Latest syllabus of P2090-080 is provided by helps big numbers of check takers to obtain high marks within their exams plus achieve their professional goals. Simply signing up and downloading material will make a person sure you may pass your IBM ISW-9.7 & Smart Analytics Technical Mastery Test v1 along with high marks. P2090-080 study guide are usually updated on a normal basis and down load is ready within your account, constantly.

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IBM ISW-9.7 & Smart Analytics Technical Mastery Test v1
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Killexams : IBM Analytics learn - BingNews Search results Killexams : IBM Analytics learn - BingNews Killexams : How one professional athlete is helping IBM encourage people to embrace a new era of data

By Noah Syken, Vice President of Sports and Entertainment Partnerships at IBM

For the last six years, IBM has been working with ESPN to infuse AI-generated insights into its fantasy football platform. But we needed someone who could help us tell the story — someone who could grab the attention of fantasy football enthusiasts, introduce them to the artificial intelligence of Watson, and encourage them to embrace the era of data-driven decision making. So we asked a guy that has a lot in common with IBM: Eli Manning.

Let me explain. Back in 2016, IBM formed a partnership with ESPN. In this relationship, we use IBM's advanced analytics and AI capabilities to analyze the massive amount of data produced by fantasy football. We then serve up insights that help guide the roster decisions of ESPN's fantasy football users. Today, those insights take the form of two features:

  • Trade Analyzer with IBM Watson, which uses AI to analyze player statistics and media commentary to help team managers understand the value of a potential trade.
  • Player Insights with IBM Watson, which helps fantasy managers estimate the potential upside and downside of a matchup, analyze boom or bust chances, and assess injuries.

IBM is in the fantasy football business for two reasons. First, we're solving a very real business problem for a valued partner. Fantasy football is a critical form of digital engagement for ESPN, and one that requires constant innovation to stay ahead of the competition. And Watson's insights make the platform more fun and engaging.

The second reason is that ESPN Fantasy Football offers IBM a powerful platform to demonstrate our AI capabilities to 11 million people. Both Trade Analyzer and Player Insights are produced by transforming vast quantities of data into insights that inform decision-making. We're analyzing the performance statistics of all 1,900 players in the league. But the numbers don't always tell the whole story. So we're also using the natural language processing capability of Watson Discovery to mine insights from millions of blogs, articles, and podcasts produced by media experts. Last year alone Watson served up more than 34 billion AI-powered insights to ESPN fantasy players.

When Eli Manning joined the New York Giants back in 2004 as the number one pick in the draft, many Giants fans thought he would be the second coming of Joe Namath: a big star in the big city. But Manning was more subtle than that. There were no flashy fur coats or movie-star girlfriends. Just an understated, workman-like grit that produced two championships. 

So what does that have to do with IBM? It has been 17 years since IBM sold its ThinkPad business to Lenovo. That was the last time our iconic "eight-bar" logo appeared on a consumer-facing device. But despite this lack of visibility, our work has never been more consequential than it is today. It's not flashy, but our technology and expertise support the operation of the most mission-critical systems on the planet: electrical grids, airlines, telecommunications networks, banks, government services, and many others.

Technologies like hybrid cloud and AI are powerful, complex, and often difficult for people to comprehend. They operate behind the scenes, in data centers and back offices. But they are critically important to our clients and the world. That's why we showcase the work of IBM Consulting through partnerships like the Masters, the US Open, and ESPN's Fantasy Football. And that is why Eli Manning is helping us tell our story.

Learn more about how IBM and ESPN are working together to bring data-driven insights to fantasy football. 

This post was created by IBM with Insider Studios

Fri, 14 Oct 2022 01:50:00 -0500 en-US text/html
Killexams : Learning Analytics Market is Expected to Record the Massive Growth, with Prominent Key Players Oracle, Blackboard, IBM

New Jersey, United States, Oct. 12, 2022 /DigitalJournal/ In a nutshell, learning analytics is primarily focused on collecting and leveraging student learning progress data to optimize learning outcomes. This includes understanding each students time on course work and determining a students progress over time.

The learning analytics (LA) market is growing rapidly in educational institutions worldwide, especially for higher education and MOOC providers, primarily driven by the need to Boost the student success rate, material efficiency of the course, retention, and learning experience. Learning analytics follows learners digital footprints, transforming how they view learning processes and enabling data-driven decisions to maximize student success. It has continued to evolve as technology continues to be one of the main drivers of educational change.

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

The Learning Analytics 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 Learning Analytics market research report tracks all the latest 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.

Competitive landscape:

This Learning Analytics 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:Oracle, Blackboard, IBM, Microsoft, Pearson, Saba Software, Sum Total System, Mcgraw-Hill Education, SAP, Desire2learn,

Market Scenario:

Firstly, this Learning Analytics 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 Learning Analytics 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

On-premises, Cloud Based,

Market Segmentation: By Application

Academic, Enterprise

For Any Query or Customization:

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 Learning Analytics 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 Learning Analytics report helps to save you time and money by delivering unbiased information under one roof.

Table of Contents

Global Learning Analytics Market Research Report 2022 – 2029

Chapter 1 Learning Analytics 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 Learning Analytics Market Forecast

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Wed, 12 Oct 2022 04:08:00 -0500 A2Z Market Research en-US text/html
Killexams : Top Predictive Analytics Solutions

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.


  • 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 provide the platform Full Marks for its interface and ease of use.


  • 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.


  • 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.


  • 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.


  • 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.


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


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.


  • 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.


  • 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.


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


  • 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.


  • 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.


  • 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.


  • 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.


  • 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.


  • 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.


  • 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.


  • 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.


  • Customizations can be time consuming and difficult.


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.


  • The platform gets Full 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.


  • 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
Killexams : Cloud Reports Offer Skills Gap Solutions


Cloud Reports Offer Skills Gap Solutions

Two latest "state of cloud" reports offer advice on how to address the everlasting, crippling cloud skills dearth.

The inability to find IT pros with requisite cloud skills is persistently identified as a major challenge to organizations moving to cloud computing, as we have covered in latest articles like "Cloud Strategy Survey Highlights Skills Shortage, Cloud Overspend" and "IBM Cloud Study: 'Initial Excitement' Bends to Skills, Security, Compliance Challenges" and even articles from years ago like this 2018 article, "Report: 'Cloud and Distributed Computing' Are Skills Companies Need Most."

In fact, only 8 percent of global technologists have significant cloud-related skills and experience, says a latest "2022 State of Cloud Report" report from Pluralsight, a technology workforce development company known for its training courses.

That report, like many before it, identifies security skills as the most coveted in this age of rampant ransomware and other cybersecurity threats, though it also highlighted a need for database analytics, networking, machine learning and several other highly sought-after skills.

Pluralsight said security was the No. 1 obstacle preventing organizations from achieving cloud maturity. "When 40 percent of leaders and learners agree that security is the top skills gap, we have a serious problem," the company said in a blog post last month. "That is, unless you want to see your organization's name splashed across the headlines. Cloud computing is the future. That much is clear. But to provide consumers with reliable solutions, we have to prioritize security."

The 8 percent figure mentioned above led off Pluralsight's list of major takeaways from its report:

Pluralsight's report compiled survey results from more than 1,000 technologists and leaders in the United States, Europe, Australia and India on the most current trends and challenges in cloud strategy and learning.

A second report, "IBM Transformation Index: State of Cloud," also emphasized how crucial security concerns are. That report is designed to help organizations gauge how they fare against industry and local cloud norms in a variety of cloud areas. IBM said the report is based on its own research with more than 3,000 IT and business decision makers in 12 countries and 23 industries, revealing the areas where teams face the biggest challenges and opportunities. There, of course, security is also front and center, along with the skills gap.

"More than 90 percent of financial services, telecommunications and government organizations who responded have adopted security tools such as confidential computing capabilities, multifactor authentication and others," IBM said in a Sept. 29 blog post. "However, gaps remain that prevent organizations from driving innovation. In fact, 32 percent of respondents cite security as the top barrier for integrated workloads across environments, and more than 25 percent of respondents agree security concerns present a roadblock to achieving their cloud business goals.

"When it comes to managing their cloud applications, 69 percent of respondents say their team lacks the skills needed to be proficient. This, combined with each cloud generating its own operating silo, puts constraints on the efficiency and effectiveness of people's work."

The skills shortage was discussed further in the actual report, which said: "Cloud complexity continues to grow. Many IT teams lack the necessary cloud skills to manage this complexity successfully and will need to reskill, hire for new skills, or 'borrow' skills via free agents in a gig model. Almost seven out of 10 respondents say their organization's IT team lacks the skills to architect or manage cloud applications."

Both reports go into much greater detail about the talent dearth, cloud computing obstacles and much more, while also offering advice to organizations to address those issues.

When it comes to offering solutions to mitigate the cloud skills shortage, Pluralsight unsurprisingly emphasized training, or "upskilling."

To shrink skills gaps and reduce lost internal knowledge, the company said: "Employees need to be aligned with company strategy while they begin to upskill to build within your cloud structure. Institutional knowledge is hard to come by and critical to maintain, which means it's essential that you don't allow critical internal knowledge to leave with an employee if they leave your company. Providing upskilling tools and opportunities is a worthwhile investment, but an investment nonetheless, so take steps to assure it properly benefits your long-term goals."

Pluralsight said organizations can mitigate this risk in a few ways:

IBM, meanwhile, said that to develop a cadre of cloud-skilled resources and create a single effective hybrid cloud operating model, organizations should consider the following steps:

Other bullet lists of advice came from our May article, "How to Address Crippling Cloud Skills Shortage?"

That article features the above graphic from Deloitte and bullet lists from that company and others including McKinsey & Company, The Linux Foundation's Clyde Seepersad and others. Here are some samples:

About the Author

David Ramel is an editor and writer for Converge360.

Mon, 17 Oct 2022 07:21:00 -0500 en-US text/html Killexams : IT services sector (read: IBM, others) faces talent challenges but new opportunities emerge

Editor’s note: Technology Business Research analysts focuses on technology markets and companies that move and shape those markets. 


HAMPTON, N.H. – Revenue expansion in the IT services sector continues, driven by vendors’ investments in talent and portfolio expansion and emphasis on strengthening relationships with customers and alliance partners.

While political and macroeconomic challenges such as rising inflation and the natural gas crisis are factors that might create pockets of slower growth, TBR expects the overall IT services market to continue to grow in the coming quarters. IT systems have become corporate utilities that enable clients to transform business models, contain costs and accelerate growth, and TBR expects demand for IT services around digital transformation to remain elevated. For the rest of 2022, attracting and managing talent will remain vendors’ core challenge to successfully growing revenue and managing costs.

Prediction No. 1: Focus on talent management, refined during the pandemic, will recede in a post-pandemic environment

Senior Analyst Elitsa Bakalova: Talent management remained a core priority and challenge for IT services providers, and none of the standard HR approaches changed during the first nine months of 2022 as vendors strived to capture rising demand for digital transformation.

As TBR predicted at the end of 2021, attracting, retaining, upskilling, promoting and rewarding talent are all necessary HR motions and further accelerated during the past three quarters. There is an ever-increasing need for people as vendors build their benches to capture opportunities and support revenue growth. New job creation and the gradual alleviation of pandemic pressures have encouraged employees to pursue career-building opportunities, leading to elevated employee attrition of 20.8% in 2Q22 compared to 16% in 2Q21, 14.1% in 2Q20 and 17.6% in 2Q19, on average, for the 31 vendors in TBR’s IT Services Vendor Benchmark. While vendors continue to recruit via traditional methods, more are investing in reskilling and upskilling as well as launching educational initiatives.

Finding and keeping employees in the IT services market is increasingly difficult as talent poaching intensifies for a finite number of resources and companies’ bookings remain high. Vendors continue to place a premium on skilled resources, offering sizable signing bonuses and higher wages. Increasing labor costs due to wage hikes and robust retention bonuses along with rising facility, travel and communication expenses are pressuring IT services vendors’ profitability.

Prediction No. 2: The decarbonization shift from promises to actual results opens a massive opportunity for IT services

Elitsa: This prediction remained true during the first nine months of 2022 as vendors TBR identified as decarbonization leaders continued to invest in developing their services and solutions portfolios to support clients’ sustainability initiatives and address their internal decarbonization-related pledges. As we anticipated, IT services vendors are increasingly bringing clarity to decarbonization by harnessing emerging technologies such as blockchain as well as established analytics and AI solutions.

According to TBR’s first Decarbonization Market Landscape, “Although some firms have been active over the last few decades around developing and acting on decarbonization strategies, many were induced — be it from competition, stakeholders or regulatory evolution — to improve, update, revisit or outright announce new net-zero targets, which in latest years have become somewhat of a comprehensive measure of a firm’s overall decarbonization efforts. … With a wider set of buyers relying heavily on technology to measure and manage emissions as well as advisory services to assess, plan and verify new initiatives, professional services vendors will continue to be key players in the enterprise decarbonization space. … Vendors must take care to continue to learn and stay up to date on reporting standards and regulatory change, supporting both internal and commercial efforts.”

Prediction No. 3: Blockchain winter ends and 5G & edge bloom in 2022, bringing new enhanced revenue streams to IT services vendors

Elitsa: While IT services vendors have increasingly announced investments in professional and managed services to enable adoption of blockchain, 5G and edge solutions, the trend is not mainstream across all 31 vendors in TBR’s IT Services Vendor Benchmark. However, select vendors have invested in expansion in the segments to benefit from diversified revenue streams.

As TBR expected, partnerships between IT services vendors and technology providers have been a key lever for increasing the value of vendors’ solutions and expanding their portfolio and client reach. For example, IBM partnered with Telus to deploy an edge computing platform across Canada, which expanded the reach of IBM Cloud Satellite by running the distributed cloud solution on Telus’ 5G network. Telus will leverage IBM Consulting services to implement AI and automation solutions, including products such as Cloud Pak for Network Automation. Atos partnered with Verizon to integrate Atos Computer Vision into Verizon’s multi-access edge computing network. This integration will bring video analytics services that utilize AI to customers and will provide Verizon with access to Atos’ BullSequana Edge servers to further advance 5G solutions.

During 2022 vendors have also leveraged acquisitions to expand their capabilities. For example, Atos acquired U.K.-based Ipsotek in 2021, adding software and IP to its solutions offerings to expand its edge AI/machine learning offerings and introduce video analytics solutions through Ipsotek’s VISuite. In 2022 IBM acquired U.S.-based Sentaca, a telecom consultancy and systems integrator, which strengthened IBM Consulting’s capabilities around helping communication service providers integrate with cloud-native services and architectures to better enable 5G for their customers.


Fri, 14 Oct 2022 04:26:00 -0500 en-US text/html Killexams : Vaagdevi College of Engineering organises interaction with IBM

Published: Published Date - 07:20 PM, Fri - 14 October 22

Warangal: Vaagdevi College of Engineering has organised an industry interaction meeting with IBM Career Education Software Group-India/South Asia at their campus here on Friday in an attempt to prepare the next generation of IT professionals for careers in Artificial Intelligence, Machine Learning, Big Data, Electric Vehicles, etc.

Delivering a presentation at the programme on AI, ML, Big-data, Analytics, Blockchain, Cloud, and Cybersecurity tools, IBM Regional Manager, RD Madhusudhana Rao said the IBM Career Education Programme aids students in developing skills in a variety of cutting-edge emerging technologies, including Artificial Intelligence/Machine Learning, Analytics, Blockchain, Cloud, Cybersecurity, etc.

“I hope IBM collaborates at various levels, whether it be to co-create learning paths, develop software skills, build capabilities, or engage in experiential learning, to customize offerings to ensure the best outcomes. IBM provides a cutting-edge educational platform with the most latest software content, real-world business knowledge, practical labs, and best practices,” he said.

“In this process, the Industry Institute Interaction Cell (IIIC) is playing a pivotal role in addressing this skill gap need, and we are excited to partner with the leading engineering schools to address the burgeoning skills gap issue faced by the IT sector,” Rao said. Principal Dr K Prakash has given his observations on the programme.

IBM Partners, Vice-Principal Dr Thirupathy Rao, Dean Administration Dr Shishidhar, Industry Institute Interaction Cell (IIIC) Coordinator Professor Chintakindi Raju, senior faculty, and IIIC members attended the programme.

Fri, 14 Oct 2022 01:50:00 -0500 en text/html
Killexams : Augmented Analytics Global Market Report 2022 No result found, try new keyword!This report provides strategists, marketers and senior management with the critical information they need to assess the global augmented analytics market. This report focuses on augmented analytics ... Wed, 12 Oct 2022 23:37:00 -0500 en-US text/html Killexams : Learning Analytics Market Analysis, Forecast, Size, New Trends and Insights. Update, COVID-19 Impact

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

Sep 20, 2022 (Reportmines via Comtex) -- Pre and Post Covid is covered and Report Customization is available.

The "Learning Analytics Market" provides a holistic analysis, market size and forecast from year to year, trends, growth drivers, and challenges, as well as vendor analysis forecast 2022 to2028. The Learning Analytics report offers an up-to-date analysis of the current global market scenario, the latest trends and drivers, and the overall market environment. The analysis of the Learning Analytics market research report is designed to help clients Boost their market position, and is in line with this. This Learning Analytics market report provides a detailed analysis of several leading Learning Analytics market vendors, including a Oracle,Blackboard,IBM,Microsoft,Pearson,Saba Software,Sum Total System,Mcgraw-Hill Education,SAP,Desire2learn.

The global Learning Analytics market size is projected to reach multi million by 2028, in comparision to 2021, at unexpected CAGR during 2022-2028 (Ask for sample Report).

The Learning Analytics market is segmented into On-premises,Cloud Based based on type. And Learning Analytics market applications include Academic,Enterprise. Geographic breakdown and analysis of each of the previously mentioned segments include regions comprising the North America: United States, Canada, Europe: GermanyFrance, U.K., Italy, Russia,Asia-Pacific: China, Japan, South, India, Australia, China, Indonesia, Thailand, Malaysia, Latin America:Mexico, Brazil, Argentina, Colombia, Middle East & Africa:Turkey, Saudi, Arabia, UAE, Korea. The report is of 102 pages.

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The top competitors in the Learning Analytics Market, as highlighted in the report, are:

  • Oracle
  • Blackboard
  • IBM
  • Microsoft
  • Pearson
  • Saba Software
  • Sum Total System
  • Mcgraw-Hill Education
  • SAP
  • Desire2learn

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Market Segmentation

The worldwide Learning Analytics Market is categorized on Component, Deployment, Application, and Region.

The Learning Analytics Market Analysis by types is segmented into:

The Learning Analytics Market Industry Research by Application is segmented into:

In terms of Region, the Learning Analytics Market Players available by Region are:

  • North America:
  • Europe:
    • Germany
    • France
    • U.K.
    • Italy
    • Russia
  • Asia-Pacific:
    • China
    • Japan
    • South Korea
    • India
    • Australia
    • China Taiwan
    • Indonesia
    • Thailand
    • Malaysia
  • Latin America:
    • Mexico
    • Brazil
    • Argentina Korea
    • Colombia
  • Middle East & Africa:
    • Turkey
    • Saudi
    • Arabia
    • UAE
    • Korea

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Key Benefits for Industry Participants & Stakeholders:

The Learning Analytics market research report's value chain analysis, sales breakdown, and competitive situation are combined with regional-level forecasts. Players, stakeholders, and other stakeholders in the Learning Analytics market industry research will be able to gain an advantage by utilizing the Learning Analytics market research report as a resource.

The Learning Analytics market research report contains the following TOC:

  • Report Overview
  • Global Growth Trends
  • Competition Landscape by Key Players
  • Data by Type
  • Data by Application
  • North America Market Analysis
  • Europe Market Analysis
  • Asia-Pacific Market Analysis
  • Latin America Market Analysis
  • Middle East & Africa Market Analysis
  • Key Players Profiles Market Analysis
  • Analysts Viewpoints/Conclusions
  • Appendix

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Highlights of The Learning Analytics Market Report

The Learning Analytics Market Industry Research Report contains:

  • The Learning Analytics market share, size, and growth comprehension study
  • This Learning Analytics market research report contains, the most common growth strategies employed by business owners
  • International and local market segmentation
  • Major changes to the Learning Analytics market research structure
  • Regional and country-level competitive analysis

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COVID 19 Impact Analysis:

The Learning Analytics market industry trend segmentation is based on various characteristics such as kinds, applications, and regions. In addition, the Learning Analytics market research report evaluates the impact of the new COVID-19 pandemic on the Learning Analytics market research and provides a unique assessment of the predicted market changes during the forecasted time frame.

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Learning Analytics Market Size and Industry Challenges:

The Learning Analytics market research report includes Company Profile, Product Specifications, Production Capacity/Sales, Revenue, Price, and Gross Margin Sales, as well as a comprehensive analysis of the Learning Analytics market competitive landscape and detailed information on vendors, as well as comprehensive details of factors that will challenge the growth of major market vendors.

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Reasons to Purchase the Learning Analytics Market Report:

  • Tactical initiatives were made by studying the focus areas of major corporations.
  • planning mergers and acquisitions in a respectable manner.
  • Identify rising players with potentially strong product portfolios in Learning Analytics market research and develop effective counter-strategies to acquire a competitive edge.

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Contact Us:

Name: Aniket Tiwari


Phone: USA:+1 917 267 7384 / IN:+91 777 709 3097


Report Published by: Predictive Market Research

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Global Learning Management System (LMS) Market Research Report 2022

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To view the original version on Lemon PR Wire visit Learning Analytics Market Analysis, Forecast, Size, New Trends and Insights. Update, COVID-19 Impact


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Tue, 20 Sep 2022 00:02:00 -0500 en-US text/html
Killexams : Hadoop Big Data Analytics Market Growth Insights with Leading Companies – 2030

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

Oct 10, 2022 (Alliance News via COMTEX) -- The global hadoop big data analytics market size was US$ 14.1 billion in 2021. The global hadoop big data analytics market is forecast to grow to US$ 33.7 billion by 2030 by growing at a compound annual growth rate (CAGR) of 12.8% during the forecast period from 2022 to 2030.

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Factors Influencing the Market

Growing digitalization in enterprises combined with the increasing adoption of smart payment technologies will fuel the growth of the global hadoop big data analytics market. Furthermore, the rising traffic on social media applications for client engagement will escalate the growth of the global hadoop big data analytics market.

The global hadoop big data analytics market may benefit from growing strategic alliances and launches by the industry players. For instance, Dell Inc. unveiled its ready AI solutions on August 8, 2018, which included Hadoop machine learning and Nvidia deep learning. In addition, Cloudera and IBM inked a partnership agreement in June 2019 with the aim to bring AI solutions and advanced data to organizations on the Apache Hadoop ecosystem. Thus, such strategies are forecast to benefit the overall hadoop big data analytics market during the forecast period.

Telecom operators require efficient solutions to manage growing data from connected devices and call data records. In addition, the rising demand to mitigate fraudulent risks and enhance the business revenues will benefit the global hadoop big data analytics market during the forecast period.

Big Data analytics helps financial firms in analyzing the choices of consumers and retain them by identifying behavioral patterns. Thus, it will contribute to the growth of the global hadoop big data analytics market. On the contrary, a lack of awareness related to hadoop big data analytics may limit the growth of the overall market.

COVID-19 Impact Analysis

The COVID-19 pandemic has upsurged the demand for digital banking globally. In addition, social media platforms also witnessed an abrupt increase in traffic. As a result, it has been beneficial for the global hadoop big data analytics market. Furthermore, the growing adoption of technology and remote working culture further contributed to the growth of the global hadoop big data analytics market.

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Regional Analysis

North America is forecast to dominate the hadoop big data analytics market, followed by Asia-Pacific. The adoption of Big Data analytics is steeply growing in the region. Furthermore, the presence of prominent vendors will help the region have a strong foothold in the hadoop big data analytics market.

Asia-Pacific is forecast to hold the second-largest share in the global hadoop big data analytics market. It is due to the contribution of emerging economies such as India, Singapore, Hong Kong, Taiwan, and others. In addition, the growing popularity of online payment platforms is forecast to benefit the overall hadoop big data analytics market in the coming years.

Competitors in the Market

  • Microsoft
  • AWS
  • Cloudera
  • HPE
  • IBM
  • Oracle
  • SAP
  • Google
  • SAS Institute
  • Salesforce
  • Other Promient Players

Market Segmentation

The global hadoop big data analytics market segmentation focuses on Deployment, Organization, Component, Business Function, Vertical, and Region.

By Deployment Mode

By Organization Size

By Component

By Business Function

  • Marketing and Sales
  • Operations
  • Finance
  • Human Resources

By Vertical

  • BFSI
  • Transportation and Logistics
  • Retail and eCommerce
  • Manufacturing
  • Telecommunications and IT
  • Healthcare and Life Sciences
  • Government and Public Sector
  • Media and Entertainment
  • Travel and Hospitality
  • Others

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By Regional Outlook

  • North America
  • The U.S.
  • Canada
  • Mexico
  • Europe
  • Western Europe
  • The UK
  • Germany
  • France
  • Italy
  • Spain
  • Rest of Western Europe
  • Eastern Europe
  • Poland
  • Russia
  • Rest of Eastern Europe
  • Asia Pacific
  • China
  • India
  • Japan
  • Australia & New Zealand
  • Rest of Asia Pacific
  • Middle East & Africa (MEA)
  • UAE
  • Saudi Arabia
  • South Africa
  • Rest of MEA
  • South America
  • Brazil
  • Argentina
  • Rest of South America

What are the key findings of the report?

?This report provides comprehensive information on factors expected to influence the market growth and market share in the future.
?The report offers the current state of the market and future prospects for various geographical regions.
?This report provides both qualitative and quantitative information about the competitive landscape of the market.

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Mon, 10 Oct 2022 01:01:00 -0500 en-US text/html
Killexams : MSc Business Analytics

The MSc Business Analytics is a one-year specialist programme for graduates with a bachelor's degree with a substantial quantitative component, and highly qualified graduates from other backgrounds with demonstrable advanced quantitative skills. It will suit graduates or early career professionals who wish to pursue a career in business analytics across sectors such as digital marketing, human resources, logistics, retail, finance, banking, insurance, and healthcare.

The MSc Business Analytics has been created in partnership with industry professionals from IBM, LV & UCL/IBM Industry Exchange Network. It provides students with the opportunity to work on business analytics projects and provide data-driven solutions to a real business problem or challenge, where possible, in partnership with IBM and other private, charity, and public sector organisations.

Students will gain a critical understanding of organisational, societal, and ethical issues in the use of Business Analytics. These issues are crucial for many organisations that seek to provide data-driven services while trying to balance innovation and competitiveness with public trust and corporate social responsibility. Examples of projects include optimisation of resource allocation, people analytics to support hiring decisions, customer segmentation, and sentiment analysis to Boost a business strategic direction.

At the end of the programme, students will have learned technical skills in data preparation (such as identification, extraction, and cleaning of data); the use of statistical and machine learning techniques to perform data mining and predictive analytics; the formulation and execution of statistical and mathematical models to optimise complex business decisions; the visualisation, interpretation, and reporting/communication of results from statistical analysis. Students will learn how to perform ad-hoc data analytics in Python and through specialised software such as IBM SPSS Modeler and IBM CPLEX.

You will be taught by leading academics whose research tackles the major issues in business analytics. 88% of our Business and Management research is rated as world leading or internationally excellent (REF 2021), reflecting its impact in shaping policy and practice. Bristol is a vibrant, ambitious and entrepreneurial city and home to SETSquared, the world's top university business incubator (UBI Global).

Fri, 08 Oct 2021 14:57:00 -0500 en text/html
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