Rapport d’analyse de la taille, de la part et des tendances du marché mondial des plateformes de science des données – Aperçu et prévisions du secteur jusqu’en 2031

Analyse du marché des plateformes de science des données

Le marché des plateformes de science des données connaît une croissance rapide en raison de l'intégration de technologies avancées telles que l'intelligence artificielle (IA), l'apprentissage automatique (ML) et le cloud computing . L'une des dernières méthodes à l'origine du marché est l'utilisation d'outils AutoML (apprentissage automatique automatisé), qui simplifient le processus de création de modèles, permettant aux entreprises moins expertes d'exploiter efficacement l'IA. Ces plateformes permettent aux data scientists de se concentrer sur l'innovation, tandis que l'automatisation gère les tâches répétitives.

Les plateformes de science des données basées sur le cloud, telles que Google Cloud AI et AWS SageMaker, favorisent encore davantage l'évolutivité et la rentabilité. En utilisant le cloud, les entreprises peuvent accéder à une immense puissance de calcul à la demande, garantissant ainsi le traitement rapide de vastes ensembles de données.

Une autre avancée réside dans l’adoption d’outils collaboratifs qui permettent aux équipes de travailler simultanément sur des projets, ce qui accroît l’efficacité et réduit le délai de mise sur le marché des solutions d’IA. Ces plateformes s’intègrent souvent aux écosystèmes de données existants, ce qui les rend accessibles à un large éventail de secteurs tels que la santé, la finance et la vente au détail. À mesure que les organisations prennent conscience de la valeur des informations basées sur les données, la demande de plateformes complètes de science des données devrait augmenter considérablement, stimulant ainsi la croissance du marché.

Taille du marché des plateformes de science des données

Français La taille du marché mondial des plateformes de science des données était évaluée à 158,59 milliards USD en 2023 et devrait atteindre 1 216,19 milliards USD d'ici 2031, avec un TCAC de 29,00 % au cours de la période de prévision de 2024 à 2031. En plus des informations sur le marché telles que la valeur du marché, le taux de croissance, les segments de marché, la couverture géographique, les acteurs du marché et le scénario du marché, le rapport de marché organisé par l'équipe de recherche sur le marché de Data Bridge comprend une analyse approfondie des experts, une analyse des importations/exportations, une analyse des prix, une analyse de la consommation de production et une analyse du pilon.

Tendances du marché des plateformes de science des données

« L’essor de l’apprentissage automatique automatisé (AutoML) »

One significant trend driving the growth of the data science platform market is the rise of Automated Machine Learning (AutoML). This technology simplifies and accelerates the model development process, allowing users with limited data science expertise to build predictive models. For instance, in January 2023, Science Applications International Corp. introduced the "Tenjin" data science platform, a versatile solution that supports low-code to full-code development for AI and machine learning applications. Powered by Dataiku, Tenjin facilitates the entire lifecycle of AI and ML model development, from deployment to training and automation, along with advanced data visualization tools. This platform aims to simplify complex processes, making AI accessible to a wider range of businesses.

Report Scope and Data Science Platform Market Segmentation       

Data Science Platform Market Definition

A data science platform is an integrated environment that provides tools, libraries, and infrastructure for data scientists to develop, manage, and execute data-driven projects. It enables users to collect, analyze, and visualize large datasets while facilitating collaboration between teams. These platforms often support various programming languages (such as Python, R, and SQL), machine learning algorithms, and data pipelines for efficient model building and deployment. Data science platforms also offer capabilities such as version control, automation, and scalability, making it easier for organizations to leverage insights from data in a structured and repeatable way for decision-making.

Data Science Platform Market Dynamics

Drivers

• Demand for Data-Driven Decision Making

The increasing reliance on data-driven decision-making is a major driver of the data science platform market. Organizations across industries are shifting towards using data insights to enhance strategy, improve customer engagement, and streamline operations. Data science platforms enable businesses to efficiently process and analyze vast datasets, leading to more accurate and informed decisions. For instance, in October 2023, GoodData Corporation unveiled its latest AI-driven data analytics platform, designed to enhance machine learning (ML), AI, and business intelligence (BI) workflows. This platform incorporates various generative AI capabilities, including a virtual assistant that provides summaries and insights. By streamlining data discovery and development processes, it enables users to make informed decisions faster, ultimately improving efficiency and effectiveness in data-driven environments.

• Growth of Big Data

The exponential rise in data generated from various sources such as IoT devices, social media platforms, and e-commerce activities is a key driver of the data science platform market. These vast volumes of unstructured and structured data require robust platforms for efficient storage, processing, and analysis. For instance, in January 2024, Databricks launched a new business intelligence platform specifically designed for telecom carriers and network service providers (NSPs). This innovative platform empowers these companies by providing a comprehensive view of their networks, operations, and customer interactions. Importantly, it ensures data privacy and protects confidential intellectual property, enabling telecom firms to make informed decisions while maintaining high standards of security in their operations.

Opportunities

• Open-Source Innovation

Open-source innovation significantly enhances the data science platform market by providing accessible tools that foster collaboration and rapid development. Platforms such as Apache Spark and TensorFlow exemplify this trend, allowing data scientists to leverage robust libraries without hefty licensing fees. As organizations seek cost-effective solutions for machine learning and big data processing, they increasingly adopt these open-source frameworks, leading to a surge in community contributions and enhancements. This collaborative environment not only accelerates the development of new features but also attracts a larger talent pool, creating opportunities for businesses to innovate and maintain competitive advantages in a data-driven landscape.

• Advances in Predictive Analytics

The surge in predictive analytics across healthcare, finance, and retail sectors presents significant opportunities in the data science platform market. In healthcare, predictive models are used to forecast patient outcomes and optimize treatment plans, as seen with tools such as IBM Watson Health. In finance, companies leverage predictive analytics for credit scoring and fraud detection, exemplified by FICO's advanced scoring algorithms. For instance, in October 2022, IBM Corporation launched the Diamondback tape library, an advanced storage solution utilizing LTO technology. This innovative product boasts an impressive capacity of up to 27 petabytes (PB) of data storage within a single server rack. The Diamondback is designed to meet the increasing demands for data storage, offering scalability and reliability for organizations needing to manage vast amounts of information securely and efficiently. As organizations recognize the value of predictive insights for decision-making, the demand for sophisticated data science platforms capable of handling complex modeling and forecasting continues to grow, creating lucrative market prospects.

Restraints/Challenges

• Data Privacy and Security Concerns

Data privacy and security concerns significantly hinder the data science platform market. As organizations rely more on data analytics, they face mounting pressure to comply with stringent regulations such as GDPR and CCPA. Non-compliance can result in hefty fines and reputational damage, leading organizations to be cautious in their data handling practices. This trepidation restricts the adoption of advanced data science solutions, as companies may prioritize security over innovation. In addition, the need for robust security measures can increase implementation costs and complexity, further deterring organizations from investing in new data science platforms and slowing overall market growth.

• Lack of Skilled Professionals

A lack of skilled professionals significantly hinders the data science platform market. The rapid evolution of data science technologies has resulted in a substantial talent gap, with many organizations struggling to find qualified data scientists and analysts. This shortage impedes the effective utilization of advanced data science platforms, leading to underperformance in analytics initiatives. Companies often invest in sophisticated tools but cannot maximize their potential due to insufficient expertise in interpreting data and deriving actionable insights. Consequently, this talent deficit stifles innovation, slows project timelines, and ultimately limits market growth as businesses fail to leverage data science capabilities to their fullest extent.

This market report provides details of new recent developments, trade regulations, import-export analysis, production analysis, value chain optimization, market share, impact of domestic and localized market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on the market contact Data Bridge Market Research for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

Data Science Platform Market Scope

The market is segmented on the basis of component type, function division, deployment model, organization size and end user application. The growth amongst these segments will help you analyze meagre growth segments in the industries and provide the users with a valuable market overview and market insights to help them make strategic decisions for identifying core market applications.

Component Type

• Platform
• Services

Professional Services

• Support and Maintenance
• Consulting
• Deployment and Integration

Managed Services

 Function Division

• Marketing
• Sales
• Logistics
• Finance and Accounting
• Customer Support
• Business Operations
• Others

 Deployment Model

• On-Premises
• Cloud based

 Organization Size

• Small and Medium-sized Enterprises (SMEs)
• Large Enterprises

 End User Application

• Banking, Financial Services, and Insurance (BFSI)
• Telecom and IT
• Retail and E-commerce
• Healthcare and Life sciences
• Manufacturing
• Energy and Utilities
• Media and Entertainment
• Transportation and Logistics
• Government
• Others

Data Science Platform Market Regional Analysis

The market is analyzed and market size insights and trends are provided by component type, function division, deployment model, organization size and end user application as referenced above.

The countries covered in the market report are U.S., Canada, Mexico in North America, Germany, Sweden, Poland, Denmark, Italy, U.K., France, Spain, Netherland, Belgium, Switzerland, Turkey, Russia, Rest of Europe in Europe, Japan, China, India, South Korea, New Zealand, Vietnam, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in Asia-Pacific (APAC), Brazil, Argentina, Rest of South America as a part of South America, U.A.E, Saudi Arabia, Oman, Qatar, Kuwait, South Africa, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA).

North America is expected to dominate the data science platform market due to the presence of a well-established infrastructure and low labor costs in the advancing countries. Moreover, the effective after-sale services offered by manufacturers within the economies are further estimated to accelerate the expansion over the forecast period.

Asia-Pacific is expected to witness significant growth during the forecast period due to rapid growth in the oil and gas exploration operation in the area within the region. China's large base for producing electronics items makes it a significant contributor to the regional market expansion.

The country section of the report also provides individual market impacting factors and changes in market regulation that impact the current and future trends of the market. Data points such as down-stream and upstream value chain analysis, technical trends and porter's five forces analysis, case studies are some of the pointers used to forecast the market scenario for individual countries. Also, the presence and availability of global brands and their challenges faced due to large or scarce competition from local and domestic brands, impact of domestic tariffs and trade routes are considered while providing forecast analysis of the country data.

Data Science Platform Market Share

The market competitive landscape provides details by competitor. Details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, global presence, production sites and facilities, production capacities, company strengths and weaknesses, product launch, product width and breadth, application dominance. The above data points provided are only related to the companies' focus related to market.

Data Science Platform Market Leaders Operating in the Market Are:

• IBM (U.S.)
• DataRobot Inc., (U.S.)
• apheris AI GmbH (Germany)
• The Digital Talent Ecosystem (U.S.)
• Databand (Israel)
• dotData (U.S.)
• Explorium Inc., (U.S.)
• Noogata (Israel)
• Tecton Inc., (U.S.)
• Spell Designs Pty Ltd (U.S.)
• Arrikto Inc., (U.S.)
• Iterative (U.S.)
• Google Inc (U.S.)
• Microsoft (U.S.)
• SAS Institute Inc., (U.S.)
• Amazon Web Services, Inc. (U.S.)
• The MathWorks, Inc. (U.S.)
• Cloudera Inc., (U.S.)
• Teradata (U.S.)
• TIBCO Software Inc. (U.S.)
• ALTERYX, INC. (U.S.)
• RapidMiner (U.S.),
• Databricks (U.S.)
• Snowflake Inc., (U.S.)
• H2O.ai (États-Unis)
• Altair Inc., (États-Unis)
• Anaconda Inc., (États-Unis)
• SAP SE (États-Unis)
• Domino Data Lab Inc., (États-Unis)
• Dataiku (États-Unis)

Dernières évolutions du marché des plateformes de science des données

• En juin 2024, IBM Corporation a annoncé une collaboration stratégique avec Telefónica Tech visant à favoriser l'adoption de solutions de pointe en matière d'intelligence artificielle (IA), d'analyse et de gouvernance des données. Ce partenariat vise à répondre aux besoins évolutifs des entreprises, en leur permettant de tirer parti des technologies avancées pour améliorer la prise de décision, l'efficacité opérationnelle et l'expérience client dans un environnement commercial de plus en plus complexe
• En mars 2024, Microsoft a annoncé une collaboration avec NVIDIA visant à améliorer l’innovation dans le domaine des soins de santé et des sciences de la vie grâce à l’IA dans le cloud et aux technologies de calcul accéléré. Ce partenariat vise à révolutionner les soins aux patients en accélérant l’accès à la médecine de précision et aux diagnostics basés sur l’IA. L’initiative devrait faire progresser considérablement le secteur de la santé en fournissant des solutions plus rapides et plus précises pour diagnostiquer et traiter les patients, améliorant ainsi les résultats de santé.
• En janvier 2023, Science Applications International Corp. a présenté la plateforme de science des données « Tenjin », une solution polyvalente qui prend en charge le développement low-code ou full-code pour les applications d'IA et d'apprentissage automatique. Propulsée par Dataiku, Tenjin facilite l'ensemble du cycle de vie du développement de modèles d'IA et d'apprentissage automatique, du déploiement à la formation et à l'automatisation, ainsi que des outils avancés de visualisation des données. Cette plateforme vise à simplifier les processus complexes, rendant l'IA accessible à un plus large éventail d'entreprises
• En octobre 2022, IBM Corporation a lancé la bibliothèque de bandes Diamondback, une solution de stockage avancée utilisant la technologie LTO. Ce produit innovant offre une capacité impressionnante allant jusqu'à 27 pétaoctets (Po) de stockage de données dans un seul rack de serveur. La Diamondback est conçue pour répondre aux demandes croissantes de stockage de données, offrant évolutivité et fiabilité aux organisations qui ont besoin de gérer de grandes quantités d'informations de manière sûre et efficace.
• En juin 2022, SAS Institute a élargi ses capacités en acquérant Kamakura Corporation, enrichissant ainsi son portefeuille de solutions de gestion des risques intégrées. Cette acquisition se concentre sur la fourniture de services professionnels spécialisés dans la gestion des actifs et des passifs (ALM) et dans d'autres secteurs financiers, notamment le secteur bancaire. En combinant ses ressources et son expertise, SAS vise à offrir des solutions complètes qui répondent aux défis complexes de la gestion des risques, aidant les organisations à prendre des décisions financières éclairées et à gérer efficacement les incertitudes du marché

SKU-19222