In 2024, distrust of the output of artificial intelligence (AI) code assistants or generative AI (GenAI) tools was the greatest challenge to working with AI in the development work flow, according to very two in three developers worldwide. Moreover, nearly 30 percent of developers reported that lack of proper training and education on new tools was a challenge to working with AI. Interestingly, around 12 percent of developers reported that lack of executive buy-in was one of their challenges to working with AI.

During a 2022 survey conducted in the United States, it was found that 26 percent of respondents thought that artificial intelligence will not make their lives either easier or harder. Moreover, 31 percent of respondents aged between 30 and 44 years stated that AI will make their lives much easier.

Artificial intelligence

Artificial intelligence (AI) is the ability of a computer or machine to mimic the competencies of the human mind, learning from previous experiences to understand and respond to language, decisions, and problems. Particularly, a large amount of data is often used to train AI into developing algorithms and skills. The AI ecosystem consists of machine learning (ML), robotics, artificial neural networks, and natural language processing (NLP). Nowadays, tech and telecom, financial services, healthcare, and pharmaceutical industries are prominent for AI adoption in companies.

AI companies and startups

More and more companies and startups are engaging in the artificial intelligence market, which is forecast to grow rapidly in the coming years. Examples of big tech firms are IBM, Microsoft, Baidu, and Tencent, with the last owning the highest number of AI and ML patent families, amounting to over nine thousand. Moreover, driven by the excitement for this new technology and by the large investments in it, the number of startups involved in the industry around the world has grown in recent years. For instance, in the United States, the New York company UiPath was the top-funded AI startup.

During a 2022 survey conducted in the United States, it was found that 18 percent of respondents thought that artificial intelligence will lead to there being many fewer jobs. By contrast, 25 percent of respondents aged between 30 and 44 years stated that AI will create many more jobs.

Artificial intelligence

Artificial intelligence (AI) is the ability of a computer or machine to mimic the competencies of the human mind, learning from previous experiences to understand and respond to language, decisions, and problems. Particularly, a large amount of data is often used to train AI into developing algorithms and skills. The AI ecosystem consists of machine learning (ML), robotics, artificial neural networks, and natural language processing (NLP). Nowadays, tech and telecom, financial services, healthcare, and pharmaceutical industries are prominent for AI adoption in companies.

AI companies and startups

More and more companies and startups are engaging in the artificial intelligence market, which is forecast to grow rapidly in the coming years. Examples of big tech firms are IBM, Microsoft, Baidu, and Tencent, with the last owning the highest number of AI and ML patent families, amounting to over nine thousand. Moreover, driven by the excitement for this new technology and by the large investments in it, the number of startups involved in the industry around the world has grown in recent years. For instance, in the United States, the New York company UiPath was the top-funded AI startup.

The advent of artificial intelligence (AI) technologies has emerged as a promising solution to enhance healthcare efficiency and improve patient outcomes. The objective of this study is to analyse the knowledge, attitudes, and perceptions of healthcare professionals in Pakistan about AI in healthcare. We conducted a cross-sectional study using a questionnaire distributed via Google Forms. This was distributed to healthcare professionals (e.g., doctors, nurses, medical students, and allied healthcare workers) working or studying in Pakistan. Consent was taken from all participants before initiating the questionnaire. The questions were related to participant demographics, basic understanding of AI, AI in education and practice, AI applications in healthcare systems, AI’s impact on healthcare professions and the socio-ethical consequences of the use of AI. We analyzed the data using Statistical Package for Social Sciences (SPSS) statistical software, version 26.0. Overall, 616 individuals responded to the survey while n = 610 (99.0%) of respondents consented to participate. The mean age of participants was 32.2 ± 12.5 years. Most of the participants (78.7%, n = 480) had never received any formal sessions or training in AI during their studies/employment. A majority of participants, 70.3% (n = 429), believed that AI would raise more ethical challenges in healthcare. In all, 66.4% (n = 405) of participants believed that AI should be taught at the undergraduate level. The survey suggests that there is insufficient training about AI in healthcare in Pakistan despite the interest of many in this area. Future work in developing a tailored curriculum regarding AI in healthcare will help bridge the gap between the interest in use of AI and training.

Improving the accuracy of prediction on future values based on the past and current observations has been pursued by enhancing the prediction's methods, combining those methods or performing data pre-processing. In this paper, another approach is taken, namely by increasing the number of input in the dataset. This approach would be useful especially for a shorter time series data. By filling the in-between values in the time series, the number of training set can be increased, thus increasing the generalization capability of the predictor. The algorithm used to make prediction is Neural Network as it is widely used in literature for time series tasks. For comparison, Support Vector Regression is also employed. The dataset used in the experiment is the frequency of USPTO's patents and PubMed's scientific publications on the field of health, namely on Apnea, Arrhythmia, and Sleep Stages. Another time series data designated for NN3 Competition in the field of transportation is also used for benchmarking. The experimental result shows that the prediction performance can be significantly increased by filling in-between data in the time series. Furthermore, the use of detrend and deseasonalization which separates the data into trend, seasonal and stationary time series also improve the prediction performance both on original and filled dataset. The optimal number of increase on the dataset in this experiment is about five times of the length of original dataset.

In a 2024 global survey of senior travel technology leaders, data security ranked as the main barrier that prevented travel companies from implementing generative artificial intelligence (AI). While ** percent of respondents said so, ** percent of the sample mentioned the lack of generative AI expertise and training.

The AI market share of the IT services industry in India reached **** percent in 2021. Artificial intelligence has been responsible for drastic changes in the technology sector where it can greatly improve productivity through process simplification and automation. It is also an integral part and one of the fundamental bases of Industry 4.0. In several developed countries, AI could potentially maximize labor productivity by more than ** percent in the next 15 years. AI application in India As India is a country with huge linguistic diversity, it imposes a great challenge to governments and companies when conducting business with people of different linguistic backgrounds. As a result, one of the first applications for AI in India is in the field of customer service. The Indian government has increased public investment to promote the Digital India initiative in the fields of AI, IoT, big data, machine learning, and robotics. Challenges of AI adoption in India However, there are several obstacles India faces in the process of AI adoption. India has a comparatively small number of scientists and researchers in the field of machine learning and artificial intelligence. It also lacks sufficient qualified specialists to localize and implement the latest technologies in the field. However, the Ministry of Electronics and Information Technology, along with various industrial bodies have introduced several programs of personnel training and technical infrastructure building to lay the foundation for future AI development in India.

The Kuwaiti ICT market, valued at $24.69 million in 2025, is projected to experience robust growth, driven by increasing government investments in digital infrastructure, rising smartphone penetration, and the expanding adoption of cloud computing and big data analytics across various sectors. The 9.84% CAGR forecast for the period 2025-2033 indicates a significant expansion, fueled by the nation's strategic focus on digital transformation and economic diversification. Key growth drivers include the burgeoning need for enhanced cybersecurity solutions, the escalating demand for efficient business process outsourcing services, and the widespread adoption of mobile technologies for both personal and commercial use. The increasing reliance on digital platforms across sectors like oil and gas, finance, and healthcare further contributes to the market's expansion. While the market presents significant opportunities, certain challenges exist. These include the need for skilled ICT professionals, potential regulatory hurdles in data privacy and security, and the need for robust cybersecurity infrastructure to address potential threats. However, ongoing government initiatives aimed at developing human capital and fostering a robust regulatory framework are anticipated to mitigate these challenges. The segmentation of the market by technology (Big Data Analytics, Cloud Computing, etc.), component (Hardware, Software, etc.), and end-user industry allows for a granular understanding of market dynamics and allows businesses to target specific niche segments effectively. Leading players like Ooredoo, Zain, IBM, and Huawei are actively shaping the market landscape through strategic investments and innovative service offerings. This competitive landscape is fostering innovation and driving the overall growth of the Kuwaiti ICT sector. Recent developments include: August 2024: The General Authority for Statistics (GASTAT) has launched the Saudi Statistician Program, designed to nurture a fresh wave of domestic data specialists. This initiative calls on graduates holding degrees in fields such as statistics, mathematics, big data and analytics, economics, data science, quantitative methods, or data engineering to submit their applications. Those who are selected will undergo a comprehensive year-long training at GASTAT, post which they will receive sponsorship to pursue a master's degree., March 2024: Zain has partnered with global firm Huawei to set up an Artificial Intelligence (AI) Center of Excellence. This collaboration seeks to advance both companies' initiatives in AI-driven innovative services and the automation of the 5.5G network in the region. The initiative will focus on training AI algorithms to boost network automation, enabling quicker customer service, superior network solutions, and optimized maintenance. Ultimately, this endeavor aims to cultivate high-quality networks, deliver swift and efficient services, and ensure top-tier user experiences.. Key drivers for this market are: Government policies and PPP initiatives such as National Development Plan called New Kuwait, Early adoption of 5G network; Increasing penetration of technology giants. Potential restraints include: Government policies and PPP initiatives such as National Development Plan called New Kuwait, Early adoption of 5G network; Increasing penetration of technology giants. Notable trends are: Early Adoption of 5G Network Drives the Market Growth.

BackgroundThe advancement of Artificial Intelligence, particularly Large Language Models (LLMs), is rapidly progressing. LLMs, such as OpenAI’s GPT, are becoming vital in scientific and medical processes, including text production, knowledge synthesis, translation, patient communication and data analysis. However, the outcome quality needs to be evaluated to assess the full potential for usage in statistical applications. LLMs show potential for all research areas, including teaching. Integrating LLMs in research, education and medical care poses opportunities and challenges, depending on user competence, experience and attitudes.ObjectiveThis project aims at exploring the use of LLMs in supporting statistical consulting by evaluating the utility, efficiency and satisfaction related to the use of LLMs in statistical consulting from both advisee and consultant perspective. Within this project, we will develop, execute and evaluate a training module for the use of LLMs in statistical consulting. In this context, we aim to identify the strengths, limitations and areas for potential improvement. Furthermore, we will explore experiences, attitudes, fears and current practices regarding the use of LLMs of the staff at the Medical Center and the University of Freiburg.Materials and methodsThis multimodal study includes four study parts using qualitative and quantitative methods to gather data. Study part (I) is designed as mixed mode study to explore the use of LLMs in supporting statistical consulting and to evaluate the utility, efficiency and satisfaction related to the use of LLMs. Study part (II) uses a standardized online questionnaire to evaluate the training module. Study part (III) evaluates the consulting sessions using LLMs from advisee perspective. Study part (IV) explores experiences, attitudes, fears and current practices regarding the use of LLMs of the staff at the Medical Center and the University of Freiburg. This study is registered at the Freiburg Registry of Clinical Studies under the ID: FRKS004971.

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Data Analytics Market Size 2025-2029

The data analytics market size is forecast to increase by USD 288.7 billion, at a CAGR of 14.7% between 2024 and 2029.

The market is driven by the extensive use of modern technology in company operations, enabling businesses to extract valuable insights from their data. The prevalence of the Internet and the increased use of linked and integrated technologies have facilitated the collection and analysis of vast amounts of data from various sources. This trend is expected to continue as companies seek to gain a competitive edge by making data-driven decisions. However, the integration of data from different sources poses significant challenges. Ensuring data accuracy, consistency, and security is crucial as companies deal with large volumes of data from various internal and external sources. Additionally, the complexity of data analytics tools and the need for specialized skills can hinder adoption, particularly for smaller organizations with limited resources. Companies must address these challenges by investing in robust data management systems, implementing rigorous data validation processes, and providing training and development opportunities for their employees. By doing so, they can effectively harness the power of data analytics to drive growth and improve operational efficiency.

What will be the Size of the Data Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleIn the dynamic and ever-evolving the market, entities such as explainable AI, time series analysis, data integration, data lakes, algorithm selection, feature engineering, marketing analytics, computer vision, data visualization, financial modeling, real-time analytics, data mining tools, and KPI dashboards continue to unfold and intertwine, shaping the industry's landscape. The application of these technologies spans various sectors, from risk management and fraud detection to conversion rate optimization and social media analytics. ETL processes, data warehousing, statistical software, data wrangling, and data storytelling are integral components of the data analytics ecosystem, enabling organizations to extract insights from their data. Cloud computing, deep learning, and data visualization tools further enhance the capabilities of data analytics platforms, allowing for advanced data-driven decision making and real-time analysis. Marketing analytics, clustering algorithms, and customer segmentation are essential for businesses seeking to optimize their marketing strategies and gain a competitive edge. Regression analysis, data visualization tools, and machine learning algorithms are instrumental in uncovering hidden patterns and trends, while predictive modeling and causal inference help organizations anticipate future outcomes and make informed decisions. Data governance, data quality, and bias detection are crucial aspects of the data analytics process, ensuring the accuracy, security, and ethical use of data. Supply chain analytics, healthcare analytics, and financial modeling are just a few examples of the diverse applications of data analytics, demonstrating the industry's far-reaching impact. Data pipelines, data mining, and model monitoring are essential for maintaining the continuous flow of data and ensuring the accuracy and reliability of analytics models. The integration of various data analytics tools and techniques continues to evolve, as the industry adapts to the ever-changing needs of businesses and consumers alike.

How is this Data Analytics Industry segmented?

The data analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ComponentServicesSoftwareHardwareDeploymentCloudOn-premisesTypePrescriptive AnalyticsPredictive AnalyticsCustomer AnalyticsDescriptive AnalyticsOthersApplicationSupply Chain ManagementEnterprise Resource PlanningDatabase ManagementHuman Resource ManagementOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Component Insights

The services segment is estimated to witness significant growth during the forecast period.The market is experiencing significant growth as businesses increasingly rely on advanced technologies to gain insights from their data. Natural language processing is a key component of this trend, enabling more sophisticated analysis of unstructured data. Fraud detection and data security solutions are also in high demand, as companies seek to protect against threats and maintain customer trust. Data analytics platforms, including cloud-based offeri