for Brett. Given all the challenges that you guys mentioned, what gives you the confidence that you're not going to have to take numbers down again in the back half of the year?

Brett Tighe

Look, we've baked everything in that we know at this point, right? We've taken into account from regardless of what number you're looking at, it's current RPO, revenue, billings, we baked in those headwinds that we've talked about today, whether it be the sales integration issues we've talked about the attrition or even the macro. So we do feel confident in the guidance and taking a similar approach and being very prudent about that like we have in the past.

Okta’s basic issues and how the company is moving to fix its problems

The most significant issues for this company has essentially been that of sales force execution, and go-to-market messaging. These are the issues that have upended revenue projections for now. Those kinds of errors should never happen as they are entirely within the control of the company. That said, were there not these issues, the shares would never have imploded to this valuation, even in this toxic market for growth shares. I believe that the greatest percentage returns are going to come from identifying those companies with a strong competitive position in a hot space that is generally recession resistant and that is what Okta is, despite the unforced miscues.

The CEO had much to explain in terms of what has been happening to Okta and how it can be fixed. One issue, which seems to be very basic, relates to defining which product within the Okta offering is appropriate for which use case. There is overlap between identity management for employees and customer identity management and apparently the overlap has led to salesforce dissension and elevated turnover. The problem surfaced in the wake of combining the two sales forces at the start of this current fiscal year. Okta, before the merge, had a solution for customer identification, as well as a solution for employee identification, essentially its core business. AuthO only focused on identity management solutions that are incorporated by developers into web sites that customers access. The customer identity space is newer, and enjoys stronger growth. The TAM of both spaces is comparable. AuthO certainly has had marketplace momentum in the customer space, most often called CIAM (customer identity access management), and that continued long after the merger. When the sales forced were merged, it became very difficult to match use cases with solutions. The company needed to offer a single customer identity solution, and that solution needed to be transcendent for all B2B SaaS applications that developers are building.

While it sounds as though it ought to be a simple problem to remediate, like many other things the devil is in the details, and apparently, the details were not carefully considered in advance of combining the two sales forces. Even on the conference call, after explaining about the two product families that are offered by Okta, some analysts were a bit confused about how the integration of the two sales forces might actually work in practice. There was a need to consolidate and train the sales forces with messaging that identified which solution was appropriate for which use case.

One of the advantages that Okta has is that it is a platform neutral solution that offers enterprises the opportunity to optimize their identity management paradigm by standardizing on a single vendor. To sell that paradigm, the company has to reach the CEO level of its prospects to explain why identity management of both customers, and of a workforce are priorities that cannot be properly handled by software in which identity management is an afterthought. The message is simple; it is desirable to partner with a vendor who can offer the whole range of identity management solutions on a multiplicity of cloud deployments and for many different use cases. It seems obvious from afar, but part of the sales force integration issue was dysfunction in Okta’s go-to-market sales motion. At least the company has identified the problem; fixing the sales motion is not going to happen instantaneously but is more of a process.

The most visible element of the integration issue comes from the salesforce attrition/turnover rate. Depending on the place in the economic cycle, Okta indicated it had averaged about 15% turnover or a bit less in the years before the pandemic. Turnover is now a bit greater than 20%. And much of that turnover has been amongst the former salespeople of AuthO. Here is some of the comment from Todd Mackinnon specifically on the issue.

Like if you're working for Auth0, this pre-IPO company your -- it's smaller, your territory is probably eight states in the US. And now you're working for Okta and you're expected -- as of the first of this year, you're being asked to sell to these multiple buyers with multiple products and your number of states or your territory really got smaller, because we have this much more scale that sales team. I could see why some of them decided to go maybe work for a smaller company and so forth.

I have to confess that in listening to that part of the conference call, and contrasting it with my own experience, I got quite agitated. What’s being described is a rookie mistake, and seems… well the term unforced error comes to mind. I can personally certain that the combination of smaller territories with an aggressive hiring plan that will make territories even smaller will inevitably lead to massive sales turnover because salespeople can’t see how they will be able to achieve objectives and make any money-no commission accelerators to be found. About the best thing about that is that it is relatively easy to fix, and from what I gleaned from the conference call, the company is taking steps to remediate that issue. Again, from Mr. Mackinnon:

But we're starting to see a lot of these trends reverse already, which is great. We've talked about a lot of the things we're doing. I'm sure those are having some effect, although some of them are recent. But just in terms of the industry, I think a lot of small companies; especially the prospects don't look as good. The valuations aren't as high. The money is not flowing there as much as it was. I've already seen a few go-to-market folks that left for smaller companies, and they've come back and the grass wasn't always greener.

While I won’t accuse the rest of the answer as being the most articulate, it probably is reflecting some early trends in which salespeople are discovering that the environment has changed, and there prospects are currently better in a larger, more stable organization than in a start-up whose prospects of going public any time soon are limited. It isn’t terribly surprising that AuthO’s sales force has been composed of risk-takers who wanted to bet on a huge pay day from an IPO, and when, instead, the company was bought, and they weren’t catered to, they went to another situation that appeared to have a similar upside. Without the potential income from an IPO, selling identity management for Okta is probably a more attractive alternative currently than had been the case recently, particularly after some remediation of the comp plan as management has spoken about.

The other major issue is that of competition. Recently, Gartner held what it called an identity management conference. Yes, there really are such things as identity management summits. At the conference, some of the presenters and attendees talked about an increased presence by Microsoft (MSFT) in the space, and it was apparently these presentations that were a precipitating factor in the exact Cleveland Research downgrade cited earlier. The fact is, however, that the Microsoft solution is really focused on applications running in Azure. Microsoft has been in the space for almost 10 years at this point, and a company such as Microsoft that sells applications more or less has to have an identity management solution in order to be considered as a real vendor. Microsoft's solution was initially built for to be an on-prem product-there was no Azure back when the product was first launched. Although, of course, Microsoft has a cloud based solution, it is unlikely to have the competitive chops that Okta brings to this market.

Most customers, at least those who are serious about identity management, are going to want a specialist vendor because almost inevitably they have a multiplicity of clouds, and they need to work with a vendor whose specialty is a multi-cloud environment-and that obviously is not Microsoft. Identity management can get far more complicated than it might seem, and when the subtleties are properly presented and explained, Okta’s position is very strong. But of course, the issue is carefully crafting the right message and making sure that sales reps are well trained to explain that message carefully and to the right audience. Okta’s problem isn’t that Microsoft had an enhanced set of functionality, but that the company needs to do a better job with trained reps presenting the benefits the company offers to the appropriate audience.

Other presenters at Gartner's summit conference focused on what are called Identity Governance ('IGA') and Okta Privileged Access Management ('PAM') which are different sub-categories in the space, where Okta has introduced exact offerings. Of course I wouldn’t expect the CEO, especially given his role and history at Okta to ever admit to a competitive deficiency but I thought he response to the queries was credible and struck me as more likely to be accurate than some of what can be presented in Gartner forums.

And so when I hear people say that IGA is IGA light, it's great because that means it's working. That means it's so simple that employees can do these access requests and Excellerate these things just in their chat. They don't have to go to some legacy tool. It means that the integrations are a snap. It comes pre-integrated to thousands of apps. So I think there's -- I think you're going to -- I think the industry is going to see that first of all, IGA is much bigger than we think it is because the solutions have been constraining the size of the pie. It's kind of like everyone said that the ITSM market was very small in ServiceNow started, but a better product made the market bigger. I think you'll see a similar thing here.

Investor concerns about competition aren’t going to abate just because of company presentations. And outsiders making some competitive assessment are always in a position of having to avoid sensational claims, or stories based on an agenda. I confess to the temptation myself; I know a couple of very satisfied Okta users in a large enterprise. But looking at the preponderance of the evidence, as best as I can, I don’t think Okta’s problems are competitive, but self-inflicted wounds stemming from very flawed strategies to integrate two dramatically dissimilar sales paradigms.

Okta’s Opportunities - Identity management at a high level is required as part of a cyber-security solution

There are two primary components to the access market, one having to do with the ability of employees to sign on to applications within their corporate firewalls, and the other market in which customers can sign on to manage their accounts and to order from vendors. There are obviously similarities between both markets, but until Okta bought AuthO competitors had focused on one segment or the other. As mentioned earlier, the key to Okta’s future operational performance is to present to CEO’s and other C-suite decision makers, the benefits they will enjoy by partnering with a leading vendor who has the most complete set of identity management solutions in both spaces and whose solutions are basically cloud-neutral. While market share data can be slippery at best, the Okta future is predicated on its ability to grab market share, and to achieve the kind of market dominance it enjoys in the Single User Sign-on market, a subset of employee identity management.

The employee identity management market where Okta started is currently estimated to have revenues of $13 billion rising to $37 billion by the end of the decade. Identity theft use cases continue to rise. They actually increased 45% in 2020, and rose further last year. Just in North America, the cost of identity theft was said to be $56 billion, according to the study linked here. Attackers use machine learning to generate multiple variants of malicious code every day.

The Consumer Identity Access Management market is thought to be a bit smaller at this time than the workforce identity market, but it is growing more rapidly. This linked analysis forecasts that CIAM, as it is often abbreviated, has a multi-year CAGR of 17%. Okta developed a solution of its own in CIAM. But with the acquisition of AuthO it now has the leading market share in the space. AuthO had been Okta’s leading competitor.

Okta’s investor presentation speaks to a TAM of $80 billion, composed of Workforce Identity+ IGA and PAM of $50 billion, with the TAM of the CIAM space being estimated at $30 billion. The TAM is based on estimates for 2025 Regardless of the precise number of the TAM, the identity management space is very large, and affords Okta a substantial growth opportunity for years to come. The issue is sales execution, messaging and overall go-to-market tactics and not demand opportunities for the solution.

Okta has the leading market share in both categories. The analysis linked here by a market research firm puts its market share at 37%. It has a higher share than that in Single User Sign-on, perhaps the most basic identity management solution. As the analysis linked here shows, there is considerable overlap in terms of functionality between CIAM and IAM, which plays strongly to Okta’s strength.

I have linked here to the latest Gartner Magic Quadrant analysis for Access Management which continues to show Okta and AuthO in the leaders quadrant along with Microsoft. I also have used Forrester’s Wave analysis which shows Okta with an unambiguous lead in the space. The Forrester Wave review basically suggests that Microsoft, despite efforts to move beyond its own base of Azure and other Microsoft-centric users is far better suited for Microsoft-centric users than as a general competitive offering.

There are, to be sure, many competitors in the identity management space. And there are even more analysis of the vendors in the space. I have chosen one to link here identifying many competitors, but there wasn’t anything particular about its analysis that makes it better than the others. For some years now, Okta has been gaining market share in the space as it continues its move upmarket and to add features, functions and integrations, which is a key differentiator for most users.

Okta has been able to gain market share because of best of breed functionality, and because it is platform/cloud neutral, and because it has always been a cloud first solution. So, when looking at CAGR estimates, it is important to note that they do not differentiate between on-prem and cloud. Thus, Okta’s CAGR estimates are always going to be greater than the CAGR for the market as a whole since cloud is growing far faster than on-prem in access management. In addition, almost all application software vendors have some kind of identity management built into their software. But increasingly, users want more sophisticated solutions with more features and which work across multiple clouds than are offered by stack application vendors. Most market research analysts highlight that trend, and it should, over time, be the other major factor in Okta’s market share gains.

Okta’s revenue forecast obviously should be considered as quite disappointing when considering the trends in the market. While it is probably impossible to identify which factor is causing a specific percentage of the decline in bookings (cRPO) balances, I think it is fair to conclude that even with such macro headwinds as their might be, the company should be able to grow its revenues by near-30%, so the forecast it has presented, of 2% sequential growth, followed by 5% in a quarter that usually has significant positive seasonality, is far below what I think ought to be a realistic goal for Okta.

Should Investors Buy Okta Shares Now/Reviewing the company's business model

Okta shares are so beaten down that they could have a dead cat bounce, and at these valuations rumors of PE interest and potential investment by activist investors are likely - it was a significant factor in the initiation of the shares at a buy rating by the Jeffries analyst. That said, the ability of the shares to achieve sustainable appreciation will await a more benign market environment for high growth IT shares. And that in turn will be a function of changes in macro conditions such as inflation, changes in employment, and the tenor of Fed speakers.

But beyond that, Okta will have to demonstrate that it is returning to sustained market share gains. The opportunity is there, and Okta certainly has the most complete and functional set of solutions particularly for larger enterprises. And the need to prevent identity theft just keeps growing, and the consequences for ignoring solutions to remediate the threat are also escalating. So, it is really a matter of basic blocking and tackling.

The company management has articulated the tactics to achieve that, but it will almost certainly be 2-3 quarters between the identification of problems and their remediation becomes discernible. Replacing and training lost salespeople and seeing them reach full productivity is always a process of some duration. Okta is still in the process of realizing some cost synergies inherent in the combination with AuthO, and a bit of that was discernible in the results of the previous quarter.

Non-GAAP gross margins ticked up slight on a sequential basis, rising to 77% last quarter. Non-GAAP research and development spending actually declined sequentially, and was about 21% of revenues. The Non-GAAP sales and marketing expense ratio also showed a small sequential decline, although at 37% of revenues, there is still much room for improvement. Although general and administrative expense also fell noticeably, at 14% of revenues, non-GAAP, that ratio is still very elevated. The company has a clear path to profitability, given the progress it has been making in that direction and the unit economics of its business, and so far as it goes, even SBC expense fell from year earlier levels, although much of that has to do with the timing for the recognition of option expenses which are recorded when they vest in accordance with the Black-Scholes paradigm. As mentioned, SBC expense last quarter was still elevated at 38% of revenue, down from 41% the prior quarter. The year earlier figure was much higher, but is not terribly relevant given the timing of vesting.

The company’s operating cash flow margin last quarter was about nil. The year over year comparison is not relevant because of the impact of AuthO on year earlier results. The biggest element of the performance in free cash flow this past quarter was the increase in A/R balances. The quarter, according to the CFO, was more back-end weighted than usual, reflecting extended decisioning on the part of some enterprise customers, and some large deals were with government agencies such that average duration of contracts in the quarter declined, and impacted the increase in deferred revenues. The current quarter is also likely to see an elevated proportion of Federal cyber-security spend as it extends beyond the end of the government’s fiscal year, and cyber-security spending by the government is apparently at rates of close to 40%.

Okta shares, even after their mini-bounce since the low that was made in the wake of the downgrade by Cleveland Research, sell at an EV/S of about 4.5X. That ratio is noticeably less than the average EV/S for a low 30% 3 year CAGR. I expect that the company, after it remediates its sales execution issues, and in a less stressed economic environment, can return to mid-high 30% revenue growth.

The company has forecast that it will achieve single digit free cash flow margins this year, considerably greater than its forecast for non-GAAP EPS. Part of that is seasonal and p[art will be a function of the likely fall in receivables days outstanding. Changes in free cash flow are often a function of duration of larger deals and are thus notoriously difficult to forecast, but the path to non-GAAP profitability that is discernible, will also encompass rising free cash flow margins.

Having written analysis for donkey’s years (donkeys have a life span of 40 years so I suppose I have exceeded that), it is often the custom to provide a catalyst to support a recommendation. I don’t really like to suggest that a catalyst for this recommendation is the percentage by which shares have fallen, although, to be sure, Okta screens well on that dubious distinction. This is a contrarian call. No one really likes recommending companies with so many moving parts in senior management, or whose management team has made some significant unforced errors. But that said, Okta is still the leading company in a hot space, and despite some assertions to the contrary, I don’t see any degradation in its competitive position. And as I see it, its position is such that its long term market share gains should resume once it addresses sales force turnover and improves its messaging to the enterprise.

Okta shares, as mentioned, have many attractions for institutional investors including size/liquidity and a very specific position within cyber-security, a market space that many institutions want to overweight in this very perilous macro environment. Despite my previous unpleasant experience in owning the shares, I am planning to return for what is hopefully a more favorable experience this time around. As with most contrarian calls, timing is never exact or guaranteed, but my view is that by the time everyone agrees that the company’s problems are solved, its relative valuation will balloon. This is one case in which I would rather be early than late.

"Editor's Note: This article was submitted as part of Seeking Alpha’s best contrarian investment competition which runs through October 10. With cash prizes and a chance to chat with the CEO, this competition – open to all contributors - is not one you want to miss. Click here to find out more and submit your article today!"

Mon, 03 Oct 2022 11:11:00 -0500 en text/html https://seekingalpha.com/article/4544473-okta-market-leader-at-a-bargain-valuation
Killexams : Artificial Intelligence In Fintech Market Projections and Regional Outlook, Estimates & Forecast, By Application, segments 2022-2030

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

Sep 21, 2022 (Alliance News via COMTEX) -- The report provides a comprehensive analysis of segments in this market, covering all the major regions and countries. The major regions analyzed in the study are North America, Europe, Asia Pacific, Latin America and Middle East & Africa. The report also includes additional information about other factors such as drivers, restraints and challenges faced by this market along with an overview for each mentioned segment in the study.

The Global Artificial Intelligence In Fintech Market size is expected to reach $25.8 billion by 2028, rising at a market growth of 16.8% CAGR during the forecast period.

Statistical and cogent models for the market were used to assess and forecast the market data. Additionally, market shares and important trends were taken into account when creating the study. The Market Time Line Analysis, Vendor Positioning Grid, Market Overview and Guide, Company Market Share Analysis, Company Positioning Grid, Standards of Measurement, Top to Bottom Analysis, and Vendor Share Analysis are additional data models that can use.

Download Free demo of This Strategic Report :-https://www.quadintel.com/request-sample/global-artificial-intelligence-in-fintech-market/QI046

The study offers in-depth regional analysis of market scenarios, by analyzing previous trends and covering future forecast. It also offers an in-depth analysis of major driving factors, segments, regions & countries and key players in the market. Moreover, the report outlines the competitive scenarios across different geographies, along with some key market strategies such as mergers and acquisitions, new product developments, R&D activities and more.

GlobalArtificial Intelligence In Fintech MarketSize, Share & Industry Trends Analysis Report By Component (Solutions and Services), By Deployment (On-premise and Cloud), By Application, By Regional Outlook and Forecast, 2022 ? 2028

Insurance executives and future banking agents would ask the proper questions to robots rather than human experts as a result of data-driven management decisions at a reduced cost. Machines would then analyze the data and provide recommendations that will aid leaders and subordinates in making better decisions. Users can employ automated financial assistants and planners to help them make financial decisions. These include events tracking, stock and bond price trends based on the user's financial goals and personal portfolio, that can aid in the recommendation of bonds and stocks to buy or sell. These systems, dubbed "Robo-Advisors," are highly being provided by both traditional financial firms and Fintech startups.

UPI is one of the most widely used digital payment systems in India, and the system was created to enable payments to be executed in seconds. To generate critical insights, financial firms utilize AI to handle and assess data from a variety of sources. Banks might use such inventive solutions to solve challenges they have when providing services like payment processing and loan management. Many banking apps offer personalized financial advice to assist users in achieving their financial goals, tracking their income & expenditures, and performing other financial chores. AI-powered finance advances are primarily responsible for this customization.

Bright well Payments, a financial services firm that offers financial solutions to transport money safely anywhere in the world, announced the launching of ARDEN in May 2022. This risk-detection engine powered by AI helps fintech companies protect their cardholders and financial assets. Globally, banks are implementing AI-enabled solutions to enhance safety, and AI provides banks the advantage of digitization. Additionally, it enables them engage with other fintech businesses. Apps that necessitate UPI, a fingerprint, or facial recognition are available from financial institutions.

COVID-19 Impact

The latest coronavirus outbreak has been beneficial to the market. Due to the coronavirus pandemic, business activity has been halted, resulting in disruptions in border restrictions, supply chains, and travel restrictions imposed by government bodies. As a result, banks and fintech companies are adopting a work-from-home attitude. Moreover, banks and financial institutions are implementing AI technologies to extract information and insights from unstructured documents and automate the laborious procedure that banks have traditionally completed in shorter period of time. For example, Temenos, a banking software business, announced the introduction of eight propositions in April 2020, utilizing breakthrough Explainable AI (XAI) and cloud technologies to assist banks and financial institutions in responding to the COVID-19 situation.

Market Growth Factors

Reduction in cost and better efficiency along with enhanced wealth management

Artificial intelligence in fintech is enabling businesses to minimize costs, automate processes, and lessen the risk of human mistake. Companies utilize AI Chatbots as customer assistants for a variety of tasks, including sales, customer service (over the phone), and online chat. AI is enabling small finance organizations since it is cost-effective and has a minimal risk of error. Furthermore, the end user is gaining momentum for the insightful facts regarding cash flow, income, and expense, as this would assist organizations cut their expenses. Lesser net worth market groups are provided digital and wealth management advice services, leading to low fee-based commissions.

Various technological enhancements would increase the popularity of AI in fintech

Credit card fraud is one of the most common types of cybercrime. As a result, firms are developing the next-generation of algorithms called Convolutional Neural Networks, which are based on the visual cortex, a small portion of cells in the human body that is sensitive to particular regions of the visual field. They can extract basic visual elements such as aligned edges, end-points, and corners in this way. This system can analyze an individual's funding data and establish whether they made the most exact credit card transaction or if their credit card data was used by someone else based on that data.

Market Restraining Factors

Data security and privacy concerns of the users

Most fintech businesses are dealing with the sensitive course of data privacy and security that is the largest hurdle with AI. Because any data breach or security failure might be disastrous, the fintech sector is overseen by tight adherence to standards and governance. Since businesses nourish more and more user and provider information into advanced, AI-fueled algorithms, innovative bits of personal data are created without the knowledge of the way it affected clients and employees, which ultimately leads to the rising privacy concerns. This is especially true in the retail banking industry, in which the collection of consumer data is at the forefront of big data challenges.

Access full Report Description, TOC, Table of Figure, Chart, etc. @https://www.quadintel.com/request-sample/global-artificial-intelligence-in-fintech-market/QI046

Component Outlook

Based on Component, the market is segmented into Solutions and Services. The solution segment procured the highest revenue share in the Artificial Intelligence In Fintech Market in 2021. The high proportion can be due to software tools, which help banks adopt AI-enabled solutions that extract correct and comprehensive data from large amounts of data in a timely manner. Some firms' solutions help them accomplish things like develop their retail banking company with next-best-action software, identify and battle financial fraud, and Excellerate client relationships with multichannel user experience solutions.

Deployment Outlook

Based on Deployment, the market is segmented into On-premise and Cloud. The cloud segment garnered a substantial revenue share in the Artificial Intelligence In Fintech Market in 2021. From 2022 to 2030, the cloud segment will grow at the quickest rate. AI-based algorithms that learn from historical data in a public cloud, detect current norms, and make recommendations are credited with the increase. In data handling and authenticity, the cloud and AI may boost efficiency, and digital security, and this automated technique removes human errors throughout data processing

Application Outlook

Based on Application, the market is segmented into Business Analytics & Reporting, Customer Behavioral Analytics, Fraud Detection, Virtual Assistant (Chatbots), Quantitative & Asset Management and Others. Business Analytics and Reporting segment witnessed the maximum revenue share in the Artificial Intelligence In Fintech Market in 2021. Regulatory and compliance management, as well as customer behaviour monitoring, benefit from business analytics and reporting. More efficiency, more educated decision, and higher revenues are all elements that have contributed to the segment's growth.

Regional Outlook

Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa. North America emerged as a leading region in the Artificial Intelligence In Fintech Market with the largest revenue share in 2021. It is due to the industrialized economies of the United States and Canada placing a major focus on R&D-derived technologies. In fintech, this region has the most competitive and rapidly developing AI technology. Many startups and rising firms that provide AI services to the finance sector are also fueling the trend.

The major strategies followed by the market participants are Partnerships. Based on the Analysis presented in the Cardinal matrix; Microsoft Corporation and Google LLC are the forerunners in the Artificial Intelligence In Fintech Market. Companies such as Oracle Corporation, Intel Corporation and IBM Corporation are some of the key innovators in the Market.

The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include IBM Corporation, Oracle Corporation, Microsoft Corporation, Google LLC, Intel Corporation, Salesforce.com, Inc., Amazon Web Services, Inc., ComplyAdvantage, Amelia US LLC, and Inbenta Technologies, Inc.

Recent Strategies deployed in Artificial Intelligence In Fintech Market

Partnerships, Collaborations and Agreements:

Jun-2022: Amazon Web Services (AWS) teamed up with Hong Kong Science and Technology Parks Corporation (HKSTP). This collaboration aimed to boost a robust Innovation and Technology (I&T) ecosystem in Hong Kong. Through this collaboration, AWS and HKSTP would introduce a series of programs under four key pillars to accelerate the innovation of IT companies, startups, and researchers across their whole growth cycle.

May-2022: Amazon Web Services (AWS) joined hands with RBL Bank and Amazon Pay. This collaboration aimed to introduce UPI payments, along with offering peer-to-peer and peer-to-merchant transactions. Through this integration, Amazon Pay would issue NPCI?s allocated UPI ID with the manage @rapl, to RBL Bank.

May-2022: Oracle PartnerNetwork (OPN) collaborated with Temenos, the cloud banking platform. The collaboration aimed to allow Oracle?s global customers like financial services organizations across the world, to implement its robust Explainable AI and machine learning capabilities through Oracle Cloud Marketplace.

May-2022: Salesforce came into a partnership with Upstart, a leading artificial intelligence (AI) lending platform. This partnership aimed to bring AI-enabled lending to the financial services industry, which can assist financial institutions to modernize lending, stay competitive, and delivering better customer service to users.

Apr-2022: ComplyAdvantage came into a partnership with Xapien, a deep-technology company. This partnership aimed to provide a ground-breaking due diligence solution to the market, wherein there is a huge requirement for a deep understanding of sanctioned parties or PEPs.

Apr-2022: IBM formed a partnership with Skyscend, a fintech start-up with headquarters in Atlanta, GA. Under this partnership, IBM's Embedded Solution Agreement (ESA) would enable Skyscend to integrate IBM's cutting-edge technologies with its Skyscend Pay B2B SaaS fintech platform to service the global marketplace.

Mar-2022: Google Cloud came into a partnership with Mizuho Financial Group (MFG), a Japanese bank holding company. This partnership aimed to show the firm modernize its systems, develop a new digital marketing platform on Google Cloud, and release a new digital financial service portfolio like Banking-as-a-Service (BaaS).

Feb-2022: Google Cloud entered into a partnership with KeyBank, and Deloitte. This partnership aimed to boost KeyBank?s commitment to a cloud-first approach to banking. In this partnership, KeyBank would become the largest regional banks in the United States to manage its primary platforms and applications on Google Cloud infrastructure, enabling the financial institution to shift the way it creates, operationalizes, and provides digital experiences to customers, partners, and teammates with security at its core.

Feb-2022: Microsoft came into partnership with U.S. Bank as part of a significant investment by US bank in its technology infrastructure. This partnership aimed to use Artificial intelligence (AI) and machine learning (ML) in order to support the bank?s applications and infrastructure along with augmenting customer privacy and the security of data and financial assets.

Oct-2021: Oracle NetSuite teamed up with HSBC, a British multinational universal bank and financial services holding company. This collaboration aimed to introduce a Banking as a Service (BaaS) offering, which would allow customers to provide business banking services via their own platforms.

Feb-2021: Google Cloud formed a partnership with BBVA, a customer-centric global financial services group. This partnership aimed to transform the bank?s security strategy by enhancing and optimizing its security infrastructure. Under this partnership, BBVA would collaborate with Google Cloud in the development of the new artificial intelligence (AI) and machine learning (ML) models to forecast and prevent cyberattacks against its banking infrastructure, offering a more secure experience for the bank and its customers.

Dec-2020: Google Cloud formed a partnership with Deutsche Bank, one of the world's leading financial service providers. This partnership aimed to boost the transformation of the bank to the cloud. For Deutsche Bank?s customers, the agreement would reshape the way products and services are developed and delivered.

Jul-2020: Microsoft signed a multi-year cloud agreement with Finastra, a financial software company. This agreement aimed to assist the digital transformation of financial services. Together, the companies would support banks, credit unions, and other firms in the sector to utilize Power Platform, Azure, and Microsoft 365.

Product Launches and Product Expansions:

Apr-2022: Salesforce released CRM Analytics, AI-based insights for sales, marketing, and service teams in every industry. These technologies would assist sales leaders, service leaders, and employees across any industry like financial services, consumer goods, manufacturing, and communications, put data at the center of each customer relationship, and eventually provide more customized experiences.

Apr-2022: IBM introduced IBM z16, Real-Time AI for Transaction Processing. This technology would bring AI inferencing, through its IBM Telum Processor, with highly protected and reliable high-volume transaction processing.

Jan-2022: Google introduced a Google Cloud digital assets team. This team would support customers' requirements in building, transacting, storing value, and deploying new products on blockchain-based platforms.

May-2021: IBM introduced new advances in artificial intelligence (AI), hybrid cloud, and quantum computing. These advanced would assist IBM's customers and partners boost their digital transformations, returning to work smarter, and developing strategic ecosystems that would generate better business results.

Apr-2021: ComplyAdvantage released a new early-stage anti-money laundering (AML) program. The program would offer qualified startups free access to the company?s leading AML and Know Your Customer (KYC) tools and resources required to uncover and decrease the threat of money-laundering activities.

Nov-2020: Google Cloud unveiled the new Document AI (DocAI) platform, a unified console for document processing. Through this latest DocAI platform, customers can rapidly access all parsers, tools, and solutions with a unified API, allowing an end-to-end document solution from evaluation to deployment.

Acquisitions and Mergers:

Mar-2022: Microsoft took over Nuance Communications, artificial intelligence (AI), and speech technology firm. This acquisition aimed to bring together Nuance?s best-in-class conversational AI and ambient intelligence with Microsoft?s safe and trusted industry cloud portfolio.

Jan-2022: Oracle took over Federos, a provider of unified service management solutions for service providers. This acquisition aimed to expand Oracle Communications? application portfolio by introducing AI-optimized assurance, analytics, and automation solutions to maintain the accessibility and performance of crucial networks and systems.

Dec-2020: IBM took over Expertus Technologies, a Montreal-based fintech company. This acquisition aimed to strengthen IBM's portfolio as an end-to-end digital payments solution provider and Excellerate IBM's hybrid cloud and AI strategy.

Oct-2020: Intel completed the acquisition of SigOpt, a startup out of San Francisco. Through this acquisition, Intel would double down on building chips and related architecture for the next generation of computing, which would boost Intel's expertise in the area of future technology: artificial intelligence.

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Scope of the Study

Market Segments covered in the Report:

By Component

Solutions
Services
By Deployment
On-premise
Cloud
By Application
Business Analytics & Reporting
Customer Behavioral Analytics
Fraud Detection
Virtual Assistant (Chatbots)
Quantitative & Asset Management
Others
By Geography
North America
US
Canada
Mexico
Rest of North America
Europe
Germany
UK
France
Russia
Spain
Italy
Rest of Europe
Asia Pacific
China
Japan
India
South Korea
Singapore
Malaysia
Rest of Asia Pacific
LAMEA
Brazil
Argentina
UAE
Saudi Arabia
South Africa
Nigeria
Rest of LAMEA
Companies Profiled
IBM Corporation
Oracle Corporation
Microsoft Corporation
Google LLC
Intel Corporation
Salesforce.com, Inc.
Amazon Web Services, Inc.
ComplyAdvantage
Amelia US LLC
Inbenta Technologies, Inc.

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About Quadintel:

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.

  • Robust, detailed segmentation
  • In-depth analysis in all geographies.
  • Detailed breakup in various segmentation.
  • Rigorous primary and secondary research

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Address: Office - 500 N Michigan Ave, Suite 600, Chicago, Illinois 60611, UNITED STATES
Tel: +1 888 212 3539 (US - TOLL FREE)
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COMTEX_414929243/2796/2022-09-21T02:07:01

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

Tue, 20 Sep 2022 18:07:00 -0500 en-US text/html https://www.marketwatch.com/press-release/artificial-intelligence-in-fintech-market-projections-and-regional-outlook-estimates-forecast-by-application-segments-2022-2030-2022-09-21
Killexams : AI In Education Market 2022 Current Price Trends, Application Growth Potential, Key Players, And Technology Type Analysis 2030

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

Oct 12, 2022 (Alliance News via COMTEX) -- The report provides a comprehensive analysis of segments in this market, covering all the major regions and countries. The major regions analyzed in the study are North America, Europe, Asia Pacific, Latin America and Middle East & Africa. The report also includes additional information about other factors such as drivers, restraints and challenges faced by this market along with an overview for each mentioned segment in the study.

The Global AI In Education Market size is expected to reach $12.8 billion by 2028, rising at a market growth of 33.5% CAGR during the forecast period.

Statistical and cogent models for the market were used to assess and forecast the market data. Additionally, market shares and important trends were taken into account when creating the study. The Market Time Line Analysis, Vendor Positioning Grid, Market Overview and Guide, Company Market Share Analysis, Company Positioning Grid, Standards of Measurement, Top to Bottom Analysis, and Vendor Share Analysis are additional data models that can use.

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The study offers in-depth regional analysis of market scenarios, by analyzing previous trends and covering future forecast. It also offers an in-depth analysis of major driving factors, segments, regions & countries and key players in the market. Moreover, the report outlines the competitive scenarios across different geographies, along with some key market strategies such as mergers and acquisitions, new product developments, R&D activities and more.

GlobalAI In Education MarketSize, Share & Industry Trends Analysis Report By Component (Solution and Services), By Application, By End-use, By Deployment Mode (Cloud and On-premise), By Technology, By Regional Outlook and Forecast, 2022 - 2028

Giving teachers, students, and educators a better experience is part of the application of AI in education. Numerous service providers are developing unique AI education solutions or services that connect to numerous education platforms. The demand for AI in education is also rising as online learning is becoming a more popular choice for skill development. As there is a growth in AI-backed business applications, tech corporations cooperated with colleges to introduce new training formats for AI talent in order to better fit with the development trends and latest industry developments.

Giving teachers, students, and educators a better experience is part of the application of AI in education. Numerous service providers are developing unique AI education solutions or services that connect to numerous education platforms. The demand for AI in education is also rising as online learning is becoming a more popular choice for skill development. As there is a growth in AI-backed business applications, tech corporations cooperated with colleges to introduce new training formats for AI talent in order to better fit with the development trends and latest industry developments.

Artificial intelligence has the ability to address some of the most significant issues facing education nowadays, such as innovative teaching and learning methods, and eventually quicken the realization of SDG 4. However, these quick technological advancements necessarily carry with them a number of risks and difficulties that have thus far surpassed regulatory structures and policy discussions. Enhanced education experience can be achieved with the help of AI technologies, and UNESCO is committed to assisting Member States in doing so while ensuring that the use of AI in educational settings is governed by the fundamental values of equity and inclusion.

COVID-19 Impact Analysis

The COVID-19 pandemic delivered a significant impact on the worldwide economy. Various businesses all over the world were majorly affected due to the pandemic. In addition, the advent of the pandemic also significantly disrupted the education infrastructure all over the world. Various industries were severely affected by the COVID-19 outbreak. The COVID-19 pandemic had a detrimental effect on the education sector. Schools colleges and other educational institutes all over the world were shut down, due to which, there was a drop in the demand for AI learning in in-person education.

Market Growth Factors

Enhanced Student And Teacher Experience

One of the key factors that are driving the growth of the AI in education market is the fact that it significantly improves the learning and teaching experience. Vendors of AI technology are creating electronic gadgets with AI capabilities by creating sophisticated learning systems that enhance learning procedures. For educational institutions to survive in a world with cutthroat competition, they must provide the finest learning environment. For instance, Century Intelligent Learning created a classroom employing AI technology that allows teachers to create academic curricula online, allowing students to access their curricula whenever they choose.

Saves A Lot Of Time And Cost

The administrative team works continuously for hours, whether it be filing paperwork, sending texts and emails, getting in touch with students or their parents, or producing periodic reports. In a similar manner, professors and teachers must grade papers for tests and homework, plan lesson plans, prepare for upcoming classes, etc. It consumes a significant amount of time for these educators. The remaining time is set aside for marking test papers, creating lesson plans for future classes, and finishing up administrative tasks. The artificial intelligence expert can assist teachers in developing an AI system that would increase staff and student productivity.

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Market Restraining Factors

Slow Digitalization Due To The Digital Divide

The diffusion of digitalization in LMIC, or low- and middle-income countries, is very slow. This is attributed to the prevalence of the digital divide all over the world. The digital divide can be defined as the reduced digital growth of developing and underdeveloped countries in contrast to developed countries due to the lower penetration of technologies. There is a significant number of LMICs that are struggling to bring technological advancements. AI-enabled deployments are completely reliant on accessible data, IT infrastructures, and Internet of Things devices for gathering instances to produce correct results due to the absence of funding for the fundamental requirements of IT infrastructures within poor nations.

Component Outlook

Based on component, the AI in education market is classified into solutions and services. In 2021, the services segment registered a significant revenue share of the AI in the education market. The growth of the segment is increasing due to the rise in online education brought on by the pandemic. Additionally, the market is being driven by rising government and private sector investments in AI education for the improvement of the educational system. Venture capitalists (VCs) invested significant amounts in AI businesses in 2020.

Deployment Mode Outlook

On the basis of deployment, the AI in education market is bifurcated into Cloud and On-premises. In 2021, the cloud segment procured the highest revenue share of the AI in the education market. Reduced ownership costs along with a growing demand for educational data sharing among international campuses are two reasons that have contributed to the expansion of the cloud segment. Additionally, it enables academic institutions to integrate cutting-edge AI technology into their current operating model without having to increase their capital expenditures.

Technology Outlook

By technology, the AI in education market is segmented into Natural Language Processing (NLP) and Machine Learning. In 2021, the natural language processing segment registered a significant revenue share of the AI in the education market. The bolstering growth of the segment is owing to an increase in the demand for virtual learning among colleges and universities. Investments from educational facilities are increasing in technological advancements in order to offer the best-in-class learning experience to their students. In addition, it would also streamline several teaching processes for teachers.

Application Outlook

Based on application, the AI in education market is fragmented into Learning Platform & Virtual Facilitators, Intelligent Tutoring System (ITS), Smart content, Fraud and Risk Management and Others. In 2021, the learning platform and virtual facilitators segment acquired the biggest revenue share of the Ai in the education market. The growth of this segment is owing to the expansion of digital education learning technology. Additionally, employing a variety of abilities and advice relevant to social growth, virtual facilitators assist students in resolving challenging real-time social issues.

End-Use Outlook

On the basis of end-use, the AI in education market is categorized into K-12 Education, Higher Education and Corporate Training & Learning. In 2021, the Corporate Training & Learning segment held a significant revenue share of the AI in the education market. Some of the major factors driving businesses to make the most of their resources are the rapidly shifting market conditions and growing market competition. Therefore, in order to stay up with the constantly shifting market expectations, firms must promptly train their staff members and keep them informed of the latest developments.

Regional Outlook

Region-wise, the AI in education market is analyzed across North America, Europe, Asia Pacific and LAMEA. In 2021, North America procured the maximum revenue share of the AI in the education market. The market in the region is being driven by elements including the presence of top organizations that create technology infrastructure facilities, solutions, and services, and the massive number of end-users utilizing educational gadgets for AI in education. For instance, the IBM corporation announced that it would train people in new skills through partnerships with business and academia.

The major strategies followed by the market participants are Partnerships. Based on the Analysis presented in the Cardinal matrix; Microsoft Corporation and Google LLC are the forerunners in the AI In Education Market. Companies such as Amazon Web Services, Inc., IBM Corporation and Pearson PLC are some of the key innovators in AI In Education Market.

The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include IBM Corporation, Microsoft Corporation, Google LLC, Cognizant Technology Solutions Corporation, Pearson PLC, Amazon Web Services, Inc., Carnegie Learning, Inc., Blackboard, Inc., BridgeU Limited, and DreamBox Learning, Inc.

Recent Strategies deployed in AI In Education Market

Partnerships, Collaborations and Agreements:

May-2022: Microsoft collaborated with ITE, an education institution. following this collaboration, the companies would integrate an AI lab in ITE College East to work as an innovation center in order to launch AI concepts, applications, and skills across industries for ITE students. Moreover, the companies would also expand the exposure of AI principles and offer skilling opportunities to more than 4,000 students.

Feb-2022: IBM came into a collaboration with the University of Florida, a public land-grant research university. This collaboration aimed to launch a public land-grant research university in order to extend UF?s strategy to become an international leader in AI, fintech, data science, and other associated technologies, which can help in solving major challenges in the society.

Feb-2022: IBM entered into an agreement with IE University, a private university. This agreement aimed to accelerate the strategy to make Madrid a key Artificial Intelligence ecosystem across Europe via the development of Pier 17. in addition, IBM's experts would join IE's adjunct faculty to aid in the identification of professors for the Educating AI innovators as well as Tech-makers program.

May-2021: Microsoft entered into an agreement with the Ministry of Tribal Affairs, a branch of the Government of India. Under this agreement, the entities would develop an AI curriculum for tribal students in Hindi and English at the Eklavya Model Residential Schools as well as Ashram Schools in the country.

Nov-2020: Microsoft signed an MoU with the Education University of Hong Kong, a publicly funded tertiary institution. With this agreement, the entities would focus on the integration of Artificial Intelligence into its Bachelor of Education programs curriculum.

Oct-2020: Microsoft came into a partnership with the All India Council For Technical Education, a statutory body to empower learners and educators. Under this partnership, Microsoft would offer more than 1500 courses to educators and students via AICTE's e-learning portal ELIS. for no cost.

Jun-2020: IBM joined hands with the Central Board of Secondary Education, an Indian national board of education. This collaboration aimed to add a curriculum with the purpose of integrating artificial intelligence in the education of classes XI and XII. Moreover, this collaboration would also complement CBSE's SEWA, or Social Empowerment through Work Education and Action, program.

Mar-2020: Pearson came into a partnership with MindBridge, a leader in financial risk discovery. This partnership aimed to integrate artificial intelligence into academic institutions.

Feb-2020: Pearson teamed up with the UCL Institute of Education, an education school of University College London. Through this collaboration, the companies would focus on the end-to-end experience of test-takers in taking PTE for study or migration purposes.

Acquisitions and Mergers:

Sep-2021: Blackboard announced a merger with Anthology, a leading provider of higher education solutions. This merger aimed to develop a comprehensive as well as modern EdTech ecosystem across the world. In addition, it would expedite the transformation at education institutions to Excellerate earner experience and propel institution and student success.

Sep-2020: Carnegie Learning acquired Scientific Learning, a leader in brain-based learning technology and research. With this partnership, the company aimed to strengthen the education technology offerings of both companies in order to drive a mutual vision to Excellerate student learning outcomes via leading-edge technology developed through rigorous research.

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Scope of the Study

Market Segments covered in the Report:

By Component

Solution
Services
By Application
Learning Platform & Virtual Facilitators
Intelligent Tutoring System (ITS)
Smart content
Fraud & Risk Management
Others
By End Use
Higher Education
Corporate Training & Learning
K-12 Education
By Deployment Mode
Cloud
On-premise
By Technology
Machine Learning
Natural Language Processing (NLP)
By Geography
North America
US

Canada

Mexico

Rest of North America
Europe
Germany

UK

France

Russia

Spain

Italy

Rest of Europe
Asia Pacific
China

Japan

India

South Korea

Singapore

Malaysia

Rest of Asia Pacific
LAMEA
Brazil

Argentina

UAE

Saudi Arabia

South Africa

Nigeria

Rest of LAMEA

Companies Profiled
IBM Corporation
Microsoft Corporation
Google LLC
Cognizant Technology Solutions Corporation
Pearson PLC
Amazon Web Services, Inc.
Carnegie Learning, Inc.
Blackboard, Inc.
BridgeU Limited
DreamBox Learning, Inc.

Request Full Report : –https://www.quadintel.com/request-sample/global-ai-in-education-market/QI046

About Quadintel:

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.

  • Robust, detailed segmentation
  • In-depth analysis in all geographies.
  • Detailed breakup in various segmentation.
  • Rigorous primary and secondary research

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.

Get in Touch with Us:

Quadintel:
Email:sales@quadintel.com
Address: Office – 500 N Michigan Ave, Suite 600, Chicago, Illinois 60611, UNITED STATES
Tel: +1 888 212 3539 (US – TOLL FREE)
Website :https://www.quadintel.com/

COMTEX_416437630/2796/2022-10-12T01:19:14

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

Tue, 11 Oct 2022 17:19:00 -0500 en-US text/html https://www.marketwatch.com/press-release/ai-in-education-market-2022-current-price-trends-application-growth-potential-key-players-and-technology-type-analysis-2030-2022-10-12
Killexams : IBM doubles down on partner ecosystem investment
Kate Woolley (IBM)

Kate Woolley (IBM)

Credit: Supplied

IBM is on a mission to double its revenue via its partner ecosystem in the next three to five years, making some significant updates to its PartnerWorld program along the way. 

As part of its efforts to re-position ecosystem partners at the center of the company’s go-to-market strategy, partners will now have access to the same badges and selling enablement materials as IBM sellers.

This is part of IBM’s ongoing commitment to growing its ecosystem.

“We will continue to make investments in the partner experience so that together, as a single team, we can achieve our goal of doubling revenue through the IBM ecosystem in the next 3–5 years,” said Kate Woolley, IBM’s ecosystem general manager.

The badges and additional materials are available through a new learning hub, designed to Excellerate the digital experience for partners.

“Users will notice a more modernised and consistent experience on the IBM training site, making it easier to find resources,” Woolley said.

All registered partners have access to these resources at the same time as IBM sellers, and at no cost.