Lewis Wynne-Jones is the Vice President of Product at ThinkData Works.

In 2012, two things happened that set the tone for the next decade of data investments. One was technological, the other was professional, and they both revolutionized the way we think about data. Together, these events directly led to the emergence of artificial intelligence as a business prerogative. Today, however, AI is fraught with problems, and fewer businesses, not more, are saying they’re data-driven.

Where did things go wrong? And why, as we see year-over-year increases in data investment, are innovation projects often failing? The unfortunate reality is that decisions made in the past 10 years have had a lasting negative impact on data strategies, leading to more investment, less innovation and patchwork data environments held together by the heroic work of a few misunderstood data engineers.

In the early 2000s, data became top-of-mind for many organizations. The dot-com explosion in the ’90s and the dawn of “big data” made many businesses start to reconsider how they managed data as an asset—not just how they stored it, but how they’d use it to Improve business outcomes. The idea that data was not an exhaust pipe, but rather a fuel source, took root in organizations of every size, and technology cropped up to support them in their effort to deliver data-driven value.

One of the technologies that emerged was cloud computing. Between 2006 and 2012, four technology companies (Amazon, Google, Microsoft and Snowflake) launched cloud platforms. The emergence of cloud tech was interesting, as it was a departure from on-premises infrastructure and private data centers, which up to then were necessary realities for data-rich enterprises. The rising cost of on-prem infrastructure (the power, people and hardware) was daunting for any company that sensed the exponential growth of data they would need to store, and the idea of elastic utilization—the ability to scale up and scale down without buying and selling machines—was instinctively appealing to IT divisions that were waking up to the idea that streamlined, easy access to data was going to be a game changer. The cloud could enable this future—and do so relatively cheaply.

The development of this new technology underscored an ideological shift toward decentralization and agile data consumption that soon became the norm. It also laid the technological groundwork for a new type of data professional to emerge and take the business world by storm.

In 2008, when DJ Patil and Jeff Hammerbacher were building the data teams at LinkedIn and Facebook, respectively, they realized that they didn’t know what to call themselves and the teams they were building. “Analyst” was too Wall Street, and “statistician” was too academic. Patil wanted to test what term was best, and since he worked at LinkedIn, he had the data he needed to find out what the market was looking for. He found one, and the “data scientist” was born.

Four years later, Patil and Thomas H. Davenport wrote a frequently cited article calling data science the “sexiest job of the 21st century,” which pointed toward a bright future in which this new breed of business-facing, data-savvy professional with the “training and curiosity to make discoveries in the world of big data” could transform the business landscape into a data-driven utopia. Businesses, already starting to modernize their tech stack to manage the increasing flow of data, embraced the idea and hired data scientists in droves.

These two events in 2012—the emergence of new technology and the rise of a profession that would use it—permanently altered the tech landscape. Cloud technology took off, and data science became one of the most in-demand skills in the market.

The emergence of AI at this time coincides neatly with the investments businesses started making in data science three years previously. Essentially, the hiring boom of data scientists was focused more on developing a talent pool than solutioning a specific problem. Data science as a discipline was poorly understood, and most organizations had not yet implemented a data strategy aligned with their business objectives. Therefore, the first wave of data scientists had the time, training and support of the business to experiment and explore possibilities, just as Patil and Davenport had recommended. “Experimentation” is not scoped to any specific strategic priority, however. In practice, data science was science—a pursuit of knowledge. Real-world applications would have to wait.

The consequence is that AI as a concept matured but AI in practice faltered. Over time, data science divisions moved further from the business strategy they were supposed to support. Silos emerged between business and technical units. Small successes were celebrated and held up as indicators that the process was working. But scaling them proved difficult. Executives, unsure why the whole process isn’t automated, continue to invest in people and technology to try to narrow the gap.

The problem they face isn’t technological, though. It’s cultural. The goal of a company is not to set up a robust data environment; it’s to build, use and sell data products. And while executives may think they’ve been clear about their strategic objectives, the reality is that they have set up R&D departments, not production lines.

AI is a worthwhile pursuit. The premise that lots of data plus lots of modeling equals interesting stuff isn’t flawed; it just ignores the necessity of starting with a problem and working toward fixing it. The problem comes before the solution, and strategy comes before technology. The hiring boom and infrastructure investments made over the past decade laid the groundwork for organizations to support innovation, but many lack a mechanism to connect the people, process and technology in a way that addresses core strategic priorities. Making this connection is what matters. Setting up a truly democratic data environment—one where anyone can access data and use it to solve problems—should be the goal. Until then, artificial intelligence will have to wait.

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It took less than two years in graduate school for me to burn out. I was spending long hours in the laboratory, yet I worried that I wasn’t making satisfactory progress. Overwork-induced exhaustion made me sloppy and impaired my productivity, creating a vicious cycle.

My experience is far from unique. A 2022 Nature survey found that 70% of graduate students work more than 40 hours per week and that 68% have difficulty maintaining a good work–life balance. And, according to Nature’s 2021 salary and jobs survey, nearly half (45%) of scientists across all career stages report signs of burnout and overwork.

How to find, read and organize papers

Time-management strategies can help to rein in work hours, but can also promote a culture of unhealthy hyper-productivity or act as a Band-Aid to cover up toxic research environments. Time management shouldn’t be about maximizing the amount of work you can cram into your day; instead, it should help you to prioritize your professional goals without sacrificing personal well-being.

In my case, that meant rethinking how I structured my days, weeks and months. Inspired by computer scientist Cal Newport, author of the 2016 book Deep Work, I’ve cut back my hours in the lab substantially, which has improved my mental and physical health. Yet I have seen no noticeable decline in productivity. To use my time more efficiently and prioritize my most important tasks, I track my goals on three timescales.

Level 1: Big picture

Big-picture planning encompasses long-term goals. For graduate students, this might include your ideal time to graduation, learning specific skills or career exploration; for a research professor, it might be completing projects, applying for funding or improving teaching and mentoring skills.

One of my favourite tools for big-picture planning is the Gantt chart, a type of horizontal bar chart that plots project timelines (Microsoft Excel and Google Sheets have templates for creating these). I created a Gantt chart shortly after joining my PhD lab, and update it a few times per year. It includes timelines for key experiments, major conferences, milestones for my doctoral programme and career exploration.

To avoid distraction from day-to-day tasks, keep your Gantt chart somewhere prominent, such as on a bulletin board near your desk.

Level 2: Academic term

At the end of each academic term, write out a plan for the next quarter or semester that considers three elements:

Reflect. What goals did you set in the previous quarter? Did you accomplish them? Why or why not?

Forecast. What events, commitments and deadlines are coming up this term?

Prioritize. What goals will you set for this quarter? Which ones will you prioritize?

Collection: Work–life balance

Your targets for the next few months should arise from and contribute to your big-picture goals. These might include completing particular experiments, writing a paper or grant application, or preparing to teach a course. Laying out your research goals alongside other upcoming commitments (teaching-assistant appointments, committee obligations and so on) helps you to set reasonable expectations.

One tool that can be helpful at this level is the Kanban board, which features columns with different labels for you to categorize and track tasks or projects. You might assign tasks based on status (such as ‘In progress’, ‘To do ASAP’, ‘To do next month’ and ‘Not started’) or priority (high, medium and low). Several online tools provide Kanban boards, including Trello and Notion. Alternatively, you can make an old-school board using sticky notes.

Level 3: Daily or weekly

For finest-grained planning, you can use a daily planner, but I prefer to work on a weekly timescale because my research often involves experiments that run over several days. In either case, you can assign specific slots for each task, known as time blocking, or use to-do lists.

Time blocking is ideal for planning experiments around other commitments and for organizing protocols across multiple days; it works best for tasks that require at least 30 minutes to complete. All you need is an electronic or paper planner. Keep in mind that your plan should be flexible: if an experiment takes longer than expected or if journal editors request manuscript revisions on a tight timescale, you’ll need to accommodate these changes.

To-do lists save you from having to schedule every minor task and can help to remind you of small but important jobs. However, they’re less than ideal for longer tasks that take several hours or stretch across several days. Without blocking out specific times, it can be difficult to predict whether your immediate to-do list is reasonable.

Time to work

Here’s how I use these three elements in practice.

First, decide how many hours you’re comfortable working in a week. This is highly personal — don’t let your answer be swayed by your perceptions of other people. Block out any fixed appointments, such as classes or meetings. Second, create slots for the most urgent and important tasks. Try to schedule recurring tasks (such as reading papers, preparing lecture materials or building next week’s plan) at the same time each week. Finally, fill any remaining time with lower-priority tasks, including responding to e-mails, ordering supplies and so on.

Collection: Time management

If you cannot fit everything you want to accomplish into your allotted hours, your options are to delay the remaining tasks or remove them entirely. Delaying a few tasks is fine, but do so sparingly or you might find your task list ballooning.

If a task does not contribute to the goals you outlined in your big-picture and academic-term plans, it’s a candidate for removal. For research tasks, it’s helpful to envision the story you plan to tell in your future paper: does this experiment contribute to that story or fall outside its scope?

Simple as it seems, this strategy has completely changed how I spend my time. I’ve found it easier to decline new commitments when I could clearly see that they would not fit into my schedule or contribute to my goals. I’ve become a better mentor for my undergraduate researchers, and carved out space for my hobbies, health and social life.

At the same time, my research is more streamlined. I don’t begin a new experiment until I convince myself that it’s both necessary and has the best experimental design to test my hypothesis. Looking back, many of the experiments that once kept me in the lab late into the night will never end up in a publication; they were often weakly designed or unnecessary.

When we value our time, we spend it more wisely.

In the world of artificial intelligence (AI) and machine learning (ML), 2022 has arguably been the year of foundation models, or AI models trained on a massive scale. From GPT-3 to DALL-E, from BLOOM to Imagen — another day, it seems, another large language model (LLM) or text-to-image model. But until now, there have been no AI benchmarks to provide a standardized way to evaluate these models, which have developed at a rapidly-accelerated pace over the past couple of years.

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LLMs have particularly captivated the AI community, but according to the Stanford Institute for Human-Centered AI (HAI)’s Center for Research on Foundation Models, the absence of an evaluation standard has compromised the community’s ability to understand these models, as well as their capabilities and risks. 

To that end, today the CRFM announced the Holistic Evaluation of Language Models (HELM), which it says is the first benchmarking project aimed at improving the transparency of language models and the broader category of foundation models. 

“Historically, benchmarks have pushed the community to rally around a set of problems that the research community believes are valuable,” Percy Liang, associate professor in computer science at Stanford University and director of the CRFM, told VentureBeat. “One of the challenges with language models, and foundation models in general, is that they’re multipurpose, which makes benchmarking extremely difficult.” 

HELM, he explained, takes a holistic approach to the problem by evaluating language models based on a recognition of the limitations of models; on multi-metric measurement; and direct model comparison, with a goal of transparency. The core tenets used in HELM for model evaluation include accuracy, calibration, robustness, fairness, bias, toxicity, and efficiency, pointing to the key elements that make a model sufficient. 

Liang and his team evaluated 30 language models from 12 organizations: AI21 Labs, Anthropic, BigScience, Cohere, EleutherAI, Google, Meta, Microsoft, NVIDIA, OpenAI, Tsinghua University and Yandex. Some of these models are open-source to the public, others are available through commercial APIs, and others are private. 

A ‘comprehensive approach’ to LLM evaluation

“I applaud the Stanford group’s initiative,” Eric Horvitz, chief scientific officer at Microsoft, told VentureBeat by email. “They have taken a comprehensive approach to evaluating language models by creating a taxonomy of scenarios and measuring multiple aspects of performance across them.” 

Benchmarking neural language models is crucial for directing innovation and progress in both industry and academia, he added.

“Evaluation is essential for advancing the science and engineering of neural models, as well as for assessing their strengths and limitations,” he said.  “We conduct rigorous benchmarking on our models at Microsoft, and we welcome the Stanford team’s comparative evaluation within their holistic framework, which further enriches our knowledge and insights.” 

Stanford’s AI benchmark lays foundation for LLM standards

Liang says HELM lays the foundation for a new set of industry standards and will be maintained and updated as an ongoing community effort. 

“It’s a living benchmark that is not going to be done, there are things that we’re missing and that we need to cover as a community,” he said. “This is really a dynamic process, so part of the challenge will be to maintain this benchmark over time.” 

Many of the choices and ideas in HELM can serve as a basis for further discussion and improvement, agreed Horvitz. 

“Moving forward, I hope to see a community-wide process for refining and expanding the ideas and methods introduced by the Stanford team,” he said. “There’s an opportunity to involve stakeholders from academia, industry, civil society, and government—and to extend the evaluation to new scenarios, such as interactive AI applications, where we seek to measure how well AI can empower people at work and in their daily lives.” 

AI benchmarking project is a ‘dynamic’ process

Liang emphasized that the benchmarking project is a “dynamic” process. “When I tell you about the results, tomorrow they could change because new models are possibly coming out,” he said. 

One of the main things that the benchmark seeks to do, he added, is capture the differences between the models. When this reporter suggested it seemed a bit like a Consumer Reports analysis of different car models, he said that “is actually a great analogy — it is trying to provide consumers or users or the public in general with information about the various products, in this case models.” 

What is unique here, he added, is the pace of change. “Instead of being a year, it might be a month before things change,” he said, pointing to Galactica, Meta’s newly released language model for scientific papers, as an example. 

“This is something that will add to our benchmark,” he said. “So it’s like having Toyota putting out a new model every month instead of every year.” 

Another difference, of course, is the fact that LLMs are poorly understood and have such a “vast surface area of use cases,” as opposed to a car that is only driven. In addition, the automobile industry has a variety of standards — something that the CRFM is trying to build. “But we’re still very early in this process,” Liang said. 

HELM AI benchmark is a ‘Herculean’ task

“I commend Percy and his team for taking on this Herculean task,” Yoav Shoham, co-founder at AI21 Labs, told VentureBeat by email. “It’s important that a neutral, scientifically-inclined [organization] undertake it.” 

The HELM benchmark should be evergreen, he added, and updated on a regular basis. 

“This is for two reasons,” he said. “One of the challenges is that it’s a fast-moving target and in many cases the models tested are out of date. For example, J1-Jumbo v1 is a year-old and J1-Grande v1 is 6-months-old, and both have newer versions that haven’t been ready for testing by a third-party.” 

Also, what to test models for is notoriously difficult, he added. “General considerations such as perplexity (which is objectively defined) or bias (which has a subjective component) are certainly relevant, but the set of yardsticks will also evolve, as we understand better what actually matters in practice,” he said. “I expect future versions of the document to refine and expand these measurements.” 

Shoham sent one parting note to Liang about the HELM benchmark: “Percy, no good deed goes unpunished,” he joked. “You’re stuck with it.” 

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© Thomson Reuters FILE PHOTO: Japanese national flags flutter in front of buildings at Tokyo's business district

By Makiko Yamazaki

TOKYO (Reuters) -Singapore-based hedge fund 3D Investment Partners got two of its nominees on the board of software developer Fuji Soft Inc on Sunday, in the latest proxy fight by activist investors seeking more independent oversight of Japanese firms.

Three battles in exact months have questioned how independent from management Japan's outside board members really are after nearly a decade of governance reform has sharply increased the number of independent directors.

Governance experts say outside directors are only nominally independent if they have close ties to management or fail to supply proper oversight.

3D, which owns more than 20% of Fuji Soft, has nominated four additional members for the company's nine-member board at Sunday's extraordinary general meeting (EGM), saying existing outside directors have failed to address years of inefficient capital allocation.

Fuji Soft has endorsed two of the fund's candidates.

The Yokohama-based firm defended its current board, telling Reuters in a statement that the independence of its outside directors "has been ensured with no conflict of interests with shareholders. They have given objective opinions and have contributed to fostering active debate".

3D this year sought an EGM at Toshiba Corp to restart a strategic review of the conglomerate to explore options including going private.

Hong Kong-based Oasis Management on Thursday asked Fujitec Co Ltd to hold an EGM to remove all six incumbent outside directors and appoint seven new ones nominated by the fund.

The elevator maker, just an hour before its June shareholders meeting, withdrew a proposal to re-elect its chief executive to the board following revelations of real estate transactions involving his family. The board then appointed him unelected chairman.

Oasis, which owns 16.5% of Fujitec, said the board had "decided to egregiously breach shareholders' most basic right – the right to vote and hold directors accountable," demonstrating a complete lack of independent counterbalancing power.

Fujitec declined to comment, saying the company has not yet confirmed the content of Oasis's EGM request.

HOW INDEPENDENT?

The Tokyo Stock Exchange says 92% of the roughly 1,800 firms on its prime section define at least one-third of their directors as independent. But it is hard to gauge their independence from management beyond a set of written criteria.

Governance experts say having a committee to nominate directors would help ensure such independence, but only 3.9% of the top-tier companies have a statutory nomination committee, where a majority of its members must be outside directors.

Even such a committee may not ensure effective independence.

Tokyo-based Strategic Capital has sought an EGM at Japan Securities Finance Co (JSF) for an independent investigation into the securities finance provider's long-held practice of nominating former Bank of Japan, Finance Ministry and Tokyo Stock Exchange officials to top management and director positions.

The practice of senior government officials landing post-retirement jobs in the private sector has long been criticized as a source of corruption in the Japanese bureaucracy.

JSF has a statutory nomination committee, but Strategic Capital said its inaction over the troubled practice shows the committee is dysfunctional.

The company has said the appointments have been made based on the people's qualifications.

(Reporting by Makiko Yamazaki; Editing by William Mallard and Lincoln Feast.)

NEW YORK, Nov. 30, 2022 /PRNewswire/ -- The test preparation market size in the US is projected to grow by USD 11.93 billion, progressing at a CAGR of 6.39% from 2021 to 2026. The market size growth of the university exams will be higher than the other segments during the forecast period. The rising number of enrollments in nationalized as well as standardized tests in the US and rising demand for private tutoring because of increased competition among the students are the prominent factors facilitating the growth during the forecast period.

For insights on the other segments - Request a free sample!

Test Preparation Market in the US: The increasing use of analytical tools in test preparations drives the growth

• The increasing use of analytical tools in test preparations is one of the key drivers supporting the test preparation market growth in the US. The analytical tools provide not only effective and efficient test preparation modules but also offer various insights that can be used by teachers, trainers, as well as students to identify their progress by means of reporting tools, thereby optimizing the entire learning process. For instance, Kaplan, through its corporate partnership programs, provides various Kaplan Partner Solutions (KPS) consisting of test preparation programs that offer performance analysis tools. These performance-tracking tools help educators identify program trends and any outstanding matriculation and retention issues associated with individual students. Other factors such as the growth of the test prep market in India and other countries will drive the test preparation market growth during the forecast period.

Test Preparation Market in the US: Technological advances in test preparation services are the key trend in the market

• Technological advances in test preparation services are one of the key trends in the market. The rising tech-savvy population and rising adoption of tablets, mobile phones, and other mobile devices have led to an increased demand for the technologically advanced mode of delivery, as well as innovative test preparation through the incorporation of various visual technologies. Moreover, educators are emphasizing the use of visual technologies such as AR and VR and other technologies such as virtual schools to impart education to students. For instance, MITAR games developed by the Massachusetts Institute of Technology's Teacher Education Program and The Education Arcade blend real-life locations with virtual individuals and scenarios to enhance the learning skills of the student. Hence, these factors fuel the growth of the test preparation market in the US.

Story continues

Download our exclusive report on the test preparation market in the US covering market challenges, vendor analysis, and growth opportunities

Test Preparation Market in the US: Segmentation Analysis

This market research report segments the test preparation market in the US by Product (University exams, Certification exams, High school exams, Elementary exams, and Other exams) and End-user (Higher education and K-12).

The competitive scenario categorizes companies based on various performance indicators. Some of the factors considered include the financial performance of companies over the past few years, growth strategies, product innovations, new product launches, investments, and growth in market share, among others.  buy the report!

Some of the key syllabus covered in the report include:

Market Drivers

Market Challenges

Market Trends

Vendor Landscape

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Related Reports:

Test Preparation Market in India by Product, End-user, and Learning Method – Forecast and Analysis 2022-2026: The test preparation market size in India is projected to grow by USD 9.07 billion from 2021 to 2026. This report extensively covers market segmentation in India by product (university exams, certification exams, high school exams, elementary exams, and other exams), end-user (post-secondary and K-12), and learning method (offline learning and online learning).

Sample Preparation Market by Product, End-user, and Geography – Forecast and Analysis 2020-2024: The demo preparation market size is projected to grow by USD 2.29 billion during 2020-2024. This report extensively covers market segmentation by product (consumables and instruments), end-user (pharmaceutical and biotechnology companies, academic and research institutes, clinical, and others), and geography (North America, Europe, Asia, and ROW).

Table of Contents:

1. Executive Summary                           

1.1 Market Overview

Exhibit 01:  Key Finding 1

Exhibit 02:  Key Finding 2

Exhibit 03:  Key Finding 5

Exhibit 04:  Key Finding 6

Exhibit 05:  Key Finding 7

Exhibit 06:  Key Finding 8

2. Market Landscape                             

2.1 Market ecosystem

Exhibit 07:  Parent market

Exhibit 08:  Market characteristics

2.2     Value chain analysis

Exhibit 09:  Value chain analysis: Education services

2.2.1    Inputs

2.2.2    Operations                                                         

2.2.3    Marketing and sales

2.2.4    Support activities

2.2.5    Innovations

3. Market Sizing                       

3.1 Market Definition

Exhibit 10:  Offerings of vendors included in the market definition

3.2     Market segment analysis

Exhibit 11: Market segments

3.3 Market size 2021

3.4 Market outlook: Forecast for 2021-2026

Exhibit 12:  US - Market size and forecast 2021 - 2026 ($ million)

Exhibit 13:  US market: Year-over-year growth 2021 - 2026 (%)

4. Five Forces Analysis                          

4.1 Five Forces Summary

Exhibit 14:  Five forces analysis 2021 & 2026

4.2 Bargaining power of buyers

Exhibit 15:  Bargaining power of buyers

4.3 Bargaining power of suppliers

Exhibit 16:  Bargaining power of suppliers

4.4 Threat of new entrants

Exhibit 17:  Threat of new entrants

4.5 Threat of substitutes

Exhibit 18:  Threat of substitutes

4.6 Threat of rivalry

Exhibit 19:  Threat of rivalry

4.7 Market condition

Exhibit 20:  Market condition - Five forces 2021

5.    Market Segmentation by Product

5.1 Market segments

Exhibit 21:  Product - Market share 2021-2026 (%)

5.2     Comparison by Product

Exhibit 22:  Comparison by Product

5.3     University exams - Market size and forecast 2021-2026

Exhibit 23:  University exams - Market size and forecast 2021-2026 ($ million)

Exhibit 24:  University exams - Year-over-year growth 2021-2026 (%)

5.4     Certifications exams - Market size and forecast 2021-2026

Exhibit 25:  Certifications exams - Market size and forecast 2021-2026 ($ million)

Exhibit 26:  Certifications exams - Year-over-year growth 2021-2026 (%)

5.5     High school exams - Market size and forecast 2021-2026

Exhibit 27:  High school exams - Market size and forecast 2021-2026 ($ million)

Exhibit 28:  High school exams - Year-over-year growth 2021-2026 (%)

5.6     Elementary exams - Market size and forecast 2021-2026

Exhibit 29:  Elementary exams - Market size and forecast 2021-2026 ($ million)

Exhibit 30:  Elementary exams - Year-over-year growth 2021-2026 (%)

5.7     Other exams - Market size and forecast 2021-2026

Exhibit 31:  Other exams - Market size and forecast 2021-2026 ($ million)

Exhibit 32:  Other exams - Year-over-year growth 2021-2026 (%)

5.8     Market opportunity by Product

Exhibit 33:  Market opportunity by Product

6.    Market Segmentation by End-user

6.1     Market segments

Exhibit 34:  End-user - Market share 2021-2026 (%)

6.2     Comparison by End-user

Exhibit 35:  Comparison by End-user

6.3     Higher education - Market size and forecast 2021-2026

Exhibit 36:  Higher education - Market size and forecast 2021-2026 ($ million)

Exhibit 37:  Higher education - Year-over-year growth 2021-2026 (%)

6.4     K-12 - Market size and forecast 2021-2026

Exhibit 38:  K-12 - Market size and forecast 2021-2026 ($ million)

Exhibit 39:  K-12 - Year-over-year growth 2021-2026 (%)

6.5     Market opportunity by End-user

Exhibit 40:  Market opportunity by End-user

7.    Market Segmentation by Learning model

7.1     Market segments

Exhibit 41:  Learning model - Market share 2021-2026 (%)

7.2     Comparison by Learning model

Exhibit 42:  Comparison by Learning model

7.3     Blended - Market size and forecast 2021-2026

Exhibit 43:  Blended - Market size and forecast 2021-2026 ($ million)

Exhibit 44:  Blended - Year-over-year growth 2021-2026 (%)

7.4     Online - Market size and forecast 2021-2026

Exhibit 45:  Online - Market size and forecast 2021-2026 ($ million)

Exhibit 46:  Online - Year-over-year growth 2021-2026 (%)

7.5     Market opportunity by Learning model

Exhibit 47:  Market opportunity by Learning model

8. Customer landscape                         

Technavio's customer landscape matrix comparing Drivers or price sensitivity, Adoption lifecycle, importance in customer price basket, Adoption rate and Key purchase criteria

8.1     Overview

Exhibit 48:  Customer landscape

9. Drivers, Challenges, and Trends                   

9.1 Market drivers

9.1.1    Increasing use of analytical tools in test preparations

9.1.2    Increasing emphasis on online mode of test preparation

9.1.3    Rising demand for test preparation services in the US

9.2 Market challenges

9.2.1    Availability of open-source test preparation materials and growth of the unorganized sector

9.2.2    Rising number of test-optional institutes

9.2.3    Increase in education debts

Exhibit 49:  Impact of drivers and challenges

9.3 Market trends

9.3.1    Technological advances in test preparation services

9.3.2    Rising emphasis on private tutoring

9.3.3    Increase in M&A activities and the number of startups

10. Vendor Landscape                           

10.1  Competitive scenario

Exhibit 50:  Vendor landscape

10.2  Landscape disruption

Exhibit 51:  Landscape disruption

Exhibit 52:  Industry risks

11. Vendor Analysis               

11.1 Vendors covered

Exhibit 53:  Vendors covered

11.2 Market positioning of vendors

Exhibit 54:  Matrix on vendor position and classification

11.3  ArborBridge Inc.

Exhibit 55:  ArborBridge Inc. - Overview

Exhibit 56:  ArborBridge Inc. - Product and service

Exhibit 57:  ArborBridge Inc. - Key offerings

11.4  Cengage Learning Holdings II Inc

Exhibit 58:  Cengage Learning Holdings II Inc - Overview

Exhibit 59:  Cengage Learning Holdings II Inc - Business segments

Exhibit 60:  Cengage Learning Holdings II Inc - Key News

Exhibit 61:  Cengage Learning Holdings II Inc - Key offerings

Exhibit 62:  Cengage Learning Holdings II Inc - Segment focus

11.5  Club Z Inc.

Exhibit 63:  Club Z Inc. - Overview

Exhibit 64:  Club Z Inc. - Product and service

Exhibit 65:  Club Z Inc. - Key offerings

11.6  CogniFit Inc.

Exhibit 66:  CogniFit Inc. - Overview

Exhibit 67:  CogniFit Inc. - Product and service

Exhibit 68:  CogniFit Inc. - Key offerings

11.7  Edgenuity Inc.

Exhibit 69:  Edgenuity Inc. - Overview

Exhibit 70:  Edgenuity Inc. - Product and service

Exhibit 71:  Edgenuity Inc. - Key offerings

11.8  Graham Holdings Co.

Exhibit 72:  Graham Holdings Co. - Overview

Exhibit 73:  Graham Holdings Co. - Business segments

Exhibit 74:  Graham Holdings Co. - Key News

Exhibit 75:  Graham Holdings Co. - Key offerings

Exhibit 76:  Graham Holdings Co. - Segment focus

11.9  Instructure Inc.

Exhibit 77:  Instructure Inc.  - Overview

Exhibit 78:  Instructure Inc.  - Product and service

Exhibit 79:  Instructure Inc.  - Key offerings

11.10                McGraw Hill Education Inc.

Exhibit 80:  McGraw Hill Education Inc. - Overview

Exhibit 81:  McGraw Hill Education Inc. - Business segments

Exhibit 82:  McGraw Hill Education Inc. - Key offerings

Exhibit 83:  McGraw Hill Education Inc. - Segment focus

11.11                Pearson Plc

Exhibit 84:  Pearson Plc - Overview

Exhibit 85:  Pearson Plc - Business segments

Exhibit 86:  Pearson Plc - Key offerings

Exhibit 87:  Pearson Plc - Segment focus

11.12                TPR Education IP Holdings LLC

Exhibit 88:  TPR Education IP Holdings LLC - Overview

Exhibit 89:  TPR Education IP Holdings LLC - Product and service

Exhibit 90:  TPR Education IP Holdings LLC - Key offerings

12. Appendix                            

12.1 Scope of the report

12.1.1 ????Definition

12.1.2 ????Objectives

12.1.3 Notes and caveats

12.2  Currency conversion rates for US$

Exhibit 91:  Currency conversion rates for US$

12.3  Research methodology

Exhibit 92:  Research Methodology

Exhibit 93:  Validation techniques employed for market sizing

Exhibit 94:  Information sources

12.4 List of abbreviations

Exhibit 95: List of abbreviations

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At the touch of a button, a soldier holding a laptop sends sparks flying on a circuit board, causing a power generator to flash bright red as a beeping sound grows louder. This is the representation of a country’s power infrastructure coming under a cyber attack.

Though the map of circuit boards depicts a fictional island, with streets called “Blockchain Street” and “Macintosh Street,” a real-life cyber attack may not be as visible as this. Still, the effects on infrastructure can be just as devastating, causing homes to lose power or water.

The scenario is just a simulation but it serves as a training ground for soldiers who are at the NATO Cyber Range in Estonia’s capital Tallinn.

At the CR14 NATO Cyber Range, around 145 on-site commanders from as many as 30 countries - most of them NATO countries but some not - are put to the test on how they would prevent a cyber attack.

Inside the three-storey building which houses it, the first floor is where food and refreshments are provided and some of the innovations are showcased. The second floor is used for training and where phones are not allowed. And the third floor is where the real action happens, but is out of bounds for journalists.

Ukraine and Article 5

NATO’s week-long cyber operation, which took place last week, is an annual affair. This year has seen the most participants, which comes as no surprise given the war in Ukraine.

“What we've seen in Ukraine is really nonstop cyberattacks since February, since really just even before the war started,” said David Cattler, assistant secretary general for intelligence and security at NATO.

“Additional cyber operations are ongoing… Some of these operations have been linked to the Russian military intelligence, to the GRU, and are clearly designed to cause psychological effects and to exhaust cyber defence resources, which again highlights the role that cyber plays in a crisis and is played in this war,” he said.

NATO takes cyberattacks so seriously that its secretary-general, Jens Stoltenberg said this year that cyberattacks against a NATO member could trigger Article 5, meaning it is considered an attack on all NATO members and the alliance could react.

Truth in fiction

Back at the Cyber Range, made-up storylines for the participants to solve involve the fictitious island of “Icebergen,” home to the nations of supposed NATO member “Anduaria” and “Harbardus,” an enemy.

“It [the situation in Ukraine] brings more of a seriousness in terms of how this actually happens. It's not as fictitious anymore. And that's the difference that this thing brings,” Bernd Hansen, Branch Head of Cyberspace at the NATO Allied Command Transformation, told Euronews Next.

Although the storylines are kept very secret by the participants and NATO, they do say it can include infrastructure attacks, network intrusion, and potential insider threats.

But the focus is on how each participating country shares information and can help the other out in the event of an attack, rather than competing with each other.

This is called the “locked shields” operation, a real-life exercise in which they would react and help other countries.

“I believe, it sets the focus on collaboration and nothing else, because if you start competing, you tend to be in a situation where you don't share as much because you want to be in a good situation in locked shields and you could get points by sharing,” said Tobias Malm, a Swedish Armed Forces major in cyber defence.

“Of course, there will always be a competitive side in that sense that all technicians want to solve the technical issues by themselves and be the first ones to solve them. So in that sense, that's a competitive ingredient in the exercise,” he told Euronews Next.

Although it’s not currently a NATO member, Sweden may soon be after NATO members quickly welcomed its application to join the alliance alongside neighbour Finland following Russia’s invasion of Ukraine.

But it is not the first time that Sweden has participated in NATO’s training programmes.

“We have been in this exercise for I think 10 or 12 years. So it's nothing new,” Malm said.

“But for us working together regarding cyber defence is good, we learn a lot and I think we can bring something new to NATO with regards to how we work and also regarding how we work”.

Although both Sweden and Finland have been here before, this year has been important for the countries.

“This year we have made improvements in capability and in information exchange,” said Markus Riihonen, Major Defence Commander in the Finish Defence Forces.

“I have felt really, really proud to be treated really warm-heartedly. And part of this is because there are people who I walk around with here and they sort of treat me as an ally,” he told Euronews Next.

“I'm looking forwards to having two very high-level cyber security countries [Finland and Sweden], which is very beneficial for them [NATO]. This is what they [NATO] have let me know that they are very much looking forward to the accession and integration in the future”.

Tech innovations

With the numerous cyber security start-ups in Finland and Sweden, NATO has all the reason to be excited about the countries joining.

“We're very much looking forward to being able to welcome Finland and Sweden into our innovation ecosystem to maintain our technological edge. I mean, they're their world-class partners or invitees now, and there'll be work real close allies as well” said David van Weel, NATO’s Assistant Secretary-General for Emerging Security Challenges.

“These are two very capable countries, especially when it comes down to the field of innovation. They have a history of strong engagement with their private sectors. They have flourishing innovation ecosystems,” he said.

Van Weel said working with the private sector and academia will prove crucial to cyber defence, which can be seen in how companies like Starlink and Microsoft have helped Ukraine.

“NATO is committed to maintaining its technological edge and exercises like the cyber coalition, help us to test and put these new technologies into practice,” he said.

“The threat from cyberspace is real and it's growing and we need to do more investment more in our enhancing our cyber defences, more expertise, more enhance cooperation, also with the private sector”.

In the NATO Cyber Range building, innovation is on show in the shape of the world’s first 5G scooters that whizz around the long corridors. Although it is only there for inspiration, it shows how transportation operating on 5G could come under attack.

Similarly, there is also a battleship simulator, which could come under cyber attack if the digital map the vessel uses were hacked and countries or islands disappeared.

But the biggest test for the participating countries is on infrastructure, such as street lights, water supply and heating coming under cyber attack.

This, in the real world, is a serious threat, which Ukraine has experienced since October after Russia began attacking its energy infrastructure, leaving around 30 per cent of power stations across the country destroyed and many without heating or light in the winter.

Georgia, which also neighbours Russia, is also worried about cyber attacks. The post-soviet country, which is not a NATO member, was attacked by Russia 13 years ago in a five-day war.

Much of Georgia’s infrastructure is also Soviet in design and installation.

“For the Ministry of Defence cyber security is one of the top priorities because we are facing a lot of a lot of challenges,” said Nika Gogindze from Georgia’s ministry of defence on cybersecurity.

He said NATO’s cyber security week has allowed him to Improve Georgia’s cyber cooperation with other countries.

“Our aim was to Improve the coordination with our allies and its nature and decrease the time in finding new ways for communicating with them during the cyber attack crisis.

“So the goal was achieved and I am so happy with that”.

As the week ends, so does of course any cyber trace of the operations that happened. All email logins are wiped from the building and a new week begins for new cyber training operations.

The Army will likely have to implement further changes to its embattled fitness test, just months after the long-in-development Army Combat Fitness Test, or ACFT, was finally rolled out, as Congress pushes for gender-neutral and potentially job-specific standards.

The must-pass National Defense Authorization Act, or NDAA, which sets funding and policy priorities for the Pentagon, would require Army Secretary Christine Wormuth to revamp the ACFT within 180 days of the bill's passing, establishing the same fitness standards for men and women "that ensure soldiers can perform the duties of their respective" jobs. The language of the bill was unveiled late Tuesday and is expected to become law in the coming weeks.

The requirement would largely take the ACFT back to what Army planners were hoping for, though widespread skepticism from Capitol Hill and Wormuth effectively forced the service to have gender-specific standards shortly after a Military.com report on early test data showed that half of women could not pass the planned test.

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The poor pass rate for women was partly attributed to the leg tuck, an exercise in which soldiers would touch their knees to their elbows on a pull-up bar. That exercise was eliminated in favor of the plank, which is generally seen as a better measurement of core strength. However, the two-mile run is still the most failed event.

The service was also aiming for job-specific scoring, the idea being an infantryman needs to be held to a higher standard of physical fitness than a soldier who works in an admin job.

Army officials interviewed by Military.com said that differentiating based on jobs quickly gets complicated and test developers who had tried to create such standards before the ACFT was put into use couldn't figure out critical logistics, including whether to grade a soldier based on their job title on paper or responsibilities within a unit. For example, someone in combat arms might be in a recruiting position or a non-combat arms soldier might be assigned to a cavalry unit.

Story continues

Army planners are already in the early phases of developing separate job standards, but only for combat arms troops, while the rest of the force would likely keep the existing scoring standards. Military.com was first to report in October that the service is testing a completely separate gender-neutral fitness test, but exclusively for expert soldier, infantryman and field medical badges.

The ACFT went live for active-duty soldiers in October and is set to be the test that determines career advancement and potential separation for part-time soldiers in the National Guard and reserve in April. Soldiers have been taking the test in a beta period, during which there are no consequences for results, since 2019.

The ACFT took more than a decade to develop, replacing the decades-old Army Physical Fitness Test, or APFT, which was widely seen as a poor measurement of fitness, grading soldiers only on push-ups, sit-ups and a two-mile run. Soldiers now are evaluated on deadlifts, a plank, hand-release push-ups, a two-mile run, and throwing a 10-lb. ball behind them as far as possible, among other fitness tests.

-- Steve Beynon can be reached at Steve.Beynon@military.com. Follow him on Twitter @StevenBeynon.

Related: The Army's New Fitness Test Is Here. For Real This Time. No, Seriously. But Its Future Is Uncertain.